diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
new file mode 100644
index 000000000..ea280821a
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -0,0 +1,118 @@
+name: "\U0001F41B Bug Report"
+description: Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
+labels: ["02 Bug Report"]
+body:
+  - type: markdown
+    attributes:
+      value: >
+        Thank you for taking the time to file a bug report. 
+        
+        Use this to report bugs in LangChain. 
+        
+        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
+        to ask for help with your issue.
+        
+        Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
+        if there's another way to solve your problem:
+        
+        [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+        [API Reference](https://api.python.langchain.com/en/stable/),
+        [GitHub search](https://github.com/langchain-ai/langchain),
+        [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+        [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
+        [LangChain ChatBot](https://chat.langchain.com/)
+  - type: checkboxes
+    id: checks
+    attributes:
+      label: Checked other resources
+      description: Please confirm and check all the following options.
+      options:
+        - label: I added a very descriptive title to this issue.
+          required: true
+        - label: I searched the LangChain documentation with the integrated search.
+          required: true
+        - label: I used the GitHub search to find a similar question and didn't find it.
+          required: true
+        - label: I am sure that this is a bug in LangChain rather than my code.
+          required: true
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Example Code
+      description: |
+        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
+        
+        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
+        
+        **Important!** 
+        
+        * Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
+        * INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
+        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
+        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+
+      placeholder: |
+        The following code: 
+        
+        ```python
+        from langchain_core.runnables import RunnableLambda
+
+        def bad_code(inputs) -> int:
+          raise NotImplementedError('For demo purpose')
+          
+          chain = RunnableLambda(bad_code)
+          chain.invoke('Hello!')
+        ```
+  - type: textarea
+    id: error
+    validations:
+      required: false
+    attributes:
+      label: Error Message and Stack Trace (if applicable)
+      description: |
+        If you are reporting an error, please include the full error message and stack trace.
+      placeholder: |
+        Exception + full stack trace
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      description: |
+        What is the problem, question, or error?
+
+        Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
+      placeholder: |
+        * I'm trying to use the `langchain` library to do X.
+        * I expect to see Y.
+        * Instead, it does Z.
+    validations:
+      required: true
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: |
+        Please share your system info with us. 
+        
+        "pip freeze | grep langchain" 
+        platform (windows / linux / mac)
+        python version
+        
+        OR if you're on a recent version of langchain-core you can paste the output of:
+        
+        python -m langchain_core.sys_info
+      placeholder: |
+        "pip freeze | grep langchain"
+        platform
+        python version
+        
+        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
+        
+        python -m langchain_core.sys_info
+        
+        These will only surface LangChain packages, don't forget to include any other relevant
+        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
+    validations:
+      required: true
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..746565c48
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,15 @@
+blank_issues_enabled: false
+version: 2.1
+contact_links:
+  - name: 🤔 Question or Problem
+    about: Ask a question or ask about a problem in GitHub Discussions.
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/q-a
+  - name: Discord
+    url: https://discord.gg/6adMQxSpJS
+    about: General community discussions
+  - name: Feature Request
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/ideas
+    about: Suggest a feature or an idea
+  - name: Show and tell
+    about: Show what you built with LangChain
+    url: https://www.github.com/langchain-ai/langchain/discussions/categories/show-and-tell
diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml
new file mode 100644
index 000000000..61a3ad496
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation.yml
@@ -0,0 +1,19 @@
+name: Documentation
+description: Report an issue related to the LangChain documentation.
+title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
+labels: [03 - Documentation]
+
+body:
+- type: textarea
+  attributes: 
+    label: "Issue with current documentation:"
+    description: >
+      Please make sure to leave a reference to the document/code you're
+      referring to.
+
+- type: textarea
+  attributes:
+    label: "Idea or request for content:"
+    description: >
+      Please describe as clearly as possible what topics you think are missing
+      from the current documentation.
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/privileged.yml b/.github/ISSUE_TEMPLATE/privileged.yml
new file mode 100644
index 000000000..692a5bde6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/privileged.yml
@@ -0,0 +1,25 @@
+name: 🔒 Privileged
+description: You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for your interest in LangChain! 🚀
+        
+        If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
+        
+        You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository 
+        or are a regular contributor to LangChain with previous merged merged pull requests.
+  - type: checkboxes
+    id: privileged
+    attributes:
+      label: Privileged issue
+      description: Confirm that you are allowed to create an issue here.
+      options:
+        - label: I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
+          required: true
+  - type: textarea
+    id: content
+    attributes:
+      label: Issue Content
+      description: Add the content of the issue here.
diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml
new file mode 100644
index 000000000..df04e1e71
--- /dev/null
+++ b/.github/actions/poetry_setup/action.yml
@@ -0,0 +1,88 @@
+# An action for setting up poetry install with caching.
+# Using a custom action since the default action does not
+# take poetry install groups into account.
+# Action code from:
+# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
+name: poetry-install-with-caching
+description: Poetry install with support for caching of dependency groups.
+
+inputs:
+  python-version:
+    description: Python version, supporting MAJOR.MINOR only
+    required: true
+
+  poetry-version:
+    description: Poetry version
+    required: true
+
+  cache-key:
+    description: Cache key to use for manual handling of caching
+    required: true
+
+runs:
+  using: composite
+  steps:
+    - uses: actions/setup-python@v5
+      name: Setup python ${{ inputs.python-version }}
+      id: setup-python
+      with:
+        python-version: ${{ inputs.python-version }}
+
+    - uses: actions/cache@v3
+      id: cache-bin-poetry
+      name: Cache Poetry binary - Python ${{ inputs.python-version }}
+      env:
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
+      with:
+        path: |
+          /opt/pipx/venvs/poetry
+        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
+        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
+
+    - name: Refresh shell hashtable and fixup softlinks
+      if: steps.cache-bin-poetry.outputs.cache-hit == 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      run: |
+        set -eux
+
+        # Refresh the shell hashtable, to ensure correct `which` output.
+        hash -r
+
+        # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks.
+        # Delete and recreate the softlinks pipx expects to have.
+        rm /opt/pipx/venvs/poetry/bin/python
+        cd /opt/pipx/venvs/poetry/bin
+        ln -s "$(which "python$PYTHON_VERSION")" python
+        chmod +x python
+        cd /opt/pipx_bin/
+        ln -s /opt/pipx/venvs/poetry/bin/poetry poetry
+        chmod +x poetry
+
+        # Ensure everything got set up correctly.
+        /opt/pipx/venvs/poetry/bin/python --version
+        /opt/pipx_bin/poetry --version
+
+    - name: Install poetry
+      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      # Install poetry using the python version installed by setup-python step.
+      run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
+
+    - name: Restore pip and poetry cached dependencies
+      uses: actions/cache@v3
+      env:
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
+      with:
+        path: |
+          ~/.cache/pip
+          ~/.cache/pypoetry/virtualenvs
+          ~/.cache/pypoetry/cache
+          ~/.cache/pypoetry/artifacts
+          ./.venv
+        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('./poetry.lock') }}
diff --git a/.github/workflows/_test_release.yml b/.github/workflows/_test_release.yml
new file mode 100644
index 000000000..322b25bf0
--- /dev/null
+++ b/.github/workflows/_test_release.yml
@@ -0,0 +1,87 @@
+name: test-release
+
+on:
+  workflow_call:
+
+env:
+  POETRY_VERSION: "1.7.1"
+  PYTHON_VERSION: "3.10"
+
+jobs:
+  build:
+    if: github.ref == 'refs/heads/master'
+    runs-on: ubuntu-latest
+
+    outputs:
+      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
+      version: ${{ steps.check-version.outputs.version }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release
+
+      # We want to keep this build stage *separate* from the release stage,
+      # so that there's no sharing of permissions between them.
+      # The release stage has trusted publishing and GitHub repo contents write access,
+      # and we want to keep the scope of that access limited just to the release job.
+      # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
+      # could get access to our GitHub or PyPI credentials.
+      #
+      # Per the trusted publishing GitHub Action:
+      # > It is strongly advised to separate jobs for building [...]
+      # > from the publish job.
+      # https://github.com/pypa/gh-action-pypi-publish#non-goals
+      - name: Build project for distribution
+        run: poetry build
+
+      - name: Upload build
+        uses: actions/upload-artifact@v3
+        with:
+          name: test-dist
+          path: ./langgraph/dist/
+
+      - name: Check Version
+        id: check-version
+        shell: bash
+        run: |
+          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
+          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
+
+  publish:
+    needs:
+      - build
+    runs-on: ubuntu-latest
+    permissions:
+      # This permission is used for trusted publishing:
+      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
+      #
+      # Trusted publishing has to also be configured on PyPI for each package:
+      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
+      id-token: write
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/download-artifact@v3
+        with:
+          name: test-dist
+          path: ./langgraph/dist/
+
+      - name: Publish to test PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: ./langgraph/dist/
+          verbose: true
+          print-hash: true
+          repository-url: https://test.pypi.org/legacy/
+
+          # We overwrite any existing distributions with the same name and version.
+          # This is *only for CI use* and is *extremely dangerous* otherwise!
+          # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
+          skip-existing: true
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 000000000..2d527582e
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,94 @@
+name: lint
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+  # This env var allows us to get inline annotations when ruff has complaints.
+  RUFF_OUTPUT_FORMAT: github
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        # Only lint on the min and max supported Python versions.
+        # It's extremely unlikely that there's a lint issue on any version in between
+        # that doesn't show up on the min or max versions.
+        #
+        # GitHub rate-limits how many jobs can be running at any one time.
+        # Starting new jobs is also relatively slow,
+        # so linting on fewer versions makes CI faster.
+        python-version:
+          - "3.9"
+          - "3.11"
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: lint-with-extras
+
+      - name: Check Poetry File
+        shell: bash
+        run: poetry check
+
+      - name: Check lock file
+        shell: bash
+        run: poetry lock --check
+
+      - name: Install dependencies
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
+        run: poetry install --with lint,typing
+
+      - name: Get .mypy_cache to speed up mypy
+        uses: actions/cache@v3
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
+        with:
+          path: |
+            ./.mypy_cache
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ hashFiles('./poetry.lock') }}
+
+
+      - name: Analysing the code with our lint
+        run: |
+          make lint_package
+
+      - name: Install test dependencies
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
+        run: |
+          poetry install --with test
+
+      - name: Get .mypy_cache_test to speed up mypy
+        uses: actions/cache@v3
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
+        with:
+          path: ./.mypy_cache_test
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ hashFiles('./poetry.lock') }}
+
+      - name: Analysing the code with our lint
+        run: |
+          make lint_tests
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 000000000..5bb9dace2
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,209 @@
+name: release
+run-name: Release by @${{ github.actor }}
+on:
+  workflow_dispatch
+
+env:
+  PYTHON_VERSION: "3.10"
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    if: github.ref == 'refs/heads/master'
+    runs-on: ubuntu-latest
+
+    outputs:
+      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
+      version: ${{ steps.check-version.outputs.version }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release
+
+      # We want to keep this build stage *separate* from the release stage,
+      # so that there's no sharing of permissions between them.
+      # The release stage has trusted publishing and GitHub repo contents write access,
+      # and we want to keep the scope of that access limited just to the release job.
+      # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
+      # could get access to our GitHub or PyPI credentials.
+      #
+      # Per the trusted publishing GitHub Action:
+      # > It is strongly advised to separate jobs for building [...]
+      # > from the publish job.
+      # https://github.com/pypa/gh-action-pypi-publish#non-goals
+      - name: Build project for distribution
+        run: poetry build
+
+      - name: Upload build
+        uses: actions/upload-artifact@v3
+        with:
+          name: dist
+          path: ./dist/
+
+      - name: Check Version
+        id: check-version
+        shell: bash
+        run: |
+          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
+          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
+
+  test-pypi-publish:
+    needs:
+      - build
+    uses:
+      ./.github/workflows/_test_release.yml
+    secrets: inherit
+
+  pre-release-checks:
+    needs:
+      - build
+      - test-pypi-publish
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      # We explicitly *don't* set up caching here. This ensures our tests are
+      # maximally sensitive to catching breakage.
+      #
+      # For example, here's a way that caching can cause a falsely-passing test:
+      # - Make the langchain package manifest no longer list a dependency package
+      #   as a requirement. This means it won't be installed by `pip install`,
+      #   and attempting to use it would cause a crash.
+      # - That dependency used to be required, so it may have been cached.
+      #   When restoring the venv packages from cache, that dependency gets included.
+      # - Tests pass, because the dependency is present even though it wasn't specified.
+      # - The package is published, and it breaks on the missing dependency when
+      #   used in the real world.
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+
+      - name: Import published package
+        shell: bash
+        env:
+          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
+          VERSION: ${{ needs.build.outputs.version }}
+        # Here we use:
+        # - The default regular PyPI index as the *primary* index, meaning 
+        #   that it takes priority (https://pypi.org/simple)
+        # - The test PyPI index as an extra index, so that any dependencies that
+        #   are not found on test PyPI can be resolved and installed anyway.
+        #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
+        #   package because VERSION will not have been uploaded to regular PyPI yet.
+        # - attempt install again after 5 seconds if it fails because there is
+        #   sometimes a delay in availability on test pypi
+        run: |
+          poetry run pip install \
+            --extra-index-url https://test.pypi.org/simple/ \
+            "$PKG_NAME==$VERSION" || \
+          ( \
+            sleep 5 && \
+            poetry run pip install \
+              --extra-index-url https://test.pypi.org/simple/ \
+              "$PKG_NAME==$VERSION" \
+          )
+
+          # Replace all dashes in the package name with underscores,
+          # since that's how Python imports packages with dashes in the name.
+          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
+
+          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+
+      - name: Import test dependencies
+        run: poetry install --with test
+
+      # Overwrite the local version of the package with the test PyPI version.
+      - name: Import published package (again)
+        shell: bash
+        env:
+          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
+          VERSION: ${{ needs.build.outputs.version }}
+        run: |
+          poetry run pip install \
+            --extra-index-url https://test.pypi.org/simple/ \
+            "$PKG_NAME==$VERSION"
+
+      - name: Run unit tests
+        run: make tests
+
+  publish:
+    needs:
+      - build
+      - test-pypi-publish
+      - pre-release-checks
+    runs-on: ubuntu-latest
+    permissions:
+      # This permission is used for trusted publishing:
+      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
+      #
+      # Trusted publishing has to also be configured on PyPI for each package:
+      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
+      id-token: write
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release
+
+      - uses: actions/download-artifact@v3
+        with:
+          name: dist
+          path: ./dist/
+
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: ./dist/
+          verbose: true
+          print-hash: true
+
+  mark-release:
+    needs:
+      - build
+      - test-pypi-publish
+      - pre-release-checks
+      - publish
+    runs-on: ubuntu-latest
+    permissions:
+      # This permission is needed by `ncipollo/release-action` to
+      # create the GitHub release.
+      contents: write
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: release
+
+      - uses: actions/download-artifact@v3
+        with:
+          name: dist
+          path: ./dist/
+
+      - name: Create Release
+        uses: ncipollo/release-action@v1
+        with:
+          artifacts: "dist/*"
+          token: ${{ secrets.GITHUB_TOKEN }}
+          draft: false
+          generateReleaseNotes: true
+          tag: v${{ needs.build.outputs.version }}
+          commit: master
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 000000000..0109f16e4
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,50 @@
+name: test
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+
+env:
+  POETRY_VERSION: "1.7.1"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: core
+
+      - name: Install dependencies
+        shell: bash
+        run: poetry install --with test
+
+      - name: Run core tests
+        shell: bash
+        run: |
+          make test
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'
diff --git a/Makefile b/Makefile
index f228a3fed..6a4b86629 100644
--- a/Makefile
+++ b/Makefile
@@ -18,7 +18,7 @@ test:
 	poetry run pytest
 
 test_watch:
-	poetry run ptw --snapshot-update --now . -- -vv -x tests
+	poetry run ptw --snapshot-update --now . -- -vv -x --ff tests
 
 ######################
 # LINTING AND FORMATTING
@@ -26,13 +26,18 @@ test_watch:
 
 # Define a variable for Python and notebook files.
 PYTHON_FILES=.
+MYPY_CACHE=.mypy_cache
 lint format: PYTHON_FILES=.
 lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langgraph
+lint_tests: PYTHON_FILES=tests
+lint_tests: MYPY_CACHE=.mypy_cache_test
 
-lint lint_diff:
+lint lint_diff lint_package lint_tests:
 	poetry run ruff .
-	poetry run ruff format $(PYTHON_FILES) --check
-	poetry run mypy $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
+	[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES)
+	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
 format format_diff:
 	poetry run ruff format $(PYTHON_FILES)
diff --git a/README.md b/README.md
index 87aab4f13..173831445 100644
--- a/README.md
+++ b/README.md
@@ -4,14 +4,14 @@
 
 ## Overview
 
-LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) [LangChain](https://github.com/langchain-ai/langchain).
+[LangGraph](https://github.com/langchain-ai/langgraph) is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) [LangChain](https://github.com/langchain-ai/langchain).
 It extends the [LangChain Expression Language](https://python.langchain.com/docs/expression_language/) with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner.
 It is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/).
 The current interface exposed is one inspired by [NetworkX](https://networkx.org/documentation/latest/).
 
 The main use is for adding **cycles** to your LLM application.
 Crucially, this is NOT a **DAG** framework.
-If you want to build a DAG, you should use just use [LangChain Expression Language](https://python.langchain.com/docs/expression_language/).
+If you want to build a DAG, you should just use [LangChain Expression Language](https://python.langchain.com/docs/expression_language/).
 
 Cycles are important for agent-like behaviors, where you call an LLM in a loop, asking it what action to take next.
 
@@ -24,7 +24,7 @@ pip install langgraph
 ## Quick Start
 
 Here we will go over an example of creating a simple agent that uses chat models and function calling.
-This agent will represent all state as a list of messages.
+This agent will represent all its state as a list of messages.
 
 We will need to install some LangChain packages, as well as [Tavily](https://app.tavily.com/sign-in) to use as an example tool.
 
@@ -32,7 +32,7 @@ We will need to install some LangChain packages, as well as [Tavily](https://app
 pip install -U langchain langchain_openai tavily-python
 ```
 
-We also need to export some environment variables needed for our agent.
+We also need to export some environment variables for OpenAI and Tavily API access.
 
 ```shell
 export OPENAI_API_KEY=sk-...
@@ -44,7 +44,6 @@ Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for bes
 ```shell
 export LANGCHAIN_TRACING_V2="true"
 export LANGCHAIN_API_KEY=ls__...
-export LANGCHAIN_ENDPOINT=https://api.langchain.plus
 ```
 
 ### Set up the tools
@@ -59,9 +58,9 @@ from langchain_community.tools.tavily_search import TavilySearchResults
 tools = [TavilySearchResults(max_results=1)]
 ```
 
-We can now wrap these tools in a simple ToolExecutor.
-This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.
-A ToolInvocation is any class with `tool` and `tool_input` attribute.
+We can now wrap these tools in a simple LangGraph `ToolExecutor`.
+This is a simple class that receives `ToolInvocation` objects, calls that tool, and returns the output.
+`ToolInvocation` is any class with `tool` and `tool_input` attributes.
 
 ```python
 from langgraph.prebuilt import ToolExecutor
@@ -74,8 +73,8 @@ tool_executor = ToolExecutor(tools)
 Now we need to load the chat model we want to use.
 Importantly, this should satisfy two criteria:
 
-1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.
-2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.
+1. It should work with lists of messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.
+2. It should work with the OpenAI function calling interface. This means it should either be an OpenAI model or a model that exposes a similar interface.
 
 Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.
 
@@ -97,7 +96,6 @@ functions = [format_tool_to_openai_function(t) for t in tools]
 model = model.bind_functions(functions)
 ```
 
-
 ### Define the agent state
 
 The main type of graph in `langgraph` is the `StatefulGraph`.
@@ -135,8 +133,11 @@ The reason they are conditional is that based on the output of a node, one of se
 The path that is taken is not known until that node is run (the LLM decides).
 
 1. Conditional Edge: after the agent is called, we should either:
+
    a. If the agent said to take an action, then the function to invoke tools should be called
+   
    b. If the agent said that it was finished, then it should finish
+
 2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next
 
 Let's define the nodes, as well as a function to decide how what conditional edge to take.
@@ -294,7 +295,7 @@ Output from node '__end__':
 
 ### Streaming LLM Tokens
 
-You can also access the LLM tokens as they are produced by each node. 
+You can also access the LLM tokens as they are produced by each node.
 In this case only the "agent" node produces LLM tokens.
 In order for this to work properly, you must be using an LLM that supports streaming as well as have set it when constructing the LLM (e.g. `ChatOpenAI(model="gpt-3.5-turbo-1106", streaming=True)`)
 
@@ -418,49 +419,67 @@ Langchain Expression Language allows you to easily define chains (DAGs) but does
 
 ## Examples
 
-
 ### ChatAgentExecutor: with function calling
 
-This agent executor takes a list of messages as input and outputs a list of messages. 
+This agent executor takes a list of messages as input and outputs a list of messages.
 All agent state is represented as a list of messages.
 This specifically uses OpenAI function calling.
 This is recommended agent executor for newer chat based models that support function calling.
 
-- [Getting Started Notebook](examples/chat_agent_executor_with_function_calling/base.ipynb): Walks through creating this type of executor from scratch
-- [High Level Entrypoint](examples/chat_agent_executor_with_function_calling/high-level.ipynb): Walks through how to use the high level entrypoint for the chat agent executor.
+- [Getting Started Notebook](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/base.ipynb): Walks through creating this type of executor from scratch
+- [High Level Entrypoint](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/high-level.ipynb): Walks through how to use the high level entrypoint for the chat agent executor.
 
 **Modifications**
 
-We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](examples/chat_agent_executor_with_function_calling/base.ipynb) so it is recommended you start with that first.
-- [Human-in-the-loop](examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb): How to add a human-in-the-loop component
-- [Force calling a tool first](examples/chat_agent_executor_with_function_calling/force-calling-a-tool-first.ipynb): How to always call a specific tool first
-- [Respond in a specific format](examples/chat_agent_executor_with_function_calling/respond-in-format.ipynb): How to force the agent to respond in a specific format
-- [Dynamically returning tool output directly](examples/chat_agent_executor_with_function_calling/dynamically-returning-directly.ipynb): How to dynamically let the agent choose whether to return the result of a tool directly to the user
-- [Managing agent steps](examples/chat_agent_executor_with_function_calling/managing-agent-steps.ipynb): How to more explicitly manage intermediate steps that an agent takes
+We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/base.ipynb) so it is recommended you start with that first.
+
+- [Human-in-the-loop](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb): How to add a human-in-the-loop component
+- [Force calling a tool first](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/force-calling-a-tool-first.ipynb): How to always call a specific tool first
+- [Respond in a specific format](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/respond-in-format.ipynb): How to force the agent to respond in a specific format
+- [Dynamically returning tool output directly](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/dynamically-returning-directly.ipynb): How to dynamically let the agent choose whether to return the result of a tool directly to the user
+- [Managing agent steps](https://github.com/langchain-ai/langgraph/blob/main/examples/chat_agent_executor_with_function_calling/managing-agent-steps.ipynb): How to more explicitly manage intermediate steps that an agent takes
 
 ### AgentExecutor
 
 This agent executor uses existing LangChain agents.
 
-- [Getting Started Notebook](examples/agent_executor/base.ipynb): Walks through creating this type of executor from scratch
-- [High Level Entrypoint](examples/agent_executor/high-level.ipynb): Walks through how to use the high level entrypoint for the chat agent executor.
+- [Getting Started Notebook](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/base.ipynb): Walks through creating this type of executor from scratch
+- [High Level Entrypoint](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/high-level.ipynb): Walks through how to use the high level entrypoint for the chat agent executor.
 
 **Modifications**
 
-We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](examples/agent_executor/base.ipynb) so it is recommended you start with that first.
-- [Human-in-the-loop](examples/agent_executor/human-in-the-loop.ipynb): How to add a human-in-the-loop component
-- [Force calling a tool first](examples/agent_executor/force-calling-a-tool-first.ipynb): How to always call a specific tool first
-- [Managing agent steps](examples/agent_executor/managing-agent-steps.ipynb): How to more explicitly manage intermediate steps that an agent takes
+We also have a lot of examples highlighting how to slightly modify the base chat agent executor. These all build off the [getting started notebook](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/base.ipynb) so it is recommended you start with that first.
+
+- [Human-in-the-loop](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/human-in-the-loop.ipynb): How to add a human-in-the-loop component
+- [Force calling a tool first](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/force-calling-a-tool-first.ipynb): How to always call a specific tool first
+- [Managing agent steps](https://github.com/langchain-ai/langgraph/blob/main/examples/agent_executor/managing-agent-steps.ipynb): How to more explicitly manage intermediate steps that an agent takes
+
+### Multi-agent Examples
+
+- [Multi-agent collaboration](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb): how to create two agents that work together to accomplish a task
+- [Multi-agent with supervisor](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/agent_supervisor.ipynb): how to orchestrate individual agents by using an LLM as a "supervisor" to distribute work
+- [Hierarchical agent teams](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb): how to orchestrate "teams" of agents as nested graphs that can collaborate to solve a problem
+
+### Chatbot Evaluation via Simulation
+
+It can often be tough to evaluation chat bots in multi-turn situations. One way to do this is with simulations.
+
+- [Chat bot evaluation as multi-agent simulation](https://github.com/langchain-ai/langgraph/blob/main/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): How to simulate a dialogue between a "virtual user" and your chat bot
 
 ### Async
 
 If you are running LangGraph in async workflows, you may want to create the nodes to be async by default.
-In order for a walkthrough on how to do that, see [this documentation](examples/async.ipynb)
+In order for a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/async.ipynb)
 
 ### Streaming Tokens
 
 Sometimes language models take a while to respond and you may want to stream tokens to end users.
-For a guide on how to do this, see [this documentation](examples/streaming-tokens.ipynb)
+For a guide on how to do this, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/streaming-tokens.ipynb)
+
+### Persistence
+
+LangGraph comes with built-in persistence, allowing you to save the state of the graph at point and resume from there.
+In order for a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/persistence.ipynb)
 
 ## Documentation
 
@@ -478,7 +497,6 @@ This class is responsible for constructing the graph.
 It exposes an interface inspired by [NetworkX](https://networkx.org/documentation/latest/).
 This graph is parameterized by a state object that it passes around to each node.
 
-
 #### `__init__`
 
 ```python
@@ -634,7 +652,6 @@ It can be used in two places:
 - As the `end_key` in `add_edge`
 - As a value in `conditional_edge_mapping` as passed to `add_conditional_edges`
 
-
 ## Prebuilt Examples
 
 There are also a few methods we've added to make it easy to use common, prebuilt graphs and components.
diff --git a/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb b/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb
index c2e919376..e2c9d4ac2 100644
--- a/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb
+++ b/examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb
@@ -44,10 +44,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n",
+      "Tavily API Key: ········\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "import getpass\n",
@@ -66,10 +75,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "LangSmith API Key: ········\n"
+     ]
+    }
+   ],
    "source": [
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
     "os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"
@@ -89,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
    "metadata": {},
    "outputs": [],
@@ -111,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
    "metadata": {},
    "outputs": [],
@@ -139,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
    "metadata": {},
    "outputs": [],
@@ -163,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
    "metadata": {},
    "outputs": [],
@@ -194,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "ea793afa-2eab-4901-910d-6eed90cd6564",
    "metadata": {},
    "outputs": [],
@@ -237,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "b547109f-f9e8-4e77-a7e7-ed2bae7a72ab",
    "metadata": {},
    "outputs": [],
@@ -277,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "73fd6432-42e8-472a-89ca-bb5ddbbcc35a",
    "metadata": {},
    "outputs": [],
@@ -293,7 +310,7 @@
     "        tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n",
     "        tool_input=json.loads(last_message.additional_kwargs[\"function_call\"][\"arguments\"]),\n",
     "    )\n",
-    "    response = input(prompt=f\"[y/n] continue with: {action}?\")\n",
+    "    response = input(f\"[y/n] continue with: {action}?\")\n",
     "    if response == \"n\":\n",
     "        raise ValueError\n",
     "    # We call the tool_executor and get back a response\n",
@@ -316,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
    "metadata": {},
    "outputs": [],
@@ -377,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "f544977e-31f7-41f0-88c4-ec9c27b8cecb",
    "metadata": {},
    "outputs": [
@@ -397,31 +414,31 @@
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "[y/n] continue with: tool='tavily_search_results_json' tool_input={'query': 'weather in San Francisco'}? n\n"
+      "[y/n] continue with: tool='tavily_search_results_json' tool_input={'query': 'weather in San Francisco'}? y\n"
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[10], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmessages\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage\n\u001b[1;32m      3\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: [HumanMessage(content\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhat is the weather in sf\u001b[39m\u001b[38;5;124m\"\u001b[39m)]}\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mapp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# stream() yields dictionaries with output keyed by node name\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mOutput from node \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mkey\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m:\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:528\u001b[0m, in \u001b[0;36mPregel.transform\u001b[0;34m(self, input, config, output_keys, input_keys, **kwargs)\u001b[0m\n\u001b[1;32m    519\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\n\u001b[1;32m    520\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    521\u001b[0m     \u001b[38;5;28minput\u001b[39m: Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    527\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Union[\u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, Any], Any]]:\n\u001b[0;32m--> 528\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_stream_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    529\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    530\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    531\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    532\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    533\u001b[0m \u001b[43m        \u001b[49m\u001b[43minput_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    534\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    536\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01myield\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:1226\u001b[0m, in \u001b[0;36mRunnable._transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1224\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m-> 1226\u001b[0m         chunk: Output \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mnext\u001b[39m, iterator)  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m   1227\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m chunk\n\u001b[1;32m   1228\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m final_output_supported:\n",
-      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:313\u001b[0m, in \u001b[0;36mPregel._transform\u001b[0;34m(self, input, run_manager, config, input_keys, output_keys)\u001b[0m\n\u001b[1;32m    303\u001b[0m done, inflight \u001b[38;5;241m=\u001b[39m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mwait(\n\u001b[1;32m    304\u001b[0m     [\n\u001b[1;32m    305\u001b[0m         executor\u001b[38;5;241m.\u001b[39msubmit(proc\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    309\u001b[0m     timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep_timeout,\n\u001b[1;32m    310\u001b[0m )\n\u001b[1;32m    312\u001b[0m \u001b[38;5;66;03m# interrupt on failure or timeout\u001b[39;00m\n\u001b[0;32m--> 313\u001b[0m \u001b[43m_interrupt_or_proceed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdone\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minflight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;66;03m# apply writes to channels\u001b[39;00m\n\u001b[1;32m    316\u001b[0m _apply_writes(checkpoint, channels, pending_writes, config, step \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[0;32m~/workplace/permchain/langgraph/pregel/__init__.py:611\u001b[0m, in \u001b[0;36m_interrupt_or_proceed\u001b[0;34m(done, inflight, step)\u001b[0m\n\u001b[1;32m    609\u001b[0m             inflight\u001b[38;5;241m.\u001b[39mpop()\u001b[38;5;241m.\u001b[39mcancel()\n\u001b[1;32m    610\u001b[0m         \u001b[38;5;66;03m# raise the exception\u001b[39;00m\n\u001b[0;32m--> 611\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    612\u001b[0m         \u001b[38;5;66;03m# TODO this is where retry of an entire step would happen\u001b[39;00m\n\u001b[1;32m    614\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inflight:\n\u001b[1;32m    615\u001b[0m     \u001b[38;5;66;03m# if we got here means we timed out\u001b[39;00m\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.11.1/lib/python3.11/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:3596\u001b[0m, in \u001b[0;36mRunnableBindingBase.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3590\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m   3591\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   3592\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   3593\u001b[0m     config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   3594\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Optional[Any],\n\u001b[1;32m   3595\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 3596\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3597\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3598\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_merge_configs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3599\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3600\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:1774\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   1772\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1773\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 1774\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1775\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1776\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m   1777\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1778\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1779\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1780\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1781\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   1782\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:3074\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   3072\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   3073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 3074\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3075\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3076\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3077\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3078\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3079\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3080\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   3081\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3082\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3083\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3084\u001b[0m     )\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:975\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m    972\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[1;32m    973\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m    974\u001b[0m         Output,\n\u001b[0;32m--> 975\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    978\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    979\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    982\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    983\u001b[0m     )\n\u001b[1;32m    984\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    985\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    325\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/base.py:2950\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m   2948\u001b[0m                 output \u001b[38;5;241m=\u001b[39m chunk\n\u001b[1;32m   2949\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2950\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2951\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   2952\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2953\u001b[0m \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   2954\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
-      "File \u001b[0;32m~/workplace/langchain/libs/core/langchain_core/runnables/config.py:326\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    325\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[0;32m--> 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[7], line 14\u001b[0m, in \u001b[0;36mcall_tool\u001b[0;34m(state)\u001b[0m\n\u001b[1;32m     12\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(prompt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[y/n] continue with: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maction\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;66;03m# We call the tool_executor and get back a response\u001b[39;00m\n\u001b[1;32m     16\u001b[0m response \u001b[38;5;241m=\u001b[39m tool_executor\u001b[38;5;241m.\u001b[39minvoke(action)\n",
-      "\u001b[0;31mValueError\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output from node 'action':\n",
+      "---\n",
+      "{'messages': [FunctionMessage(content=\"[{'url': 'https://www.weather2travel.com/california/san-francisco/january/', 'content': 'San Francisco weather in January 2024 Expect 13°C daytime maximum temperatures  long-term weather averages for San Francisco in January before you book your next holiday to California in 2024/2025.  San Francisco January sunrise & sunset times  How sunny is it in San Francisco in January?Expect 13°C daytime maximum temperatures in the shade with on average 6 hours of sunshine per day in San Francisco in January. Check more long-term weather averages for San Francisco in January before you book your next holiday to California in 2024/2025. 13. 13°C max day temperature. 6. 6 hours of sunshine per day. 10. 10 days with some ...'}]\", name='tavily_search_results_json')]}\n",
+      "\n",
+      "---\n",
+      "\n",
+      "Output from node 'agent':\n",
+      "---\n",
+      "{'messages': [AIMessage(content='The weather in San Francisco varies depending on the time of year. In January, the average daytime maximum temperature is around 13°C (55°F) with about 6 hours of sunshine per day. If you need more detailed information or want to check the weather for a specific date, you can visit this [website](https://www.weather2travel.com/california/san-francisco/january/).')]}\n",
+      "\n",
+      "---\n",
+      "\n",
+      "Output from node '__end__':\n",
+      "---\n",
+      "{'messages': [HumanMessage(content='what is the weather in sf'), AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n  \"query\": \"weather in San Francisco\"\\n}', 'name': 'tavily_search_results_json'}}), FunctionMessage(content=\"[{'url': 'https://www.weather2travel.com/california/san-francisco/january/', 'content': 'San Francisco weather in January 2024 Expect 13°C daytime maximum temperatures  long-term weather averages for San Francisco in January before you book your next holiday to California in 2024/2025.  San Francisco January sunrise & sunset times  How sunny is it in San Francisco in January?Expect 13°C daytime maximum temperatures in the shade with on average 6 hours of sunshine per day in San Francisco in January. Check more long-term weather averages for San Francisco in January before you book your next holiday to California in 2024/2025. 13. 13°C max day temperature. 6. 6 hours of sunshine per day. 10. 10 days with some ...'}]\", name='tavily_search_results_json'), AIMessage(content='The weather in San Francisco varies depending on the time of year. In January, the average daytime maximum temperature is around 13°C (55°F) with about 6 hours of sunshine per day. If you need more detailed information or want to check the weather for a specific date, you can visit this [website](https://www.weather2travel.com/california/san-francisco/january/).')]}\n",
+      "\n",
+      "---\n",
+      "\n"
      ]
     }
    ],
@@ -463,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb b/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
new file mode 100644
index 000000000..0510118be
--- /dev/null
+++ b/examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb
@@ -0,0 +1,424 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
+   "metadata": {},
+   "source": [
+    "# Chat Bot Evaluation as Multi-agent Simulation\n",
+    "\n",
+    "When building a chat bot, such as a customer support assistant, it can be hard to properly evalute your bot's performance. It's time-consuming to have to manually interact with it intensively for each code change.\n",
+    "\n",
+    "One way to make the evaluation process easier and more reproducible is to simulate a user interaction.\n",
+    "\n",
+    "With LangGraph, it's easy to set this up. Below is an example of how to create a \"virtual user\" to simulate a conversation.\n",
+    "\n",
+    "The overall simulation looks something like this:\n",
+    "\n",
+    "![diagram](./img/virtual_user_diagram.png)\n",
+    "\n",
+    "First, we'll set up our environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%capture --no-stderr\n",
+    "# %pip install -U langgraph langchain langchain_openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "import uuid\n",
+    "\n",
+    "\n",
+    "def _set_if_undefined(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass(f\"Please provide your {var}\")\n",
+    "\n",
+    "\n",
+    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
+    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
+    "\n",
+    "# Optional, add tracing in LangSmith.\n",
+    "# This will help you visualize and debug the control flow\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Agent Simulation Evaluation\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ef4528d-6b2a-47c7-98b5-50f14984a304",
+   "metadata": {},
+   "source": [
+    "## 1. Define Chat Bot\n",
+    "\n",
+    "Next, we will define our chat bot. For this notebook, we assume the bot's API accepts a list of messages and responds with a message. If you want to update this, all you'll have to change is this section and the \"get_messages_for_agent\" function in \n",
+    "the simulator below.\n",
+    "\n",
+    "The implementation within `my_chat_bot` is configurable and can even be run on another system (e.g., if your system isn't running in python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "828479af-cf9c-4888-a365-599643a96b55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "import openai\n",
+    "\n",
+    "\n",
+    "# This is flexible, but you can define your agent here, or call your agent API here.\n",
+    "def my_chat_bot(messages: List[dict]) -> dict:\n",
+    "    system_message = {\"role\": \"system\", \"content\": \"You are a customer support agent for an airline.\"}\n",
+    "    messages = [system_message] + messages\n",
+    "    completion = openai.chat.completions.create(\n",
+    "        messages=messages, model=\"gpt-3.5-turbo\"\n",
+    "    )\n",
+    "    return completion.choices[0].message.model_dump()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f58959bf-2ab5-4330-9ac2-c00f45237e24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'content': 'Hello! How can I assist you today?',\n",
+       " 'role': 'assistant',\n",
+       " 'function_call': None,\n",
+       " 'tool_calls': None}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_chat_bot([{\"role\": \"user\", \"content\": \"hi!\"}])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "419340a3-5ecf-48e7-9028-4f2fad750502",
+   "metadata": {},
+   "source": [
+    "## 2. Define Simulated User\n",
+    "\n",
+    "We're now going to define the simulated user. \n",
+    "This can be anything we want, but we're going to build it as a LangChain bot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "32c147df-7f90-4b0d-9a6b-671677020353",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables import chain\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "system_prompt_template = \"\"\"You are a customer of an airline company. \\\n",
+    "You are interacting with a user who is a customer support person. \\\n",
+    "\n",
+    "{instructions}\n",
+    "\n",
+    "When you are finished with the conversation, respond with a single word 'FINISHED'\"\"\"\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system_prompt_template),\n",
+    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "    ]\n",
+    ")\n",
+    "instructions = \"\"\"Your name is Harrison. You are tyring to get a refund for the trip you took to Alaska. \\\n",
+    "You want them to give you ALL the money back. \\\n",
+    "This trip happened 5 years ago.\"\"\"\n",
+    "\n",
+    "prompt = prompt.partial(name=\"Harrison\", instructions=instructions)\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "simulated_user = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6f80669e-aa78-4666-b67c-a539366d5aab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Hi, I would like to request a refund for a trip I took with your airline company to Alaska. Is it possible to get a refund for that trip?')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "\n",
+    "messages = [HumanMessage(content=\"Hi! How can I help you?\")]\n",
+    "simulated_user.invoke({\"messages\": messages})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "321312b4-a1f0-4454-a481-fdac4e37cb7d",
+   "metadata": {},
+   "source": [
+    "## 3. Define the Agent Simulation\n",
+    "\n",
+    "The code below creates a LangGraph workflow to run the simulation. The main components are:\n",
+    "\n",
+    "1. The two nodes: one for the simulated user, the other for the chat bot.\n",
+    "2. The graph itself, with a conditional stopping criterion.\n",
+    "\n",
+    "Read the comments in the code below for more information.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65bc4446-462b-4ee8-b017-2862fbbdfaf5",
+   "metadata": {},
+   "source": [
+    "**Nodes**\n",
+    "\n",
+    "First, we define the nodes in the graph. These should take in a list of messages and return a list of messages to ADD to the state.\n",
+    "These will be thing wrappers around the chat bot and simulated user we have above.\n",
+    "\n",
+    "**Note:** one tricky thing here is which messages are which. Because both the chat bot AND our simulated user are both LLMs, both of them will resond with AI messages. Our state will be a list of alternating Human and AI messages. This means that for one of the nodes, there will need to be some logic that flips the AI and human roles. In this example, we will assume that HumanMessages are messages from the simulated user. This means that we need some logic in the simulated user node to swap AI and Human messages.\n",
+    "\n",
+    "First, let's define the chat bot node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "69e2a3a3-40f3-4223-9136-113738440be9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.adapters.openai import convert_message_to_dict\n",
+    "from langchain_core.messages import AIMessage\n",
+    "\n",
+    "\n",
+    "def chat_bot_node(messages):\n",
+    "    # Convert from LangChain format to the OpenAI format, which our chatbot function expects.\n",
+    "    messages = [convert_message_to_dict(m) for m in messages]\n",
+    "    # Call the chat bot\n",
+    "    chat_bot_response = my_chat_bot(messages)\n",
+    "    # Respond with an AI Message\n",
+    "    return AIMessage(content=chat_bot_response[\"content\"])\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "694c3c0c-56c5-4410-8fa8-ea2c0f11f506",
+   "metadata": {},
+   "source": [
+    "Next, let's define the node for our simulated user. This will involve a little logic to swap the roles of the messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7cad7527-ffa5-4c30-8585-b54a7a18bd98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _swap_roles(messages):\n",
+    "    new_messages = []\n",
+    "    for m in messages:\n",
+    "        if isinstance(m, AIMessage):\n",
+    "            new_messages.append(HumanMessage(content=m.content))\n",
+    "        else:\n",
+    "            new_messages.append(AIMessage(content=m.content))\n",
+    "    return new_messages\n",
+    "\n",
+    "\n",
+    "def simulated_user_node(messages):\n",
+    "    # Swap roles of messages\n",
+    "    new_messages = _swap_roles(messages)\n",
+    "    # Call the simulated user\n",
+    "    response = simulated_user.invoke({\"messages\": new_messages})\n",
+    "    # This response is an AI message - we need to flip this to be a human message\n",
+    "    return HumanMessage(content=response.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a48d8a3e-9171-4c43-a595-44d312722148",
+   "metadata": {},
+   "source": [
+    "**Edges**\n",
+    "\n",
+    "We now need to define the logic for the edges. The main logic occurs after the simulated user goes, and it should lead to one of two outcomes:\n",
+    "\n",
+    "- Either we continue and call the customer support bot\n",
+    "- Or we finish and the conversation is over\n",
+    "\n",
+    "So what is the logic for the conversation being over? We will define that as either the Human chatbot responds with `FINISHED` (see the system prompt) OR the conversation is more than 6 messages long (this is an arbitrary number just to keep this example short)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "28004fbf-a2f3-46b7-bde7-46c7adaf97fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def should_continue(messages):\n",
+    "    if len(messages) > 6:\n",
+    "        return \"end\"\n",
+    "    elif messages[-1].content == \"FINISHED\":\n",
+    "        return \"end\"\n",
+    "    else:\n",
+    "        return \"continue\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0856d4f-9334-4f28-944b-06d303e913a4",
+   "metadata": {},
+   "source": [
+    "**Graph**\n",
+    "\n",
+    "We can now define the graph that sets up the simulation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0b597e4b-4cbb-4bbc-82e5-f7e31275964c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, MessageGraph\n",
+    "\n",
+    "\n",
+    "graph_builder = MessageGraph()\n",
+    "graph_builder.add_node(\"user\", simulated_user_node)\n",
+    "graph_builder.add_node(\"chat_bot\", chat_bot_node)\n",
+    "# Every response from  your chat bot will automatically go to the\n",
+    "# simulated user\n",
+    "graph_builder.add_edge(\"chat_bot\", \"user\")\n",
+    "graph_builder.add_conditional_edges(\n",
+    "    \"user\",\n",
+    "    should_continue,\n",
+    "    # If the finish criteria are met, we will stop the simulation,\n",
+    "    # otherwise, the virtual user's message will be sent to your chat bot\n",
+    "    {\n",
+    "        \"end\": END,\n",
+    "        \"continue\": \"chat_bot\",\n",
+    "    },\n",
+    ")\n",
+    "# The input will first go to your chat bot\n",
+    "graph_builder.set_entry_point(\"chat_bot\")\n",
+    "simulation = graph_builder.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e0bd26e-8c1d-471d-9fef-d95dc0163491",
+   "metadata": {},
+   "source": [
+    "## 4. Run Simulation\n",
+    "\n",
+    "Now we can evaluate our chat bot! We can invoke it with empty messages (this will simulate letting the chat bot start the initial conversation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "32848c2e-be82-46f3-81db-b23fea45461c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'chat_bot': AIMessage(content='How may I assist you today regarding your flight or any other concerns?')}\n",
+      "----\n",
+      "{'user': HumanMessage(content='Hi, my name is Harrison. I am reaching out to request a refund for a trip I took to Alaska with your airline company. The trip occurred about 5 years ago. I would like to receive a refund for the entire amount I paid for the trip. Can you please assist me with this?')}\n",
+      "----\n",
+      "{'chat_bot': AIMessage(content=\"Hello, Harrison. Thank you for reaching out to us. I understand you would like to request a refund for a trip you took to Alaska five years ago. I'm afraid that our refund policy typically has a specific timeframe within which refund requests must be made. Generally, refund requests need to be submitted within 24 to 48 hours after the booking is made, or in certain cases, within a specified cancellation period.\\n\\nHowever, I will do my best to assist you. Could you please provide me with some additional information? Can you recall any specific details about the booking, such as the flight dates, booking reference or confirmation number? This will help me further look into the possibility of processing a refund for you.\")}\n",
+      "----\n",
+      "{'user': HumanMessage(content=\"Hello, thank you for your response. I apologize for not requesting the refund earlier. Unfortunately, I don't have the specific details such as the flight dates, booking reference, or confirmation number at the moment. Is there any other way we can proceed with the refund request without these specific details? I would greatly appreciate your assistance in finding a solution.\")}\n",
+      "----\n",
+      "{'chat_bot': AIMessage(content=\"I understand the situation, Harrison. Without specific details like flight dates, booking reference, or confirmation number, it becomes challenging to locate and process the refund accurately. However, I can still try to help you.\\n\\nTo proceed further, could you please provide me with any additional information you might remember? This could include the approximate date of travel, the departure and arrival airports, the names of the passengers, or any other relevant details related to the booking. The more information you can provide, the better we can investigate the possibility of processing a refund for you.\\n\\nAdditionally, do you happen to have any documentation related to your trip, such as receipts, boarding passes, or emails from our airline? These documents could assist in verifying your trip and processing the refund request.\\n\\nI apologize for any inconvenience caused, and I'll do my best to assist you further based on the information you can provide.\")}\n",
+      "----\n",
+      "{'user': HumanMessage(content=\"I apologize for the inconvenience caused. Unfortunately, I don't have any additional information or documentation related to the trip. It seems that I am unable to provide you with the necessary details to process the refund request. I understand that this may limit your ability to assist me further, but I appreciate your efforts in trying to help. Thank you for your time. \\n\\nFINISHED\")}\n",
+      "----\n",
+      "{'chat_bot': AIMessage(content=\"I understand, Harrison. I apologize for any inconvenience caused, and I appreciate your understanding. If you happen to locate any additional information or documentation in the future, please don't hesitate to reach out to us again. Our team will be more than happy to assist you with your refund request or any other travel-related inquiries. Thank you for contacting us, and have a great day!\")}\n",
+      "----\n",
+      "{'user': HumanMessage(content='FINISHED')}\n",
+      "----\n"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in simulation.stream([]):\n",
+    "    # Print out all events aside from the final end chunk\n",
+    "    if END not in chunk:\n",
+    "        print(chunk)\n",
+    "        print(\"----\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dde4f2b5-cfe8-4ff0-99ea-fe2c5fed70c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png b/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png
new file mode 100644
index 000000000..a0cefb06b
Binary files /dev/null and b/examples/chatbot-simulation-evaluation/img/virtual_user_diagram.png differ
diff --git a/examples/multi_agent/agent_supervisor.ipynb b/examples/multi_agent/agent_supervisor.ipynb
new file mode 100644
index 000000000..53c0d6b30
--- /dev/null
+++ b/examples/multi_agent/agent_supervisor.ipynb
@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
+   "metadata": {},
+   "source": [
+    "## Agent Supervisor\n",
+    "\n",
+    "The [previous example](multi-agent-collaboration.ipynb) routed messages automatically based on the output of the initial researcher agent.\n",
+    "\n",
+    "We can also choose to use an LLM to orchestrate the different agents.\n",
+    "\n",
+    "Below, we will create an agent group, with an agent supervisor to help delegate tasks.\n",
+    "\n",
+    "![diagram](./img/supervisor-diagram.png)\n",
+    "\n",
+    "To simplify the code in each agent node, we will use the AgentExecutor class from LangChain. This and other \"advanced agent\" notebooks are designed to show how you can implement certain design patterns in LangGraph. If the pattern suits your needs, we recommend combining it with some of the other fundamental patterns described elsewhere in the docs for best performance.\n",
+    "\n",
+    "Before we build, let's configure our environment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%capture --no-stderr\n",
+    "# %pip install -U langchain langchain_openai langchain_experimental langsmith pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_if_undefined(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass(f\"Please provide your {var}\")\n",
+    "\n",
+    "\n",
+    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
+    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
+    "_set_if_undefined(\"TAVILY_API_KEY\")\n",
+    "\n",
+    "# Optional, add tracing in LangSmith\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ac25624-4d83-45a4-b9ef-a10589aacfb7",
+   "metadata": {},
+   "source": [
+    "## Create tools\n",
+    "\n",
+    "For this example, you will make an agent to do web research with a search engine, and one agent to create plots. Define the tools they'll use below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f04c6778-403b-4b49-9b93-678e910d5cec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated, List, Tuple, Union\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_experimental.tools import PythonREPLTool\n",
+    "\n",
+    "tavily_tool = TavilySearchResults(max_results=5)\n",
+    "\n",
+    "# This executes code locally, which can be unsafe\n",
+    "python_repl_tool = PythonREPLTool()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d58d1e85-22d4-4c22-9062-72a346a0d709",
+   "metadata": {},
+   "source": [
+    "## Helper Utilites\n",
+    "\n",
+    "Define a helper function below, which make it easier to add new agent worker nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c4823dd9-26bd-4e1a-8117-b97b2860211a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
+    "from langchain_core.messages import BaseMessage, HumanMessage\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "\n",
+    "def create_agent(\n",
+    "    llm: ChatOpenAI, tools: list, system_prompt: str\n",
+    "):\n",
+    "    # Each worker node will be given a name and some tools.\n",
+    "    prompt = ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\n",
+    "                \"system\",\n",
+    "                system_prompt,\n",
+    "            ),\n",
+    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "            MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
+    "        ]\n",
+    "    )\n",
+    "    agent = create_openai_tools_agent(llm, tools, prompt)\n",
+    "    executor = AgentExecutor(agent=agent, tools=tools)\n",
+    "    return executor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c302b0-cd57-4913-986f-5dc7d6d77386",
+   "metadata": {},
+   "source": [
+    "We can also define a function that we will use to be the nodes in the graph - it takes care of converting the agent response to a human message. This is important because that is how we will add it the global state of the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "80862241-a1a7-4726-bce5-f867b233832e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def agent_node(state, agent, name):\n",
+    "    result = agent.invoke(state)\n",
+    "    return {\"messages\": [HumanMessage(content=result[\"output\"], name=name)]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d32962d2-5487-496d-aefc-2a3b0d194985",
+   "metadata": {},
+   "source": [
+    "### Create Agent Supervisor\n",
+    "\n",
+    "It will use function calling to choose the next worker node OR finish processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "311f0a58-b425-4496-adac-dc4cd8ffb912",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "\n",
+    "members = [\"Researcher\", \"Coder\"]\n",
+    "system_prompt = (\n",
+    "    \"You are a supervisor tasked with managing a conversation between the\"\n",
+    "    \" following workers:  {members}. Given the following user request,\"\n",
+    "    \" respond with the worker to act next. Each worker will perform a\"\n",
+    "    \" task and respond with their results and status. When finished,\"\n",
+    "    \" respond with FINISH.\"\n",
+    ")\n",
+    "# Our team supervisor is an LLM node. It just picks the next agent to process\n",
+    "# and decides when the work is completed\n",
+    "options = [\"FINISH\"] + members\n",
+    "# Using openai function calling can make output parsing easier for us\n",
+    "function_def = {\n",
+    "    \"name\": \"route\",\n",
+    "    \"description\": \"Select the next role.\",\n",
+    "    \"parameters\": {\n",
+    "        \"title\": \"routeSchema\",\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"next\": {\n",
+    "                \"title\": \"Next\",\n",
+    "                \"anyOf\": [\n",
+    "                    {\"enum\": options},\n",
+    "                ],\n",
+    "            }\n",
+    "        },\n",
+    "        \"required\": [\"next\"],\n",
+    "    },\n",
+    "}\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system_prompt),\n",
+    "        MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"Given the conversation above, who should act next?\"\n",
+    "            \" Or should we FINISH? Select one of: {options}\",\n",
+    "        ),\n",
+    "    ]\n",
+    ").partial(options=str(options), members=\", \".join(members))\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
+    "\n",
+    "supervisor_chain = (\n",
+    "    prompt\n",
+    "    | llm.bind_functions(functions=[function_def], function_call=\"route\")\n",
+    "    | JsonOutputFunctionsParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a07d507f-34d1-4f1b-8dde-5e58d17b2166",
+   "metadata": {},
+   "source": [
+    "## Construct Graph\n",
+    "\n",
+    "We're ready to start building the graph. Below, define the state and worker nodes using the function we just defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a430af7-8fce-4e66-ba9e-d940c1bc48e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Any, Dict, List, Optional, Sequence, TypedDict\n",
+    "import functools\n",
+    "\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langgraph.graph import StateGraph, END\n",
+    "\n",
+    "\n",
+    "# The agent state is the input to each node in the graph\n",
+    "class AgentState(TypedDict):\n",
+    "    # The annotation tells the graph that new messages will always\n",
+    "    # be added to the current states\n",
+    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
+    "    # The 'next' field indicates where to route to next\n",
+    "    next: str\n",
+    "\n",
+    "\n",
+    "research_agent = create_agent(llm, [tavily_tool], \"You are a web researcher.\")\n",
+    "research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n",
+    "\n",
+    "# NOTE: THIS PERFORMS ARBITRARY CODE EXECUTION. PROCEED WITH CAUTION\n",
+    "code_agent = create_agent(llm, [python_repl_tool], \"You may generate safe python code to analyze data and generate charts using matplotlib.\")\n",
+    "code_node = functools.partial(agent_node, agent=code_agent, name=\"Coder\")\n",
+    "\n",
+    "workflow = StateGraph(AgentState)\n",
+    "workflow.add_node(\"Researcher\", research_node)\n",
+    "workflow.add_node(\"Coder\", code_node)\n",
+    "workflow.add_node(\"supervisor\", supervisor_chain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c1593d5-39f7-4819-96d2-4ad7d7991d72",
+   "metadata": {},
+   "source": [
+    "Now connect all the edges in the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "14778e86-077b-4e6a-893c-400e59b0cdbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for member in members:\n",
+    "    # We want our workers to ALWAYS \"report back\" to the supervisor when done\n",
+    "    workflow.add_edge(member, \"supervisor\")\n",
+    "# The supervisor populates the \"next\" field in the graph state\n",
+    "# which routes to a node or finishes\n",
+    "conditional_map = {k: k for k in members}\n",
+    "conditional_map[\"FINISH\"] = END\n",
+    "workflow.add_conditional_edges(\"supervisor\", lambda x: x[\"next\"], conditional_map)\n",
+    "# Finally, add entrypoint\n",
+    "workflow.set_entry_point(\"supervisor\")\n",
+    "\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36496de-7121-4c49-8cb6-58c943c66628",
+   "metadata": {},
+   "source": [
+    "## Invoke the team\n",
+    "\n",
+    "With the graph created, we can now invoke it and see how it performs!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "56ba78e9-d9c1-457c-a073-d606d5d3e013",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'supervisor': {'next': 'Coder'}}\n",
+      "----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Python REPL can execute arbitrary code. Use with caution.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Coder': {'messages': [HumanMessage(content=\"The code `print('Hello, World!')` was executed, and the output is:\\n\\n```\\nHello, World!\\n```\", name='Coder')]}}\n",
+      "----\n",
+      "{'supervisor': {'next': 'FINISH'}}\n",
+      "----\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in graph.stream(\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            HumanMessage(content=\"Code hello world and print it to the terminal\")\n",
+    "        ]\n",
+    "    }\n",
+    "):\n",
+    "    if \"__end__\" not in s:\n",
+    "        print(s)\n",
+    "        print(\"----\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "45a92dfd-0e11-47f5-aad4-b68d24990e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'supervisor': {'next': 'Researcher'}}\n",
+      "----\n",
+      "{'Researcher': {'messages': [HumanMessage(content='**Research Report on Pikas**\\n\\nPikas are small mammals related to rabbits, known for their distinctive chirping sounds. They inhabit some of the most challenging environments, particularly boulder fields at high elevations, such as those found along the treeless slopes of the Southern Rockies, where they can be found at altitudes of up to 14,000 feet. Pikas are well-adapted to cold climates and typically do not fare well in warmer temperatures.\\n\\nRecent studies have shown that pikas are being impacted by climate change. Research by Peter Billman, a Ph.D. student from the University of Connecticut, indicates that pikas have moved upslope by approximately 1,160 feet. This upslope retreat is a direct response to changing climatic conditions, as pikas seek cooler temperatures at higher elevations.\\n\\nPikas are also known to be industrious foragers, particularly during the summer months when they gather vegetation to create haypiles for winter sustenance. Their behavior is encapsulated in the saying, \"making hay while the sun shines,\" reflecting their proactive approach to survival in harsh conditions.\\n\\nThe effects of climate change on pikas are not limited to the Southern Rockies. Studies published in Global Change Biology suggest that climate change is influencing pikas even in areas where they were previously thought to be less vulnerable, such as the Northern Rockies. These findings point to a broader trend of pikas moving to higher elevations, a behavior that may indicate a search for cooler, more suitable habitats.\\n\\nMoreover, researchers are exploring the possibility that pikas at lower elevations may have developed warm adaptations that could be beneficial for their future survival, given the ongoing climatic shifts. This line of research could help conservationists understand how pikas might cope with a warming world.\\n\\nIn conclusion, pikas are a species that not only fascinate with their unique behaviors and adaptations but also serve as indicators of environmental changes. Their upslope migration in response to climate change highlights the urgency for understanding and mitigating the effects of global warming on mountain ecosystems and the species that inhabit them.\\n\\n**Sources:**\\n- [Colorado Sun](https://coloradosun.com/2023/08/27/colorado-pika-population-climate-change/)\\n- [Wildlife.org](https://wildlife.org/climate-change-affects-pikas-even-in-unlikely-areas/)', name='Researcher')]}}\n",
+      "----\n",
+      "{'supervisor': {'next': 'FINISH'}}\n",
+      "----\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in graph.stream(\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            HumanMessage(content=\"Write a brief research report on pikas.\")\n",
+    "        ]\n",
+    "    },\n",
+    "    {\"recursion_limit\": 100},\n",
+    "):\n",
+    "    if \"__end__\" not in s:\n",
+    "        print(s)\n",
+    "        print(\"----\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d363d2c-e0da-4cce-ba47-ad2aa9df0fef",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/multi_agent/hierarchical_agent_teams.ipynb b/examples/multi_agent/hierarchical_agent_teams.ipynb
new file mode 100644
index 000000000..a4794a319
--- /dev/null
+++ b/examples/multi_agent/hierarchical_agent_teams.ipynb
@@ -0,0 +1,859 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3e3ebc4-57af-4fe4-bdd3-36aff67bf276",
+   "metadata": {},
+   "source": [
+    "## Hierarchical Agent Teams\n",
+    "\n",
+    "In our previous example ([Agent Supervisor](./agent_supervisor.ipynb)), we introduced the concept of a single supervisor node to route work between different worker nodes.\n",
+    "\n",
+    "But what if the job for a single worker becomes too complex? What if the number of workers becomes too large?\n",
+    "\n",
+    "For some applications, the system may be more effective if work is distributed _hierarchically_.\n",
+    "\n",
+    "You can do this by composing different subgraphs and creating a top-level supervisor, along with mid-level supervisors.\n",
+    "\n",
+    "To do this, let's build a simple research assistant! The graph will look something like the following:\n",
+    "\n",
+    "![diagram](./img/hierarchical-diagram.png)\n",
+    "\n",
+    "This notebook is inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al. In the rest of this notebook, you will:\n",
+    "\n",
+    "1. Define the agents' tools to access the web and write files\n",
+    "2. Define some utilities to help create the graph and agents\n",
+    "3. Create and define each team (web research + doc writing)\n",
+    "4. Compose everything together.\n",
+    "\n",
+    "But before all of that, some setup:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d30b6f7-3bec-4d9f-af50-43dfdc81ae6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%capture --no-stderr\n",
+    "# %pip install -U langgraph langchain langchain_openai langchain_experimental"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "30c2f3de-c730-4aec-85a6-af2c2f058803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "import uuid\n",
+    "\n",
+    "\n",
+    "def _set_if_undefined(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass(f\"Please provide your {var}\")\n",
+    "\n",
+    "\n",
+    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
+    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
+    "_set_if_undefined(\"TAVILY_API_KEY\")\n",
+    "\n",
+    "# Optional, add tracing in LangSmith.\n",
+    "# This will help you visualize and debug the control flow\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354568e2-aef0-4af9-8a79-e64d3eea752f",
+   "metadata": {},
+   "source": [
+    "## Create Tools\n",
+    "\n",
+    "Each team will be composed of one or more agents each with one or more tools. Below, define all the tools to be used by your different teams.\n",
+    "\n",
+    "We'll start with the research team.\n",
+    "\n",
+    "**Research team tools**\n",
+    "\n",
+    "The research team can use a search engine and url scraper to find information on the web. Feel free to add additional functionality below to boost the team performance!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4024eb89-843d-4cc3-ab3f-e1eb4d031179",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated, List, Tuple, Union\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_core.tools import tool\n",
+    "from langsmith import trace\n",
+    "\n",
+    "tavily_tool = TavilySearchResults(max_results=5)\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def scrape_webpages(urls: List[str]) -> str:\n",
+    "    \"\"\"Use requests and bs4 to scrape the provided web pages for detailed information.\"\"\"\n",
+    "    loader = WebBaseLoader(urls)\n",
+    "    docs = loader.load()\n",
+    "    return \"\\n\\n\".join(\n",
+    "        [\n",
+    "            f'<Document name=\"{doc.metadata.get(\"title\", \"\")}\">\\n{doc.page_content}\\n</Document>'\n",
+    "            for doc in docs\n",
+    "        ]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c427982-fadf-4721-a77e-2465df9fc6bc",
+   "metadata": {},
+   "source": [
+    "**Document writing team tools**\n",
+    "\n",
+    "Next up, we will give some tools for the doc writing team to use.\n",
+    "We define some bare-bones file-access tools below.\n",
+    "\n",
+    "Note that this gives the agents access to your file-system, which can be unsafe. We also haven't optimized the tool descriptions for performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f20a18ca-2709-4c12-84f3-88678591a9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from tempfile import TemporaryDirectory\n",
+    "from typing import Dict, Optional\n",
+    "\n",
+    "from langchain_experimental.utilities import PythonREPL\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "_TEMP_DIRECTORY = TemporaryDirectory()\n",
+    "WORKING_DIRECTORY = Path(_TEMP_DIRECTORY.name)\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def create_outline(\n",
+    "    points: Annotated[List[str], \"List of main points or sections.\"],\n",
+    "    file_name: Annotated[str, \"File path to save the outline.\"],\n",
+    ") -> Annotated[str, \"Path of the saved outline file.\"]:\n",
+    "    \"\"\"Create and save an outline.\"\"\"\n",
+    "    with (WORKING_DIRECTORY / file_name).open(\"w\") as file:\n",
+    "        for i, point in enumerate(points):\n",
+    "            file.write(f\"{i + 1}. {point}\\n\")\n",
+    "    return f\"Outline saved to {file_name}\"\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def read_document(\n",
+    "    file_name: Annotated[str, \"File path to save the document.\"],\n",
+    "    start: Annotated[Optional[int], \"The start line. Default is 0\"] = None,\n",
+    "    end: Annotated[Optional[int], \"The end line. Default is None\"] = None,\n",
+    ") -> str:\n",
+    "    \"\"\"Read the specified document.\"\"\"\n",
+    "    with (WORKING_DIRECTORY / file_name).open(\"r\") as file:\n",
+    "        lines = file.readlines()\n",
+    "    if start is not None:\n",
+    "        start = 0\n",
+    "    return \"\\n\".join(lines[start:end])\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def write_document(\n",
+    "    content: Annotated[str, \"Text content to be written into the document.\"],\n",
+    "    file_name: Annotated[str, \"File path to save the document.\"],\n",
+    ") -> Annotated[str, \"Path of the saved document file.\"]:\n",
+    "    \"\"\"Create and save a text document.\"\"\"\n",
+    "    with (WORKING_DIRECTORY / file_name).open(\"w\") as file:\n",
+    "        file.write(content)\n",
+    "    return f\"Document saved to {file_name}\"\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def edit_document(\n",
+    "    file_name: Annotated[str, \"Path of the document to be edited.\"],\n",
+    "    inserts: Annotated[\n",
+    "        Dict[int, str],\n",
+    "        \"Dictionary where key is the line number (1-indexed) and value is the text to be inserted at that line.\",\n",
+    "    ],\n",
+    ") -> Annotated[str, \"Path of the edited document file.\"]:\n",
+    "    \"\"\"Edit a document by inserting text at specific line numbers.\"\"\"\n",
+    "\n",
+    "    with (WORKING_DIRECTORY / file_name).open(\"r\") as file:\n",
+    "        lines = file.readlines()\n",
+    "\n",
+    "    sorted_inserts = sorted(inserts.items())\n",
+    "\n",
+    "    for line_number, text in sorted_inserts:\n",
+    "        if 1 <= line_number <= len(lines) + 1:\n",
+    "            lines.insert(line_number - 1, text + \"\\n\")\n",
+    "        else:\n",
+    "            return f\"Error: Line number {line_number} is out of range.\"\n",
+    "\n",
+    "    with (WORKING_DIRECTORY / file_name).open(\"w\") as file:\n",
+    "        file.writelines(lines)\n",
+    "\n",
+    "    return f\"Document edited and saved to {file_name}\"\n",
+    "\n",
+    "\n",
+    "# Warning: This executes code locally, which can be unsafe when not sandboxed\n",
+    "\n",
+    "repl = PythonREPL()\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def python_repl(\n",
+    "    code: Annotated[str, \"The python code to execute to generate your chart.\"]\n",
+    "):\n",
+    "    \"\"\"Use this to execute python code. If you want to see the output of a value,\n",
+    "    you should print it out with `print(...)`. This is visible to the user.\"\"\"\n",
+    "    try:\n",
+    "        result = repl.run(code)\n",
+    "    except BaseException as e:\n",
+    "        return f\"Failed to execute. Error: {repr(e)}\"\n",
+    "    return f\"Succesfully executed:\\n```python\\n{code}\\n```\\nStdout: {result}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "504ee1c6-2b6a-439d-9046-df54e1e15698",
+   "metadata": {},
+   "source": [
+    "## Helper Utilities\n",
+    "\n",
+    "We are going to create a few utility functions to make it more concise when we want to:\n",
+    "\n",
+    "1. Create a worker agent.\n",
+    "2. Create a supervisor for the sub-graph.\n",
+    "\n",
+    "These will simplify the graph compositional code at the end for us so it's easier to see what's going on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e09fb60f-1aac-455b-b67d-8d2e4ccfd747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Any, Callable, List, Optional, TypedDict, Union\n",
+    "\n",
+    "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
+    "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables import Runnable\n",
+    "from langchain_core.tools import BaseTool\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "\n",
+    "def create_agent(\n",
+    "    llm: ChatOpenAI,\n",
+    "    tools: list,\n",
+    "    system_prompt: str,\n",
+    ") -> str:\n",
+    "    \"\"\"Create a function-calling agent and add it to the graph.\"\"\"\n",
+    "    system_prompt += \"\\nWork autonomously according to your specialty, using the tools available to you.\"\n",
+    "    \" Do not ask for clarification.\"\n",
+    "    \" Your other team members (and other teams) will collaborate with you with their own specialties.\"\n",
+    "    \" You are chosen for a reason! You are one of the following team members: {team_members}.\"\n",
+    "    prompt = ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\n",
+    "                \"system\",\n",
+    "                system_prompt,\n",
+    "            ),\n",
+    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "            MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
+    "        ]\n",
+    "    )\n",
+    "    agent = create_openai_functions_agent(llm, tools, prompt)\n",
+    "    executor = AgentExecutor(agent=agent, tools=tools)\n",
+    "    return executor\n",
+    "\n",
+    "\n",
+    "def agent_node(state, agent, name):\n",
+    "    result = agent.invoke(state)\n",
+    "    return {\"messages\": [HumanMessage(content=result[\"output\"], name=name)]}\n",
+    "\n",
+    "\n",
+    "def create_team_supervisor(\n",
+    "    llm: ChatOpenAI, system_prompt, members\n",
+    ") -> str:\n",
+    "    \"\"\"An LLM-based router.\"\"\"\n",
+    "    options = [\"FINISH\"] + members\n",
+    "    function_def = {\n",
+    "        \"name\": \"route\",\n",
+    "        \"description\": \"Select the next role.\",\n",
+    "        \"parameters\": {\n",
+    "            \"title\": \"routeSchema\",\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"next\": {\n",
+    "                    \"title\": \"Next\",\n",
+    "                    \"anyOf\": [\n",
+    "                        {\"enum\": options},\n",
+    "                    ],\n",
+    "                },\n",
+    "            },\n",
+    "            \"required\": [\"next\"],\n",
+    "        },\n",
+    "    }\n",
+    "    prompt = ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\"system\", system_prompt),\n",
+    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "            (\n",
+    "                \"system\",\n",
+    "                \"Given the conversation above, who should act next?\"\n",
+    "                \" Or should we FINISH? Select one of: {options}\",\n",
+    "            ),\n",
+    "        ]\n",
+    "    ).partial(options=str(options), team_members=\", \".join(members))\n",
+    "    return (\n",
+    "        prompt\n",
+    "        | llm.bind_functions(functions=[function_def], function_call=\"route\")\n",
+    "        | JsonOutputFunctionsParser()\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00282b1f-bb4d-4ee7-9bae-e8e6f586f12e",
+   "metadata": {},
+   "source": [
+    "## Define Agent Teams\n",
+    "\n",
+    "Now we can get to define our hierachical teams. \"Choose your player!\"\n",
+    "\n",
+    "### Research Team\n",
+    "\n",
+    "The research team will have a search agent and a web scraping \"research_agent\" as the two worker nodes. Let's create those, as well as the team supervisor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "53db0c78-e357-48ba-ae5f-3fc04735a3b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import functools\n",
+    "import operator\n",
+    "\n",
+    "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "import functools\n",
+    "\n",
+    "\n",
+    "# Research team graph state\n",
+    "class ResearchTeamState(TypedDict):\n",
+    "    # A message is added after each team member finishes\n",
+    "    messages: Annotated[List[BaseMessage], operator.add]\n",
+    "    # The team members are tracked so they are aware of\n",
+    "    # the others' skill-sets\n",
+    "    team_members: List[str]\n",
+    "    # Used to route work. The supervisor calls a function\n",
+    "    # that will update this every time it makes a decision\n",
+    "    next: str\n",
+    "\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
+    "\n",
+    "search_agent = create_agent(llm, [tavily_tool], \"You are a research assistant who can search for up-to-date info using the tavily search engine.\")\n",
+    "search_node = functools.partial(agent_node, agent=search_agent, name=\"Search\")\n",
+    "\n",
+    "research_agent = create_agent(llm, [scrape_webpages], \"You are a research assistant who can scrape specified urls for more detailed information using the scrape_webpages function.\")\n",
+    "research_node = functools.partial(agent_node, agent=research_agent, name=\"Web Scraper\")\n",
+    "\n",
+    "supervisor_agent = create_team_supervisor(\n",
+    "    llm,\n",
+    "    \"You are a supervisor tasked with managing a conversation between the\"\n",
+    "    \" following workers:  Search, Web Scraper. Given the following user request,\"\n",
+    "    \" respond with the worker to act next. Each worker will perform a\"\n",
+    "    \" task and respond with their results and status. When finished,\"\n",
+    "    \" respond with FINISH.\",\n",
+    "    [\"Search\", \"Web Scraper\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b01c6ee8-a461-4081-8a97-a3a06ec0f994",
+   "metadata": {},
+   "source": [
+    "Now that we've created the necessary components, defining their interactions is easy. Add the nodes to the team graph, and define the edges, which determine the transition criteria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1a7a1260-d9f6-4011-b2b1-13fab5126997",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "research_graph = StateGraph(ResearchTeamState)\n",
+    "research_graph.add_node(\"Search\", search_node)\n",
+    "research_graph.add_node(\"Web Scraper\", research_node)\n",
+    "research_graph.add_node(\"supervisor\", supervisor_agent)\n",
+    "\n",
+    "# Define the control flow\n",
+    "research_graph.add_edge(\"Search\", \"supervisor\")\n",
+    "research_graph.add_edge(\"Web Scraper\", \"supervisor\")\n",
+    "research_graph.add_conditional_edges(\n",
+    "    \"supervisor\",\n",
+    "    lambda x: x[\"next\"],\n",
+    "    {\n",
+    "        \"Search\": \"Search\",\n",
+    "        \"Web Scraper\": \"Web Scraper\",\n",
+    "        \"FINISH\": END\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "\n",
+    "research_graph.set_entry_point(\"supervisor\")\n",
+    "chain = research_graph.compile()\n",
+    "\n",
+    "# The following functions interoperate between the top level graph state\n",
+    "# and the state of the research sub-graph\n",
+    "# this makes it so that the states of each graph don't get intermixed\n",
+    "def enter_chain(message: str):\n",
+    "    results = {\n",
+    "        \"messages\": [HumanMessage(content=message)],\n",
+    "    }\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "\n",
+    "research_chain = (\n",
+    "    enter_chain\n",
+    "    | chain\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63ee8f2c-fbde-427b-ba54-ae0c7ce5fbfb",
+   "metadata": {},
+   "source": [
+    "We can give this team work directly. Try it out below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "912b0604-a178-4246-a36f-2dedae606680",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'supervisor': {'next': 'Search'}}\n",
+      "---\n",
+      "{'Search': {'messages': [HumanMessage(content='Taylor Swift\\'s next tour, named \"The Eras Tour,\" has dates scheduled for both 2023 and 2024. Some of the upcoming dates mentioned in the search results include:\\n\\n- November 25-26, 2023, in São Paulo, Brazil at the Allianz Parque\\n- February 7-9, 2024, in Tokyo, Japan at the Tokyo Dome\\n\\nFor a complete and detailed list of all the dates and locations for Taylor Swift\\'s \"The Eras Tour,\" you would need to visit official sources or ticketing websites such as Ticketmaster, as the search results suggest additional dates have been added due to overwhelming demand. The tour is set to wrap up in Indianapolis on November 3, 2024. Keep in mind that the schedule may be subject to change, and it\\'s a good idea to verify the dates on an official ticketing or event site for the latest information.', name='Search')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'FINISH'}}\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in research_chain.stream(\n",
+    "        \"when is Taylor Swift's next tour?\",\n",
+    "        {\"recursion_limit\": 100}\n",
+    "    ):\n",
+    "    if \"__end__\" not in s:\n",
+    "        print(s)\n",
+    "        print(\"---\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "749b99ab-f6f0-4c5d-a90b-10102465d186",
+   "metadata": {},
+   "source": [
+    "### Document Writing Team\n",
+    "\n",
+    "Create the document writing team below using a similar approach. This time, we will give each agent access to different file-writing tools.\n",
+    "\n",
+    "Note that we are giving file-system access to our agent here, which is not safe in all cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1bcdbf44-9481-430c-8429-fa142ed8a626",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "# Document writing team graph state\n",
+    "class DocWritingState(TypedDict):\n",
+    "    # This tracks the team's conversation internally\n",
+    "    messages: Annotated[List[BaseMessage], operator.add]\n",
+    "    # This provides each worker with context on the others' skill sets\n",
+    "    team_members: str\n",
+    "    # This is how the supervisor tells langgraph who to work next\n",
+    "    next: str\n",
+    "    # This tracks the shared directory state\n",
+    "    current_files: str\n",
+    "\n",
+    "\n",
+    "# This will be run before each worker agent begins work\n",
+    "# It makes it so they are more aware of the current state\n",
+    "# of the working directory.\n",
+    "def prelude(state):\n",
+    "    written_files = []\n",
+    "    if not WORKING_DIRECTORY.exists():\n",
+    "        WORKING_DIRECTORY.mkdir()\n",
+    "    try:\n",
+    "        written_files = [\n",
+    "            f.relative_to(WORKING_DIRECTORY) for f in WORKING_DIRECTORY.rglob(\"*\")\n",
+    "        ]\n",
+    "    except:\n",
+    "        pass\n",
+    "    if not written_files:\n",
+    "        return {**state, \"current_files\": \"No files written.\"}\n",
+    "    return {\n",
+    "        **state,\n",
+    "        \"current_files\": \"\\nBelow are files your team has written to the directory:\\n\"\n",
+    "        + \"\\n\".join([f\" - {f}\" for f in written_files]),\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
+    "\n",
+    "doc_writer_agent = create_agent(\n",
+    "    llm,\n",
+    "    [write_document, edit_document, read_document],\n",
+    "    \"You are an expert writing a research document.\\n\"\n",
+    "    # The {current_files} value is populated automatically by the graph state\n",
+    "    \"Below are files currently in your directory:\\n{current_files}\",\n",
+    ")\n",
+    "# Injects current directory working state before each call\n",
+    "context_aware_doc_writer_agent = prelude | doc_writer_agent\n",
+    "doc_writing_node = functools.partial(agent_node, agent=context_aware_doc_writer_agent, name=\"Doc Writer\")\n",
+    "\n",
+    "note_taking_agent = create_agent(\n",
+    "    llm,\n",
+    "    [create_outline, read_document],\n",
+    "    \"You are an expert senior researcher tasked with writing a paper outline and\"\n",
+    "    \" taking notes to craft a perfect paper.{current_files}\",\n",
+    ")\n",
+    "context_aware_note_taking_agent = prelude | note_taking_agent\n",
+    "note_taking_node = functools.partial(agent_node, agent=context_aware_note_taking_agent, name=\"Note Taker\")\n",
+    "\n",
+    "chart_generating_agent = create_agent(\n",
+    "    llm,\n",
+    "    [read_document, python_repl],\n",
+    "    \"You are a data viz expert tasked with generating charts for a research project.\"\n",
+    "    \"{current_files}\",\n",
+    ")\n",
+    "context_aware_chart_generating_agent = prelude | chart_generating_agent\n",
+    "chart_generating_node = functools.partial(agent_node, agent=context_aware_note_taking_agent, name=\"Chart Generator\")\n",
+    "\n",
+    "doc_writing_supervisor = create_team_supervisor(\n",
+    "    llm,\n",
+    "    \"You are a supervisor tasked with managing a conversation between the\"\n",
+    "    \" following workers:  {team_members}. Given the following user request,\"\n",
+    "    \" respond with the worker to act next. Each worker will perform a\"\n",
+    "    \" task and respond with their results and status. When finished,\"\n",
+    "    \" respond with FINISH.\",\n",
+    "    [\"Doc Writer\", \"Note Taker\", \"Chart Generator\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aee2cd9b-29aa-458e-903d-4e49179e5d59",
+   "metadata": {},
+   "source": [
+    "With the objects themselves created, we can form the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9c5c644f-8966-4d2e-98d2-80d73520e9fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the graph here:\n",
+    "# Note that we have unrolled the loop for the sake of this doc\n",
+    "authoring_graph = StateGraph(DocWritingState)\n",
+    "authoring_graph.add_node(\"Doc Writer\", doc_writing_node)\n",
+    "authoring_graph.add_node(\"Note Taker\", note_taking_node)\n",
+    "authoring_graph.add_node(\"Chart Generator\", chart_generating_node)\n",
+    "authoring_graph.add_node(\"supervisor\", doc_writing_supervisor)\n",
+    "\n",
+    "# Add the edges that always occur\n",
+    "authoring_graph.add_edge(\"Doc Writer\", \"supervisor\")\n",
+    "authoring_graph.add_edge(\"Note Taker\", \"supervisor\")\n",
+    "authoring_graph.add_edge(\"Chart Generator\", \"supervisor\")\n",
+    "\n",
+    "# Add the edges where routing applies\n",
+    "authoring_graph.add_conditional_edges(\n",
+    "    \"supervisor\",\n",
+    "    lambda x: x[\"next\"],\n",
+    "    {\n",
+    "        \"Doc Writer\": \"Doc Writer\",\n",
+    "        \"Note Taker\": \"Note Taker\",\n",
+    "        \"Chart Generator\": \"Chart Generator\",\n",
+    "        \"FINISH\": END\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "authoring_graph.set_entry_point(\"supervisor\")\n",
+    "chain = research_graph.compile()\n",
+    "\n",
+    "# The following functions interoperate between the top level graph state\n",
+    "# and the state of the research sub-graph\n",
+    "# this makes it so that the states of each graph don't get intermixed\n",
+    "def enter_chain(message: str, members: List[str]):\n",
+    "    results = {\n",
+    "        \"messages\": [HumanMessage(content=message)],\n",
+    "        \"team_members\": \", \".join(members)\n",
+    "    }\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "# We re-use the enter/exit functions to wrap the graph\n",
+    "authoring_chain = (\n",
+    "    functools.partial(enter_chain, members=authoring_graph.nodes)\n",
+    "    | authoring_graph.compile()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9860fd46-c24d-40a5-a6ba-e8fddcd43369",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'supervisor': {'next': 'Note Taker'}}\n",
+      "---\n",
+      "{'Note Taker': {'messages': [HumanMessage(content='The outline and poem have been successfully written and saved. The poem, titled \"Whispers of the Wind,\" captures the essence of the wind\\'s journey from a gentle beginning to a powerful crescendo and then a calming end, concluding with the cyclical nature of this elemental force.\\n\\nHere is the completed poem:\\n\\n---\\n\\n**Whispers of the Wind**\\n\\nEmbracing the essence of nature\\'s breath,\\nA zephyr stirs, the dawn\\'s own cheer,\\nWhispering secrets, meant for no ear,\\nThe gentle beginnings, a soft caress.\\n\\nA gale ascends, midday\\'s fierce guest,\\nTwirling leaves in a wild, untamed dance,\\nNature\\'s raw power in its vast expanse,\\nThe crescendo of gusts, a forceful dance.\\n\\nAs dusk falls, the tempest does wane,\\nA soft sighing through the willow\\'s mane,\\nIn the quietude, peace finds its chance,\\nThe calming aftermath, a serene trance.\\n\\nThrough whispers, roars, and silent chants,\\nThe wind carries on, no pause nor glance,\\nIn its journey, a boundless, spirited lance,\\nThe enduring cycle, a timeless romance.\\n\\n---\\n\\nThe poem is a testament to the wind\\'s ever-present and ever-changing presence in the world around us.', name='Note Taker')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'FINISH'}}\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in authoring_chain.stream(\n",
+    "        \"Write an outline for poem and then write the poem to disk.\",\n",
+    "        {\"recursion_limit\": 100}\n",
+    "    ):\n",
+    "    if \"__end__\" not in s:\n",
+    "        print(s)\n",
+    "        print(\"---\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4b5b08d-9a9a-474a-94b4-f7aaa8ff19e6",
+   "metadata": {},
+   "source": [
+    "## Add Layers\n",
+    "\n",
+    "In this design, we are enforcing a top-down planning policy. We've created two graphs already, but we have to decide how to route work between the two.\n",
+    "\n",
+    "We'll create a _third_ graph to orchestrate the previous two, and add some connectors to define how this top-level state is shared between the different graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "95ae7e52-92ed-41a3-88c4-21b6d7c8b041",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.messages import AIMessage, BaseMessage, HumanMessage\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
+    "\n",
+    "supervisor_node = create_team_supervisor(\n",
+    "    llm,\n",
+    "    \"You are a supervisor tasked with managing a conversation between the\"\n",
+    "    \" following teams: {team_members}. Given the following user request,\"\n",
+    "    \" respond with the worker to act next. Each worker will perform a\"\n",
+    "    \" task and respond with their results and status. When finished,\"\n",
+    "    \" respond with FINISH.\",\n",
+    "    [\"Research team\", \"Paper writing team\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4880e573-612f-4d24-97c1-2079382a4a2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Top-level graph state\n",
+    "class State(TypedDict):\n",
+    "    messages: Annotated[List[BaseMessage], operator.add]\n",
+    "    next: str\n",
+    "\n",
+    "def get_last_message(state: State) -> str:\n",
+    "    return state[\"messages\"][-1].content\n",
+    "\n",
+    "\n",
+    "def join_graph(response: dict):\n",
+    "    return {\"messages\": [response[\"messages\"][-1]]}\n",
+    "\n",
+    "# Define the graph.\n",
+    "super_graph = StateGraph(State)\n",
+    "# First add the nodes, which will do the work\n",
+    "super_graph.add_node(\"Research team\", get_last_message | research_chain | join_graph)\n",
+    "super_graph.add_node(\n",
+    "    \"Paper writing team\", get_last_message | authoring_chain | join_graph\n",
+    ")\n",
+    "super_graph.add_node(\"supervisor\", supervisor_node)\n",
+    "\n",
+    "# Define the graph connections, which controls how the logic\n",
+    "# propagates through the program\n",
+    "super_graph.add_edge(\"Research team\", \"supervisor\")\n",
+    "super_graph.add_edge(\"Paper writing team\", \"supervisor\")\n",
+    "super_graph.add_conditional_edges(\n",
+    "    \"supervisor\",\n",
+    "    lambda x: x[\"next\"],\n",
+    "    {\n",
+    "        \"Paper writing team\": \"Paper writing team\",\n",
+    "        \"Research team\": \"Research team\",\n",
+    "        \"FINISH\": END\n",
+    "    }\n",
+    ")\n",
+    "super_graph.set_entry_point(\"supervisor\")\n",
+    "super_graph = super_graph.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6b8badbf-d728-44bd-a2a7-5b4e587c92fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.', name='Web Scraper')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='Based on the information gathered from the provided URLs, here is a detailed research report on the North American sturgeon along with a chart that summarizes some of the key data:\\n\\n**North American Sturgeon Research Report**\\n\\n**Overview:**\\nNorth American sturgeons are ancient fish that belong to the Acipenseridae family. These bony, bottom-dwelling fish are characterized by their elongated bodies, scutes (bony plates), and barbels near the mouth. They are anadromous, meaning they migrate from saltwater to freshwater to spawn.\\n\\n**Species and Distribution:**\\n- Atlantic Sturgeon (*Acipenser oxyrinchus*): Found along the East Coast from Canada to Florida.\\n- Green Sturgeon (*Acipenser medirostris*): Found on the West Coast from Alaska to California.\\n- Shortnose Sturgeon (*Acipenser brevirostrum*): Distributed along the East Coast in estuarine and riverine habitats.\\n\\n**Conservation Status:**\\n- Atlantic Sturgeon: Multiple distinct population segments (DPSs) listed as endangered or threatened under the Endangered Species Act (ESA).\\n- Green Sturgeon: Southern DPS listed as threatened under the ESA.\\n- Shortnose Sturgeon: Listed as endangered throughout its range under the ESA.\\n\\n**Threats:**\\n- Bycatch in commercial fisheries.\\n- Habitat degradation and loss due to dams, pollution, and development.\\n- Climate change affecting water temperatures and spawning conditions.\\n- Vessel strikes, particularly for Atlantic sturgeon in high-traffic rivers.\\n\\n**Conservation Efforts:**\\nEfforts include habitat restoration, removal of barriers to improve fish passage, protection from bycatch, and public education. Critical habitat has been designated for certain species, and regulations prohibit the retention of green sturgeon in recreational and commercial fisheries.\\n\\n**Chart Information:**\\nThe chart would include species names, conservation status, geographic distribution, and key threats.\\n\\n| Species             | Conservation Status | Distribution                        | Key Threats                                    |\\n|---------------------|---------------------|-------------------------------------|------------------------------------------------|\\n| Atlantic Sturgeon   | Endangered/Threatened | East Coast (Canada to Florida)     | Bycatch, habitat loss, vessel strikes          |\\n| Green Sturgeon      | Southern DPS Threatened | West Coast (Alaska to California) | Bycatch, habitat degradation, climate change   |\\n| Shortnose Sturgeon  | Endangered            | East Coast estuarine and riverine habitats | Bycatch, habitat degradation, dams          |\\n\\n**Conclusion:**\\nNorth American sturgeons face numerous threats that have led to a decline in their populations. However, ongoing conservation efforts, including habitat restoration and protective regulations, are crucial in supporting their recovery and ensuring the survival of these ancient and ecologically important species.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Paper writing team'}}\n",
+      "---\n",
+      "{'Paper writing team': {'messages': [HumanMessage(content='The contents of the \"AnalysisOfWhispersOfTheWindPoem.txt\" file have been successfully retrieved. It appears to contain the structured paper that was conceptualized earlier, including the introduction, a detailed analysis of the poem\\'s structure and content, exploration of poetic devices and imagery, discussion on the philosophical and environmental implications, and the paper\\'s conclusion.\\n\\nWith this analysis, the paper provides a comprehensive examination of the poem \"Whispers of the Wind,\" capturing the essence of the poem\\'s use of the wind as a symbol to articulate themes related to the natural world. It reflects on how the poet utilizes language and form to convey the power and beauty of nature, as well as the philosophical undertones that underscore human connection with the environment.\\n\\nThe conclusion of the paper emphasizes the significance of the poem in fostering a greater appreciation for nature and highlights the relevance of its message in the context of contemporary environmental discourse. This analysis serves as an important contribution to the understanding of the poem\\'s artistic and thematic dimensions.', name='Doc Writer')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'Research team'}}\n",
+      "---\n",
+      "{'Research team': {'messages': [HumanMessage(content='The contents of the \"AnalysisOfWhispersOfTheWindPoem.txt\" file have been successfully retrieved. It appears to contain the structured paper that was conceptualized earlier, including the introduction, a detailed analysis of the poem\\'s structure and content, exploration of poetic devices and imagery, discussion on the philosophical and environmental implications, and the paper\\'s conclusion.\\n\\nWith this analysis, the paper provides a comprehensive examination of the poem \"Whispers of the Wind,\" capturing the essence of the poem\\'s use of the wind as a symbol to articulate themes related to the natural world. It reflects on how the poet utilizes language and form to convey the power and beauty of nature, as well as the philosophical undertones that underscore human connection with the environment.\\n\\nThe conclusion of the paper emphasizes the significance of the poem in fostering a greater appreciation for nature and highlights the relevance of its message in the context of contemporary environmental discourse. This analysis serves as an important contribution to the understanding of the poem\\'s artistic and thematic dimensions.')]}}\n",
+      "---\n",
+      "{'supervisor': {'next': 'FINISH'}}\n",
+      "---\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in super_graph.stream(\n",
+    "        {\n",
+    "            \"messages\": [\n",
+    "                HumanMessage(content=\"Write a brief research report on the North American sturgeon. Include a chart.\")\n",
+    "            ],\n",
+    "        },\n",
+    "        {\"recursion_limit\": 150},\n",
+    "    ):\n",
+    "    if \"__end__\" not in s:\n",
+    "        print(s)\n",
+    "        print(\"---\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb1b06f5-648c-459c-84f3-431807373782",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/examples/multi_agent/multi-agent-collaboration.ipynb b/examples/multi_agent/multi-agent-collaboration.ipynb
new file mode 100644
index 000000000..8afa66194
--- /dev/null
+++ b/examples/multi_agent/multi-agent-collaboration.ipynb
@@ -0,0 +1,520 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39fd1948-b5c3-48c4-b10e-2ae7e8c83334",
+   "metadata": {},
+   "source": [
+    "# Basic Multi-agent Collaboration\n",
+    "\n",
+    "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n",
+    "\n",
+    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\".\n",
+    "\n",
+    "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n",
+    "\n",
+    "The resulting graph will look something like the following diagram:\n",
+    "\n",
+    "![multi_agent diagram](./img/simple_multi_agent_diagram.png)\n",
+    "\n",
+    "Before we get started, a quick note: this and other multi-agent notebooks are designed to show _how_ you can implement certain design patterns in LangGraph. If the pattern suits your needs, we recommend combining it with some of the other fundamental patterns described elsewhere in the docs for best performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d7b6dcc-c985-46e2-8457-7e6b0298b950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U langchain langchain_openai langsmith pandas langchain_experimental matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "743c19df-6da9-4d1e-b2d2-ea40080b9fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_if_undefined(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass(f\"Please provide your {var}\")\n",
+    "\n",
+    "\n",
+    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
+    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
+    "_set_if_undefined(\"TAVILY_API_KEY\")\n",
+    "\n",
+    "# Optional, add tracing in LangSmith\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "075c91c3-c249-471d-b259-41975faa83fb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4344a7-21df-4d54-90d2-9d19b3416ffb",
+   "metadata": {},
+   "source": [
+    "## Create Agents\n",
+    "\n",
+    "The following helper functions will help create agents. These agents will then be nodes in the graph.\n",
+    "\n",
+    "You can skip ahead if you just want to see what the graph looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4325a10e-38dc-4a98-9004-e1525eaba377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain_core.messages import (\n",
+    "    AIMessage,\n",
+    "    BaseMessage,\n",
+    "    ChatMessage,\n",
+    "    FunctionMessage,\n",
+    "    HumanMessage,\n",
+    ")\n",
+    "from langchain.tools.render import format_tool_to_openai_function\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation\n",
+    "\n",
+    "\n",
+    "def create_agent(llm, tools, system_message: str):\n",
+    "    \"\"\"Create an agent.\"\"\"\n",
+    "    functions = [format_tool_to_openai_function(t) for t in tools]\n",
+    "\n",
+    "    prompt = ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\n",
+    "                \"system\",\n",
+    "                \"You are a helpful AI assistant, collaborating with other assistants.\"\n",
+    "                \" Use the provided tools to progress towards answering the question.\"\n",
+    "                \" If you are unable to fully answer, that's OK, another assistant with different tools \"\n",
+    "                \" will help where you left off. Execute what you can to make progress.\"\n",
+    "                \" If you or any of the other assistants have the final answer or deliverable,\"\n",
+    "                \" prefix your response with FINAL ANSWER so the team knows to stop.\"\n",
+    "                \" You have access to the following tools: {tool_names}.\\n{system_message}\",\n",
+    "            ),\n",
+    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
+    "        ]\n",
+    "    )\n",
+    "    prompt = prompt.partial(system_message=system_message)\n",
+    "    prompt = prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
+    "    return prompt | llm.bind_functions(functions)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b40de2-5dd4-4d5b-882e-577210723ff4",
+   "metadata": {},
+   "source": [
+    "## Define tools\n",
+    "\n",
+    "We will also define some tools that our agents will use in the future"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ca076f3b-a729-4ca9-8f91-05c2ba58d610",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.tools import tool\n",
+    "from typing import Annotated\n",
+    "from langchain_experimental.utilities import PythonREPL\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "tavily_tool = TavilySearchResults(max_results=5)\n",
+    "\n",
+    "# Warning: This executes code locally, which can be unsafe when not sandboxed\n",
+    "\n",
+    "repl = PythonREPL()\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def python_repl(\n",
+    "    code: Annotated[str, \"The python code to execute to generate your chart.\"]\n",
+    "):\n",
+    "    \"\"\"Use this to execute python code. If you want to see the output of a value,\n",
+    "    you should print it out with `print(...)`. This is visible to the user.\"\"\"\n",
+    "    try:\n",
+    "        result = repl.run(code)\n",
+    "    except BaseException as e:\n",
+    "        return f\"Failed to execute. Error: {repr(e)}\"\n",
+    "    return f\"Succesfully executed:\\n```python\\n{code}\\n```\\nStdout: {result}\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1b54c0c-0b09-408b-abc5-86308929afb6",
+   "metadata": {},
+   "source": [
+    "## Create graph\n",
+    "\n",
+    "Now that we've defined our tools and made some helper functions, will create the individual agents below and tell them how to talk to each other using LangGraph."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c6a8c3c-86a0-46aa-b970-ab070fb787d9",
+   "metadata": {},
+   "source": [
+    "### Define State\n",
+    "\n",
+    "We first define the state of the graph. This will just a list of messages, along with a key to track the most recent sender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "290c91d4-f6f4-443c-8181-233d39102974",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, List, Sequence, Tuple, TypedDict, Union\n",
+    "\n",
+    "from langchain.agents import create_openai_functions_agent\n",
+    "from langchain.tools.render import format_tool_to_openai_function\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "# This defines the object that is passed between each node\n",
+    "# in the graph. We will create different nodes for each agent and tool\n",
+    "class AgentState(TypedDict):\n",
+    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
+    "    sender: str"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "911a283e-ea04-40c1-b792-f9e5f7d81203",
+   "metadata": {},
+   "source": [
+    "### Define Agent Nodes\n",
+    "\n",
+    "We now need to define the nodes. First, let's define the nodes for the agents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "71b790ca-9cef-4b22-b469-4b1d5d8424d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import functools\n",
+    "\n",
+    "\n",
+    "# Helper function to create a node for a given agent\n",
+    "def agent_node(state, agent, name):\n",
+    "    result = agent.invoke(state)\n",
+    "    # We convert the agent output into a format that is suitable to append to the global state\n",
+    "    if isinstance(result, FunctionMessage):\n",
+    "        pass\n",
+    "    else:\n",
+    "        result = HumanMessage(**result.dict(exclude={\"type\", \"name\"}), name=name)\n",
+    "    return {\n",
+    "        \"messages\": [result],\n",
+    "        # Since we have a strict workflow, we can\n",
+    "        # track the sender so we know who to pass to next.\n",
+    "        \"sender\": name,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
+    "\n",
+    "# Research agent and node\n",
+    "research_agent = create_agent(\n",
+    "    llm, \n",
+    "    [tavily_tool], \n",
+    "    system_message=\"You should provide accurate data for the chart generator to use.\",\n",
+    ")\n",
+    "research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n",
+    "\n",
+    "# Chart Generator\n",
+    "chart_agent = create_agent(\n",
+    "    llm,\n",
+    "    [python_repl],\n",
+    "    system_message=\"Any charts you display will be visible by the user.\",\n",
+    ")\n",
+    "chart_node = functools.partial(agent_node, agent=chart_agent, name=\"Chart Generator\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71c7f1b2-24a3-4340-bcb2-feb22e344fb6",
+   "metadata": {},
+   "source": [
+    "### Define Tool Node\n",
+    "\n",
+    "We now define a node to run the tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d9a79c76-5c7c-42f6-91cf-635bc8305804",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [tavily_tool, python_repl]\n",
+    "tool_executor = ToolExecutor(tools)\n",
+    "\n",
+    "def tool_node(state):\n",
+    "    \"\"\"This runs tools in the graph\n",
+    "\n",
+    "    It takes in an agent action and calls that tool and returns the result.\"\"\"\n",
+    "    messages = state[\"messages\"]\n",
+    "    # Based on the continue condition\n",
+    "    # we know the last message involves a function call\n",
+    "    last_message = messages[-1]\n",
+    "    # We construct an ToolInvocation from the function_call\n",
+    "    tool_input = json.loads(\n",
+    "        last_message.additional_kwargs[\"function_call\"][\"arguments\"]\n",
+    "    )\n",
+    "    # We can pass single-arg inputs by value\n",
+    "    if len(tool_input) == 1 and \"__arg1\" in tool_input:\n",
+    "        tool_input = next(iter(tool_input.values()))\n",
+    "    tool_name = last_message.additional_kwargs[\"function_call\"][\"name\"]\n",
+    "    action = ToolInvocation(\n",
+    "        tool=tool_name,\n",
+    "        tool_input=tool_input,\n",
+    "    )\n",
+    "    # We call the tool_executor and get back a response\n",
+    "    response = tool_executor.invoke(action)\n",
+    "    # We use the response to create a FunctionMessage\n",
+    "    function_message = FunctionMessage(\n",
+    "        content=f\"{tool_name} response: {str(response)}\", name=action.tool\n",
+    "    )\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [function_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcb30498-dbc4-4b20-980f-da08ebc9da56",
+   "metadata": {},
+   "source": [
+    "### Define Edge Logic\n",
+    "\n",
+    "We can define some of the edge logic that is needed to decide what to do based on results of the agents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4f4b4d37-e8a3-4abb-8d42-eaea26016f35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Either agent can decide to end\n",
+    "def router(state):\n",
+    "    # This is the router\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    if \"function_call\" in last_message.additional_kwargs:\n",
+    "        # The previus agent is invoking a tool\n",
+    "        return \"call_tool\"\n",
+    "    if \"FINAL ANSWER\" in last_message.content:\n",
+    "        # Any agent decided the work is done\n",
+    "        return \"end\"\n",
+    "    return \"continue\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9359c34-e191-43a2-a3d4-f2dea636dfd2",
+   "metadata": {},
+   "source": [
+    "### Define the Graph\n",
+    "\n",
+    "We can now put it all together and define the graph!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4dce3901-6ad5-4df5-8528-6e865cf96cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "workflow = StateGraph(AgentState)\n",
+    "\n",
+    "workflow.add_node(\"Researcher\", research_node)\n",
+    "workflow.add_node(\"Chart Generator\", chart_node)\n",
+    "workflow.add_node(\"call_tool\", tool_node)\n",
+    "\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"Researcher\",\n",
+    "    router,\n",
+    "    {\"continue\": \"Chart Generator\", \"call_tool\": \"call_tool\", \"end\": END},\n",
+    ")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"Chart Generator\",\n",
+    "    router,\n",
+    "    {\"continue\": \"Researcher\", \"call_tool\": \"call_tool\", \"end\": END},\n",
+    ")\n",
+    "\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"call_tool\",\n",
+    "    # Each agent node updates the 'sender' field\n",
+    "    # the tool calling node does not, meaning\n",
+    "    # this edge will route back to the original agent\n",
+    "    # who invoked the tool\n",
+    "    lambda x: x[\"sender\"],\n",
+    "    {\n",
+    "        \"Researcher\": \"Researcher\",\n",
+    "        \"Chart Generator\": \"Chart Generator\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.set_entry_point(\"Researcher\")\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c9447e7-9ab6-43eb-8ae6-9b52f8ba8425",
+   "metadata": {},
+   "source": [
+    "## Invoke\n",
+    "\n",
+    "With the graph created, you can invoke it! Let's have it chart some stats for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "176a99b0-b457-45cf-8901-90facaa852da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Researcher': {'messages': [HumanMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"UK GDP data for the past 5 years\"}', 'name': 'tavily_search_results_json'}}, name='Researcher')], 'sender': 'Researcher'}}\n",
+      "----\n",
+      "{'call_tool': {'messages': [FunctionMessage(content=\"tavily_search_results_json response: [{'url': 'https://www.statista.com/topics/3795/gdp-of-the-uk/', 'content': 'Monthly GDP of the UK 2019-2023  Monthly GDP growth of the UK 2019-2023  Quarterly GDP growth of the UK 2019-2023  Quarterly GDP per capita in the UK 2019-2023Monthly index of gross domestic product in the United Kingdom from January 2019 to November 2023 (2019=100) GVA of the UK 2022, by sector GVA of the UK 2022, by sector Gross value added...'}, {'url': 'https://www.statista.com/topics/6500/the-british-economy/', 'content': 'Monthly GDP growth of the UK 2020-2023  Quarterly GDP growth of the UK 2015-2023  Monthly growth of gross domestic product in the United Kingdom from January 2020 to September 2023  Monthly GDP of the UK 1997-2023Economy The UK economy - Statistics & Facts United Kingdom The gross domestic product of the British economy was 2.23 trillion British pounds in 2022 and was the fifth- largest global...'}, {'url': 'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/november2023', 'content': 'data from January 2022 to September 2023, as published in our\\\\xa0GDP quarterly national accounts, UK: July to September  Figure 1: UK GDP is estimated to have grown by 0.3% in November 2023  GDP monthly estimate, UK: November 2023  Source: Monthly GDP estimate from Office for National Statistics The main reasons for revisions in October 2023 are:Monthly GDP is estimated to have grown by 0.3% in November 2023, following an unrevised fall of 0.3% in October 2023. This release provides data for November 2023 and has revisions from January 2022 to September 2023, consistent with our Quarterly national accounts bulletin, published on 22 December 2023. October 2023 is also open for revision.'}, {'url': 'https://www.statista.com/statistics/375195/gdp-growth-forecast-uk/', 'content': 'Forecasted annual growth of gross domestic product in the United Kingdom from 2000 to 2028 Additional Information  GDP growth forecast for the UK 2000-2028 Weak economic growth throughout 2023  United Kingdom 2000 to 2028 *Forecast data Other statistics on the topicThe UK economy Economy  Economy Average annual earnings for full-time employees in the UK 1999-2023 Economy Inflation rate in the UK 1989-2023In 2022 the gross domestic product (GDP) of the United Kingdom grew by four percent but is expected to grow by just 0.6 percent in 2023, and 0.7 percent in 2024. More robust growth is...'}, {'url': 'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/october2023', 'content': 'GDP monthly estimate, UK: October 2023  Figure 1: UK GDP is estimated to have fallen by 0.3% in October 2023  Monthly estimate of gross domestic product (GDP) containing constant price gross value added (GVA) data for the UK.  domestic product (GDP) in October 2023.1. Main points Monthly real gross domestic product (GDP) is estimated to have shown no growth in the three months to October 2023, compared with the three months to July 2023. Monthly GDP...'}]\", name='tavily_search_results_json')]}}\n",
+      "----\n",
+      "{'Researcher': {'messages': [HumanMessage(content=\"The search results provide information on the UK's GDP, including monthly and quarterly data from various years, but they do not provide a clear year-on-year GDP figure for each of the past five years. To graph the UK's GDP over the past five years, we need annual GDP figures for each year.\\n\\nI will attempt to extract the annual GDP figures mentioned in the search results and then graph them. However, please note that the information provided in the search results may be incomplete or not formatted in a way that allows for a straightforward extraction of annual GDP figures for each of the last five years. If necessary, I may need to perform additional searches to fill in any gaps.\\n\\nFrom the search results, we have the following relevant information regarding the UK's GDP:\\n\\n- The gross domestic product of the British economy was 2.23 trillion British pounds in 2022.\\n- In 2022, the gross domestic product (GDP) of the United Kingdom grew by four percent.\\n\\nUnfortunately, the search results do not provide explicit annual GDP figures for 2018, 2019, 2020, or 2021. Therefore, I will need to perform an additional search to find the missing GDP figures for these years. Let's proceed with that search.\", additional_kwargs={'function_call': {'arguments': '{\"query\":\"UK GDP 2018 2019 2020 2021\"}', 'name': 'tavily_search_results_json'}}, name='Researcher')], 'sender': 'Researcher'}}\n",
+      "----\n",
+      "{'call_tool': {'messages': [FunctionMessage(content='tavily_search_results_json response: [{\\'url\\': \\'https://www.statista.com/topics/3795/gdp-of-the-uk/\\', \\'content\\': \"GDP of the UK 2021, by country Gross domestic product of the United Kingdom in 2021, by country (in million GBP)  Monthly GDP of the UK 2019-2023  United Kingdom\\'s share of global gross domestic product (GDP) 2028  Quarterly GDP per capita in the UK 2019-2023In 2022, the gross domestic product of the United Kingdom amounted to approximately 2.2 trillion British pounds, compared with 2.14 trillion pounds in 2021, and 1.99 trillion in 2020. Although the ...\"}, {\\'url\\': \\'https://www.beta.ons.gov.uk/economy/grossdomesticproductgdp/compendium/unitedkingdomnationalaccountsthebluebook/2022/nationalaccountsataglance\\', \\'content\\': \"of UK GDP over 2020 and 2021  Figure 1: The UK economy increased by 7.5% in 2021, having seen the largest fall in over 300 years in 2020  accounts at a glance, UK National Accounts, The Blue Book: 2022  Figure 3: In 2021, nominal GDP in most of the G20 countries had recovered to its 2019 levelsIn 2021, the UK\\'s implied GDP deflator rose by 0.4%, the lowest among the developed countries shown (Figure 4). However, these comparisons are likely to reflect some of the measurement challenges over the coronavirus pandemic. Movements in the implied GDP deflator in 2020 and 2021 have been largely affected by the government consumption deflator.\"}, {\\'url\\': \\'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/december2021\\', \\'content\\': \\'GDP monthly estimate, UK : December 2021  gross domestic product (GDP), UK.  data from January 2021 to November 2021. This is consistent with GDP first quarterly estimate, UK: October to December  on gross domestic product (GDP) in December 2021, with output of these services increasing by 51% and 19% respectively2. Monthly GDP. Monthly real gross domestic product (GDP) is estimated to have fallen by 0.2% in December 2021, compared with a 0.7% growth in November 2021 (revised down from 0.9% growth). This release includes revisions to the monthly data back to January 2021, consistent with the first quarterly estimate of GDP.\\'}, {\\'url\\': \\'https://www.beta.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/september2021\\', \\'content\\': \\'GDP monthly estimate, UK: September 2021  2021, while detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.  More detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.  while detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.Source: Office for National Statistics - GDP monthly estimate. Download this chart. Image .csv .xls. Monthly real gross domestic product (GDP) grew by 0.6% in September 2021, and follows a revised 0.2% growth in August 2021 (down from 0.4% growth) and a revised 0.2% fall in July 2021 (down from a 0.1% fall).\\'}, {\\'url\\': \\'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/october2021\\', \\'content\\': \\'GDP monthly estimate, UK : October 2021  Figure 1: UK GDP is estimated to have grown by 0.1% in October 2021, but remains 0.5% below its pre-pandemic level  Monthly estimate of gross domestic product (GDP) containing constant price gross value added (GVA) data for the UK.  Source: Office for National Statistics – GDP monthly estimate NotesTable 1: UK GDP in October 2021 was 0.5% below its pre-pandemic level, however services has now returned to its pre-pandemic levelChange in output, percentage change, February 2020 to October 2021, UK. Source: Office for National Statistics - GDP monthly estimate. This table uses data from the output measure of GDP.\\'}]', name='tavily_search_results_json')]}}\n",
+      "----\n",
+      "{'Researcher': {'messages': [HumanMessage(content=\"Based on the search results, we can now extract some annual GDP figures for the UK:\\n\\n- In 2022, the gross domestic product of the United Kingdom amounted to approximately 2.2 trillion British pounds.\\n- In 2021, the gross domestic product of the United Kingdom was about 2.14 trillion British pounds.\\n- In 2020, the gross domestic product of the United Kingdom was about 1.99 trillion British pounds.\\n\\nUnfortunately, the search results still do not provide explicit figures for 2018 and 2019. To complete the dataset, we would need to perform another search or access historical economic data from a reliable source such as the Office for National Statistics (ONS) or an economic database. If additional searches are not possible, we may need to use these three data points to give a partial visualization of the trend. \\n\\nSince we cannot perform additional searches, I will provide the data points we have, and you will need to add the missing years' data manually if required:\\n\\n- 2020: 1.99 trillion GBP\\n- 2021: 2.14 trillion GBP\\n- 2022: 2.2 trillion GBP\\n\\nPlease note that the data for 2018 and 2019 is missing, and you would need to find these figures to complete the five-year view. With the data at hand, the line graph would only represent the years 2020 to 2022.\", name='Researcher')], 'sender': 'Researcher'}}\n",
+      "----\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Python REPL can execute arbitrary code. Use with caution.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Chart Generator': {'messages': [HumanMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"code\":\"import matplotlib.pyplot as plt\\\\n\\\\n# Define the data\\\\nyears = [2020, 2021, 2022]\\\\ngdp_values = [1.99, 2.14, 2.2] # Trillion GBP\\\\n\\\\n# Create the line graph\\\\nplt.figure(figsize=(10, 5))\\\\nplt.plot(years, gdp_values, marker=\\'o\\')\\\\n\\\\n# Labeling the graph\\\\nplt.title(\\'UK GDP from 2020 to 2022\\')\\\\nplt.xlabel(\\'Year\\')\\\\nplt.ylabel(\\'GDP (Trillion GBP)\\')\\\\nplt.grid(True)\\\\nplt.xticks(years)\\\\nplt.tight_layout()\\\\n\\\\n# Display the graph\\\\nplt.show()\"}', 'name': 'python_repl'}}, name='Chart Generator')], 'sender': 'Chart Generator'}}\n",
+      "----\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'call_tool': {'messages': [FunctionMessage(content=\"python_repl response: Succesfully executed:\\n```python\\nimport matplotlib.pyplot as plt\\n\\n# Define the data\\nyears = [2020, 2021, 2022]\\ngdp_values = [1.99, 2.14, 2.2] # Trillion GBP\\n\\n# Create the line graph\\nplt.figure(figsize=(10, 5))\\nplt.plot(years, gdp_values, marker='o')\\n\\n# Labeling the graph\\nplt.title('UK GDP from 2020 to 2022')\\nplt.xlabel('Year')\\nplt.ylabel('GDP (Trillion GBP)')\\nplt.grid(True)\\nplt.xticks(years)\\nplt.tight_layout()\\n\\n# Display the graph\\nplt.show()\\n```\\nStdout: \", name='python_repl')]}}\n",
+      "----\n",
+      "{'Chart Generator': {'messages': [HumanMessage(content=\"Here is the line graph representing the UK's GDP from 2020 to 2022:\\n\\n[Please see the graph above]\\n\\nNote that the data for 2018 and 2019 is missing, and the graph only shows the years for which we have data. If you acquire the missing figures, you can manually add them to the graph to complete the five-year view.\", name='Chart Generator')], 'sender': 'Chart Generator'}}\n",
+      "----\n",
+      "{'Researcher': {'messages': [HumanMessage(content=\"FINAL ANSWER:\\n\\nHere is the line graph representing the UK's GDP from 2020 to 2022:\\n\\n[Please see the graph above]\\n\\nNote that the data for 2018 and 2019 is missing, and the graph only shows the years for which we have data. If you acquire the missing figures, you can manually add them to the graph to complete the five-year view.\", name='Researcher')], 'sender': 'Researcher'}}\n",
+      "----\n",
+      "{'__end__': {'messages': [HumanMessage(content=\"Fetch the UK's GDP over the past 5 years, then draw a line graph of it. Once you code it up, finish.\"), HumanMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"UK GDP data for the past 5 years\"}', 'name': 'tavily_search_results_json'}}, name='Researcher'), FunctionMessage(content=\"tavily_search_results_json response: [{'url': 'https://www.statista.com/topics/3795/gdp-of-the-uk/', 'content': 'Monthly GDP of the UK 2019-2023  Monthly GDP growth of the UK 2019-2023  Quarterly GDP growth of the UK 2019-2023  Quarterly GDP per capita in the UK 2019-2023Monthly index of gross domestic product in the United Kingdom from January 2019 to November 2023 (2019=100) GVA of the UK 2022, by sector GVA of the UK 2022, by sector Gross value added...'}, {'url': 'https://www.statista.com/topics/6500/the-british-economy/', 'content': 'Monthly GDP growth of the UK 2020-2023  Quarterly GDP growth of the UK 2015-2023  Monthly growth of gross domestic product in the United Kingdom from January 2020 to September 2023  Monthly GDP of the UK 1997-2023Economy The UK economy - Statistics & Facts United Kingdom The gross domestic product of the British economy was 2.23 trillion British pounds in 2022 and was the fifth- largest global...'}, {'url': 'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/november2023', 'content': 'data from January 2022 to September 2023, as published in our\\\\xa0GDP quarterly national accounts, UK: July to September  Figure 1: UK GDP is estimated to have grown by 0.3% in November 2023  GDP monthly estimate, UK: November 2023  Source: Monthly GDP estimate from Office for National Statistics The main reasons for revisions in October 2023 are:Monthly GDP is estimated to have grown by 0.3% in November 2023, following an unrevised fall of 0.3% in October 2023. This release provides data for November 2023 and has revisions from January 2022 to September 2023, consistent with our Quarterly national accounts bulletin, published on 22 December 2023. October 2023 is also open for revision.'}, {'url': 'https://www.statista.com/statistics/375195/gdp-growth-forecast-uk/', 'content': 'Forecasted annual growth of gross domestic product in the United Kingdom from 2000 to 2028 Additional Information  GDP growth forecast for the UK 2000-2028 Weak economic growth throughout 2023  United Kingdom 2000 to 2028 *Forecast data Other statistics on the topicThe UK economy Economy  Economy Average annual earnings for full-time employees in the UK 1999-2023 Economy Inflation rate in the UK 1989-2023In 2022 the gross domestic product (GDP) of the United Kingdom grew by four percent but is expected to grow by just 0.6 percent in 2023, and 0.7 percent in 2024. More robust growth is...'}, {'url': 'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/october2023', 'content': 'GDP monthly estimate, UK: October 2023  Figure 1: UK GDP is estimated to have fallen by 0.3% in October 2023  Monthly estimate of gross domestic product (GDP) containing constant price gross value added (GVA) data for the UK.  domestic product (GDP) in October 2023.1. Main points Monthly real gross domestic product (GDP) is estimated to have shown no growth in the three months to October 2023, compared with the three months to July 2023. Monthly GDP...'}]\", name='tavily_search_results_json'), HumanMessage(content=\"The search results provide information on the UK's GDP, including monthly and quarterly data from various years, but they do not provide a clear year-on-year GDP figure for each of the past five years. To graph the UK's GDP over the past five years, we need annual GDP figures for each year.\\n\\nI will attempt to extract the annual GDP figures mentioned in the search results and then graph them. However, please note that the information provided in the search results may be incomplete or not formatted in a way that allows for a straightforward extraction of annual GDP figures for each of the last five years. If necessary, I may need to perform additional searches to fill in any gaps.\\n\\nFrom the search results, we have the following relevant information regarding the UK's GDP:\\n\\n- The gross domestic product of the British economy was 2.23 trillion British pounds in 2022.\\n- In 2022, the gross domestic product (GDP) of the United Kingdom grew by four percent.\\n\\nUnfortunately, the search results do not provide explicit annual GDP figures for 2018, 2019, 2020, or 2021. Therefore, I will need to perform an additional search to find the missing GDP figures for these years. Let's proceed with that search.\", additional_kwargs={'function_call': {'arguments': '{\"query\":\"UK GDP 2018 2019 2020 2021\"}', 'name': 'tavily_search_results_json'}}, name='Researcher'), FunctionMessage(content='tavily_search_results_json response: [{\\'url\\': \\'https://www.statista.com/topics/3795/gdp-of-the-uk/\\', \\'content\\': \"GDP of the UK 2021, by country Gross domestic product of the United Kingdom in 2021, by country (in million GBP)  Monthly GDP of the UK 2019-2023  United Kingdom\\'s share of global gross domestic product (GDP) 2028  Quarterly GDP per capita in the UK 2019-2023In 2022, the gross domestic product of the United Kingdom amounted to approximately 2.2 trillion British pounds, compared with 2.14 trillion pounds in 2021, and 1.99 trillion in 2020. Although the ...\"}, {\\'url\\': \\'https://www.beta.ons.gov.uk/economy/grossdomesticproductgdp/compendium/unitedkingdomnationalaccountsthebluebook/2022/nationalaccountsataglance\\', \\'content\\': \"of UK GDP over 2020 and 2021  Figure 1: The UK economy increased by 7.5% in 2021, having seen the largest fall in over 300 years in 2020  accounts at a glance, UK National Accounts, The Blue Book: 2022  Figure 3: In 2021, nominal GDP in most of the G20 countries had recovered to its 2019 levelsIn 2021, the UK\\'s implied GDP deflator rose by 0.4%, the lowest among the developed countries shown (Figure 4). However, these comparisons are likely to reflect some of the measurement challenges over the coronavirus pandemic. Movements in the implied GDP deflator in 2020 and 2021 have been largely affected by the government consumption deflator.\"}, {\\'url\\': \\'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/december2021\\', \\'content\\': \\'GDP monthly estimate, UK : December 2021  gross domestic product (GDP), UK.  data from January 2021 to November 2021. This is consistent with GDP first quarterly estimate, UK: October to December  on gross domestic product (GDP) in December 2021, with output of these services increasing by 51% and 19% respectively2. Monthly GDP. Monthly real gross domestic product (GDP) is estimated to have fallen by 0.2% in December 2021, compared with a 0.7% growth in November 2021 (revised down from 0.9% growth). This release includes revisions to the monthly data back to January 2021, consistent with the first quarterly estimate of GDP.\\'}, {\\'url\\': \\'https://www.beta.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/september2021\\', \\'content\\': \\'GDP monthly estimate, UK: September 2021  2021, while detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.  More detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.  while detail on the quarterly path can also be found in GDP first quarterly estimate, UK: July to September 2021.Source: Office for National Statistics - GDP monthly estimate. Download this chart. Image .csv .xls. Monthly real gross domestic product (GDP) grew by 0.6% in September 2021, and follows a revised 0.2% growth in August 2021 (down from 0.4% growth) and a revised 0.2% fall in July 2021 (down from a 0.1% fall).\\'}, {\\'url\\': \\'https://www.ons.gov.uk/economy/grossdomesticproductgdp/bulletins/gdpmonthlyestimateuk/october2021\\', \\'content\\': \\'GDP monthly estimate, UK : October 2021  Figure 1: UK GDP is estimated to have grown by 0.1% in October 2021, but remains 0.5% below its pre-pandemic level  Monthly estimate of gross domestic product (GDP) containing constant price gross value added (GVA) data for the UK.  Source: Office for National Statistics – GDP monthly estimate NotesTable 1: UK GDP in October 2021 was 0.5% below its pre-pandemic level, however services has now returned to its pre-pandemic levelChange in output, percentage change, February 2020 to October 2021, UK. Source: Office for National Statistics - GDP monthly estimate. This table uses data from the output measure of GDP.\\'}]', name='tavily_search_results_json'), HumanMessage(content=\"Based on the search results, we can now extract some annual GDP figures for the UK:\\n\\n- In 2022, the gross domestic product of the United Kingdom amounted to approximately 2.2 trillion British pounds.\\n- In 2021, the gross domestic product of the United Kingdom was about 2.14 trillion British pounds.\\n- In 2020, the gross domestic product of the United Kingdom was about 1.99 trillion British pounds.\\n\\nUnfortunately, the search results still do not provide explicit figures for 2018 and 2019. To complete the dataset, we would need to perform another search or access historical economic data from a reliable source such as the Office for National Statistics (ONS) or an economic database. If additional searches are not possible, we may need to use these three data points to give a partial visualization of the trend. \\n\\nSince we cannot perform additional searches, I will provide the data points we have, and you will need to add the missing years' data manually if required:\\n\\n- 2020: 1.99 trillion GBP\\n- 2021: 2.14 trillion GBP\\n- 2022: 2.2 trillion GBP\\n\\nPlease note that the data for 2018 and 2019 is missing, and you would need to find these figures to complete the five-year view. With the data at hand, the line graph would only represent the years 2020 to 2022.\", name='Researcher'), HumanMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"code\":\"import matplotlib.pyplot as plt\\\\n\\\\n# Define the data\\\\nyears = [2020, 2021, 2022]\\\\ngdp_values = [1.99, 2.14, 2.2] # Trillion GBP\\\\n\\\\n# Create the line graph\\\\nplt.figure(figsize=(10, 5))\\\\nplt.plot(years, gdp_values, marker=\\'o\\')\\\\n\\\\n# Labeling the graph\\\\nplt.title(\\'UK GDP from 2020 to 2022\\')\\\\nplt.xlabel(\\'Year\\')\\\\nplt.ylabel(\\'GDP (Trillion GBP)\\')\\\\nplt.grid(True)\\\\nplt.xticks(years)\\\\nplt.tight_layout()\\\\n\\\\n# Display the graph\\\\nplt.show()\"}', 'name': 'python_repl'}}, name='Chart Generator'), FunctionMessage(content=\"python_repl response: Succesfully executed:\\n```python\\nimport matplotlib.pyplot as plt\\n\\n# Define the data\\nyears = [2020, 2021, 2022]\\ngdp_values = [1.99, 2.14, 2.2] # Trillion GBP\\n\\n# Create the line graph\\nplt.figure(figsize=(10, 5))\\nplt.plot(years, gdp_values, marker='o')\\n\\n# Labeling the graph\\nplt.title('UK GDP from 2020 to 2022')\\nplt.xlabel('Year')\\nplt.ylabel('GDP (Trillion GBP)')\\nplt.grid(True)\\nplt.xticks(years)\\nplt.tight_layout()\\n\\n# Display the graph\\nplt.show()\\n```\\nStdout: \", name='python_repl'), HumanMessage(content=\"Here is the line graph representing the UK's GDP from 2020 to 2022:\\n\\n[Please see the graph above]\\n\\nNote that the data for 2018 and 2019 is missing, and the graph only shows the years for which we have data. If you acquire the missing figures, you can manually add them to the graph to complete the five-year view.\", name='Chart Generator'), HumanMessage(content=\"FINAL ANSWER:\\n\\nHere is the line graph representing the UK's GDP from 2020 to 2022:\\n\\n[Please see the graph above]\\n\\nNote that the data for 2018 and 2019 is missing, and the graph only shows the years for which we have data. If you acquire the missing figures, you can manually add them to the graph to complete the five-year view.\", name='Researcher')], 'sender': 'Researcher'}}\n",
+      "----\n"
+     ]
+    }
+   ],
+   "source": [
+    "for s in graph.stream(\n",
+    "    {\n",
+    "        \"messages\": [\n",
+    "            HumanMessage(\n",
+    "                content=\"Fetch the UK's GDP over the past 5 years,\"\n",
+    "                \" then draw a line graph of it.\"\n",
+    "                \" Once you code it up, finish.\"\n",
+    "            )\n",
+    "        ],\n",
+    "    },\n",
+    "    # Maximum number of steps to take in the graph\n",
+    "    {\"recursion_limit\": 150},\n",
+    "):\n",
+    "    print(s)\n",
+    "    print(\"----\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "010fc36e-4116-4758-bcac-b02c7dcd405d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/persistence.ipynb b/examples/persistence.ipynb
new file mode 100644
index 000000000..98edfb1b0
--- /dev/null
+++ b/examples/persistence.ipynb
@@ -0,0 +1,469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
+   "metadata": {},
+   "source": [
+    "# Persistence\n",
+    "\n",
+    "When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbd446a-808f-4394-be92-d45ab818953c",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First we need to install the packages required"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --quiet -U langchain langchain_openai tavily-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
+   "metadata": {},
+   "source": [
+    "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key: ········\n",
+      "Tavily API Key: ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
+    "os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
+   "metadata": {},
+   "source": [
+    "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21ac643b-cb06-4724-a80c-2862ba4773f1",
+   "metadata": {},
+   "source": [
+    "## Set up the tools\n",
+    "\n",
+    "We will first define the tools we want to use.\n",
+    "For this simple example, we will use a built-in search tool via Tavily.\n",
+    "However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "tools = [TavilySearchResults(max_results=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
+   "metadata": {},
+   "source": [
+    "We can now wrap these tools in a simple ToolExecutor.\n",
+    "This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n",
+    "A ToolInvocation is any class with `tool` and `tool_input` attribute.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import ToolExecutor\n",
+    "\n",
+    "tool_executor = ToolExecutor(tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5497ed70-fce3-47f1-9cad-46f912bad6a5",
+   "metadata": {},
+   "source": [
+    "## Set up the model\n",
+    "\n",
+    "Now we need to load the chat model we want to use.\n",
+    "Importantly, this should satisfy two criteria:\n",
+    "\n",
+    "1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
+    "2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
+    "\n",
+    "Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# We will set streaming=True so that we can stream tokens\n",
+    "# See the streaming section for more information on this.\n",
+    "model = ChatOpenAI(temperature=0, streaming=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a77995c0-bae2-4cee-a036-8688a90f05b9",
+   "metadata": {},
+   "source": [
+    "\n",
+    "After we've done this, we should make sure the model knows that it has these tools available to call.\n",
+    "We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.utils.function_calling import convert_to_openai_function\n",
+    "\n",
+    "functions = [convert_to_openai_function(t) for t in tools]\n",
+    "model = model.bind_functions(functions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
+   "metadata": {},
+   "source": [
+    "## Define the nodes\n",
+    "\n",
+    "We now need to define a few different nodes in our graph.\n",
+    "In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n",
+    "There are two main nodes we need for this:\n",
+    "\n",
+    "1. The agent: responsible for deciding what (if any) actions to take.\n",
+    "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
+    "\n",
+    "We will also need to define some edges.\n",
+    "Some of these edges may be conditional.\n",
+    "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
+    "The path that is taken is not known until that node is run (the LLM decides).\n",
+    "\n",
+    "1. Conditional Edge: after the agent is called, we should either:\n",
+    "   a. If the agent said to take an action, then the function to invoke tools should be called\n",
+    "   b. If the agent said that it was finished, then it should finish\n",
+    "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
+    "\n",
+    "Let's define the nodes, as well as a function to decide how what conditional edge to take."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.prebuilt import ToolInvocation\n",
+    "import json\n",
+    "from langchain_core.messages import FunctionMessage\n",
+    "\n",
+    "# Define the function that determines whether to continue or not\n",
+    "def should_continue(messages):\n",
+    "    last_message = messages[-1]\n",
+    "    # If there is no function call, then we finish\n",
+    "    if \"function_call\" not in last_message.additional_kwargs:\n",
+    "        return \"end\"\n",
+    "    # Otherwise if there is, we continue\n",
+    "    else:\n",
+    "        return \"continue\"\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(messages):\n",
+    "    response = model.invoke(messages)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return response\n",
+    "\n",
+    "# Define the function to execute tools\n",
+    "def call_tool(messages):\n",
+    "    # Based on the continue condition\n",
+    "    # we know the last message involves a function call\n",
+    "    last_message = messages[-1]\n",
+    "    # We construct an ToolInvocation from the function_call\n",
+    "    action = ToolInvocation(\n",
+    "        tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n",
+    "        tool_input=json.loads(last_message.additional_kwargs[\"function_call\"][\"arguments\"]),\n",
+    "    )\n",
+    "    # We call the tool_executor and get back a response\n",
+    "    response = tool_executor.invoke(action)\n",
+    "    # We use the response to create a FunctionMessage\n",
+    "    function_message = FunctionMessage(content=str(response), name=action.tool)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return function_message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
+   "metadata": {},
+   "source": [
+    "## Define the graph\n",
+    "\n",
+    "We can now put it all together and define the graph!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "812b4e70-4956-4415-8880-db48b3dcbad2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import MessageGraph, END\n",
+    "# Define a new graph\n",
+    "workflow = MessageGraph()\n",
+    "\n",
+    "# Define the two nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", call_model)\n",
+    "workflow.add_node(\"action\", call_tool)\n",
+    "\n",
+    "# Set the entrypoint as `agent`\n",
+    "# This means that this node is the first one called\n",
+    "workflow.set_entry_point(\"agent\")\n",
+    "\n",
+    "# We now add a conditional edge\n",
+    "workflow.add_conditional_edges(\n",
+    "    # First, we define the start node. We use `agent`.\n",
+    "    # This means these are the edges taken after the `agent` node is called.\n",
+    "    \"agent\",\n",
+    "    # Next, we pass in the function that will determine which node is called next.\n",
+    "    should_continue,\n",
+    "    # Finally we pass in a mapping.\n",
+    "    # The keys are strings, and the values are other nodes.\n",
+    "    # END is a special node marking that the graph should finish.\n",
+    "    # What will happen is we will call `should_continue`, and then the output of that\n",
+    "    # will be matched against the keys in this mapping.\n",
+    "    # Based on which one it matches, that node will then be called.\n",
+    "    {\n",
+    "        # If `tools`, then we call the tool node.\n",
+    "        \"continue\": \"action\",\n",
+    "        # Otherwise we finish.\n",
+    "        \"end\": END\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# We now add a normal edge from `tools` to `agent`.\n",
+    "# This means that after `tools` is called, `agent` node is called next.\n",
+    "workflow.add_edge('action', 'agent')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc9c8536-f90b-44fa-958d-5df016c66d8f",
+   "metadata": {},
+   "source": [
+    "**Persistence**\n",
+    "\n",
+    "To add in persistence, we pass in a checkpoint when compiling the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6845ed6a-d155-4105-9160-28849877248b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "\n",
+    "memory = SqliteSaver.from_conn_string(\":memory:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "79d29875-8aa8-434c-9f20-1c58346a6249",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally, we compile it!\n",
+    "# This compiles it into a LangChain Runnable,\n",
+    "# meaning you can use it as you would any other runnable\n",
+    "app = workflow.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
+   "metadata": {},
+   "source": [
+    "## Interacting with the Agent\n",
+    "\n",
+    "We can now interact with the agent and see that it remembers previous messages!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='Hello Bob! How can I assist you today?'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "inputs = [HumanMessage(content=\"hi! I'm bob\")]\n",
+    "for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
+    "    for k, v in event.items():\n",
+    "        if k != \"__end__\":\n",
+    "            print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "08ae8246-11d5-40e1-8567-361e5bef8917",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='Your name is Bob.'\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = [HumanMessage(content=\"what is my name?\")]\n",
+    "for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
+    "    for k, v in event.items():\n",
+    "        if k != \"__end__\":\n",
+    "            print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f47bbfc-d9ef-4288-ba4a-ebbc0136fa9d",
+   "metadata": {},
+   "source": [
+    "If we want to start a new conversation, we can pass in a different thread id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "273d56a8-f40f-4a51-a27f-7c6bb2bda0ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=\"I'm sorry, but I don't have access to personal information.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = [HumanMessage(content=\"what is my name?\")]\n",
+    "for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"3\"}}):\n",
+    "    for k, v in event.items():\n",
+    "        if k != \"__end__\":\n",
+    "            print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8578a66d-6489-4e03-8c23-fd0530278455",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_agentic_rag.ipynb b/examples/rag/langgraph_agentic_rag.ipynb
new file mode 100644
index 000000000..ecd65ba90
--- /dev/null
+++ b/examples/rag/langgraph_agentic_rag.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "625868e8-46cb-4232-99de-e95aee53c3a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "425fb020-e864-40ce-a31f-8da40c73d14b",
+   "metadata": {},
+   "source": [
+    "# LangGraph Retrieval Agent\n",
+    "\n",
+    "We can implement [Retrieval Agents](https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents) in [LangGraph](https://python.langchain.com/docs/langgraph).\n",
+    "\n",
+    "## Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "urls = [\n",
+    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
+    "]\n",
+    "\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=100, chunk_overlap=50\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools.retriever import create_retriever_tool\n",
+    "\n",
+    "tool = create_retriever_tool(\n",
+    "    retriever,\n",
+    "    \"retrieve_blog_posts\",\n",
+    "    \"Search and return information about Lilian Weng blog posts.\",\n",
+    ")\n",
+    "\n",
+    "tools = [tool]\n",
+    "\n",
+    "from langgraph.prebuilt import ToolExecutor\n",
+    "\n",
+    "tool_executor = ToolExecutor(tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe6e8f78-1ef7-42ad-b2bf-835ed5850553",
+   "metadata": {},
+   "source": [
+    "## Agent state\n",
+    " \n",
+    "We will defined a graph.\n",
+    "\n",
+    "A `state` object that it passes around to each node.\n",
+    "\n",
+    "Our state will be a list of `messages`.\n",
+    "\n",
+    "Each node in our graph will append to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0e378706-47d5-425a-8ba0-57b9acffbd0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    messages: Annotated[Sequence[BaseMessage], operator.add]"
+   ]
+  },
+  {
+   "attachments": {
+    "f886806c-0aec-4c2a-8027-67339530cb60.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dc949d42-8a34-4231-bff0-b8198975e2ce",
+   "metadata": {},
+   "source": [
+    "## Nodes and Edges\n",
+    "\n",
+    "Each node will - \n",
+    "\n",
+    "1/ Either be a function or a runnable.\n",
+    "\n",
+    "2/ Modify the `state`.\n",
+    "\n",
+    "The edges choose which node to call next.\n",
+    "\n",
+    "We can lay out an agentic RAG graph like this:\n",
+    "\n",
+    "![Screenshot 2024-02-02 at 1.36.50 PM.png](attachment:f886806c-0aec-4c2a-8027-67339530cb60.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "278d1d83-dda6-4de4-bf8b-be9965c227fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.tools.render import format_tool_to_openai_function\n",
+    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langgraph.prebuilt import ToolInvocation\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def should_retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Decides whether the agent should retrieve more information or end the process.\n",
+    "\n",
+    "    This function checks the last message in the state for a function call. If a function call is\n",
+    "    present, the process continues to retrieve information. Otherwise, it ends the process.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state of the agent, including all messages.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: A decision to either \"continue\" the retrieval process or \"end\" it.\n",
+    "    \"\"\"\n",
+    "    print(\"---DECIDE TO RETRIEVE---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    # If there is no function call, then we finish\n",
+    "    if \"function_call\" not in last_message.additional_kwargs:\n",
+    "        print(\"---DECISION: DO NOT RETRIEVE / DONE---\")\n",
+    "        return \"end\"\n",
+    "    # Otherwise there is a function call, so we continue\n",
+    "    else:\n",
+    "        print(\"---DECISION: RETRIEVE---\")\n",
+    "        return \"continue\"\n",
+    "\n",
+    "\n",
+    "def check_relevance(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the Agent should continue based on the relevance of retrieved documents.\n",
+    "\n",
+    "    This function checks if the last message in the conversation is of type FunctionMessage, indicating\n",
+    "    that document retrieval has been performed. It then evaluates the relevance of these documents to the user's\n",
+    "    initial question using a predefined model and output parser. If the documents are relevant, the conversation\n",
+    "    is considered complete. Otherwise, the retrieval process is continued.\n",
+    "\n",
+    "    Args:\n",
+    "        state messages: The current state of the conversation, including all messages.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: A directive to either \"end\" the conversation if relevant documents are found, or \"continue\" the retrieval process.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK RELEVANCE---\")\n",
+    "\n",
+    "    # Output\n",
+    "    class FunctionOutput(BaseModel):\n",
+    "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
+    "\n",
+    "    # Create an instance of the PydanticOutputParser\n",
+    "    parser = PydanticOutputParser(pydantic_object=FunctionOutput)\n",
+    "\n",
+    "    # Get the format instructions from the output parser\n",
+    "    format_instructions = parser.get_format_instructions()\n",
+    "\n",
+    "    # Create a prompt template with format instructions and the query\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing relevance of retrieved docs to a user question. \\n \n",
+    "        Here are the retrieved docs:\n",
+    "        \\n ------- \\n\n",
+    "        {context} \n",
+    "        \\n ------- \\n\n",
+    "        Here is the user question: {question}\n",
+    "        If the docs contain keyword(s) in the user question, then score them as relevant. \\n\n",
+    "        Give a binary score 'yes' or 'no' score to indicate whether the docs are relevant to the question. \\n \n",
+    "        Output format instructions: \\n {format_instructions}\"\"\",\n",
+    "        input_variables=[\"question\"],\n",
+    "        partial_variables={\"format_instructions\": format_instructions},\n",
+    "    )\n",
+    "\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\")\n",
+    "\n",
+    "    chain = prompt | model | parser\n",
+    "\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "    score = chain.invoke(\n",
+    "        {\"question\": messages[0].content, \"context\": last_message.content}\n",
+    "    )\n",
+    "\n",
+    "    # If relevant\n",
+    "    if score.binary_score == \"yes\":\n",
+    "        print(\"---DECISION: DOCS RELEVANT---\")\n",
+    "        return \"yes\"\n",
+    "\n",
+    "    else:\n",
+    "        print(\"---DECISION: DOCS NOT RELEVANT---\")\n",
+    "        print(score.binary_score)\n",
+    "        return \"no\"\n",
+    "\n",
+    "\n",
+    "### Nodes\n",
+    "\n",
+    "\n",
+    "# Define the function that calls the model\n",
+    "def call_model(state):\n",
+    "    \"\"\"\n",
+    "    Invokes the agent model to generate a response based on the current state.\n",
+    "\n",
+    "    This function calls the agent model to generate a response to the current conversation state.\n",
+    "    The response is added to the state's messages.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state of the agent, including all messages.\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: The updated state with the new message added to the list of messages.\n",
+    "    \"\"\"\n",
+    "    print(\"---CALL AGENT---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4-0125-preview\")\n",
+    "    functions = [format_tool_to_openai_function(t) for t in tools]\n",
+    "    model = model.bind_functions(functions)\n",
+    "    response = model.invoke(messages)\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "# Define the function to execute tools\n",
+    "def call_tool(state):\n",
+    "    \"\"\"\n",
+    "    Executes a tool based on the last message's function call.\n",
+    "\n",
+    "    This function is responsible for executing a tool invocation based on the function call\n",
+    "    specified in the last message. The result from the tool execution is added to the conversation\n",
+    "    state as a new message.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state of the agent, including all messages.\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: The updated state with the new function message added to the list of messages.\n",
+    "    \"\"\"\n",
+    "    print(\"---EXECUTE RETRIEVAL---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    # Based on the continue condition\n",
+    "    # we know the last message involves a function call\n",
+    "    last_message = messages[-1]\n",
+    "    # We construct an ToolInvocation from the function_call\n",
+    "    action = ToolInvocation(\n",
+    "        tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n",
+    "        tool_input=json.loads(\n",
+    "            last_message.additional_kwargs[\"function_call\"][\"arguments\"]\n",
+    "        ),\n",
+    "    )\n",
+    "    # We call the tool_executor and get back a response\n",
+    "    response = tool_executor.invoke(action)\n",
+    "    # print(type(response))\n",
+    "    # We use the response to create a FunctionMessage\n",
+    "    function_message = FunctionMessage(content=str(response), name=action.tool)\n",
+    "\n",
+    "    # We return a list, because this will get added to the existing list\n",
+    "    return {\"messages\": [function_message]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "955882ef-7467-48db-ae51-de441f2fc3a7",
+   "metadata": {},
+   "source": [
+    "## Graph\n",
+    "\n",
+    "* Start with an agent, `call_model`\n",
+    "* Agent make a decision to call a function\n",
+    "* If so, then `action` to call tool (retriever)\n",
+    "* Then call agent with the tool output added to messages (`state`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(AgentState)\n",
+    "\n",
+    "# Define the nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", call_model)  # agent\n",
+    "workflow.add_node(\"action\", call_tool)  # retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b2158218-b21f-491b-853c-876c1afe9ba6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Call agent node to decide to retrieve or not\n",
+    "workflow.set_entry_point(\"agent\")\n",
+    "\n",
+    "# Decide whether to retrieve\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"agent\",\n",
+    "    # Assess agent decision\n",
+    "    should_retrieve,\n",
+    "    {\n",
+    "        # Call tool node\n",
+    "        \"continue\": \"action\",\n",
+    "        \"end\": END,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Edges taken after the `action` node is called.\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"action\",\n",
+    "    # Assess agent decision\n",
+    "    check_relevance,\n",
+    "    {\n",
+    "        # Call agent node\n",
+    "        \"yes\": \"agent\",\n",
+    "        \"no\": END,  # placeholder\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7649f05a-cb67-490d-b24a-74d41895139a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---CALL AGENT---\n",
+      "\"Output from node 'agent':\"\n",
+      "'---'\n",
+      "{ 'messages': [ AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory Lilian Weng\"}', 'name': 'retrieve_blog_posts'}})]}\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO RETRIEVE---\n",
+      "---DECISION: RETRIEVE---\n",
+      "---EXECUTE RETRIEVAL---\n",
+      "\"Output from node 'action':\"\n",
+      "'---'\n",
+      "{ 'messages': [ FunctionMessage(content='Citation#\\nCited as:\\n\\nWeng, Lilian. (Jun 2023). LLM-powered Autonomous Agents\". Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.\\n\\nLLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nWeng, Lilian. (Mar 2023). Prompt Engineering. Lil’Log. https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/.', name='retrieve_blog_posts')]}\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---DECISION: DOCS RELEVANT---\n",
+      "---CALL AGENT---\n",
+      "\"Output from node 'agent':\"\n",
+      "'---'\n",
+      "{ 'messages': [ AIMessage(content='Lilian Weng\\'s blog post titled \"LLM-powered Autonomous Agents\" discusses the concept of agent memory but does not provide a detailed list of the types of agent memory directly in the provided excerpt. For more detailed information on the types of agent memory, it would be necessary to refer directly to the blog post itself. You can find the post [here](https://lilianweng.github.io/posts/2023-06-23-agent/).')]}\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO RETRIEVE---\n",
+      "---DECISION: DO NOT RETRIEVE / DONE---\n",
+      "\"Output from node '__end__':\"\n",
+      "'---'\n",
+      "{ 'messages': [ HumanMessage(content=\"What are the types of agent memory based on Lilian Weng's blog post?\"),\n",
+      "                AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\"query\":\"types of agent memory Lilian Weng\"}', 'name': 'retrieve_blog_posts'}}),\n",
+      "                FunctionMessage(content='Citation#\\nCited as:\\n\\nWeng, Lilian. (Jun 2023). LLM-powered Autonomous Agents\". Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.\\n\\nLLM Powered Autonomous Agents\\n    \\nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\\n\\n\\n \\n\\n\\nTable of Contents\\n\\n\\n\\nAgent System Overview\\n\\nComponent One: Planning\\n\\nTask Decomposition\\n\\nSelf-Reflection\\n\\n\\nComponent Two: Memory\\n\\nTypes of Memory\\n\\nMaximum Inner Product Search (MIPS)\\n\\nThe design of generative agents combines LLM with memory, planning and reflection mechanisms to enable agents to behave conditioned on past experience, as well as to interact with other agents.\\n\\nWeng, Lilian. (Mar 2023). Prompt Engineering. Lil’Log. https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/.', name='retrieve_blog_posts'),\n",
+      "                AIMessage(content='Lilian Weng\\'s blog post titled \"LLM-powered Autonomous Agents\" discusses the concept of agent memory but does not provide a detailed list of the types of agent memory directly in the provided excerpt. For more detailed information on the types of agent memory, it would be necessary to refer directly to the blog post itself. You can find the post [here](https://lilianweng.github.io/posts/2023-06-23-agent/).')]}\n",
+      "'\\n---\\n'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "\n",
+    "inputs = {\n",
+    "    \"messages\": [\n",
+    "        HumanMessage(\n",
+    "            content=\"What are the types of agent memory based on Lilian Weng's blog post?\"\n",
+    "        )\n",
+    "    ]\n",
+    "}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        pprint.pprint(f\"Output from node '{key}':\")\n",
+    "        pprint.pprint(\"---\")\n",
+    "        pprint.pprint(value, indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93781e8c-dd25-4754-9c26-e5faac57e715",
+   "metadata": {},
+   "source": [
+    "Trace:\n",
+    "\n",
+    "https://smith.langchain.com/public/6f45c61b-69a0-4b35-bab9-679a8840a2d6/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "189333cc-5d34-4869-9f9b-741210e1096f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_crag.ipynb b/examples/rag/langgraph_crag.ipynb
new file mode 100644
index 000000000..a058d2aa8
--- /dev/null
+++ b/examples/rag/langgraph_crag.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "459d0bcf-7c60-495e-91c3-85b0b8c67552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python"
+   ]
+  },
+  {
+   "attachments": {
+    "5bfa38a2-78a1-4e99-80a2-d98c8a440ea2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8889a307-fa3f-4d38-9127-d41e4686ae47",
+   "metadata": {},
+   "source": [
+    "# Corrective RAG (CRAG)\n",
+    "\n",
+    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
+    "\n",
+    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
+    "\n",
+    "Here we show how to implement ideas from the `Corrective RAG (CRAG)` paper [here](https://arxiv.org/pdf/2401.15884.pdf) using LangGraph.\n",
+    "\n",
+    "## Dependencies\n",
+    "\n",
+    "Set `OPENAI_API_KEY`\n",
+    "\n",
+    "Set `TAVILY_API_KEY` to enable web search [here](https://app.tavily.com/sign-in)\n",
+    "\n",
+    "## CRAG Detail\n",
+    "\n",
+    "Corrective-RAG (CRAG) is a recent paper that introduces an interesting approach for self-reflective RAG. \n",
+    "\n",
+    "The framework grades retrieved documents relative to the question:\n",
+    "\n",
+    "1. Correct documents -\n",
+    "\n",
+    "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n",
+    "* Before generation, it performns knowledge refinement\n",
+    "* This paritions the document into \"knowledge strips\"\n",
+    "* It grades each strip, and filters our irrelevant ones                                           \n",
+    "\n",
+    "2. Ambiguous or incorrect documents -\n",
+    "\n",
+    "* If all documents fall below the relevance threshold or if the grader is unsure, then the framework seeks an additional datasource\n",
+    "* It will use web search to supplement retrieval\n",
+    "* The diagrams in the paper also suggest that query re-writing is used here \n",
+    "\n",
+    "![Screenshot 2024-02-04 at 2.50.32 PM.png](attachment:5bfa38a2-78a1-4e99-80a2-d98c8a440ea2.png)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Let's implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21f32d2-92ce-4995-b309-99347bafe3be",
+   "metadata": {},
+   "source": [
+    "## Retriever\n",
+    " \n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a566a30-cf0e-4330-ad4d-9bf994bdfa86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "urls = [\n",
+    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
+    "]\n",
+    "\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=250, chunk_overlap=0\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87194a1b-535a-4593-ab95-5736fae176d1",
+   "metadata": {},
+   "source": [
+    "## State\n",
+    " \n",
+    "We will define a graph.\n",
+    "\n",
+    "Our state will be a `dict`.\n",
+    "\n",
+    "We can access this from any graph node as `state['keys']`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "94b3945f-ef0f-458d-a443-f763903550b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Dict, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        keys: A dictionary where each key is a string.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    keys: Dict[str, any]"
+   ]
+  },
+  {
+   "attachments": {
+    "3b65f495-5fc4-497b-83e2-73844a97f6cc.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f81239f2-314d-41fe-9af9-d19b5b193b53",
+   "metadata": {},
+   "source": [
+    "## Nodes and Edges\n",
+    "\n",
+    "Each `node` will simply modify the `state`.\n",
+    "\n",
+    "Each `edge` will choose which `node` to call next.\n",
+    "\n",
+    "We can make some simplifications from the paper:\n",
+    "\n",
+    "* Let's skip the knowledge refinement phase as a first pass. This can be added back as a node, if desired. \n",
+    "* If *any* document is irrelevant, let's opt to supplement retrieval with web search. \n",
+    "* We'll use [Tavily Search](https://python.langchain.com/docs/integrations/tools/tavily_search) for web search.\n",
+    "* Let's use query re-writing to optimize the query for web search.\n",
+    "\n",
+    "Here is our graph flow:\n",
+    "\n",
+    "![Screenshot 2024-02-04 at 1.32.52 PM.png](attachment:3b65f495-5fc4-497b-83e2-73844a97f6cc.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "efd639c5-82e2-45e6-a94a-6a4039646ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema import Document\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
+    "\n",
+    "### Nodes ###\n",
+    "\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "    # LLM\n",
+    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
+    "\n",
+    "    # Post-processing\n",
+    "    def format_docs(docs):\n",
+    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "    # Chain\n",
+    "    rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "    # Run\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\n",
+    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK RELEVANCE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Data model\n",
+    "    class grade(BaseModel):\n",
+    "        \"\"\"Binary score for relevance check.\"\"\"\n",
+    "\n",
+    "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
+    "\n",
+    "    # LLM\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Tool\n",
+    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
+    "\n",
+    "    # LLM with tool and enforce invocation\n",
+    "    llm_with_tool = model.bind(\n",
+    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
+    "    )\n",
+    "\n",
+    "    # Parser\n",
+    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
+    "        Here is the user question: {question} \\n\n",
+    "        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n",
+    "        input_variables=[\"context\", \"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Chain\n",
+    "    chain = prompt | llm_with_tool | parser_tool\n",
+    "\n",
+    "    # Score\n",
+    "    filtered_docs = []\n",
+    "    search = \"No\"  # Default do not opt for web search to supplement retrieval\n",
+    "    for d in documents:\n",
+    "        score = chain.invoke({\"question\": question, \"context\": d.page_content})\n",
+    "        grade = score[0].binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            search = \"Yes\"  # Perform web search\n",
+    "            continue\n",
+    "\n",
+    "    return {\n",
+    "        \"keys\": {\n",
+    "            \"documents\": filtered_docs,\n",
+    "            \"question\": question,\n",
+    "            \"run_web_search\": search,\n",
+    "        }\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Create a prompt template with format instructions and the query\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
+    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
+    "        Here is the initial question:\n",
+    "        \\n ------- \\n\n",
+    "        {question} \n",
+    "        \\n ------- \\n\n",
+    "        Formulate an improved question: \"\"\",\n",
+    "        input_variables=[\"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Grader\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Prompt\n",
+    "    chain = prompt | model | StrOutputParser()\n",
+    "    better_question = chain.invoke({\"question\": question})\n",
+    "\n",
+    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question using Tavily API.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with appended web results\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---WEB SEARCH---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    tool = TavilySearchResults()\n",
+    "    docs = tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "    documents.append(web_results)\n",
+    "\n",
+    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer or re-generate a question for web search.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current state of the agent, including all keys.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---DECIDE TO GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    filtered_documents = state_dict[\"documents\"]\n",
+    "    search = state_dict[\"run_web_search\"]\n",
+    "\n",
+    "    if search == \"Yes\":\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa076e90-7132-4fcf-8507-db5990314c4f",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "The just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dedae17a-98c6-474d-90a7-9234b7c8cea0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_entry_point(\"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"web_search\")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f5b7c2fe-1fc7-4b76-bf93-ba701a40aa6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('There are several types of memory in human brains, including sensory memory, '\n",
+      " 'which retains impressions of sensory information for a few seconds after the '\n",
+      " 'original stimuli have ended. Short-term memory is utilized for in-context '\n",
+      " 'learning, while long-term memory allows the agent to retain and recall '\n",
+      " 'information over extended periods by leveraging an external vector store and '\n",
+      " 'fast retrieval. Additionally, agents can use tool use to call external APIs '\n",
+      " 'for extra information that is missing from the model weights.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2bee03de-a32c-4bbe-b37a-a13bb825e4cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('The AlphaCodium paper uses a test-based, iterative approach for code '\n",
+      " 'generation. It employs a multi-stage, code-oriented flow that addresses the '\n",
+      " 'specific challenges of coding problems. Unlike traditional models, '\n",
+      " 'AlphaCodium actively engages in problem self-reflection, reasoning, and '\n",
+      " 'iterative code solution generation.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correction for question not present in context\n",
+    "inputs = {\"keys\": {\"question\": \"What is the approach for code generation taken in the AlphaCodium paper?\"}}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state \n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e44593-1959-4abf-8405-5e23aa9398f5",
+   "metadata": {},
+   "source": [
+    "LangSmith Traces - \n",
+    " \n",
+    "* https://smith.langchain.com/public/7e0b9569-abfe-4337-b34b-842b1f93df63/r\n",
+    "\n",
+    "*  https://smith.langchain.com/public/b40c5813-7caf-4cc8-b279-ee66060b2040/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_crag_mistral.ipynb b/examples/rag/langgraph_crag_mistral.ipynb
new file mode 100644
index 000000000..c1289061e
--- /dev/null
+++ b/examples/rag/langgraph_crag_mistral.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "19969669-b47f-47f3-b6d4-f7b155434840",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install --quiet langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python langchain-mistralai"
+   ]
+  },
+  {
+   "attachments": {
+    "9db7f9db-55aa-48cb-95d5-bcde3f937589.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "92ddc4f4-f7bf-4e0e-b5a5-5abd8a008b21",
+   "metadata": {},
+   "source": [
+    "# Corrective RAG\n",
+    "\n",
+    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
+    "\n",
+    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
+    "\n",
+    "Here we show how to implement self-reflective RAG using `Mistral` and `LangGraph`.\n",
+    "\n",
+    "We'll focus on ideas from one paper, `Corrective RAG (CRAG)` [here](https://arxiv.org/pdf/2401.15884.pdf).\n",
+    "\n",
+    "![Screenshot 2024-02-07 at 1.21.51 PM.png](attachment:9db7f9db-55aa-48cb-95d5-bcde3f937589.png)\n",
+    "\n",
+    "## Dependencies\n",
+    "\n",
+    "Set `MISTRAL_API_KEY` and set up Subscription to activate it.\n",
+    "\n",
+    "Set `TAVILY_API_KEY` to enable web search [here](https://app.tavily.com/sign-in).\n",
+    "\n",
+    "If you want to run this locally, use [Ollama](https://ollama.ai/library/mistral/tags):\n",
+    "\n",
+    "* Download [Ollama app](https://ollama.ai/)\n",
+    "* Download Mistral e.g., `ollama pull mistral:7b-instruct`\n",
+    "\n",
+    "Optionally, use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "abc064ab-7de1-4d03-a987-cd3078438d61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n",
+    "tavily_api_key = os.environ.get(\"TAVILY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0",
+   "metadata": {},
+   "source": [
+    "## Indexing\n",
+    "\n",
+    "First, let's index a popular blog post on agents using [Mistral embeddings](https://python.langchain.com/docs/integrations/text_embedding/mistralai).\n",
+    "\n",
+    "We'll use a local vectorstore, [Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "254ae533-79e0-42f4-b200-1ec9160e1d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_mistralai import MistralAIEmbeddings\n",
+    "\n",
+    "# Load\n",
+    "url = \"https://lilianweng.github.io/posts/2023-06-23-agent/\"\n",
+    "loader = WebBaseLoader(url)\n",
+    "docs = loader.load()\n",
+    "\n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=500, chunk_overlap=100\n",
+    ")\n",
+    "all_splits = text_splitter.split_documents(docs)\n",
+    "\n",
+    "# Embed and index\n",
+    "embedding = MistralAIEmbeddings(mistral_api_key=mistral_api_key)\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=all_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=embedding,\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "attachments": {
+    "a2fac558-b18e-4610-bfa7-0d40c92e0ede.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "fe7fd10a-f64a-48de-a116-6d5890def1af",
+   "metadata": {},
+   "source": [
+    "## Corrective RAG\n",
+    "\n",
+    "Let's implement self-reflective RAG with some ideas from the CRAG (Corrective RAG) [paper](https://arxiv.org/pdf/2401.15884.pdf):\n",
+    "\n",
+    "* Grade documents for relevance relative to the question.\n",
+    "* If any are irrelevant, then we will supplement the context used for generation with web search.\n",
+    "* For web search, we will re-phrase the question and use Tavily API.\n",
+    "* We will then pass retrieved documents and web results to an LLM for final answer generation.\n",
+    "\n",
+    "Here is a schematic of our graph in more detail:\n",
+    "\n",
+    "![crag.png](attachment:a2fac558-b18e-4610-bfa7-0d40c92e0ede.png)\n",
+    "\n",
+    "We will implement this using [LangGraph](https://python.langchain.com/docs/langgraph): \n",
+    "\n",
+    "* See video [here](https://www.youtube.com/watch?ref=blog.langchain.dev&v=pbAd8O1Lvm4&feature=youtu.be)\n",
+    "* See blog post [here](https://blog.langchain.dev/agentic-rag-with-langgraph/)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### State\n",
+    "\n",
+    "Every node in our graph will modify `state`, which is dict that contains values (`question`, `documents`, etc) relevant to RAG."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated, Dict, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        keys: A dictionary where each key is a string.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    keys: Dict[str, any]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0081ff31-4a91-4dbc-9977-8bcdc7dd0aeb",
+   "metadata": {},
+   "source": [
+    "### Nodes and Edges\n",
+    "\n",
+    "Every node in the graph we laid out above is a function.\n",
+    "\n",
+    "Each node will modify the state in some way.\n",
+    "\n",
+    "Each edge will choose which node to call next."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema import Document\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_mistralai.chat_models import ChatMistralAI\n",
+    "\n",
+    "### Nodes ###\n",
+    "\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    local = state_dict[\"local\"]\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"keys\": {\"documents\": documents, \"local\": local, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    local = state_dict[\"local\"]\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "    # LLM\n",
+    "    if local == \"Yes\":\n",
+    "        llm = ChatOllama(model=\"mistral:7b-instruct\", temperature=0)\n",
+    "    else:\n",
+    "        llm = ChatMistralAI(\n",
+    "            model=\"mistral-medium\", temperature=0, mistral_api_key=mistral_api_key\n",
+    "        )\n",
+    "\n",
+    "    # Post-processing\n",
+    "    def format_docs(docs):\n",
+    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "    # Chain\n",
+    "    rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "    # Run\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\n",
+    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK RELEVANCE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    local = state_dict[\"local\"]\n",
+    "\n",
+    "    # LLM\n",
+    "    if local == \"Yes\":\n",
+    "        llm = ChatOllama(model=\"mistral:7b-instruct\", format=\"json\", temperature=0)\n",
+    "    else:\n",
+    "        llm = ChatMistralAI(\n",
+    "            mistral_api_key=mistral_api_key, temperature=0, model=\"mistral-medium\"\n",
+    "        )\n",
+    "\n",
+    "    # Data model\n",
+    "    class grade(BaseModel):\n",
+    "        \"\"\"Binary score for relevance check.\"\"\"\n",
+    "\n",
+    "        score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
+    "\n",
+    "    # Set up a parser + inject instructions into the prompt template.\n",
+    "    parser = PydanticOutputParser(pydantic_object=grade)\n",
+    "\n",
+    "    from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "    parser = JsonOutputParser(pydantic_object=grade)\n",
+    "\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
+    "        Here is the user question: {question} \\n\n",
+    "        If the document contains keywords related to the user question, grade it as relevant. \\n\n",
+    "        It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
+    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
+    "        Provide the binary scor as a JSON with no premable or explaination and use these instructons to format the output: {format_instructions}\"\"\",\n",
+    "        input_variables=[\"query\"],\n",
+    "        partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
+    "    )\n",
+    "\n",
+    "    chain = prompt | llm | parser\n",
+    "\n",
+    "    # Score\n",
+    "    filtered_docs = []\n",
+    "    search = \"No\"  # Default do not opt for web search to supplement retrieval\n",
+    "    for d in documents:\n",
+    "        score = chain.invoke(\n",
+    "            {\n",
+    "                \"question\": question,\n",
+    "                \"context\": d.page_content,\n",
+    "                \"format_instructions\": parser.get_format_instructions(),\n",
+    "            }\n",
+    "        )\n",
+    "        grade = score[\"score\"]\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            search = \"Yes\"  # Perform web search\n",
+    "            continue\n",
+    "\n",
+    "    return {\n",
+    "        \"keys\": {\n",
+    "            \"documents\": filtered_docs,\n",
+    "            \"question\": question,\n",
+    "            \"local\": local,\n",
+    "            \"run_web_search\": search,\n",
+    "        }\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    local = state_dict[\"local\"]\n",
+    "\n",
+    "    # Create a prompt template with format instructions and the query\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
+    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
+    "        Here is the initial question:\n",
+    "        \\n ------- \\n\n",
+    "        {question} \n",
+    "        \\n ------- \\n\n",
+    "        Formulate an improved question: \"\"\",\n",
+    "        input_variables=[\"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Grader\n",
+    "    # LLM\n",
+    "    if local == \"Yes\":\n",
+    "        llm = ChatOllama(model=\"mistral:7b-instruct\", temperature=0)\n",
+    "    else:\n",
+    "        llm = ChatMistralAI(\n",
+    "            mistral_api_key=mistral_api_key, temperature=0, model=\"mistral-medium\"\n",
+    "        )\n",
+    "\n",
+    "    # Prompt\n",
+    "    chain = prompt | llm | StrOutputParser()\n",
+    "    better_question = chain.invoke({\"question\": question})\n",
+    "\n",
+    "    return {\n",
+    "        \"keys\": {\"documents\": documents, \"question\": better_question, \"local\": local}\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question using Tavily API.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Web results appended to documents.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---WEB SEARCH---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    local = state_dict[\"local\"]\n",
+    "\n",
+    "    tool = TavilySearchResults()\n",
+    "    docs = tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "    documents.append(web_results)\n",
+    "\n",
+    "    return {\"keys\": {\"documents\": documents, \"local\": local, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer or re-generate a question for web search.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current state of the agent, including all keys.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---DECIDE TO GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    filtered_documents = state_dict[\"documents\"]\n",
+    "    search = state_dict[\"run_web_search\"]\n",
+    "\n",
+    "    if search == \"Yes\":\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6096626d-dfa5-48e0-8a24-3747b298bc67",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "The just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_entry_point(\"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"web_search\")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0a868a-f0f9-4aa9-b955-a78da2359af8",
+   "metadata": {},
+   "source": [
+    "## Run\n",
+    "\n",
+    "`Mistral API -` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('Episodic memory stores specific events or experiences, making them unique to '\n",
+      " 'each individual. Semantic memory, on the other hand, involves general '\n",
+      " 'knowledge and facts that are not tied to personal experiences. Procedural '\n",
+      " 'memory is responsible for learning and remembering sequences of actions, '\n",
+      " \"such as riding a bike. These memory types contribute to an agent's learning \"\n",
+      " 'and decision-making processes by allowing it to recall past experiences '\n",
+      " '(episodic), understand and use information (semantic), and perform tasks '\n",
+      " '(procedural).')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "inputs = {\n",
+    "    \"keys\": {\n",
+    "        \"question\": \"Explain how the different types of agent memory work?\",\n",
+    "        \"local\": \"No\",\n",
+    "    }\n",
+    "}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96",
+   "metadata": {},
+   "source": [
+    "`Locall (Ollama) -` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "16ea2032-59c7-433d-aca4-2828a1239074",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: TRANSFORM QUERY and RUN WEB SEARCH---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('There are three types of agent memory in artificial intelligence systems: '\n",
+      " 'sensory memory, short-term memory, and long-term memory. Sensory memory is '\n",
+      " 'the learning embedding representations for raw inputs such as text, image or '\n",
+      " 'other modalities. Short-term memory is in-context learning that is short and '\n",
+      " 'finite, restricted by the finite context window length of Transformer. '\n",
+      " 'Long-term memory is an external vector store that the agent can attend to at '\n",
+      " 'query time, accessible via fast retrieval. The external memory can alleviate '\n",
+      " 'the restriction of finite attention span by using approximate nearest '\n",
+      " 'neighbors (ANN) algorithms such as maximum inner product search (MIPS).')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "inputs = {\n",
+    "    \"keys\": {\n",
+    "        \"question\": \"Explain how the different types of agent memory work?\",\n",
+    "        \"local\": \"Yes\",\n",
+    "    }\n",
+    "}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0931b76a-3ea8-4f2f-9d27-242d48ec3fe3",
+   "metadata": {},
+   "source": [
+    "## LangSmith Traces\n",
+    "\n",
+    "`Mistral API -` \n",
+    "\n",
+    "https://smith.langchain.com/public/1c9ce3f2-76bb-4514-a107-076823e9849e/r\n",
+    "\n",
+    "`Locall (Ollama) -` \n",
+    "\n",
+    "https://smith.langchain.com/public/6b626f5e-248d-4d52-b36b-5cc3bf0b95d3/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/rag/langgraph_self_rag.ipynb b/examples/rag/langgraph_self_rag.ipynb
new file mode 100644
index 000000000..b4b1766f3
--- /dev/null
+++ b/examples/rag/langgraph_self_rag.ipynb
@@ -0,0 +1,757 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a384cc48-0425-4e8f-aafc-cfb8e56025c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
+   ]
+  },
+  {
+   "attachments": {
+    "ea6a57d2-f2ec-4061-840a-98deb3207248.png": {
+     "image/png": 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wbWV0YT4KejsPdwAAQABJREFUeAHsnQeYFdX5xj96772DYgGlqmAXsZdo1JhYomKsMf+oicZomqZqYozGxBp77L13FCuCiooVQUF6k87S9/6/34Gzmb1subtsubv3/Z5n7sydcubMOzNnznu+ViflYhIhIASEgBAQAkJACAgBISAEhIAQyEoE6mZlrVQpISAEhIAQEAJCQAgIASEgBISAEAgIiLTpQRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhIBIm54BISAEhIAQEAJCQAgIASEgBIRAFiMg0pbFN0dVEwJCQAgIASEgBISAEBACQkAIiLTpGRACQkAICAEhIASEgBAQAkJACGQxAiJtWXxzVDUhIASEgBAQAkJACAgBISAEhEB9QSAEhIAQEAJCQAhsOQLr16+3lStXhmnVqlUFy/n5+bZhwwZjzj5xmXlyivuxT1wu6pgtr6lZnTp1rF69emGqX7/+ZvO6detacn1yOW5r0qSJtWrVKkytW7cOZVRE3VSGEBACQkAIbI6ASNvmmGiNEBACQkAI1HIEIEbLly8PxIr5ihUrwnJeXl7BPLkMGUsSMbYlt7O8du3aWo5ayZfXvHnzAgIXyRxzCF38H5c7d+5sXbt2taZNm5ZcqLYKASEgBIRAQKBOykVYCAEhIASEgBCoKQhAuJYuXWrLli0LU1yOc0hYJGKQsaKWV69eXabLbdiwoTVq1MgaN25caCptHcdFjRZztFTJ/3G5qPXFrStTxUvYec2aNZacIJ0l/U/fBpEFc6YlS5YErWEJpytyU5s2bQJ569atW5HzTp06FXmcVgoBISAEcg0BkbZcu+O6XiEgBIRAFiAQO/wQr0i24ryodXEbc44tSTDbQ+vD1KJFi0Lz5Lq4T1yXJGDJZYgaBEpSMgIQZO4PBI55nJL/4/K8efNs9uzZpd5LzDLRyDFttdVW1qdPH9t6663DnP8SISAEhECuICDSlit3WtcpBISAEKgEBNB6LVq0yBYvXhzm8+fPNzrkrCuKfMV1HJcUiBGmc2heMKVDIE5JYhWXSyNiaK8kNQMBiN2sWbNszpw5gcRB5NIn/PqKk3QSF/937NixuEO0XggIASFQIxEQaauRt02VFgJCQAhUPALpBAytCOSLKS5DziJBY47pYVLSyRdELE4QsrgcfZ3iOo6TCIGiEJg7d24gdZMnT7Yvv/zSJk2aFCYGB4qTZs2aFWjm0Mjtuuuutssuu4TgKsUdo/VCQAgIgWxGQKQtm++O6iYEhIAQKCcCkCwmiFU0WYv/47Y4j5qydAKGPxakqm3btgXz9GVIGOviekwTJUKgKhDg2YbAJYncF198sdlAQrIuw4YNszgNGTLEWrZsmdysZSEgBIRA1iIg0pa1t0YVEwJCQAhY6IBGUhVJVlHkC2JGJzbuUxR2dFDbtWsXCFb79u3DnP+RmEXiFUkY2gqJEKhpCGBeGYkcJC5q5tJNcrkuiBtaOOaDBg0yolpKhIAQEALZiIBIWzbeFdVJCAiBWolAJFuQK6ZodhiX4/rkvKiOZgSnOBIG6UqSsvifoA4SIZCrCHz99dcFBO6dd96x9957z9atW1cIjoEDB9rQoUNt8ODBgcQp2EkhePRHCAiBakRApK0awdephYAQqLkIYEr47bffBvLFPGrDmKeTMcga60rLsELgDTRfHTp0CHOIF/+T61gWCau5z41qnl0IQNzGjRtn48ePtw8++CCYEidruO222wZfuOHDhwezyi5duiQ3a1kICAEhUGUIiLRVGdQ6kRAQAtmMAGHkFy5cWDAtWLDAmCIZSydoJWnAkteJ6WEkX0XNk+uIjigRAkKg+hCYOHFiIHHvv/++ffjhhyEASqwNUUnxh4PAxUmRSiM6mgsBIVDZCIi0VTbCKl8ICIEqQ2DDhg1GrqhkQmXIWEyuzJz/kZAlSVppub+SF4GvF0l/CSuOVix9HjVlaMXUqUsip2UhULMQ+Pzzz+3dd98NBA4S99VXXxVcAP5vUQPHfJtttinYpgUhIASEQEUjINJW0YiqPCEgBMqNAMSJMN7kbIoRD5lDtpjI8YWpYVzH/qtWrbK1a9cami/m5RVCzkczxKj9gnzFKUnMmjZtWt7T6DghIARqMAL4xU2YMKGAxH388ccFVzNgwIBCJI7IqhIhIASEQEUhINJWUUiqHCEgBIpFADIFGStqev3114P2a82aNaX6fBV7ggw3EI4en5TevXtb//79Q7CBvn37BmIm08QMQdRuQkAIFCBApEo0cW+88UaYyCmHoI2PJpTMiU4pEQJCQAhsCQIibVuCno4VAkKgEAJow0iAyzRlypQQdpvlWbNmFdqvLH+IeEi+MIJ0MLGMVixOaL0gY0SBg/gxoYGLIfBLM3vEzJER8uSEVk0iBISAECgLAgxORfLGnDYwCqRt5MiRtu+++4a2Jq7XXAgIASGQKQIibZkipf2EgBAoQIDgHHRIIkGLJC2OMhfsWMQCGq0ePXpYt27dDEK2evXq4B/WtWvXgvUE74i5w7Y0TD1kLgYTIZQ++ZvI3RTzNxVF6tDE7b333rbPPvuE0fIWLVoUcSVaJQSEgBAoHgHSCrz55puByOEPF2XnnXcO5A0Sh8ZfIgSEgBDIBAGRtkxQ0j5CIIcRwGcM8x9CYhMeG9JDJMXSZOuttzbCZTNtt9121qdPn0DUWrVqVdqhVbp95syZIXdTJHKR1CUrsdtuuwUSt8suu4Tw38ltWhYCQkAIlIbAJ598UqCFe+uttwp2J7H3iBEjAonbfvvtC9ZrQQgIASGQjoBIWzoi+i8EchwBIipGksY86WhfFDS9evUqRM4gaURR21INWVHnqsp1Y8aMsVdffTWMlCfNnAhMsvvuuwfyBolTR6sq74rOJQRqPgJTp04NBC5q4fLy8sJF0a6gfYPEKRJlzb/PugIhUNEIiLRVNKIqTwjUMASmTZtWQNLQpBEdrTjBL2OnnXayfv36FWjQ8DOr7QJGkLixY8cGjSPmllHAY7/99rP9998/aBTjes2FgBAQAqUhwCBZ0g9u/vz54ZC99tqrwIQSKwWJEBACQkCkTc+AEMgxBD777LNAPDB3RJMWOwnpMBCMY+jQoYb/BXOmOnXqpO+Wk/9fe+21AgzBMQo+cJA3Jnz0JEJACAiBTBHAFD0SOAaJpk+fHg5F88bA0EEHHRT8fzMtT/sJASFQuxAQaatd91NXIwQ2QwAtERq02Bkozh9t4MCBBeQM7VH37t03K0srNkcAU6eXX345TAQeQNA+QtyiBi7b/Pg2vwqtEQJCIJsQIBLlCy+8UDDxn4BIhxxySCBvtC8SISAEcgsBkbbcut+62hxAgLxBkLT333/f3n777RA4JP2yySGE/0TUoKFNq+k+aOnXWB3/J06cGMjb6NGjjcADSNu2bQu0b3S06tWrVx1V0zmFgBCooQigcYsEDusIBF/agw8+OEyYq0uEgBCo/QiItNX+e6wrrOUIELIeEz20PMwnTJiw2RWT2wztGVEQ8ZWArEkqFwH836IGDm0nQtCW73znO3bMMcfYVlttFdbpRwgIASGQKQKQtkjgovkkwUuiBk5a/UyR1H5CoOYhINJW8+6ZaiwEDL80SNq4ceMCUUsGxgAefM8gaUxEOISokYxaUvUI5OfnF5A3NHAEHsB8EuLGhJZTIgSEgBAoCwJFmU8S2TaStz333LMsxWlfISAEagACIm014CapikIAP7QkSfv88883A4VQ+8OHDw8TRKBLly6b7aMV1YvAsmXL7LnnnrMnnnjCYq4mTJyOPvro4KdSvbXT2YWAEKiJCBRlPjl48OBwKbQvBDCRZr8m3lnVWQgURkCkrTAe+icEsgYBQu8TQYxIhRC21atXF6obZjCRpDEfMGBAoe36k90IEBgG8vbYY4/Z+vXrbdiwYQXatwYNGmR35VU7ISAEshKBpPkkFYTQ4UcLcYs+cLmQpiUrb44qJQS2EAGRti0EUIcLgYpEYNKkSfb6668Hskbi1XQZNGhQAVHbddddrXnz5um76H8NQ+Crr74KxA0CRwerb9++gbyhfevcuXMNuxpVVwgIgWxAAPNJJPq/MWddjx497LDDDrPDDz9cA33ZcKNUByFQBgRE2soAlnYVApWBAP5paNQgawSvSEqTJk2CPxo+aXvssYdtvfXWyc1arkUI5OXl2eOPPx60b2hWiToZ/d4UHa4W3WhdihCoBgSKMqE88MADA3kjOFLdunWroVY6pRAQAmVBQKStLGhpXyFQQQh89NFHBRo1wvMnBWdySFqc+C/JLQQg8RA4plQqZSeeeKL98Ic/tP79++cWELpaISAEKhwBBgefeuope/rpp23p0qVBuw9xQ/uGpl8iBIRAdiIg0pad90W1qoUIfPDBB0Gjho8ay0nBSZxoX5GooWGTCAHMZSFujzzyiBGM5oQTTgjkbbvtthM4QkAICIEtQmDevHmBvEHgPvzww6Bti+QNLZxECAiB7EJApC277odqU8sQgJxB0tCcpBO1gQMHFpA08qdJhEBxCCxYsMDuu+++MJEyAK0b2jeNiheHmNYLASFQFgReeeWVoHmDwOH7RmArNG9HHHGEde3atSxFaV8hIAQqCQGRtkoCVsXmLgIlEbUhQ4bYiBEjbJ999jGWJUKgLAiQjw/ydv/994d8b5A3tG99+vQpSzHaVwgIASFQJALTpk0L2rdnnnnGSC3TunXrgsBIO+64Y5HHaKUQEAJVg4BIW9XgrLPUcgRKI2qQNMiaiFotfxCq6PLwQ4G4QeDQvEWfNyLDSYSAEBACFYHAs88+a08++WTILUl5Rx55ZCBwfM8kQkAIVD0CIm1Vj7nOWEsQEFGrJTeyBl/GypUr7d57791M89atW7cafFWquhAQAtmEwMSJE0NUW/xrGSTC95rItkcddVQ2VVN1EQK1HgGRtlp/i3WBFYmAiFpFoqmyKgoBEq9Hs0n836LPW6dOnSrqFCpHCAiBHEeAYEgQN3JKEgGZaLbkk4TAkaJEIgSEQOUiINJWufiq9FqAgIhaLbiJOXIJ69atC+QNAof/26hRo8KkaKQ58gDoMoVAFSHw/PPPBwL33HPPWefOnQvIm4IjVdEN0GlyEgGRtpy87bro0hAQUSsNIW3PZgTy8/ODyeRdd91lLEPeCFgiEQJCQAhUJAJJ08klS5bYscceaz/4wQ/kv12RIKssIbAJAZE2PQpCYBMCImp6FGobAphN3nnnnXbHHXcYQUpOOeUUO+yww2rbZep6hIAQqGYEoukk5pMQOcwmIW+77rprNddMpxcCtQcBkbbacy91JeVAgISi5FArKo8akR4V9bEcoOqQrEOAJLoQNyaSuKN522OPPbKunqqQEBACNR+Bxx57zB566CF76623Qq43yNvee+9d8y9MVyAEqhkBkbZqvgE6fdUjMH36dHvxxRft5ZdftrFjxxaqwKBBg2zYsGEhsWiXLl2M0OqYfDBPLrMOadmypbVo0SJMLKf/Z1tyfaGT6Y8QqGIEvvzyy0Dc7rnnnmDGBHlT7qUqvgk6nRDIEQT4xkLe8H874IADguaNuUQICIHyISDSVj7cdFQNQ2D58uX20ksvBbLGfP369QVX0L59e2vcuLEtW7YsTAUbKmEBE7Xk1LNnz4L/HTp0qIQzqkghsDkC7777biBvDF6cccYZdvrppyv62+YwaY0QEAIVgMD48eMDeXv44YeDph/N2+GHH14BJasIIZBbCIi05db9zrmrffXVV+3RRx8N5o+QskykVatW1rp1a0vOi1tetWqV5eXlGfPkFNfhUxSXmc+ZM8dmzJhRiDTGOkEckyQukrttt93W+vTpE3fTXAhUGAKMhN9+++02a9asQNxIFSARAkJACFQGAp9//rlB3NC+9evXL2je8H2TCAEhkBkCIm2Z4aS9ahACfBRwhiawyIoVKwrVPJVKWZ06dcI6yNA222xj6fNCB1TSn9mzZxtmmhA45sll8mylC2aWmG7GacCAAda1a9f03fRfCJQLATpRt912m7Vp0yZo3vbdd99ylaODhIAQEAKlIcAgEW0O32osXdC8ff/737d69eqVdqi2C4GcRkCkLadvf+24+AkTJtgLL7wQtGlff/21rV27ttCF1a1bN/iojRw5shBJK7RTFv1BY5ckc5MnTw7RuD755JNCtcScMpK4gQMHGpMSnBaCSH/KgMCaNWvs5ptvtltuucX233//QN623377MpSgXYWAEBACmSOA2wLkjYkBVYgbU/PmzTMvRHsKgRxCQKQth252bblUGvo333wzaNPeeeedECgkeW2QtO7du4foeIQ333333WvFCB75tgilzPTxxx+H+RdffJG89OAfB5GDwDFXuOVC8OhPBghMnTo1kDe01fi7MaHplQgBISAEKguBBx54wO677z5bvHhxIG5o39DCSYSAEPgfAiJt/8NCS1mMAOaEBBB5+umnA1nBVywpRGjs37+/HXTQQXb88cdbkyZNkptr7fK6desKiFwkdFOmTCm43vr169tee+0VphgVs2CjFoRACQi88cYbQeuGKRPEjU6URAgIASFQmQiQLuD+++83Bo/QutHu4N8tEQJCwEykTU9B1iIwadKkEO3x2WefNRyYMZ9ICuaBw4cPDySN3FOSjQgQ8CQSuPfee8+I3MXoJdKpU6cQvWunnXYyJpm/bcRMv8UjwAj4jTfeaL169bIzzzwzaK6L31tbhIAQEAJbjgADtLQ95FKNPm/4n0uEQC4jINKWy3c/C68dv6177703JOWcNm3aZjUkdxpJOtGmkfxakhkC5OcaN25cIHDMSbaMEIgF4osZJWak7dq1y6xA7ZVTCCxatMhuuummQN5OPvnkQN40+p1Tj4AuVghUCwKkJYG8vfbaawXkDdN/iRDIRQRE2nLxrmfZNc+cOTOYPt51111GIJGkYOaI2SMJOQ8++GCFvk+CswXLEGI0cBC4t99+2zA/BetI3pjjFycRAkkE0Nyidfvoo48CccNsUiIEhIAQqGwExowZE8gbljfHHHNMMJ2Uz3Zlo67ysw0BkbZsuyM5Up+Y7Bo/NSZ8s5AGDRqEHGkjRowIDfNuu+2WI4hU72VC4t56661A4JhjTonpJB9F7gETueokQgAEHnzwQbvhhhtCigBMJhlQkQgBISAEKhuBsWPHBp83AiUdeuihQftGf0EiBHIBAZG2XLjLWXSNo0ePNhL6QtRiPjLyph144IFBm0YgEYKKSKoXAXLcoYkjOickrmHDhoG4YUJJYJO+fftWbwV19mpHIGkyeeSRR9pZZ51lO+ywQ7XXSxUQAkKg9iPw/vvvB80bppPklcTv7ZBDDqn9F64rzGkERNpy+vZXzcXTuEaNWjKyIYEwIGsQtT59+lRNZXSWMiOAVjSaUjKH0KGBI/gLBG7w4MFlLlMH1B4Eosnk66+/HqJMQt408FJ77q+uRAhkMwKkvyFJ9x133BEGFs8777wwz+Y6q25CoLwIiLSVFzkdVyICS5YsCeH5iQCFOUOU3r17F2jUdtlll7ha8xqEAB9JOuiEhOfe7rjjjgVpBfbYY48adCWqakUiEE0myZMIcSNct0QICAEhUBUIzJgxI5C3a665xnbeeWe78sorbauttqqKU+scQqDKEBBpqzKoc+NEEyZMKCBrMUJh06ZNg88LGjUCitSrVy83wMiBq5w8eXIgcJA4JsLCo32LWjjuvSR3EMBkEl+3m2++OfibnHPOOTZgwIDcAUBXKgSEQLUisGLFCoO4/ec//zF83S699FKRt2q9Izp5RSIg0laRaOZoWatWrSogakR4ikJI/sMOOyx03rp16xZXa15LEWCkE+JGaGa0cM2aNQsEbp999rH99tvPWrRoUUuvXJeVjgAa2Ouuu84wnYS4MZHoXSIEhIAQqAoENmzYYL/61a9C0BLI2wUXXKCIyFUBvM5RqQiItFUqvLW78E8//TSQtaeeesrosCN0zInoBFmjsy7JTQQIMgN5ixo4EqPz4dx///2Ds7g68LnxXNx6662BvPXs2dN+8pOfBE17bly5rlIICIFsQGDp0qWBvOGqwTfo7LPPls9bNtwY1aFcCIi0lQu23D0oPz+/QKv2wgsvFACBDXnUqnXu3LlgvRaEAB9NNLCQOKb169eHADQQuJEjR4Y0D1uCEhEuOQfmt5LsQ4Dci9dff7099NBDduKJJwbyJs179t0n1UgI1GYEZs2aZVdccYU9+eSTgbzRFhEITSIEahICIm016W5VY13peD3xxBOBsMUIkG3atCnQqikARTXenBp06pUrVwYCF0kcprWYTkLe8IXjmSqrvPrqq/bjH/84BL5gFLVr165lLUL7VwECzzzzTCBv+L1hLnnSSSdVwVl1CiEgBITA/xD44osvgs/tI488EjRuBEw6+uij/7eDloRAFiMg0pbFNycbqvbmm28GskYiy7Vr14YqEe49atXat2+fDdVUHWogAqtXrw6aNwgcE515CBzkbe+997ayaGMIgPPrX//acEK/+OKLw/NZAyGp9VXOy8sLxA3NG6ZKmEyS+kMiBISAEKhKBEhdc99994VcbwMHDgx53o477jj53lblTdC5yoyASFuZIav9B6xZs8YgaWjWSKyMEMb7qKOOChOdaokQqEgE1q1bV2A+CYGbOXNmIG5RC9e9e/dST/ftt98G8xdCz6N5g7xJshMBSDaBSl555ZVA3LhfBK6RCAEhIASqEgFS2Nx///1h2mabbQziRqLuJk2aVGU1dC4hkBECIm0ZwZQbO0UTSMja1KlTw0V36tSpgKxtv/32uQGErrJaEcBvMvq/MZ8+fXqBCSVmlB07diyxfnfeeWcgb4MGDbILL7ww5Owp8QBtrDYE7r777kDeMIvFZPLwww+vtrroxEJACOQuAp9//nkgbg888EAwsT/++OMDgVPU49x9JrLxykXasvGuVHGdGPWmoXr00UcLTCDJrRQ1a23btq3iGul0QuB/CGCiC3kjEiVRSiFuTPvuu2+xPnA805dffrlhAkOoZzQ5kuxEgAAB//rXv4Kp0ve+971A3rbeeuvsrKxqJQSEQK1G4KuvvgptEdo3+j5o3dC+tWvXLuPrJjgWA1HbbbddxsdoRyGQCQIibZmgVEv3wfQRsoZmLQqdYcjaEUccEVdpLgSyBoHx48cX5IL75ptvQhJvEnkzEVY+KfhgQtxuu+22kGoA8ta/f//kLlrOIgSeffbZQN7mzp0biNsZZ5yRRbVTVYSAEMglBPi+0D+CvDVq1CgQN8gb1kelydVXXx36VTfddJOIW2lgaXuZEBBpKxNctWPnl19+2fD7SYbsh6gxyk3nVyIEagICaNHwf0MLx3Ikb8zRFEdBg/zb3/42+GWef/75dtppp8VNmmcZAkQXRet2ww032O677x783dQmZdlNUnWEQA4hMHv27EDcCFqC6T5mk2jfevToUSIKZ555ZvgukR8uE6JXYmHaKAQ2ISDSlkOPAvlJyJWEmRmC4z9EjYnoSRIhUFMRSCdwO+64Y+j0k4qC6f3337dLLrnE8NskvDMjoZLsReDtt98O5I05JJsok2UxT8reK1PNhIAQqIkIzJs3z+69994wEawtmk0WZ8qNpu7kk0+2BQsWGOb6jRs3romXrTpnGQIibVl2QyqjOmjVUPO/9957ofguXboUkLXevXtXxilVphCoNgQigRs3bpyNHTvWeN7R2kDkHnvsMZs4caIxCkqKAEl2I4DGDc0bI9UxF19211i1EwJCoDYjkCRvy5cvLyBv/fr12+yy+f5gUolA4iRCYEsREGnbUgSz9HjyVaFVg7B99tlnoZY4xUbNmoKLZOmNU7UqFAGioGI+iWM4o518cBnxJEfcn/70JyV4rlC0K6cw2i+IGz5vhx56aNC6QcAlQkAICIHqQiBJ3kg3Azk79thjbciQIYWqFIkbUSg/+eSTQtv0RwiUFQGRtrIiluX7z5kzxx5++OFA1giVjuy8886hMYGw1a9fP8uvQNUTAqUjQBAdPpSLFy8OE4m5i1petWpViYVp9LNEeLJqIz4lkDfuNekBMJmsV69eVtVRlRECQiC3EIjkDWsm+l8EcTvmmGNsxIgRBUBg/fHd7343pBKAxEmEQHkREGkrL3JZdtyUKVMCUYOw0ZlF9tlnH/v+97+v3EdZdq9UnS1HgE47nfekoD3GFLJ58+ZBm4bPJglSmTM1bdrUGO1kYh8mdfqTCGb/cjI9ACPaPAcHHnhg9ldcNRQCQqBWI8D3iP4X06RJkwJpg7zFSNzTpk0LfTLy3SaDwNVqUHRxFY6ASFuFQ1q1BTKCgxkkEyHOERLUQtYgbRIhIASEQG1DIKYHwHSSaG74u/Xq1au2XaauRwgIgRqGwPr16wvI27vvvhvMJSFvaNoQTLt32WWXsE8NuzRVNwsQEGnLgptQniq88cYbgajFHGtoDLCnZsIcUiIEhIAQqM0I4Ld77bXXGrmQOnbsGLRup556am2+ZF2bEBACNQgBBpfQvI0ePTqYRkLcjjzyyBBVcocddrDbb7+9Bl2NqpoNCIi0ZcNdKEMdyLFG2FkaAaRVq1ZBq4a/Gmp3iRAQAkIglxAghQm+biRe32uvvQJ5I1qoRAgIASGQDQgQDIugcORsQyBuH330kUHcrr/++myooupQQxAQaashNyqdrHXt2jWQNcwgu3XrVkOuQtUUAkJACFQ8AiS9hbihecM86fTTTw/krbTcbkR8u//++yu+QipRCAiBnEAAv1oCY/Xp0ydo/Dt06GBMbdq0KZjwt+Y/eUIff/zxYCUFOPhaY9ZN29W3b9+cwEsXuWUIiLRtGX6VfnQ6WaNhIKkjZK20DkmlV04nEAJZjgCjmQSvIFR8SbJhw4awuU6dOla3bt2Sdg3b8B+NnX1Mk0888cRSj9EOlY8ASdQhbmPGjLGtttqq1Nxuv/jFL8LzgfWCRAgIASFQVgQIMILv2syZM0NwLP4Tlbi4yMRYR2HOzTcHEoc0bNjQLr30UvvhD39Y1tNr/xxDQKQtS294OlnD9BGyxsTojEQICIHSEfj3v/9tr776qj3yyCPF7rxmzRrbdtttC7YPGzYs5G+LUb8KNiQWSCXw+9//PnyY33777WI/0IlDKm3x0UcfDZ2Gyy+/PONzpFKpkLPxt7/9rQ0ePDjj42rKjv/5z3+Me79kyRLbb7/97KyzzrLhw4dvVv358+eH/Epg8I9//GOz7VohBISAECgPApAyiBuDhkSWZCKyd1xmTvtDmgDyhp555pn261//ujyn0jE5hICSdmXZzU4na4MGDSoga8qxlmU3S9XJWgRWrlxpjRo1KlP96Oj37NnTnnzySfvpT39qI0eODGkBYiGUGQdMSCVwxRVXhNDNkLbiJHkM+/AhT2rzIE+Y9qGtYxtkkHo3aNCgUJFsQwOIho/jGZmN5c2ePds+/fTTcDzr2I99ihPOSXnvvfdeIDUsI8n0B5gYsj5TDGP94nnT/xdXl8paf8YZZ4TouRA3gjXhA4zJJOSNUe4oLF944YVBI9e5c2e76KKL4ibNhYAQEALlRoD2FG0/k0QIVBQCpdsBVdSZVE6JCEDWfvSjH9lpp50WOhiMCl911VWhA4nplQhbifBpoxAoQOCSSy6x/v3729Zbb22Yy2UqPXr0CMF8/u///i8cAqlB3nzzzRCVlTJ5P6NJS9hYzM/UqVPt3HPPDfU47LDDgrkeuzKaet555xUcBanYY489wn/Sd+CYjm8D+yXPM8ITtf785z8PGkG0QlFzSIfgyiuvDE7tsYNw2223FZRf1MItt9wSsGHbKaecslnHguP33HPPEKr6b3/7W0EqkaLKYh3ElGv485//HHbBbJS6YC5UnYL2FFPJq6++Olwv13300UfbXXfdVahamM5C5q677jr773//W2ib/ggBISAEhIAQyBoEfNRVUo0IvPTSSykPU53yEf4wuU1z6qmnnqrGGunUQqDmIuCmKOE98ihdKc/hlfLBj5R31Eu8IDdNCcc899xz4Zi///3v4b9rsMJxTmBSrPvqq69SrsFJuVlkQXnPP/982LdgxaYFN3NJjRo1KjV58uSUE4eUm+iFLdSLdz0vLy/8P+igg1I+OBOW3UwzxTknTJiQ8gGc1AUXXLCptFS4Dvblmv7whz+kqBPi5n+hbk48UosXLw4T11OSsN1Nc0I9nnnmmXAM5SARv/vuuy/lZqVhH08vUlJxYZsT3LAvx3F9rt0q9Ziq3GHhwoWpyy67LNSN+vlAWMqjThaqQmyHXStXaL3+CAEhIASEgBDIBgSkaasm+vzCCy+Yd+oKNGv4XTASzEgvybElQkAIlB2BsWPHhgA9aLf69etXpncJbcvBBx8ctDN/+tOfrEuXLjZlyhSbPn16MJNE44YTObl3ShLMHdmnU6dOhukkZjJO3mzGjBnB5JJjybOIv8Pnn39u1BXBNBLtHtHFMHF86KGHwvr4c9RRR4VrOvDAA0Od2J/6YKqJOWXr1q3DVJpJI9vZF2nevHlYphyE0NQEOMJ3Fu0eofPxCSxNdtppp6BB/OUvfxm0kiX5A5ZWVmVs55pw9L/zzjsNn0Xwx+n/N7/5TcCSc/7ud7+z3r17B7+SL774ojKqoTKFgBAQAkJACJQbAfm0lRu68h3oI9sh6hy5hRDI2gknnGD7779/+QrUUTmNAD5OCxYsCL5VAEHCYTrzrlmwFi1aGEE2cHie5hGtEDrprjEyOtX4QuEk7VqfsH7vvfcOTtGYAVLuNttsExynhwwZEohB06ZNzUeaQjjjUFgW/uCLFf29qF4kJ5lU1bVmwadtl112CdfJMcuXLw+HQrpi2GZyIkaJPlzxP3MwwuEcE8mWLVuGTRBC6gXBghBB6iBxEMvtttsu7HP22WcH3L/zne8U+KeFDZt+4rWkR7fkP47s5ZF169YVOozrSS+rqGssdNCmP9EPj2cnWwUiynTzzTfbjTfeGAbJGEDDHBU/OAKzYAKLqadMJbP1LqpeQkAICIHcRECkrYruO8EN3HQojLxzSpG1KgK+lp0Gzc+kSZPCNHHiRFu2bFnQFOBXBKmAtNBppuO9wTU+jZq1srUrl9rSpUutaat2ZvnrbdGCefbUi6/Z7GmTbIWTkoaNm/j6DYFkpOrUDT5cED1yzUACCdZQv0FDa9qkcdDocC4IHpootD4EcMgW2W233UIwCTriAwYMCJqjstSNQCOQp2uuuSZojPCLYh1EmISoXDOatCgx6iR5evCJY1+0OgQxiZo1SBnHo3lD0JiRHwwieMwxx4R1lEl6AjQ/bI/+YRBrcjKWJGi5iBw5ZsyY4BMHiSstHQhEDC0a2jx8/yBc3EfuK/f37rvvDv/RFEYfv5LqgD+em5AaPnAE84AY4T+WrQJJ49mFvN1zzz2GZpVnBnL9q1/9yv7yl7+Ee6Bobtl6B1UvISAEhEDuIaCQ/5V8zx977LFA1saNGxfOJLJWyYDXwuIx5UIzwzOE5mbu3Lm20rVja5yY0dHusvUOtmjuDBvQb1vrtu1g77C3tZb111u7PoOsYYP61qHRGltep5Wb6dW1Tk3W28K1jW3ugsXWv0sjm7+6gS1etsrmfvmuNWrT1RZ+u8TmTBpvC1fXczLXzCa994q17tHPVixdbDO+mGDdthlgkz5611q3bB4690QJJGkonXQ6wQTSaN++fbXeBYJ7XH/99aF+mB7OmzevIHBHURWLIf/RtKH5ct+wEAb/j3/8o5188smBTKF1ieaKmNVFUkV5RJpkUAYh6AVkhXPefvvt9uCDDwatm/tRBZM89gEztHlo4zDnjKTsjjvuCKQH0vTjH//YbrjhBnYPZpR77bVXIE9o6cgJhLYP8t64ceNQHkTJffLCNZPDkeAkpQkkj+Am1APhOaOeBOogkibr0T5B2qIWragyqS/tGqSWIDAEIsFMMpZX1DHZtA4cIG8Qb+TYY48N2mesIuL9zKb6qi5CQAgIASGQmwiItFXSfaeDR+clRqATWaskoGtpsXSEGe3/5JNPQqe9ZbtO1qlLV5s3/SvbcdeRNmjwUGtTf7UNGD7SGjVvZVu1a2DzV6Zs3oqU9e9Q12YvT9nKdSnbvn1dm7ksZcvXpqxRXbP6Pq1Yb9akHst1bMVqX+/R4+v58hrf32fWqEEdW8Z636eBT2s9Iny3ZhtsyqwFNmXGQnvn2btt64G72aq16+21p+619Q2a2eQvPnPiONO6dO1mF17w85DnrDpvDWZ/SbO+ioi+igaT+4L/V7qJIus5HyakSUGDBgnkmEzqgKaUYzClRDvHPBmKP1l2+jLHonmF2GdyLo6HQJLLDPIX0xnE9Zh5ZlpOel2y6T8RRCHShxxySIi+ieYTck6Ey6RAVm+66aYQ9RIzYgYfMI9FE8f+EiEgBISAEBAC1YmASFsFow9RY8JcCBFZq2CAc6A4OploiwiGsM8++1jjTttYvtW1VbM/s/0PO9p67DjcGjm5at2kjs11oga5CstO1CBlEDLnZVg8WrPGdWzJqpQ7Wm0kYBj21fdjm3sB3/qxkLIWvrxsTcp8tTXyjSuc4NVhfzee9n57mCh/Qd7Gfdp4mUt9/3zf1qFZHVvs65c6WRj95P32znsf2rhXnrSTTzrJ/vrXvwafutICY+TALdUlViMCEGqP2hmCvrzzzjthTnUgqRA4fDjJh4nvJoQXrRtBoRBIc+/evc2j/Ib/+hECQkAICAEhUF0IiLRVEPIQNfxAPv7441CiyFoFAZuDxeBf8/4HH1mLZk1sl32/Y/muyerVtoHts+/+tsCJVsrZVTPXhq1w4uS8ydo6oULLhjR3rdkGX6zr5K2hq82WbNKYQcby0KSxvxOtBa6Ri8dCujzAYSB/yzyGRL1Qvtlqj1EByWvbdGM5G/xPi4Ybz0tBTZzUrXdiyD5NvD55TvaQX539Axvz6iu2194j7LgfHBv8t8IG/QiBLEAAP0EGRhgU8RQKYc46hGAvO++8c/APxfwUP0OkW7dugchB8CRCQAgIASEgBKoDAZG2LUT90UcfDVHGPLdSKElkbQsB1eH2s5/9zDVt19muu+9p5/z6b7bjoKHWufn/tFuYMEKWIGaNfVru5CoQMNeAYRKJNHSStsqX3WIvaM3QiiHtnLCheVvvTKuNkz2WG24KR0SZqNsgeJhKhjKdsGEqucpVdxy70oniet/g3C2UEcpn2bfXdeIHgXzivtvcJ+scN/PbEEzQvvzyy3DubP7BtJDgLZjFSf6HAOaWYJJuDvq/PWrHEoF6SL9AZFWSmhPwhznpHvDnw9wW3018EIkAKsluBAighMlvbTDvTSKNyTL+tUSbjr6wye1xmf2SAZMyNbGOx2cyJ3APQarQUEehDcVMvDZYV4AhU2z70v/Hay7rnHLQ/hNdGBP4Pn36FBmROe7HgBJ1wDKAQFiS3EaAgXdJORDwBNghdDcdbAgbZO3WW2+12267TeH7y4GnDvkfAi+//LL7RjVxErHK7r7xShv3/P22cPnaoCXz9t7WOUFC24ZGbLkvQ7TaO1mCsGHu6PFGgtaLl9v5VyBu65yQQbogYGv8GEwol6KFaxAO90iTG/dr6mwMU0m0ZxAwSBqEr7UTQjRpq1wTB1FEm8d5vRjvHGzUts2a/JH97Kfn2N0PPGp7u1aQD3dpUQz98GoV0h14EusQQZEQ8OmCT+G//vUvI6BQZQvBS/CrgkBmImiL0PBvqdC5w7eNTkK64Fe54447hhxn2RzKP73eZf2Pz+Guu+5qnnQ7hP0nnxuBVCBxBCSJJpQibGVFtmr3h3gTrIeASERFJfdeegqLqq1R4bPxjhEx9sMPPyy8IcN/vKukpWBAoSR54IEHwoAZfptMPNsEVsq0bSmp7LiNwEkxhyOpY44//vhgbkxEXdrUmizgjEk07cHMmTMDASZgEVFn58yZU+5L4/4ziInrAPiNHz/e5s+fX2R57Mu56GtixYWJd6bCfcZXmUG3qhDwYmCL74ikchFQyP8y4svoEi9QzLPGiBeNlfKslRFI7V4sApdddpmN8wiBw0YeaQunT7InH7nfrrn6H9bLR+SGDNvTOnfrbQcddrit2VAnaLh6tqljCzf5pxFcZIkTLbdWDCaLa5xdQbC6tKhjy52krXTS1bGlkzFfxv+tno+KEqSkgS9j4oiGDTLY0skbppVo0vjPHHPJZo3M91lvG9asDCHrv54yySa8N94+ef8d69Kpgw0atpeddva51njDUg/+8EHQTBR7oVmwgciPfETRmOPXlJQYFZKcdozaV7ag1aFDRvTCTDQEBDsiaiXRIuNocHnqeP7559sTTzwRDiXgBpEriQSJ/P73vzfyxoEFUTavuOKKsD5XfrgP+L0RmVNSdQjQCUSSzzUdwtI0RjyfvKv4LpKbkgGw+C4lj0+WH5c5H4FnYqJ5/nMMdYgd0nTSTueYfZKaJf4jHEfZDHZwHJ1wthGcjA513C95TezPe5Z+HtajDY55H8MJSvkhzyZCJN1PP/3UeM+HDx8eovxy7vTzsm8Sb/4XJUXVA7zRSBPlmMBLkB2i7Ca1cEWVVRXrGJiDGHGPYsoWyAw4k9amuGsGo+SzgVaM5yPeN+4nzwWDApTBPYuYclzEOFk+dSH6L6R91KhR1qtXr6CtpCwkBs+K/wlsRUAk7h8pZIjmm4lwvZA9nv3TPO9kZQnPNnV7/PHHQ95X8CSK9IgRIyrrlDlfrkhbho/A6NGj7d577w0fAQ4RWcsQOO1WZgQYLf7yq+m2/+EN7Xunj7KjTzjFpnz9jT113y02Y9pX9tqLT9tt1/7Z2nfpYVv37mFtO3W31m3a+chyb+vUY3tr26Gjk6u6rlFzzZgTrc5O2PJ8vhJtW6MNPvq21pa7ymz1sgWuimtqeavWWL381fatJ+ReuaG+tai3xvJWr7GZszDLqGfLFy2wr3xkd8O61bZs6RJbzgfPG+uWzRpb245dbfCQoXb+WaOsT/+hQXvXs1Vde/qF0daz77ZlvvaqPACc6dRBWAYPHlzo1JivQIgI2U9HJ13Ynoy2yEc2fuD5cKdvTz8++T9++JPrSlqO54LcE1o/2Smgo8B/PqZ0AAikkYmgnYCckkvy3HPPDVEwDzjggNDR2WOPPUIOtlNPPdXOO+88mehkAqj22SIE/vCHPwT/8EceeSSUQ2Cv7373u4GMlWQiRgea7ZjtMSdvI4L/4kEHHRQIEx1LNHB03K+99towUEJ6C7QezEmvQSAayB+dT97/mO6DwYsLL7wwlIlVDUFrKIcOOKSI9y2m8yDHH9p7yiSPIm0C/srIKaecEub8fPPNN2E5ptpg8AbCQ75A2hg6xSd5YCfKIYVJphIj2aL5wieTsiKBoY70Z3i3EepPnslkKpP080DWqAd+nqQGwVw4CtrDmHMyps6IZDnuU11ztFVYQXE/wZTrICIs8QfILZkk6ck60i5HEkabC0GP/0m78/DDDwctPPuBL4NnBx98cBgsQCuGvyykjHsWBwS5hwwm8L3heaY8CDwaS86BxRb3n7qxD+US9ZZrKE27GuvO9WEhwnMDkeR8PAtcJ8SVbwSEle9VkmjG49Pn1IuJ7wlT/M9+nAsCyjswdOjQYFYOgUPTzXsmqXgERNpKwTSdrNHw08Hh5ZIIgcpAgNHJb2bMsrVL5tj0pRtH4Hbdobf1++0fbbH7oLV0U8UZM+fY1C8/tbnfTLHPJrsPzucf29MPz7TlK1c5uVprLVq0sHwnXI0bNrDVeSutXiNvtFu2sLVrVvtHYql1JNGz+5yl6jXwMP/eEK9bZXnr61ir5s2shUcYyVubbyvyVlmrpo2tUct2vk/KRuy9h7Vs39laduhh/bbqbI1bdgx+b93Q4rm2bq5Hr+zuhG2Nk8M1dZzQ+fmyWeh80LFLJ2zUOUZ/5UOUFD6cfOgxmSM5NWZABK7AfBJTaUa3ITuMbmNWSMeouE4B5dIpJVcaQtuSibz44ovBTId9k/nf+E8nk/qgOaSThklU7EyxvTjBjHXPPfcME6O0mAdyHVFIuo3QGSip0xz311wIbAkCfGPJc8j7hnkfli10fEt79tASQ4ggaeQuHDlyZKgGHc2kJP/TeUWTyrvIenIgog3jfaBDzjP/yiuvhDx+aMI5B51VtNCYuZErk3PSHnAMZnR04PE9Iwrwm2++GUgC7yRl096Qg5H9o2aFTjllQwK7d+9uZ599dkh0T5uAZQ/aXogfA0y0PZlIDKIDkWCAinbm0EMPDcSSciGikDbI5GuvvRbe+ZLK5bowFYSQLFiwwBjEob1JCmTjhBNOsIsvvtgGDBiQ3FRty9yf7bffPtxD6g9xxZyT9rmktjlqyuI9iqSNZwSiwjMBnnwjIIScZ9GiRWE9QYwg+/jG8m3gucXUmmeBb0vbtm3DekgZbS/rKZd+JftBCAmKxPaotSspV2YS3IkTJwbyTV0QzLz7uJUOz+2wYcNC3RlM4Bnn/+mnnx7MZ5NlJJd5NhnM45nnG8e18QyguGBQhHeM5x4MeKYoF4wllYOASFsxuKaTNUahaYgZhaspQqOD0OgkR+Mrq/742EyePLlKogVipkCntCquq7LwKq5cGmeSVzds2SE05N1a1nWzxpR961Ee27tfWp6bOHbv1sUG9e1qy9fsH0gSvmz4nqFZy1s4wxa7HSTasX49O9gHn02x1p16WhePsbE8b42NHf+e7bLzTsGcZcX6+ta9TSOb9PV069lnO+vSuqHnbfOcb6vqWZd2za1Vi2Y2x8kYdWjawGza4vxAGte59RK+cd3d1JJzQtgIloKZ5VTfp2ldfPAKd5SKu97qWs/HiI9ZUlhH54iPOhJ9M0iyDRki6TS+Z5iekNOLfensYULIKOY555wTOmOMnO67777hA53esUmejyTYmLxQPp3ATISPJB9+Pux0DJNCJ5MOGonC2X7NNddkRNqSZVBfRqaTwqg5nWfwkQiBykYAM11IGs8w5IuBCt6x0oRnF3KBBg1ScdZZZwXtSmnHkVqF9xuhDLRFdEQRNCL4x6E1gVhh3sZ7QGcbcsn3FQLGO88xmDCiceMY1qN1iO1D1HwT3IdOeRTqTHm0LUxoCumDQK5wyUBr17t379BO/OMf/4iHlTiPBBftIBoe/Nw4P513iBVEE219DKKG31tJQueftieSsaJyF1I3sIR0ZovQT2AwjSBC48aNCxpCTDgPP/zwEqsYSVokbZHE0ecAR0gMmDBACvmFtKHdgrjyLYAQsi8DDwQdgchwDHMIDt+YuB/ELJIyzss543khc2yPWr4SK+0bIVL//Oc/A9GiDv/3f/8XjuVcBDXBhJV6/e1vfwsDAGh4GUigTkUJGjPuOYMHlIuWjjoxkECZvBcQf3wAGSgY4e8PJp2SykFApC0NVx48nPsxm0IYGaNhZkSiJsnbb78dfO1inVHR40RblFYh7sOcl5Fr5uNU2r7J41iuKB+b9HKT/7Gdv+SSS0JDSGPMiB4d3tokNN55ntF62ep890VzUzfn3nM9RD+50jBzdLc1I1caRG61Eye3sAjCcmMnVk3cbLKh+7UN27GvLXLNXJs+g5xcbdSAkV9t6K57WWN/8yFaXXz9ajej7LjVgEAISSWAdq9XO/dr8/xtM5bmWycnYxC2Gb6+ifu64SO32n3fIHLQsrkr88OxzX3bN0vyQ1TK1SswofQKZbHw4eJjmBTMSOh4MboeR9vZzkeIfelA0YkcOHBg6PTgf0aHiA4XzyOCPwcEhzaE9cUJZlF0/jA5QmN23HHHFbQ7xR3Dep4PRmqLi3R51FFHhWTQ+BZg5oUvRrqPTEnlcx3FBW+IHYmSjtc2IVARCGAiiI8ac95HOoyZCAMxV199dQgegzniT37yk4LD+L4haIqSnVTepygMUDD4ESVpBsg6tvEepL8j6e8G2h2EjnO6RidZPvswCEk7gmkc7dJOO+0UiEasb+ywl8WnDeKHRJNKtDY/+tGPApmljYPUoTHBpxcf1iQe4cC0H8hEcp90XNidvgNkNR2LtKKq/C/EAg0mGlsIHG1oUaQzWTHae/CHpHC/uEeQFCYILlpWTGrpk0AIafd5diDd4ET7znnQ6NFW9+3bN5SDBoz2nvvC94ZIp5GUcT6eKwgizwHCOv5TViZCHWjv2Z/yk88MZp0QVgYUsaSARKIJRLvH4EJRQjmY07IP3xMsgRgIgKRGgZgy8ADOKDjitzBu17ziEBBp24Rl9F9h5ARhJIpOVLRF3rRbjZsx+oe9MRHwsFePtvlcCI0DLzgTEhsHOqvpjtJsix1cPiBJn5247bLLNvexCQX7T3J/1tEI0bAnG6bYcMVj0uvHekZeaWy4P2g7+KjTcS5LpzSWn61z8M1fv87ae7h9ZPZyJ0K+vN7JG2uI5LjGlwOB83adQCEst2gYdg852/Arg6ARIRJyRYh/AosQfIQIkbOX5YfE2FJxfTsAAEAASURBVBtcPUe+tw7NNmrS5jg5rOf7EDkSAtbOzwt5m+eBTvx2heOXOhHs2bpuOC+kDqIHiZztJLARy35sqmlba9io6JG7jbWs/l86LdGfJNaGzhUadTpxmAiyHCU+//F/cXM6LUgMBFDcfrwDSPwYJ0feizsmk/WxnPhuZXJMch/IZnqHhg4bH+aSwowny9CyENhSBOjU/uIXvwgDiAyC0OktTdAaQEjQfvDNYdCEbwOdZAQtA51TzNHoiLIPQmee55sOLR1wBlKi8D6gPaJTimD2RkebY9E+0HllkBSNBm0EZeBTykQHPUkI+eahfeM7TDvBu8/xcVCY9gcCBTGgo8/+mMxhWgmRi2bboSKl/EQTNaxf+J5TV9qkSLwgGaxjG4O0pQkm0pBgsKFtQasChkmBdKYT1OT26lru5O4APD9obCFV3M+StEFcHyQX0sNAPveK6+U5pH3l/nM8A2Pgiakk105/kWcP7GlD42Be7969A5HhXrOdeXyeCTDCN59nZZr7n/Ec8mxBFOlb8YzwPLCdZ4vzsS7224rCNLb99KF4Dtk3SaR5Tpk4RxwQKKqcuI53iHozAEjZDAgky0PzyPNJlF2uTVJ5CGyk8pVXftaXTKABfD4YOYCwQQYYfcLsqaYTNsCnkUFjhqkIo0I0BkyE8sYWm48FdvQIoyixw4npBMcyIYzGsIy5BiNMvJx8VBmpoSFkG+pyRlmSglkAgQ3i/vHDh9aPoAZRsP2PTtHF1Y998SfCmZhGkI4kEju94U8t+KGBhlzVdz+ymU6uWjkhCiotn0Gg/DseCBljxg19PyJCkuga8hbNFAlCQtLszq6pQzP3rRMthOTb81bkB1JW3zV0+MhBzNCkzXPCRvTIzm6COWtZylr4uSBjHEvOt4a+fyCBTgip31wSfXuZHX3/BX6u9a6C6+hlzfZj89fkuXnkRlISTpyFP4wYounCrDcT4WOFUzh+ahxDIAJMqvgw88wyOILwTvCxLE14ZyCOmDjSkUCLl4nQYWBUnpFfPross25LhDLoyOKzQDvI+58UTLUQRo0lQqAqEGC0HisKSBMmiqUJHUp8vtAi8F7S2WYgkQ4lZeHTdemll4ZvPe8x3yJICwKpQ0t09NFHBwuVZPAhNC38x5wMH1E0FwxesMy3k+8YQUj4lqLB47uKiTImmhDIdMFkk28xxAECh5kc32j8YBkwxQWDsuI7F6P/URaDlplqMSBrTPgecW0MEhHgJQrrqCdtYPRZjduKmkMeISn4cdGfiGaScV/Kx3yc+5VtAjGhf4ImCIFglCZoSnmOINgErgF3nhueB0g0GmD8we64445AzPme0KYziAxJ5Di0w1ERwPniwF+SKKGxpD8DYeObglaZbwimlrTxkCXqvdADhfGt4LmlvS5J+CZBKgmGgr8izwGEj+8N56Ot512hLJ7l2O8rrkz85NAo8mxznzG/j/0vjoHA8V5wXkklI+BMPifFowelnHSkvLEOk5vZpfzBrDVYuE1+uC43fUi5zXrKG5eUN/rh+lyrmPKPUMpH7VKuiQv7+ccm5R3NlI/mhP/u7Jzylzzlo3DhGB+tSTmZDdt8VC7lqvKwzPFs80Yk5T4+BeeIQLotfmrUqFEpbzRS/hFLecMfNsWyfEQq/HcymLrqqqvCcnH1i2Uy949tOL83JMnVtWLZCUHKfaNSj77wRsoJVmrJ6vzUnOX5KTeXTK3bkErNWpafcjKXWur/py/JT81fmZ9yE8fU14s2pBavyk95vrbUVF9etmbj/nO9DPbP8/WuPQvLK9ZuXGYf5Nu8/NRXfoynCAjbZ/n5ELZP/nZDaqGX+7XPKR/hnNMWb0jl+1/qwbFr/VjXCob6+ccg5R2tsG82/3gHKrwL7uuQ8o9xQVW9wxeer4IVmxZ8FDTlI+phm484h3eLTb/5zW/Cutie8HxmIk7UUiNGjAjHum9bmMd3orjjfYCp0Lk4J+sQ3nE37w7L3jEM+3lnNvwv7idZnnfIUt4x9Pu68T5TF95xHzUO11hcGbVxvXeaUrSD3llPXf/vf6feeeWV1GO335Yac999qRv+9MfUvbfcknrV1/kIe5GXT5vIJCk/AvH75MQi40K8M5nyQYwi9+cbx7Ptgx3hu8Uy334nXmF9+rvHd5L3iW8cx6QL5ypqffp+6f85zjvhRdaTuvsAUPohqVi3ZDu12U5lWEG9nbilnJCU4ahUQT3AJLYTsQDqlr4ubquuOe2f+3KlnKik3Ec55b7DKSdDGVWH+8S+vMc+MBfuNdfHf/qLfKuZu7YsfD/Yxv2j3+VkKUVfhn4WWCHcc7419IeicAzfFdcSpzxASIq+qfsip9wHMfTB2M+Df6R8wDrl/ohh29SpU+PhRc4p0wcAwrfKBwdSTiRDW8Y95/x8a3i2ee6pX0lCX4/vG9i5tjHU0QcSUvQxEc5FO+mD8ynXWJdUlLZVAAI+Pp9bwig5E86iqJkZycMEKn3UqLagkswfx+gIwggkIzeMniCMIDEahC8Mo5JIuqM02qw4wpfus8O2onxs/GUO5iiMzmE+wugSIz6YA+DQjHBeRsAY8Ys+CyXVj2MY6UHbhm8RI6C1TcBzmUf38Pfbmrkp43w3j2zr2iw0X/M3abQwlyQ3G7nV0IYRLKS5b2/s2bTn+v74v+FjttD3R3vW3s0fF7t5ZAPXlmFeucCPRevWwvdZ6aaVi3w/zDHZx29bCDCCjxyaO8pa5tq2Du7bRpmYXZKkGxPMNb4vGrquboKJ1m6dK9d6tqpj4315AxnAs1zwfbnUR999YCH4e8Rw3rw36aaTXAr+MpgaE0AkmhmxnlF3prIK7wbn8o96ePd4tkvTHDOCz1SUJEd1MV0q6hrSjyupPLDB8gALBPxHc0nQXj7lFgY79Opps91iYIWbLb38ztgwWt7WzdgaeVvp4xRW182Ae3tY9cv8/mNqRoAaohdiyoYGh/uJVgVze0a6JaUjgGUHocN98CdoOcpicsV3Jn6r0s+EFQNSlGkZ2oLizOyLeyeTGpP0c5X0n+OK00oUV/dYt/iNLqn8krahDeSdJwolpoyYdZZFYj2KwmRL61aWemS6L/0N2jCCkPD+obUtzh84vUzuU1H7oq3CTJJvNBYYSVNB7h9mqGiv6AOxnQnhnqffd47lu0KfjPL4n14uaRvQ5jqJDP2oop7fZN0pA20i2j80g9SD+0Y90OKyjXNk8vziC0iALcpEe4dmbsSI/wUbYT0WGODBdUgqF4GcIG10XPBZwweKcKW8NEQ3IsAI6uzaLFw7plyYfURB9U4UIcLfIqj5ydGSFB+RSf4ttBxV6aX57NAo+IhUiJwUnWExDaFhpwEBf3wMaFSx/4a8IaXVj3KJtJeJyUyhiteQP3wMG9TNtxb114eIkS2cZEGuFjlRwv8Mc8TlbhLZwO0hCf8PMUNIiO1aNQ/zXycQM3zYCGKCj5lr1myDEyyOhWQRiATyxvY5boLZ0QkZy5CzPm08QIcX6Rq9UP5KPydlQNgIfgLh6+L7e1tts9zvjWMxx2Qbvm7UJ8/ZW33sNbNceA4xe8I3EvOqTCVJ2DI9prj9+JDG8uLHvbh9q3o9ZkGYQ/Nhrs1Ce4dJqw+rW0Mn0A28PXzQTZG6ewcslbfK35f6ttL9VM5zP6CR3ulBVvr+c91k6eEXX7IvP55oQzw4TTePtLbWO0iD3NztYh9U2tn3JTw4JISw2ZA3TFDlG1jy00RHk44lJo2V7aZAB5RBk6IE08QYUKSo7TVxXSQERMYlgmI2+qBVJK70N/AfwwQVclER+cNoD0trE2nLy9Kel7YvBCsTkpXEjjIjyU6uL0s54JccbIK4MSWF/iNTaZgkj9Fy+RCo9aTtDrc3xj+NDzIfAToh+GKVZeSufNBmz1FcN9GO0Cpgg49t/BgfwaQRYzQebHC6RXjpinKUZp+kzw6jSbGjyYg0H9mkj010pEajFjVrkDI6xth7I2j2GH1G+5bMJVVS/TiOc0E4qU8MusD62iL4GKxyH6kV6zf6joVIka4tW+JkiGiPEDCCkkCWWMbfrFPzuiFYCJq2Tk7MWL/C+yGtPRbI8jWE59+oDYNQ4ctGoBGIGUFO0KRB+/BX6+WkC381gpA0di0eZUMMA8Fz0kegEqJJouEjCAnb8JlDA9jDNW/ka6OcNg19BLIGdfT5uBU3wl1bnqvyXEdRo8zlKScbj0HjgB8h7c9M9ydZ5+1YfXIX+qBQB28PZ7hmrfugwTZw220852BL6+7O903cOiNKM1/e2kehf/mjU+2eZ561dyd+bGe7r+5Be+wed7E6a9dYH/cDOXr33ewTP8eNd98TRsxp95IDaQUHaCEgAD5VJfhpFyek46htQhCJTNOL1IZrR4PFJKk8BETWKg/b9JJrPWkjPwodMhwoc82JPtkJJdAKZo2YIY5yUzAEEwn+I0TMiqO/aMMY4WRUGMHkCgdjInMhYEkgE8pDMFXEqTUK24mARfloMG6//fagXkfrhrlQNOFCTY/WE8dbyo9SWv3QErI/TsKUV9sETWaqbsONxMwJGBqwEJ7fk1hDolY5MergJM3924JZY3cnWpg3Ih18/2Du6EqjNt6/JGqk+7iF4CIQNoKFQPwgWgQbgXzF8P+YOBJshCiQdX0H9m3q2yF4vmiznaRB8ND6Qd7QpBE5Eo1cF6/bemeBmGx29eAn45cRjMPZokQIZCEC5JPCDBuzoR3cpKe7R4nr6AQMfeKkObPtnocfsT2HDLazv3dMRrWf8MXn1qd7t0KEjQPd0SVMttSsn2t1/3XBz+2cP/9lM+uHjE6inYSAEBACQiCnEajjo4obe3s5DUPuXjw+GGjF0lXo+NawDW1akvyVFyk6R0QvwhSjNHvs5DmKqx+av6jpS+5fG5bnzJlr55x/gZ1z5hm2l5vuzMAE0clYPdeAkWCbaI8NnTBNdxLF+lXue7bGp66BOLnWa5O/GgSM/G7keiMXG0m5Sc5NpElyrvlYhrV1TRmkC+0ZPnOE9icHG6SNRNmsRyB4tBQ93F9toZtQYjKJdi+kI3Ai18g1fKQRCAm2vW433/WAvfTEg/bk44/Uhluia6hFCGCSTWQ+zN5OcUuA/MWLNru6X17zT2vSqKFd5r4c4cHfbI/CK06/7DLL8zbpXh+kKk2u+u/d/l6usKvdN7K+k0WJEBACQkAICIFMEPBumSSXEcC8MJ2wgQc2z2jBKoKwUR7aTsorC2HjuOLqV1sJG9fcsGEDa+w+bXU85D/+Zmi6GjopQouFZovJo0mGVADka4NkQZYgVYTzb+Lh+9GQ4bsGqSMXGySrrZM9CBvh+T02YCBslNPO94GwRZ+5ek7Y0MShtYOyQeQou7sTtmVeDkFIgjmmn4sUBNQHLRxkEvNM6typbXP3rXPWKBECWYYAbRHa/pNGjCiSsFHdbVyD/4EHE/nXfffbigx8HXt37RZIW2mXOs2tBGbPn2dNUvl20Tk/tqfcCmHDopLDd5dWprYLASEgBIRAbiAg0pYb91lXWYMQIBAJms4FK/OtuWvCggbMyRX+Y2jK5jiJQtMGscpzU8lOm3KxoTEjuTX+Z5AxIkz6bp5U2zVqvtzMyRzBSZZDujyaJASPMvGZwxftWy+X/fF1wy+OZUwqiT7ZrSXBRpzw+T4QNtajXYMExoAlBEJhuZkTuab1NkbBqkGwq6o5gAA5sTAJH+m5sMZ7RNvi5PSjj7I9Bg+xcRM/sstvubW43QrWn3DoIdbUrRKmevnFyfke8GbUb35rfbp1d7/Rej6w0c5uvvMOO8rzhP7tkotDrr/ijtV6ISAEhIAQEALe5ZIIASGQTQhA2Ja7zSPmiRvNF13D5kQIjRtasnVu6ggxg4CFMP+bzCAxaWzj+xH+n4iTaNvmu7atRSOSam8MTsIxJNymnCa+jjD/eR4oFJ85tGRo5dq5xRbBSJa4Vo2gIoT2xzcO80fI3woncfi7dQ7mkRt92zr4+tl+rgZeBsuLV20I/nfZhKvqktsIkGh2lEfKrePv10EemOnWRx62Fk1Psn7FhKm+6NRRAbA5HiGyNNnK/eHu+vOfQzTJKR4N9+PJU+wb16odsNuutoP7qF78z2t93WS79Q+/D8FL1nsd6rs1w3knHG93PfW0PfX6a/ayR/I7xxM119aouKVhqO1CQAgIASFQMgIibSXjo61CoMoRaNGihTWq50E/GqVslpsdQsYCEXICBYnCbBFTSUwf0cIFbZgTOQKA4GOGFq6Vr5+6aGPgEAgefm8cQ7438qx5fzFo08irhlYOwobJJISOwCTLfZ8FTsK6eXASgkBGIgh5W+XbujuRI2Ik5A7TzGhC2c396ha5z1veGqJP+kaJEMgSBG6+8m+2daeOdpNHEF7jIf5P8gBKz7/1drGkLVa7S1p467g+Ob//ueftydfGBBPJtaQPWLPWOrRtE8ru6bmhxnlOzCt+dn4gbBwHYYty8ncON6bbn3jSbr/11hDBl0BOEiEgBISAEBACSQRE2pJoaFkIZAEC+P2RRuHDSVNt2503kqJgvugkCfK2xEkRWjcIG8mw8THrsSmCJKF3IXUhGImTtRCq34kZ/zGFXOXBSEgXgLmjW0EGc0b85fJdw0YCbggbSbXxh+vsBAzzR8wxIWfkeQu52JywQczQ+EHqImkk5D+pBkiynbdolq1YviwL0Kw5VSCVxYQJE+y/nh+MSKuk6SDx9oABA2rORWRpTfM9x9qkKVPsjKOPsYZufsx0vmvdrr33Xtt7p6G2k+eJLK/87rrr7d1PP7FTv3uUDR+wo/VKS6D9z3vutUbupzrcc0QVJfjMjX5nnC1YtMiGenCU2267zQ444ABrqiAlRcGldUIgZxAgqvfJJ58cUlSRKonlI444ImeuXxe6OQIibZtjojVCoNoRaNq8lbVt3yWE548h//Fngyw1c7NGkl2jDSOaJMQJs0fSAQQNmJMszBc/f/8Ne/Sxx23BwkUebKaxXX7lVbaubtOQiw1/OCJIokWDsFEePm+E9g/BSZwcEtofsgfJa+XpAzC1hMhBIPGlI0XAMp+T8Juk2hA2fN7QALZ2f52KSGJa7TeiCipA/sSJron5wgNfkM4CkkYAoOeee86uueYaO/bYY+3vf/97FdSk9p7iWceSvIGH7rVnwUWO8JQhn3891c69/Arb3XNWtvFcbARTbufBmc4+9nsF+6Uv3PjQw3bUfiPdJ62tD6xMsomTvwzau56dO6fvGv7vvfNO7se2MQ9m+g5vOEn//Q03WteOHWxov/628w472Pte5p/+9KeQViV9f/2vXQhgsjt16lSbO3duyDvK+04e2TjVrqvV1WSCAD63L7/8sr355pv2zjvv2JAhQ0Keud69e4c0SqRvOvjgg0OuvVzKN5wJdrmwj0hbLtzlSrrGefPmBc0AHxw6nY8++mgYCfrjH/9YSWfMnWJT9epbiwYbChJgoz3Lcw0YWjG0bJg7YrJIfjQSZwdtm2u6iO6ICePMLz+0a/99fQFg69evc8LWJCTARmtGnrW1ztBII7B03gz76a8vCcFPiNZ5zA+Ot6MPHhF82tCsdXDzxzlO8Dr5uVDPcQ6Cn5BGgOAlJORGyBUXfOy8jsvX1rWVq5SnreAGFLHwzTff2EUXXRQ+zCeccIL95Cc/CbkHk7uSS+yf//yn7bHHHvbWW28lN2m5DAg84f5rzTxPWrr8+PvHOmEbZKPHjbfPvppiq9euDZEj0/fj/9Ovv2H/ffopH8TYYMNcowZp+/yrr625a8SKI2wcN2S77cLEcrrc//zzdoxr1ahHlKVuOnn1TTcZHfr+/fvH1ZrXIgRudTNY8p5O84TrUWLO0WeeecaYIG7XX/+/Njzup3ntRYA+FInPSXXE/X/66acLWVr87Gc/C9+LW265xY4//ni7/PLLg0VG7UVEV5aOwMbeVvpa/a92BBhhyVZZ5z4baAcOd8f5s88+2+6///6C1AB8bHbw0WLWY+IlKR8CLd1OcYMTLYJ/EEp/nROx9U6SiOpI+H3WQ6LQlKFtwxcNEob2jeAiY9y/Bunavafd8J9b7O83/tcJXr2QPBvtGWkCIGxo7Xp3aePLG5sCPha33nSDTfhkki3GHNMJG75t7XzewH3lOBflu3LOQ/v7eX095UAgG3vgE7bNxEeu7oawf6iEfjZDAJzjB5iOGR9fksWnC2ZyfLjRWp500knpm6v1/2uvvWbvvvuu0R5ksyxwTdhHEz+2n554YqFqLvR78NaHH9riZcvsvBNPsFsuu8zu/stf7FJvuxB805atXBmWJ3oQkdfff9+G7TjAHrnqqmDGyIZ6bsrcoU2bgv3Czmk/xUWUJMDJpKnT7HsH7F/oiAMGDrAd3FxTHfZCsFT5H75xDEhWpKz05+kcz/33hz/8wXbffffwHX3eiTsDOKSh4Lv/K/e1RPiWsm91y8cff2xjxoyp7mpU2/kvvPDCYOnw3nvvVWodsKzgm0AgsiuvvDK8/0WZxmM2D2kb6Tkm0boxeC7JHQRE2rLwXqMe/8EPfhBU4qeccor9y5OwvvDCC7Z8+fJqr+2cOXPs9NNPD2ZbNCiMGFI3zHn48OCTc/fdd4eGBH8cGvyaJl9++WW1V5nO4MKV64LmynlZMEGEmCEEG2nl/mkbE1q7OSLEyXcikTZh/pu6meOcuRsj3u2++662pk6zYMrI+m/dvBIzRnKxNfKDIFmt3CzsnzffZVffcLu1b9c2nGP0a2+HbYvyNhI7crHN35QigEAnc500dnG/ODRrEDnII9pAAqewb9P6zjIlxSLwi1/8wt53EkDHnBHV0gTtNdpsOpLZIk899ZR973vfC+Y7p556qj355JPZUrVQD9oq8Pr5Rb+0If22t+THDsJ2OdoOr/O/77vPfuKRH9NlrON9zqb1aOk+mTLZunXsWGi37h7YZJ4PTr376aeF1sc/k6dPt8tu8EEQN31Nly+9vezdrWsIPJLc1tDJ4g9G7mszPQrlE088kdxU5cu068cdd5xdeuml9tBDD9kU9wvMBeFb9uKLL9rw4cPt3HPPtUceecQWZhBFtCRs1q9fb9///vdt8eLFhq8SAzVHH3209Uv4U3Zxf8izzjrLxo0bF9ZD3H7ngXOqUzDbhrjss88+OWmyS/vMvT/jjDOCVgssHn/88aANq8j7gi8rfmv3eXvEc1Ka/Pa3v7VtttnG7nW/XEnuIFDvMpfcudyacaWMqg8dOjQko6ZjxwgMHaSHH344mFOQHLZPMWGqK/sKUclDzFDh8zHZaqutNjslHx4+9KvcwZ4PHstERKwJQseERvmBBx6w1atX20477VQt1X7wsaesd6+etvOQQcGvDP8xgoTMIJqkM6RAkNDCOVEKya19ubUTsNZOovBvGz36Ff+oLLa+2w+y4UO2D0mwMWskgiRBHQlYgmklvm0k1V7typLe7Rva9Jmz7Wv382nfro31H7p72BdiByFs2nBjYJNZm0gjfnAh6Igf2wmNnJddxwvs4stj3vnAJk/5yo77/jHVgh8nxT8Em39MPrNJGLGlw/ajH/0odAQyqRsfc96hv7gm6Dvf+Y61ddO86pYDDzzQMOtcunRpaJ+effZZw5wzz4N+9OrVq9oDaWBe+Marr9pWrgkbtH0/u8YHk3YdONAD6uTbxf5sfOv1PuGQQ61J4ybum/aF7ecd9Jc8IEhMAbDOO9n3e9t7ijv+t/WBjVUeEXLC55/Z/EWLbdB22wb4W/v6l7wDvnbtOtvTfU/SpV2rVn7MF3bvs8/Yce6Hwrsb5d5nnws+dHt6W0+Ot6T0aNfO8hvUt5vuvDP4NJK7sTqE8zKB5c033xxM+giUM3PmTCNgUo8ePTYjndVRz4o+J20G3y20YTzPfA8wU2YQksHTdn5/yvpNY4CTb+edfk87ppH/9Po3b97cfuiBch588EH70LXBtBXV9QxgOcPgMX0OyCTaP7Q7tEF862u7cN0MQEOk2nskWZ6BG3wgBgsjCC0BpPjONE57h8uCCz5slHnmmWdaWSLH8pwyMEU73LBhw7KcskL2RdvHwCOB0wicJB+7CoG1xEJE2kqEp/o29u7dO4xs4XC67bbbhoaBDycNBqM8EDkaThp3OnRVIYzovOqdoHvuucfosJUmu+22m33wwQehcTsxzTSptGOrazsfKEzSWnpn7LHHHrNLLrnEJrtp1IIFC0IHpaoiur38wnPWs3tX67jVoKA9I3E1fmje37Ru7rsWk1hD0liPaSKRJdGiLfVk2u+8+aot8mh0Awb0s10H9w+52Ga7OSO52SiD9AAhF5sTNjRl3Z0U0p98Y/wE+2bq19bFAycMHb57MMeMof0hb5hBNuFcThYJSLIxBcFGXzoCphDynxxw70943xbNnhZGkiGI1SFXXHFFIEd0eiAWfOCygcChkUKb/jdPttzKO/WZytae7wtTyUaNGtmwYcMyPaxS96P92W+//WzUqFH+XOUHc0k07wx+8N5g6oOvDh38qhaIxZveGfruiH1sf8freXfsf9an0d7xXOId74u8Izz2w49s7rcLnYRt7+aQ6z3s/uP2tj8vw53c3eekqqVf34HejiFDPLLj1zNn2VhPuH2gd+Yb+DU1ckJDIJ8X3n7Ljth330Kh/OP1jhy2i334xST7h/swnXjooYHkvDJ+vD3l5qWH7rW37dS/6MiVOzgh+tDNJyd89JHt62VXh0DKeNaOOuqoMFjAM8hgFs8h2iesKr766qtAbNi3OjqOlYULg6NcE+ZomCbzHZ7umlPI13/+8x/7yO8LZs4QsNIIHJqaiy++OJAf/FMzFQY/GKzlHdqxmOijmZa1Jfvx/jJAi9YJjdskD5bz17/+NQTMgMTSjmXDQNKWXGNpxzZxbTvWReRRhKzwrGMiDrGGwH39tfu3envBM1NWGT16dNC+MtBIEKpMhXty7bXXBmsH+oxVLbwT9InQHF/lZuO8E5jL87zynZJUPAK1irTB9uk4xA4C9uM0vEw1VfgY0FhjRsGoHy8IBGKaOzCP9w8/qnQaUEbh+vbtW6mXCYFhROeQQw7J+DyDBg0KI0i8yHz8aoLQsR/s0eS4VkwiMGehYcZU6FM3g+LjFJ3GK+t66PSuyq9v+3qHE7IEEcIPjbD6aL3qu0YL7RbLJMTu4iSM4CQQsJY+aP+wH0/natjQwbb1NtvZTNfEQdiIKglha+h9aAKb4JeGFq+R/5/nx37w3rs245tp1rlbDztiv903hvZ3MobvGuSNV4llt9wMgUfwt/OUbLbcyR9RLCFyBEV5b8yzNt3LYXSyut4/RsoZOOB9YcDhuuuuC6PWEDhGBPnAVofceOON1t2TMZd1IINOAp3EMWPGBC1AddS9uHMyyrz33nuHQETginkZ0c8w76Jzz3+egy3tWHDtaPMy0YCPdx+h55xcXOzkDDnKySURItu1ah3C/W/vnZw9hw5xn7blQdNGhMczjjnGHnaid8Wtt9myFcvtRO+kbu33CmHw4c0PPrQl7gN3lHfaovTbqo+TvSdCObs52StKIH5v+QDWPT7Ytpdr7wd4W92wvrfZPXvYPB9c6eSam6KkR8dOdrvj188DknTt2rWoXapsHe0e7SKdViKa0mGDlKMlwIyTQa5Zs2aFb1Flt49VdtGJE0FYDzroIDvGnxE65mhZCCaCFo7r5ttcXIedd4HBTnyVytLu0E7cfvvtwQyOtiwbBO0apIXngPvPIDLXxbtJJEwsbOi3lIV8pF8X/TgiuSYH/DhXdX1L0uvH92OvvfYKwUDQxEFcY1vHQHU9Dya0nQcgylR4hvhOnXfeeZkeUrAfx/LslWUwoODgLVzged9zzz2DOw/PBH0lBnLwueNZ4P4VZY21hafN6cPr+IvhIQVqh+D38corrwQtFCPZNCqY7KA9qU1Co0jHJU78RyBI+MKVtTOYCTaQFcw1aJDKKr/5zW9Cpw2zmposRO9DS8IEScZUFK1cUQEktvQ6Tz7tLOvWq49d/ruLQy42crOhDfvWCVHMs0Z+NKI7sp6IkficTRj7mpuqPWlz58wOVbjlputtdYM2rhGo45HxNkaAxJxyjRM2TC3RmOEfR3ASUgY8fv+d9uILz1svHyn74xV/C750EELMINc4eevhfmzkcYNEkiKgvucIJmokRHCtk0fyu1Gfm268wT77+MNgUrQlZiNbimM8HqJGW8D0+uuvB+INoeR9KetHhc4Dwoe5PAKZpAP45yL8qEorj0EaghjQvmWz0OkCawKVMLiElQBCR4bBEDRz5REGMzAxw7y0NBnr7+nlV//DHvQOZUmC3xl+bTPccuHRf/wj7Eoo/kFe15abRr3He/s3d8FC+/sddwQN3eF771WoyB+5zxf7XuPRQFduao/To1USkOR3bkq03AcTf+LPwHZOGq9wrQ2BTH5fQsCJ4y/7vf3w1FMDWSh00iz5g0afIBpoHfADowOLLxgaGTqSEL1MhGcGSXbMedfK+55lcs4t3QctPtdMMBE0jnTkyaM1YsSIQiaQdGRJ28H+ZRW0fGs9qimd85IEbQcTOOI/F9sp/rPMurjM//QpeVxcpnsI/gyEc1+Sc9bHiVQlEFe025yHNh9yhxUQBIcBh1/+8pclVb/QtssuuyxYFTHgg9DvoD9HoJbSTDJpcxjgYkLbw5zvdXIdZfJNiBMDnMUJxIM+Vpwwl2V//keMOZbAaxAV/D4JYANpRQvLYAcDWueff35xpwiB22a4DyvEr6yCFpyBgGzpX2EuiiUY7T9WFygT6CcxZTLYVtbrz7X9fXy99gi+SJA2Rn2YoxmqbsJGA4aWiYmGlyn9f3IdD3ym2zFJwNaalx3zC8yRiDwFSaKh4MUpzXY+07v/ySeflLlzG8umjtjEowktyyhjPD5b5nRAmAjTTuPKhNaETj/monRUyKnSxjthWyptW7WwTq2bhmiQ+KGRf22p+6RBtro7cSJKJKaJaL2cKwV/Mkwkn3Dzrvnz5lpLH+n83a8usnWN2nrAEU+Y7YQs+r+RJJtcbAQtgbDhA5fn2/FZG7zbiEDavvGO7EUX/Ny+d/SR1nDXfTwpt4f2b0OEyo3BTNDMNfByIXOE/4fGUE/q48o4W7RyvZ8ze0ZGeVcg2Ux0APigcP/u8E44WgNGz7l3pQkdM0YUEfxa6JjSzpT1PStvDjs6QZy3LEJHh85ccUIbRbvDFNso5sn/xR2byXqwp+NOZ56ODYNrBLagE0WI66IipCXLpXOE3wSDVJBdTJdLFe+ULpk1M+xGFEiSaRcn2/gAxZU//7md5fU60wkxBGov9zOL8p9HHrXR48e5hq6V/fncnxbaFvfp3K69NW7UMPz9yAMZveADPEkihjkmmry7XFt/y6OP2c1u8jb/20XWq2sXj1x5YiymyHmnNq1DZ5VntDhZ5to/iHzsRNPBjst0OuNyedbTgefexYlzcU+wZKEDy8Qyo+4k/0WrynsCSWGi017S8xevicEIzP/L01GnDAbV+HZGgsJy/P6yLv1/3Bbnye3py/F/spMe682c95/nmW/wr3/963Be3tUYBRotSnndGDCRzCQADPeZicBl1S2QGggME8I1lIW0MZiGhhGTQ76vDLQxKF0aYaNNx+8X8ljdwjsRMSitjcP8GbPb8gjvX+8MTCPBpjhiB8mmbxankrSkPOeQ4ThxbHKZ/6QqgaARoIrBHNpufPawtsLElkGN2qiNL8/9K+sxtYq0YXOMUyaOnHTAiHJYGcLLxcgaDXkkXMnOTnI9H4SqFl6abt5BKGtHsqR6MnqFOUx5JI604g+wpXmHwBMTsTiBdVkkdk6LmxdXFueBdNJAMqdB5gM50M2h6MRgPomfAwIxKI8WJXnuxm6/uHrNuuBD1rVVnWByiFath+dEgzih0SKQSGPPiRbMJZ1AMU594iln2Cfvv2kvjX7VLrzkt/bnv//T2rTrGAKRtGpUJ5wCU0kIWjsPJIIPHKSN3G+UueO2ve38Cy6069zpHm3diy+9Yn2H7B1MH9HJE7QEaeItB4SNYCR+avercxLoy8293G+W5FvDOj7a6qyuOEV+cZ043uFoWhPn4YSl/HAeOlTJkeSi1rEdZ3K00ZA1fAnoEDCKixaIdoMgQHQUKK84k15MgggoQlvAh4lAQUkNQSnVDSZlJY28Fnc8nYCykDbaQ/wkslVK68xQbzrjYI3JDRHTMiFt+Xkr7dslbgbrBHeua4J6egcaM0R82L7xqJKNvOMxcNttbHfvCCKQuvPdkuAqDxIBobrI72kT75iw721u9nfVLy60/v5MtPbBEEwnB7t/28F77B6OneZaBjRzV1/0i/CfPG6fOmmJMmnaNLvQ/T0e8MFEAo6ccczRYSKdQNTkxX2Zs765vwfxeermieq/LaWdQwOJ+R1kKZuENjJTs/3ydtSz9RnnuwCJI8IfQse2PAIBgrCUJrRVmGxGM0qIOsL7EifM6LZU0FrRTlMWE/0N5qxH0LgxaM7AGB1z7j9uBmURImpyzfjzMUiKNpMATKUJhCDZPoI535E4UVfwjJoy+gHxP324LRHaZiySiPKLbxeCFQ6DFlxDScJ3h8GO8ggD9gzSlyaQLZ6RMW7GWh6LqdLKz2Q7gxhxICO5f6ZRlJPH5OpyrSJt3ESIAYJ6mpexMjQ7dPaI6ohZCI1ARQgft2h6QGObXI6jpMk5tsNcIyNK8+fPD1WgsYIUETIYM42KFBq32CiXtVxGWvholIewcX10OumU1AThHqH53FKpW7eB50lbH8L5k9AaX7H2bsoI7ZrjpAs/NwKCYKYImarnGyB1++66o+2/24720SefB43bOG8kGcRgf2S2BxIh31pHLwsfOEws0bbhC4d/2pefTrRrrvp72PfII4+wvgN3Cz5vRK5k33w/V3NXKGCu2dKPgzSSM47Q/639P0FRKL+R52kDC0hSlGy/l/iGMuFUHoW8OUWRKzpifGghHXQ2MdnL1EyWCGEQPUyl4oBGPF9pczox+NVkKj/+8Y8LOnGZHhP3497FTk1R8+K0DvH4OKczwog52NImM5hEcAs6c2jekp2seExyDmFDu4l/DxptOkWlSd0Wro1r3Sp0Lhe7GVRDN++6yE0f1zj5ISn2fG8/Xxz7tu3cfwf71emnheIG+aDUsAEDbeKXk+zRl0e7P9uh9s7Ej23b3r0KyN3rfp9fe/89O/agAwuq0NtNv57617WB0LFytL9zPPtR/vv0M7aNaxrSI0QWR9iIbHnkiH3toE2ksIF/G+J3LZaZPj/ttNPC4FFxBJ360NFmBJ0pLqMFxTIAP970OfefQTLuM/Pk/+Q6NHBYYkAYmBC0SrwfF1xwQcaDfeXtqG/JM56OY1H/GfyJHftoIsec7y/PNEQZooLwjWQgD+LENzwSNqxN7rjjjrBPWX/ANtN3ftSoUcZU1UL7gG8jrgMMaEOw0LJiicAgWXkEdwyCSTEHA6J3ZiLFDbTFYyPZjP+3ZM5zwDXj08ngEsL957tBW5Vpn4cBcb4FaAljnr5M6kUfFNKGBUJpgnki/UzIKQOTPNPxeY7L3MeSptLOUZ7t1B2yXVaJfYs4uMXxtEvJtresZdaE/WsVaaMRJULTv//976D1IHAEL31FCwSEkYGqFl42RlNpIJIjJTRS2NHjo1NeYlXatWBmirlleQQn7PKaqdJRo/FHc1iS8GGNGs6o9eQ/E41QXC6r5pNGDkz5ADOnQWAElcidfKjRuCHUj8aajuiWNhrQnLz8utbc1hdorsjFRgTJqYvyrYWTo1au1SLE/lonXk0hUU6oMKGEvEHkmjZtFurlxpMFhA2/NQKR9HRtHf5p+MC18DKXr9mYi43lV94YG44bOGBHG3nE8cHnDd81tHEk+I7nIik3WrXprlVr5oQNrd3/t3ceUHIU59ouCUUkQChLKAESyCQDRmSTMyKDTY7G5HiNLxkBxgJj4g8iZ4MJJmO4BowAkXMWIJIAZQTKEaH/e2pVOr2j2d2e2dndnpq3zpmd2Znunu6nerrrrS/xvfOsAHhf2z6C0zWvLtpCX9KfhbRwY8l9DgMp+pfXuc/hRoR1NKzLezU1zg1ugPQtyyMm8gm25PoMTGkIk7SiDQGCewhlM0jekLYxGCI2DMtC2sakUl2DmLTbKmQ54tiwYjLjHuoeInCJZ+NRSGPgi2WW3ziTVWnbsiv0ci3NbfGzr79xDzz9jM8YeYOVKemxaBBJrbYZdm4k22E2UXGpDa5ffu9dyxC5kfvWLG1Y6Ej/T7bIf784wvW0kiwhOUlYFwtcaFje1li5v//3ezuXEIF/N/GSpiHkOndY3t38yMOLRRv7yG+H+BtEVk2NgSKPxmpkEsYCwoPfDdc97kP0b7D2FLovxQzUG/McZ1BInA6p7/k9cs2hca1g8oYJmXzu8Zz7CGXcgUkqlrYxGYTbKzW8stiw2vAbhwnHN3jwYIeILnQyKt+xMZCnniXWdTwf0lps822r1O9xTWMchmBjQoV9Y7KcTLqESRTTGL/hQspvKG2mUJJrhetqmu9k8oBHUzUS9+AqiUVyJ8ukS7gJCfaKafV1py7mO7OwTlSijROeTDbM8gw09xVOTlLmM0Aq54ZIuOGGG/wFn4BfGhZEjpMHJ35DN2ZDwuCrkO/iB4rrIMHU9WmNORjJt59YM7lJI1xfeeUVvwg3aooLE2uSxn0l33bzvdfc3uzQrpVrvnCBT+2PJQzBhtshCUVI7U9CEuLUfHFrE2M9Lc4Na9iPZgGbYfFuzasMa2b9auFT++P6ONOW72vC7mcTX+Mtpg0XSWq3sU3cI0nnH9Zbqk077+6ISyXLYJXDJXK6bZ8slLhCIgKJo6PswHTbNjF2bN9ntFzws1k3anaPzHfcNb3HYIBHQzRmKvH1Z9CBMGCwiSWd31SagQcDVSwXhU4GcK3COoIAS3NuM3AjDowBMde2LDYGmLiaItaS1jBmnLkO1xaTVdvxcP1G3BIjwXUo7eCtX79+7mpclUyoz7DJFdwXg2DD0vbUiJd8YezVbJshsQjJQ/a3wQQJQw44/Qw3w8Q7Qv+2Rx51f9x7Lx+XNmXa1Bp3d7YN4hGXXRe5K400l6n25jrW21ykamuswwQR7YLjj3P72fnBd+674w4+sclSdo4xcdTUDWs5vxcewQUMKwGxbPQv4rI+LasDdaziXCO4/oc4LSw2WMAYc3CO4wZXWyMBEeKrENGGSzCD+KbOHJo8LoQagp3nb8z1l0kormewqC0WKrmNNK/ZFucVA/1TLeY0Cw2BGsQacckhwUYpxpj8fhCBhPVgbKhrm7gZcn6kScrUVOywvBNaw72VyXvuqfxWKHdTV3xiXftcrDt1XdvN+udNfxcoISGKVobG7GyxPsJhG039jFCgBgfuWliKaMzsc2Pj0ZgX8pD5J+0gM7AjUQcC89AmcNkI+1CfZ25O3Kh5IJ4ZOHFzYkIAJvlmVevzfWHd1i1M/Pw4y7Uy0xmujQgkhnQ9LY4NcTTTJngRbLgpdrP3sHohriaY8CLhyAK/tDNXiLn+fdbpYcKOt8fbMq3MrRHPxaVMpXUxAYbrY3NTi+3IMGKt2UIr3L1ImJlxzvbDrH92Craw9ZY3EcnyJDTpbt/tRaAvHWDFtu17sLa1a7HQhNz8au6RfsMZ+INQwzrADRiBwUCTPmWmlAFYGDyn2VUGriEJQ5rlwzK4zVDXhkEJ7sPHH3983hlQ4idJzMDvjlIbWbtB4xLEzZiJjGRmPI6P/Q11JsNxF/PM5AjZ97CgI5LT9g/3gPVWX819Z66Z+1r/rr0oBfcL5tp+gV2XVu3Xz0RcF4tvM1Nyoq3St6+73JJafWLrkSwEN8pHnx/uttlwA7efiaiX3n3H7Wz9Rcwb1wMSlLSx12Pteo1VrLtZ8jaxUgK0WbPnWP23+X7ZxFdUe/nQf59zxL2dccTh1BVwC+2HueV6g9xL77zt+vft490uW9g2SzkgrrYDKf8hTpd+wHJNY5AZBHnKTdS5WJYG6njucJ1g4pHJCBrCjIk6wg+4VhTi+ofVntIj/IZJXlRXw9WViRAG5k3duGYycQwPXJ1xe8SqSFbEmsoclGKfsVwh2higN2Xj2EPpEu79XI+5/5f6N4n1iEk8hNtxxx3nJ+nyJbDhmosFkmUL9VxoaI54gXFPQ9TzetCgQd4CyflfbF6EfPtcrDt1vm2V03tRibZyAl/XvpIJkzguZotJ6cqJTxwI/zdVY1CL+wo/FgZjdTVuTMSjnHDCCalnx+vaZmN8zkAZ9xdusPjp0zDhc7HmQSashm7zmrVcVH+tmU/+4ZOQmBWLrJE+cQjWtkWWrmANw8LVxUSULw/Qvasb/dUX7sWXXnFrbLyDG9BzGbe0/dqpxUYzTegtctR980LL3CXJ/DhtdpW7z9Km0qjFhiBjWdwpzRjgOplgI4slwqxreyvIbdsiE2VX+16WYT9xv5w+e75bqoXF5ZlFA3fipm6hvlkQaggAzmEmRRiAFbqPxLJwQ2KiCNedYopdc04xGOA8w+rGzCODlDAQHGeuecy8IlqY9KjLVbOxGWM5YAYVlz1EGhlisYTBo9QxtRwbgfSFNPj9ZFn7Bq64ko9PY13cFS+8/gZLBLK323eHmmMDcYHkQdt/px3dSVZIGLfJC02sXWKDD8oETPzxJ/e5iS1cJWeaNa5Nq9ZuA3OD69qpo7lHVsWYbLz2r91V5tL+prkDDaqh9EwbO67OFlOWbHz3bDtHX3v/A9exaxe3ks1QN1XDqoaVlwyIuGLhxpXGslTs/mZhoI4bMgNiLAVMlJJIAys8Qq3YhCLwIFkVljPOTZJT1GSZJMMq1xbi4NK6yhXLu671uAZxDyfEgVhcJmOKzX5b13eFz7Gsk+yJgf+55tJclxUzrNcQz0ysMfFM8iqEWkNOljO2wBqLWyiinXsD34t3Ba65eDvxHhP6nJ9ZuSdggWe8iljjvsj9gN/xkCFDUnmsFNtvxbhTF/tdWVkvqjptWYFaiv1gJgfzeIiZKcU2S7EN9ouLCT9ILqY1iUjKLhBbeKylz8Z1olwaLpDcMBnk444Ffy7UjX3jPPb4E9yPlqThhtvvttgzE0Imrkw/+Tg0XBOnWmgWRjESilA3LWSEJDU/lrnRoz5xZ5x7wWLsWChuufNuN8s+Jx4Nyxv11Gab+OJ1NxNgX439yQ056zQ3c8Z0y6a4q9t5z6rYo2Ym2ubbd3Sz78LF8icTfj1s3aXNQPHtVKvjY5/zmnptiEDKB5xpaa+XXmqhO+uMPzeYNXLxwdXwAmGGSxPCm5s/8SeICR6kWC5mhjg35T+DOCYn6uvqgcWPGx43ZdKeMzDA5ZY4SQYNWWwMHHALbchBTH2Oe9JXX7oTbLZ6n222dTtvvpnf1FE2k72CJck41yag8jWsa8SvkZQk2Xj/Ihtwb7rOuu6Y3+2T/KjO18Puu98989qrbqgVzh2YM+k2xNJg9+zS1T318kt+OyRMWdFi8d4ykUdh7/c++9S1sbi8S+yaW6qkV3XucM4CDCSZzcfakcZlOGf11P/mDtSZyGjKhvsZ1/1SJzMjcQv3RKwlWKq4HpH9DyHHZBCDX0QKpQPwAMhCC67jjbUveBdxfcGiV8rQg8ba/1J8D8w5B5lATjbOF85L2DR1SyYXYyzIuJBHY4TscOx4uSAQmUzHAoxnVOxNoi32Hm6A4yO+hhkUbjD8WPiR0rjIkjkxXGQOP/xwbyVsgF1osE1iZcMVEqFGRrWmaiS6Gfn5F+74s//u67KRkXGMZWkkvm2uCShcFqnXhiWMItlkcpxm4oskJZ1N1OG6+K//vOj+89iD7icTf61bt3HnXXyF696xvRdX3W1dLGdkjWw5f5o78o/VB7EXXvJ316NnLx/jhhCkFts8+1KSlnRoQwZJyxppbpbNzNS2jBlAiJmjwPYcWwZRN+zis8wlrIWfCcRS1NiN2b4TTzzRu9SRiIMbHDe7hp4hbuzj1PfVTGChWb/OtevUGLOKXXfG6W6quXgdai5F19uja04tRSxwiLIvvv3O7WYxOsfvu6Q71odWNHdNSzgw1ixOwQpX87dX/+Tvd9zp3SrJXLly7z4+nT+CDNfJs83NbC1z5XzJztnhb7xpMaYt3a/MWkk83KsffuCus3X7rbhi9Q1G+F+lDdSZUMLSTibq0LDmhWQu4T09i0BWCTCJT4wncZoYGZqi4SYa4h5Psomx2JtEW+w93EDHR3A+M4LEtBDPQpwJqbxx9+AHTIIUteIJDLv+Rvf6ux+5/3f1VWbFqhJsJA5BaM02SxYuiDSyN5L6f765JiKgiHljme8sxozEJFjicKmcbLFvCC1cJ7uYG2RLi2WbNLOqttqY77+zWd1zzGoy3/Xo3s3ts/9Bbo1fr+MF2xxzkcRyt8By/U+zMjYItGVbV2WKNA9IKxNQFSNHbBvukdN8jF1zd84Zp7n2FkhOrFZw9yueRnFrMrmAK69a5RIgVTgTTBub1XwZs7Q+ZrFpD+RkL73m3vvcOyM/cdNt1vaIPfZcXH8tH7XJlsHxWIvt+qPFNW1t7uqFtFctKcpHo75wX9jE1o9Tp7hV+vZzO/12UzfdYsR++GmKWawtSZC5QI23TIzD33rTx7aRkGDVQYV9TyH7pGVFQAREoJwJ4EWDaz7GgqZ0o20shjasUxOBwgngqkNGyPpmhSz8m+NfY75ZyWYusFp9C6tS/n/vBZgJLRNlk02A4SppL90Ys3S1tsQgiDTTVK43iUasjbNYN+LQEGzEpZE0pINZ34iBa2+WuNZmtZtAun8TXyQ6admhl7vNYiew1lEwG/Flb5s4dJZVkli2KsFGTByCDQFIhkqsb2ShrBJ1xLPZ57YNskxOtXSTbdtVT/nvd64R/0iwNSLsjH4VbkRYLogPWmjust2WaV9tT2/814PuUUtIc6SJsN9tt221z8I/Dz833L1uLqtz5s5xpPMnRi4Its9Hj3b/NMt8B6sLh/Vu43XWdn0tNjFf24j6Xfag3fn4E+7Z119zz1miCUqJtG/dyv1CjSETbmSw3MyE2n6HHOy6rZrNTKH5jk/viYAIiEBjEch1p64EwQZbibbGOsP0PSKQkkBLE1w/T5/kZkyd7NPnI8Ba2S+V2DZqsfH5WHtNI54MARUsbwi8X0zB9TZLHLFuWNMQbLg1ItI6mqgaa0IL61xbE3wkEVneLGm4XfoEIyYYEWwzbZvEzrUwixwulHy/X8ZcMBF2WNYm2LrUi8PCN8MEW4fWVd9Bge1lrG5VW0tmguBTE4GmJEBMILGHZDucYZay0HCJvNWSHQw7+yy3bp4yCnNN5J1hiWo++Oxzi4Pr6jZZex13qBWqJwsln51z7TD34ajPXT+LP5wzd57VcHvRPWmxaWdb0opV+/ULX1Pt+UsL2D/fMlc2b7u02+PgQ9xW5loessNNtzjaBVZiYFkTf80tG6X/YVVbW/+IgAiIgAhAgBh18g5QDqKS4h4l2nT+R0XgySef9MUtQxzV448/7mOZmjI+rRjAK9qg78ORo7wFDYsZcWsd7ZmEH8SP/WwJRDqa2BprIo3kHwgtLGAUz0bYEeuGICPGjRprfN7Jlp9g1jbS/ZO2H3HVwj4gtf90i1vDsoYIRLB5i5kJu7EWR9fG3CpZngQjfDfxcJPsmYQmWP8oBcB+IQopsD1n3gK3cM50N691c18mIQxKi+GgdUSgFATIaEkWtrct+QOCq3WrVu6ZV19z66+xel7BxndSfPtHE3lDTz5picyPiDmSsDx4+eVuafM6oH1nKeJPv+oq92+r/5ZPtL1hyUWGWObKrS1r6VnnnefXSf5ZhhjaJoyjTe6LXtdNgIQiky1emAc120gmwn0nWU+PRAlkXyTum/CBmhrFyUmiQI1INREQgboJkL24EptEWyX2esTH/IUlC6C2zdlnn+0DvK+yQdROVixVx17jAAA6L0lEQVS33NrsX1paWn3LzGcC6XvL0IhFiwQjuD5SVLurxaVRDBuLV1sTTFjJsID1sayOWL4Qc4gq3Cbnm8Bbwd6n8PYCE3Vdza0RcWW1r11P2w6WuqlmiSMbJYKN2LkQt9bSBB5xbLaaF2yk9p9hy1JgGzfI2eZKiSWQ5SkdMN0ePdr94n42I/7CFm1LXsem3Pqx2P0laxgJVNRKR4C6baMsedLzlkZ/+0HrubmWBCQ5wM79phVskH2ZlV6hDluyUVPtky+/dP+0ArFBsPE5BbT323End/ujj7iVVljBrdhrBdeyRQvXxSxnuFWebZa5A63+EvWt1MqDwKhRo3zsNqVLgkBDYPEgrjtfQ7i1NRdXRD3JuX6xunsk5aJsQk0NiwGNmHCSkaiJgAiIQD4CEm35qOi9siVAHRkyBR599NG+Btfpp5/uC/K++eabjkLfzS1mhJozpFInw+Btt93mU6yTEp90/7UN4hoTysLmS7lWViKbYtmIIgQbwgxRtYKJrgnm9ogoQywhurB8kYa/pf2iyeqIOyPWNdL5Y4kjrm2uibceti7iav4iixwulNPtYdrMW9qwwmGRwyUSwYcVju1g6cONkoQnWPGWMyE3x147Wwa3SUoHsA+4aU6d8bObv7C5ZcFrLvfIAk8aymmQ9p9itmTTUysdAVxoKJR8u9UdW9ncHRfYYPoLyyxZW8sVbCz72TejXQerPdjeBua57Ter/cr9ZNa5J18a4c/9kV997bawGpvv2mTSAZbCXoItl1i2/59qfcl9Yr4JfNyxKCPCPWR5E+K8Du/xmthEEnKxDgIv2d54443kv3otAgURuOWWW7wVttB6ogV9iRYuCwISbWXRTdrJtASIpUKw4edMrbVQjoD/qR2HS8vQoUPdP//5T19vrr+l8H7ooYf8jTgrgo1j7b5sK3Mz/Nmn2e9rQoj6aMS09cRiZqKrjfkldjd3SSxduDx2M4tZO7O44fJIGBkibIqJOVwlcWs0j0VfDHuGuUHyoEYbZQEQcCxvL31yE7aDOGQZarchGCk1sKyJN9ukF41Y/RBvrIdoNL3mrX49zAqHm+W4qfPNMtjCJzlhllktPQHOQYoZa7Y9PbO0S/br189dYkWyjznmGHfVw4+YRbidG9C3r7vjscfdIbumz3bbbum2vhj2x2ZtI74t2Xp07uwO2mWw++1v1rXJixn+cbXVadvGaq4dVUNtuOT6ep0tAuutt563tNW0V++//77/vZK+/xuzwCYbtRupvUZhbjw/1ESgGAJMGlAagsL26xeYsbaY79M62SYg0Zbt/tHeFUHgwAMP9IKMWmfMfI4116RvbUadYt8LLEMbJQp4xipHjY+nn37aFzDm5pqV9ta7H7hxEye57iacyOaIuyOCCgGGDlrBBBLCaZwJKtwZiSebaOINV0hi3cabeCMujRg33CZJ80/afixvWNssv4h3kUR44U6JBW/F5Zv78gBY34ija23uk7hgtjaB2MZeE8/GtnGVpBHHhkvl6J+q6sSRtGSKZaXsvLRZ+lou5dkrEUkVq7R/sfiqNRyBdibUsLjfc8897lPLCNnGJnketTIAdz7+mLkLt3DLL7esd4dsaVa0Ndf6tWthMWYbrbuuWwUTtrm70dawiZ5/Wuzs82++tYRoC3tOchLa8VZeoJ+VnaAAu1ocBGZZiQYmVnjkFvNlUM3kAJ4d1K+iiPaxxx6b+sCZdFQTgSSBqy1+doIlTcKSq5aeAL/PnnYdXmedddKvVAZLVoRoo2o7vuJqDUMgBFFvsskm/kfSMN+SfqsEhffp08ffPFmLGU/+p2YYKcBpDI632GILN2LECO+ORjFtastlIW0smqj/muu5rlaricQguCZ2MhGFzWqGiS4yQ+KiSBZILF2k3MedEUHXpV1VGv4utnwb82scZyKKLJDNTZmR5p9lEWMkLcH9EUsa7pVY81ifpCLtzfUR10sEG0KvYyuLgbN1cYNEKLJPpsmqCmybkMPqt5ztxxjbT773l1m/mAXQXDV/tteytHG6pW7B2qsbdO3ITjvtNPed1TsjVmi77barfeGcT7k+/MFiy2i+nMW0aW7SmDHuYbO4zzLrGLFuoy2pyAdffeVGjx7tC7f2s3i1oUcf5dP6UyB7q/U3cG0swyPJSjrnSR5CvbU/MUlkWSKx7KuVP4FgVWMwyJgiNO4pW1lBdrw6uJ/8fVEdQGLYOD8LaUwqqIlAkgBjF1q4NyQ/0+uaCdx1110+pv6yyy6z/E6W5CmSVhGijTiRcCGNpN8ydRghiJr4MVyPstawtp177rnur3/9qw8MZ3+vvfZa7yPOwI94hJ3NfampikDn8jLDl1nDrE6biSNcE9ubIKK2GqIId0esZNRoQywhwnBx5NHTLGgTLdYNIdfOPmNdxBfp/b/DxXGRRY5lcZe0jPy+GDaxcPPMfEbafpKaLGPLeaudvUfSEjJIsk38I4lv4/v5n2V+tu30Wq4qWQrxcB1M2H04ZpZZMi0Zycwp3sLJzLNaOgLB0ta+ffV6YunWrpylcHX+kyUJOfnkkx0uzvvvv78rJpsY14blLNEIj9NWWy0vQCwrXNf2P+ts12P5DmZpG+D23WH7GuuxjbN4prNuuNG1N1fJK20/ZT3Ji7Us3iQByVNWh4945xdeeGHxPq+88spepCHWcGXGk4P7C8vSEOqck4U2nSuFEot/+SA4JNoK62smPp999lmft+CUU04pbOUMLx29aBtkQeBkcJJoa/izMEtBstxkkw1LGo8ZNpNOYXAacW2kZOZGSXB5ltpXNss/4Ycp5mZoQsgE0rcmnHCPbGO/WFwfEU+4MBJDRtFshBfWNgQaVjncKXFrRERhMUOIYQWbZlY1imDj2kg2yW7mZmnjVnObrBJxLE/SktnEtJlg+8G2jfBDqEEIKyCCbYoJP+Leetv34jYZMlESDzdnQUu3fLvWrsNyy7gVLIueWnoC4cYs0VY3M67piDbKfBCrRnmPIyzZBwPpUjYscxTnfv31190dt97qXh850r1oGWqXsdi23Tbfwq2/1pquu2UMnGbXkn+/9rq71+KbtjbrH67XoT9LuT/aVsMTePnll70A49wKSUXwwthmm238fQQvjdBeffVVd+aZZzqu2bQrrrjC7bnnnuHjgp5laSsIV0UtLO+Lwro71mtv1KKNiyiue8ymkpQizFgU1vVaOi2BLIm2mvY5ORjmR80MexZbp14DLDPZHEuW0MyNnlLlmoh7ItatORa31o/kJCaa+B/BhlhCUHWz5cfbe8SqkXGSJCVY7LqYRQ6RNc3S9bcz98cpJtg62ue4UE40kYYgoxYbAgwrXHcTbNNseRpCkVckLkFAEiOHSyWJSqbaMrzu36m53z7rd2lnsXW2wkIzzSmmzSNM/UeWttSo/IK4vZORESsbsR8k+xg8eLAXb8EVurAt1rz0Bhts4GtAssSnn3zibrKY2eEWF/ewWWB+sIyBC23iZ6BZ684ZMsRtv/32NW9In2SSANYyEoog1CgbQ0NE7WYF1XF9RLDliioEG662TAauaklp/ud//qfovud+RKkANRHIRyBWEZLvWEvxXqy8ohZtpNltbXEHs2fP9rNm++23XynOBW2jBgLlINpq2PXMvf3Be2+6nt06eytZSxNWCDAsYLNMJPUxF8m5lhMBixvWNp8d0lRSSO0/x5bBjdLHrZnQotYbLpG4PyLYqKWG9QwRiBWOhnVuhm2DsgDExbHNORbftkxrq/NmIg5rHO6VxNIhFqvi2xa6H00UrtixuV+WbXU3a+D7X453k6fNMpX3s4/9wH1NLR2BcKPJHRymW7tyl+LaQ4a+vfbay91q1jAG2iQaOvTQQxsknhlxdpnFrNFC6ne5tpXf+UfKfrwySEaFYCO1Pw1LGp4ZCLWa4pwRbMElF1dI3HWp0VZsozi3mgjkEgj3hPCc+7n+z08gVl5RizayBHbr1s0HrHNhlmjLf3KX6t2sWq1KdXyNuZ2Wv8xzL5mLDrFmJAkh6yMuibgjkkCEJCTUU8MqNtUsX/0s8yOiCwuar5Vm1i9cHCmqjdCabv/jOmlVBCwJS5WrJCKwKm2/swyVllnS1u2MYDPhNt1eU5eN7dOIkSOmbZKJNARfa4tfG2f7gCBE1LE/uF/aIq5Zu86u2/LLWlHhZXwSGL8B/UlFILjpasY9Fa4lFvqVZWok3m2HHXZwN954oxdvCDcEXNLKvsSK9XijVatWjoda+RCgeH0QaiGpCHX8SGqDUBs4cGCtB/Pee+95wcbvlfNt7733rnX5NB9S+01NBHIJhOtWrCIk93hL9X+svKIVbbhFEoS4ms2IkvL9+eefd2R/4sKs1jAEZGkrHdcp02eZe+R8n/Ift0bcIFcw61nIJknsWUvzaSSODfdIygKEOm6Irmkm8Kjphooab/FwCDYaIhCLHCIQax3Cbw6CzbbDa5aaZQKvowmwINiIf8PFcrwJM+LkKC/wvSVF6WzLkJVyomWWRLD5ZCn2XZ3Mw4ekJM2aqbi2h17An3CjCXGXBayqRRMEcGfjQVFaxNsjjzzirW4HHXRQYim9rCQChEsEoRbcH3GvDRlI09ZGZDKYCWCWv/jii12/fv1KglGirSQYo9tIEG2KaSusa8O9tLC1sr90tKINwbb66qv7mCVOduo1YG2TaGu4k1KirXRsx4wZa2WhzJ8R0WVukD4JiWV7JP0/2STJHEk2SSxdlpHfjbbXXc01EbdG3Bd9YWxbl2LbLU1AIboQeNRim42os9i2jiaucI4kMUkHc79sZRY4vqu9CUIscFj0rEa2xbQ18zXdsNAR94ZVrb1Z21padhKsdRTexhL3zaLYu6lLmQic28y9/syzZunuWuesdemolf+WQkybLG2l6UsSk2B1ozYb7pNk9yMGDvc3tfgJEGsWhBrPlCAhsQzus1jVeBRiJWUMQdzkiSee6OPXSkkwrWijiPcHH3zgdt11V58kZaQlxtl0001LuSvaVoYIhHFVrCKkoVDHKnJtKBdne+aZZ7xPOu4LPLawmzQ3bIoqqzUMAcV0lIYrN+QF82a53Xfbw40aO9XXWcO6hWBDrJFAZIIJJyxdbU1QkdqfWmy8JikI2R0RWyQYoS1rnlsItuAqSUbIDibYljLR9aO9394sabg84u6IJQ8ROMuscNRzW9q+j9g4XCApLzDJlkfELWffR3kBliHWDcsbcW6IunFTFrgxX39uNa+mOVJmq6UnEG7MaSxtDN4ee+wxv3Ey3L300kvpv6iCliSD6YUXXujuu+8+n+jhkEMO8cWuKX6sFicBsj+SvRNrK+m+EVvUEeU8eO6553zSGhLWFCLYIMW5RMZSEo6UuqWNaXv33XfdCSec4BBruHbedtttpd4VbS9DBIKlLdwbMrRrmd6VMAGa6Z0sYueiFG0MZrC04ZtOxyHattxyS8dNmmBjtdITgDNJX9TqT+BDy0jnJxvatHOjR31k7oYIs6oU/mSHRCBhbUO8jbXkH21NNJHV0ddTM9HE+4grhBaJSnxGRxNc1GWjwHY7W7a1Cbaq7VjWSJY3IYeIW84E3lSzwpExEhFGAhNcLdkO7pdkkGT5iSbwEHRdzLqHhQ13SDJUIiBXHdDXjRj+jK9dVGzq6/pTLM8thBtNGtGmwVthfbzhhht6d8nLL7/cwY5z84YbblCG08IwZnZp3B+vu+46t/vuu/saaXfeeafr0qWLF21khPzHP/7hDj744HrF2a655pp+ew0BIa1o47t79Ojh7r333mq7QZweExJYAT///PNqn+mf8iUQ7glpLUeazKvq61hFbpSiDSvbZptt5lZaaaXFog1LG0WVQ/HL8v0JZ3PPgwk/m3tXXnt1wAEH+JTly1uqx/NPP8VdcMEF7rvRX7nuJpwQabg7IqLI1mjzEYvT/Lcx8YbA81kmzU2yp9VgQ7Ata+ILyxjp/3GBRODxmveJjUPgzbHliWmbYoINV8rlbBlS+5OtEgsdbpcLzF+yh4k0LG8tbB+8OLTtEC/XseVcq131ubv71uvdvoO39m7JBx544OLaReXVA023t+FGk0a0sZcavBXeV2SYfPjhh30s07XXXuvFG5YYtfIjQGZo4hWPPvpob1UjxgzrPmn4ETVPPPGEt4wRKpH1VkgiL6yEL774oi81EI4LCyD3DuL4SY6iFgcBSlbRwr2hrqPSZF4VobS86uKZtc9tPj2+xg15jz328AcWLG38gx/7RRdd5LNJ9u7dO74Db8IjkmgrLXwKBBPofv/997sHH3zQz5xebd6OJA/p37ena7NsRzdz1ly349abug+WWtr16L2SW3vlblWWMXORJMvkDyauEHVYwEhkssASjnS3JCRY2FqYeMPdkWLbJCXpZctT642G8CMuzlvYrBbbTBNsLNOh2Qx338PPuxat27t5U8a5T7/40k23gLjpP4xxc+fNdeOtGPiq/fu5I488Uplaizwdwo0mrWhj8Pbf//7Xx+aEr2TwhtDH8sDg7aabbgof6XkRAWIGcTHbaaed3M033+zjlLhnEO/GoFct2wQodI7XDGJ7zJgxPtX+Pvvs471r8LDBU6HcWiH3UKwuWIoZ69BItjZu3Dh/HeD10KFDF1uQw6C/3Hhof6sIEINJIx4z7XkdJvOSni5YYrFEMzlw/PHHu1VWWaXqCyL9G+6lsR1edKKN2ikff/yxwwWGlhRtBO5eY7V1CEgmQF2tdAQKmSUs3bfGvSUGlri7MJj88ssv3YQJE2wg/rUViv/JTZk2xX331Tfu+i8+dZN+/Mn9PHuGM01mgqqtW6F7F9etZy+3fNeebq3+vV2Hbr3c7AUt3boDe7vRE1pY4etWrmeH1ibSlnI/mZWthxXSpmD3TMSelReYZWkjPx090f0yY6J72wZEn3zxrfvu8w8tMckvbt7C5q5/nxXc5B8nu8lTZ5mbZnO3/vqD/A1glVVWdX36aDKkPmdluCmndTVOO3jTwC1/r6y88sp+gBvEG1Y4Ek38/ve/r5cbXf5v07v1IRCKXyPW3nzzTR+PhkAjvozncr8HFbr/iFQKvnPslDaitiP3iS+++MLhCqzffH3Otuyti1U5bd4ATeY5l9adNHs9XfseRSfamHkbNGjQ4ox1SdFGdiasbbhPSrTVfmIU+mkhs4SFbrvSl4ftOuusUyOG2WZ9M8OZe2vUODflu5Fuiom4j0aNdlMnfO/+9cHbbuzEH9zcGVNdK4s5/NlEV1syjVhayuatl3bNrQA2k9KzrGZAy+a/uJkzZ5r1bI5r17aV69qls5UDaO66durgOnfs4JZq38n16d7Z9ezayYrQ9nVrr/sb189EmgpB19g1BX8QZgcLSZCgwVvBmJdY4be//a3jQbISygQQD0Vad8Rbnz59llhebzQOgVmzZvlJViZaeVD8evPNN/eWZMQKiUFiaYXcQxFkFP3ecccdPRPGOUOGDPHukUz2kXBFLS4ChYg2TeZVGWziOgOqjiY60UY9NmZLQ/MJHRKuEjvvvLN3Fxo1apQbMGBAWEzP9SRQyA2nnl+l1XMIkCmStt6AHq7VwB7+NcW0yThJYe3Z5t7YrqXVevtxuptmKbCbzZ/lps+a46bMmOVaLZzvZ2TnN2vl2jRf4Hr36etatu/oOltKSX47c0wQkpSEhiUPcahWGgKkoCdVNynpQwuz4/lEG9niKB6dbBq8JWnU/zUijVnqW2+91WflQ7ztu+++/oFVTq1xCJAJNQg13P6YiMWiRjbIcnbrOvbYY31ylPPPP38JkPksbVdeeaWPyUsuHEI/eI9SFqH97ne/8y6TCLhwHQmf6bl8CeAWSZszZ05BB1Hpk3mytBV0ujTNwiNGjHBkzmEmLrSkpY33sFjw+Pe//73ExTCso+fCCRQj2ugr1ZspnHVNa5AdMjQEG42kIlWvbWa283L+EZZJ8xwEG8tKsKUhln4ZBlbDhw+vJtrC2rnukffcc49PUz969OiwyOK4Xd7Q4G0xlnq/wHJMvBvu9KE491133bVYvA0cOLDe36ENLEng008/XSzUyKBL8hAGngi1tddee8kVyvCd9dZbz91xxx1u4sSJrmvXrtWOIPce+sADD7i77767oHFKsNRX27D+KRsC+YwJWJtpWNqSjf9JvJNvAkCTebK0Jc+VzL5GtDFrnSygnSva2PktttjCBzETsK9WGgK5N5w0WyXLEX2AxXPevHl+ZltFQtOQ0zIxEOA6dOaZZ1qM4hTXoUOHaoeUa2lDtJERL23T4C0tqZqX69u3r3fDQ7wlLW+4TWJ9I/27Wv0IMHGHdwyTFzyT8RmRdtZZZ/lETPXbevbWJrkUg2xKEu2///7VdjDX0kYCKrJBqlUOAbKdMi7Cwh9aqC2cK9qYqMtXa1KW2CpysVrabB4+nvboo4+67bffvtoB5bpH8iGp/z/55BPHrIZaegJYJ3HXyNeKEW1sJ2Q5Sm6zUuvNfPbZZz6IPMkizWsS7ySzRKVZp5BlGnr7hexLTMuuv/763uWFwWpuS1ra+N1heSB+Ra3xCWAdGTZsmK/pxmvqfeFCecYZZ/h+afw9Ku9vRKjdfvvtPskSXjH/+te/vNsvViXEG27DiJsYG+7Na621ls/4mnt8yXsoyVZIqpb0GspdXv/HR4D+fuGFF6pdV37+2WIUrCVFG/dkxmLcQwppTOZViutsrBOX0Yg2BvokGqE+W7JhaeORbNx4cb1gMKSWngAcGWCSrCK3JW84fPbOO+84Zqq58dTWGPxUYr0ZstXhchUaAfacu1hUCm3UJSL183fffee5f//994VuotblG3r7tX55xB+SCQxrGwPV3Ja0tHGdwgLNdUut6QjgxUEJjr/97W8+Ox+/VYm3dP3BJCmupmTDZWBKMjAyHGJZ4HH66af7czzd1sp7qY033thb2sJ1OgzGw+Cco3vsscc8j9oSUJU3Be19PgLrrruu9zxKjgOCpW3uXCuiuqiFcVWhoi2sXwnPEm0Z72VuADfccIPr3LlztT3F0pY7s0B2JepVyD2yGqo6/8FthZsvN9zclpuKFvcP3FTrmjGtLcsRbkkEoy9caBkwImvUXmH2PrSHHnrIu8jhLpe2kfYal7mTTjrJr0KWO0QAZS3qajBFjOWyJV30448/7vCjb4jt17Vflfb5lltu6SdCcJFMtmBpI84H0UbWW7VsECBZScgyyeSLxNuS/TJt2jQfgkC9QCzEPJic43zHkoBV7ZhjjqlIF9NwT3zllVc8uCDamLijUV/xqaee0m/e06i8PwcffLDDa2z8+PH+4INoC+cJb4ZJcYm2ms8Pibaa2WTmEyw7uY2OQ7jltnzL5i6j/6sTQGDtvvvueS0DSUsb8Wm5BX+rb6n6fwSbE3RNS9abwSUs1nozFPHFOszgBuHE7P2f//xnx/lKsgku3IheisEHYYVbETEOWInvvfde99ZbbzniOEPaa/zb2R7ByTSKzhI/QVYxtkPRVRoXfOInOnbs6Pr37+/7iuVgT3whGc5YplTbf+655/z31nRc/sMK/YPIpmHBTrZgaePmTZwPvzu1bBEg5TyTf4888oj/vWIdwfLGZCD9Nn369GztcAPvTbCmcW3bZJNN3KmnnuqvOQceeKC/TpGA49BDD3X9+vVr4D3J9ubxqOjdu/diL5QwGA+WtieffNJ7qeg3n+1+bKi922677fymmbCmBdGWzB7J/ZkSJWo1E4hVtC1K5l3zgZf7J/kSkZT7MTXl/nOhIOaAIp7JFNhJSxv++FgI0lh8KjXLUUi48vLLL/vuJL6SBAekt2b2DM7HHXecL/TLbD58cZ3ghk/cJgMjBBqz1bhFYh1DdIWGz/saa6zh1zv33HN9lk6y3jHLTepsxAJCDWsaQf+vv/66X5b9wX2Hxj6WYvt8f69evfwx5R5Xpbv/kIAEJsy6M0hLuh4z6EcQYNkJIi70r56zQyBkJD7yyCMdFnNqheKu3KlTJ59UA+sSDzw8Ympcf3DT4trBM6KNGGWuG3/5y1/8MwzUqhNgEpnrOJNZCLUg2pjspAUrm37z1blVyn/U38MVm+sIk7dBzIfzBMHGOIFzSK1mAhJtNbPJ9CdcIHNj2jK9wxnfOS4U1A3hgoKoCLPJSUsb7lzbbrttnXXwKjnLEe6R1BO86aabvAAmxTjvIahIB41rxLXXXuuFFNavSy65xKe9ZgYfoZsMUA/uv2FGDsEc+gUxgHijkTjhxhtv9FYdLDthsIkAxC0SVyZmyI8++mh32mmneQsP65Vi+4i/fMfF9iu9IdJDtrAZVkcvNATb1KlTq6X2D5/pOXsEcE/GysaDATnXSCawiINjILbVVlt58cZzOQ4oGDwyqcOgkQx3b7/9tuN8xVrPxMOf/vQn/yyxUfe5yfUbF1Guz8Ti0+DLeYPHRPLeWPfWtERsBLC2HXXUUe69995bwtLGb5AwoDDxG9uxl+p4Yh33V4SlLQw6S3UyVPJ2cKvDJ5+4NkQbs620INqou8ZMIe5+9WnlOKgp9HhxFdpll138ajCjcSMn1TMCLLhDtm/f3l+kuZkzm02q8WSMU0gXj9CbMGGCO/zww30gO9tj9ha3ClwdGTziskRLBjXzP1YAYkLPOecc716JFZX9ICtlKbZf03Hx3ZXeKBwMdxITJC1tCHQGb3LlLr8zBGHGA5fnIN6IfeOB1RnLGzHCPGe5cd1gkIhACyKNiVDqph122GFepG2wwQZZPoRM7hsit0uXLt5FHq8HGqJNv3mPouL/YGlbddVVvagPHjTBPZKQCCbPqSepVjOBWFP+V4Ro4yajVjoCuM+dd955/mZOXBQtiDZuOrjE5JZeKN23x7MlGOH2SJB+z549/YER64BYIrNkOG8RxsSyMYAiXfQqq6zihTMXdupFIep476CDDnK4RWIx44J/yimn+PIWbJi4KAaQDLSwuAVXnEATUXXZZZf5eDYsP8QTXnrppV60lWL7NR0XYjHWi2tgW9czacB5vPHGG95ywfLMuOO6isVTrXwJMCNOTBcPflNY3hBx/L55UGCZGFUeTMbgAp1bdLmxjp7kQ7hpcx4i1Dj/sKSRFAdhdsQRR/g4W2JtcxN+NdY+xvI9eFVgbcPSFrLCwh439xDfHcux6jiKI0AiNtyscbumhSLbuCNfffXVxW20gtaSpa1MO5uBbxj8lukhZG63Q/YrUpXjBklDfEyePNnH4CBGgnUmczufoR1CrHDTTg7SSH/NoAjrCokOcEXCeskgikEe8YQMpng/mXHwtttu82Jv6NChLszcXn755d7iiUBjkBAa6yPikg2Bx37w/WR5Qxzi0hhafbdf23GFGLrwXZX4jLWNeMMVV1zRHz6ukczGy4oRz9mAKOOBGyHijQcCietosuwD7oaIOOqJIuJYp5QtiLPPP//ci7TwHFLQM0nDeXfiiSf67+d6JJfHUvZA1bawllCjDo8KGhmXuebT72oigDcNIRK4SNLwwuDezaR4MjxCpPITiHUyWJa2/P2td2shgBWHGzlxDWR3pJGumIHmDz/84EL2o1o2oY8WEQjxZgEIg3YGTwziRo4c6S1diOQwsw17HszcJxvCJ5/4wc0019WU9XMbFzjcNXnka/Xdfl3Hle87K+k9rJtYQLGc0rBYY+lUi5MAE1vBG4F4UizkWOLef/9974bIe5wDNCxdxJ/i1cCDGKjwOvnM+8QbEzvKY9KkSUu8Jp18EGdsm2sDopCJHq5FXNdXW201PlJrYAJMeOIqS9/TyLiMm7SaCEAA7xSsbXjY0LB6Y4nFrVqT4h5JrX9kaasVT3Y/lKWtYfoGIUESDUQaDWtOsA5kPU6jYYiUbqu4mjLLloxbK93Wm25LsR5XKYhiVcOawaCaxg1b2cFKQTb728CyxiP83ok3/eijj/wMOxZ1HpQHqW8jBgZhhljEksfrfBM49f0erZ+OAN4PTNYg0GmIczIFq4lAIIBoo7wPjTAJYt9DSZ+wjJ7zE5ClLT+XzL/LTKLcI0vfTYi266+/3g8u2Drub7jxyde69Ky1xfgJMHOKaxTucjTiHMk4qFZ5BBi84yLHgzgyGum+f/rpJ//48ccf877mc1wbcXMmeUHuc8hSWHlEs3vEWDlxi6QxcaMmAkkCZHOmDis1V78xN1rCUOQynyRU82tZ2mpmk+lP6LhYO68pweNOw0wG8RA0RBsuPGG2uCn3Td8tAuVIgBv0fffd53ddMQvl2IMNt88k7OEREhY13Ddpy41JgN889TJpGow3Jvny+S6s4og2JmtwjSTrqFrdBGK1tEWfVhErG37+aqUngPsWhZ1pJK8gw6GaCIhAcQRwlWKCCbcp1eApjqHWEoFyIoB7Kr95BuKDBw8up13XvjYSASbDaYg2Cfv00GM11lREIpJQUT59d2vJNAT22Wcfd/zxx/tFyXbI/2oiIALFEejRo4ePNxo4cGBxG9BaIiACZUcgxLGW3Y5rhxuFADVbsbKTN4CJPbV0BGK1tEm0pet/LZWHAIPLIIj33nvvPEvoLREQgUIIXHfddYUsrmVFQAREQAQiJoBo23nnnX32SOLb1NIRkKUtHafMLYV7ZBAWmdu5Mt8h0riT6AW+xLipiYAIiIAIiIAIiIAIlIbAwQcf7GvgDhgwoDQbrJCtxGppiz6mDbUt0dZwv9JQCJQ6bWoiIAIiIAIiIAIiIAKlIUAtRgm2wlnGammTaCv8XNAaCQL333+/N90ro1ECil6KgAiIgAiIgAiIgAg0CYFYLW2KaWuS0ymuLx02bFhcB6SjEQEREAEREAEREAERKEsCsrSVZbc5n05X7pFl2nnabREQAREQAREQAREQAREogECslrbo3SNJRKJ4qwLOdC0qAiIgAiIgAiIgAiIgAmVKQJa2Mu44UqaqiYAIiIAIiIAIiIAIiIAIxE2AzOYxtugtbahtWdpiPHV1TCIgAiIgAiIgAiIgAiJQnYBEW3UeZfOf6rSVTVdpR0VABERABEQgkwTw2KnNa+ftt9929957byb2/T//+Y979913/SMTO6SdEIFGJiDR1sjAS/V1WNqUiKRUNLUdERCBchy8FdJrEyZMcHfeeaeum4VA07JRE/jyyy/dSiut5B/rrruuO+WUU9zEiROrHfMDDzzgLrjgAvfLL79Ue78p/rn99tvd8OHD/aMpvl/fKQJNTYCxf7NmzZp6N0r+/RXhHinRVvLzRhsUgYokUK6Dt0I669tvv3XnnHOO3MoLgaZlK4LAI4884o455hj36quvusMOO8wLtIULF3oL3JAhQ9zLL7/s8O5JNj6fPn26F3mzZ89OfuRfz5w5s9oECZNCM2bMyPv7S04YzZkzp9p6bGzq1KmO70vbwr4nl+c7CtlGcl29FoEsEYgxGUn1q0uWaJdwX7gASbiVEKg2JQIVTiC2wRvdSewvg0s1ERCB/AQGDBjgjjzySHfllVe6jz76yL3zzjvu6aef9ha4VVdd1e26667VVnzppZfcRhtt5NZYYw03aNAgt8kmmyz+HPfFY4891q222mpu5ZVXdpdddpn/jPdXX311179/f/fHP/7RffXVV/59JlOCte/uu+92fN/666/vXSARa3z3Wmut5TbbbDOHEEzTcKNkm9OmTfOLz5s3z///+OOPp1ldyywikBTT+aBk0XU2337G9l7uBEoMxxe9aAszRvyo1ERABESgFARiG7w9+OCDfpDI4PKmm24qBSJtQwSiJbDOOuv4Yxs9erTbaqutvHA666yzljjev/3tb27FFVd0L7zwgvvggw/cM888s3iZs88+279G9F199dVenGE9Q3i99tprjokhxi3Dhg3zy/Xq1ctb8vgHscUDscfz888/777//nv3xBNPuPPPP9+9//77fp26/rDv7dq1c0899ZRfFHFB23zzzf2z/tRNoBK8L+qmkM0lZGnLZr/UulfBv1yWtlox6UMREIEiCMQyeLv00ku9BWHEiBG1JlsoApFWEYHoCDAYROwwrqCIb8eOHV379u2XOM6NN97YvfLKK+6vf/2re+yxx1yrVq38Ml9//bW31J1wwgneYrbbbru5a6+91rVp08bhQvnWW2950YalgFg5Gq+XX355/3q77bZzAwcOdFdddZU74IADHL9bBNiaa67pn3/1q1/55er6w/4ceuihDssdDXGJYFtuueXqWlWf5xCIyfsiFrdZWdpyTtJy+DeINqX9L4fe0j6KQHkRiGHwhrVg3Lhx7qCDDnJ9+vRx++67b3l1gvZWBBqZAJYsXBB79+5d6zf/7//+r7v//vvdKqus4m688Ua3zz77+OXDYHLWrFlLrH/00Uc7EolgoevWrdsSn/MG26P17NnT7wPiMZktL4g7v1Adf9gnjuezzz7zVrsdd9yxjjX0cT4CMXlfxOI2K0tbvjM14+/JPbJ0HRT8tpPPhWxdWekKoaVly4FADIO34DqOxYDWoUOHckCvfRSBRieAsLn55pvdySef7GO/iCkjacjkyZN9PCiTw7zmPRrxaP369XP777+/jzkbOXKkX65v377u17/+tbviiiscLomTJk1yY8aM8YlNuKbstdde7ve///3i4xs7dqwj3uyHH37w77H98B28QRwbbpYff/yx4zvYRtqGOMQiiCsn+4vFTq14AjF4X8TiNivRVvx53GRrBtEmS1v9uiDptx2CoXkOQdJptq6sdGkoaZlyIBDT4I3fcY8ePdxdd93lmFhhllVNBERgSQJ77rmnjzFjYH7PPfd469aZZ57pKAOACyQWa17zHg0rFsKOZCTXXHONO/30090yyyzjP7vwwgu9WyXbXG+99dyJJ57oXSD//Oc/u7/85S/e/TG4XLI+8XCIMxqZK7Hihbb11ls7XCZ32mkn/xlukoW0Aw880D377LN+X2uy7hWyvUpeNgbvi1jcZoNFO6bzsUVMB5PvWIJoU0xbPjqFv0dWqXAjYW3cQ4ILKv/jj4+vf7IhmAmwVhOBWAgw0OrUqZPbdNNN3RlnnLF48Pboo48uPkQGb8SqkGSAwRsz8KHlDt5uueUWxzZpDOBIDBIGb2yfNOM0Bm8kJiDrHI3B2+DBg308DP8zeHvjjTf84A0hlnbwRn2poUOH+m1vv/32bEpNBERgEQESfuBGnK/x++aRr/FbJLMjrovcN5Mz/1jaWO+SSy7x98dll13Wb4IYM0QU91UGzyeddJJ/Zt2a9oEYtIsvvtidd955rm3btj4TbNJdMt++Jd/7zW9+4+/bhx9+ePJtvS6CQCHeF1tuuaWPR8R1lrjC//u//1tcMqIm11ksrrvsskuNscdJ11l2n7EvsZKhpXWd5Z5FnGVwmyWTabm15O+t3Pa9pv2NXrQFQSHRVtMpUNj7zMonRRtrkzUL9w7SG+Pnz02HDFY0Bp+nnnqqf63BoMegP2VMINbBG7P022yzjXfBYqCIy2Rwlyzj7tKui0CTEkA4MblTW0Nk8Ui2pODK/Sy5XO7rsGza3y7lBe644w738MMPO2LZtt1229xN6v+UBBA3gSfjpOA6O3fu3Gqus61bt/ZjqOA6y8Q3xgUssZRcSbrOUsSdOGOEGpNwCEKyju6xxx7uoosu8nuG62znzp2XcJ0N4zSss1huiVnG8sQ2KD9RV4vBbVaira5ezuDnsrSVtlO4UDCoo5H5iosFwhiXqnvvvdeLN97HMtC9e3cXstIdfPDBi4VcafdIWxOB7BPI+uANgtzQw4xsjG4l2T9LtIci0LgEEAO4Q1533XXevTIpFht3T8r/22LzvqBHsPji1YEALUe32RjvY81M1Cws/59LzUdAcC7FMDE7p02DW/PWKvcTYtoITj3kkEMWD+yOOuooP4uI+xazPczYYdHEGoGpnxkkZnlefPFFP3uET/4f/vAH9+mnny4xs1i5ZHXkIiACIiACIiAClUSAsVJNrrOBA+EmhJbgOhusRqwXXGf5nEn08FlYL98zy2KJJVwFcd6sWbN8i1V7b/z48X7cR+H3cswqyvgTqyQWy1ha9O6RQZOGDGmxdFxTHQdxNsHsntyH4CedvBAE5sFVQ1npksT0WgREQAREQAREoBIJZNn7Irh5lrvbbBoxW27nXsWINmWPLM2pOWrUqGqijXTGNbVkVjri3JSVriZSel8EREAEREAEREAEmp5ALG6zMbpHVoxoUyKS0lwIdt9992obGj58+OJsR8kPgsVNWemSVPRaBERABERABERABLJLYIMNNnA8yr3J0laGPRiyRwZXvTI8hEzscm1Z80IWI3aUH0kyLbGy0mWi+7QTIiACIiACIiACIlAxBGRpK8OuDqJN7pFN13n8cJSVrun465tFQAREQAREQAREoJIIxGhpax57B4ZEJHKPjL2ndXwiIAIiIAIiIAIiIAIiUOX5FRsHibbYelTHIwIiIAIiIAIiIAIiIAIVTCBG90iJtgo+oXXoIiACIiACIiACIiACIhAbAblHlmGPKqatDDtNuywCIiACIiACIiACIiACRRKQaCsSXFOuFmLalD2yKXtB3y0CIiACIiACIiACIiACjUNA7pGNw7mk3yJLW0lxamMiIAIiIAIiIAIiIAIikGkCsrRlunvy71ywtCl7ZH4+elcEREAEREAEREAEREAEYiIg0VaGvRlEm9wjy7DztMsiIAIiIAIiIAIiIAIiUCABibYCgWVh8SDaVFw7C72hfRABERABERABERABERCBhiUg0dawfBtk6yGmTe6RDYJXGxUBERABERABERABERCBTBGQaMtUd6TbmWBpk2hLx0tLiYAIiIAIiIAIiIAIiEA5E1D2yDLsPVnayrDTtMsiIAIiIAIiIAIiIAIiUCQBWdqKBJeF1RTTloVe0D6IgAiIgAiIgAiIgAiIQMMSwNIWWxLC5g2LrOm3Lktb0/eB9kAEREAEREAEREAEREAEGosAlrbYDDYSbY119uh7REAEREAEREAEREAEREAEGpwAok2WtgbHXNovUCKS0vLU1kRABERABERABERABEQgywRwj5SlLcs9lGffgntkbB2X51D1lgiIgAiIgAiIgAiIgAhUPAFZ2srwFAiWtthMpGXYFdplERABERABERABERABEWhwAoppa3DEpf+CINpUp630bLVFERABERABERABERABEcgaAWWPzFqPpNifINrkHpkClhYRAREQAREQAREQAREQgTInIEtbGXZgiGmTe2QZdp52WQREQAREQAREQAREQAQKJKCYtgKBZWFxWdqy0AvaBxEQAREQAREQAREQARFoHAKytDUO55J+S7C0KaatpFi1MREQAREQAREQAREQARHIJAFZ2jLZLel2SqItHSctJQIiIAIiIAIiIAIiIALlTEB12sqw92RpK8NO0y6LgAiIgAiIgAiIgAiIQJEEZGkrElxTrkZM24YbbhhdVfSmZKrvFgEREAEREAEREAEREIEsE4gtc3yLLMMuxb7179/ftW/f3h1xxBGl2Jy2IQIiIAIiIAIiIAIiIAIikHECGG1ias3MErUwpgPSsYiACIiACIiACIiACIiACIhATASax3QwOhYREAEREAEREAEREAEREAERiI1A9O6RuR02YsQId8011/i3mzVrVvBzWCdsN/xfzDNGzmLW47vDelnYj1iOI7AMfAPjQp/DdgpdL7l8LExjOY7Qp+HcKPY5LY/kuZD7XWm3kbte8n9e02r7nro+L6f98Ae76A/7na/V9/3Asr7bYd9KsY36bCccS+DU1PsT9iM8p92fcBxply90+425fDiW8J08F3pcha5T6PYLWZ7jKWT5Qve9MZfP7ZtyPa5wHE2x/6X+zo022ohTILpWcaKtW7dubvLkyW7UqFHRdaYOSAREQAREQAREQAREQAQqmcApp5ziTj755OgQKKYtui7VAYmACOQjEGbyanoO69T0eZr3k7PHaZbnO3OXi2U/snocSealfB2Ot5TbDOdG2Ga+mfDcZcJ+FPt+8hwO31vfbebbThaPpRhmWTyOXN65/9d2nOVwPLXtfzhXk89pli92mZp+L8VuL19fxdInMGmMYyH5CKItxibRFmOv6phEQAREQAREQAREQAREQASiIaBEJNF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgISbdF0pQ5EBERABERABERABERABEQgRgISbTH2qo5JBERABERABERABERABEQgGgL/HyO/Hwp37uk5AAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "919fe33c-0149-4f7d-b200-544a18986c9a",
+   "metadata": {},
+   "source": [
+    "# Self-RAG\n",
+    "\n",
+    "Self-reflection can enhance RAG, enabling correction of poor quality retrieval or generations.\n",
+    "\n",
+    "Several recent papers focus on this theme, but implementing the ideas can be tricky.\n",
+    "\n",
+    "Here we show how to implement ideas from the `Self RAG` paper [here](https://arxiv.org/abs/2310.11511) using LangGraph.\n",
+    "\n",
+    "## Dependencies\n",
+    "\n",
+    "Set `OPENAI_API_KEY`\n",
+    "\n",
+    "## Self-RAG Detail\n",
+    "\n",
+    "Self-RAG is a recent paper that introduces an interesting approach for self-reflective RAG. \n",
+    "\n",
+    "The framework trains an LLM (e.g., LLaMA2-7b or 13b) to generate tokens that govern the RAG process in a few ways:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\n",
+    "* Token: `Retrieve`\n",
+    "* Input: `x (question)` OR `x (question)`, `y (generation)`\n",
+    "* Decides when to retrieve `D` chunks with `R`\n",
+    "* Output: `yes, no, continue`\n",
+    "\n",
+    "2. Are the retrieved passages `D` relevant to the question `x` -\n",
+    "\n",
+    "* Token: `ISREL`\n",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\n",
+    "* Token: `ISSUP`\n",
+    "* Input: `x (question)`, `d (chunk)`,  `y (generation)` for `d` in `D`\n",
+    "* All of the verification-worthy statements in `y (generation)` are supported by `d`\n",
+    "* Output: `{fully supported, partially supported, no support`\n",
+    "\n",
+    "4. The LLM generation from each chunk in `D` is a useful response to `x (question)` -\n",
+    "\n",
+    "* Token: `ISUSE`\n",
+    "* Input: `x (question)`, `y (generation)` for `d` in `D`\n",
+    "* `y (generation)` is a useful response to `x (question)`.\n",
+    "* Output: `{5, 4, 3, 2, 1}`\n",
+    "\n",
+    "We can represent this as a graph:\n",
+    "\n",
+    "![Screenshot 2024-02-02 at 1.36.44 PM.png](attachment:ea6a57d2-f2ec-4061-840a-98deb3207248.png)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Let's implement some of these ideas from scratch using [LangGraph](https://python.langchain.com/docs/langgraph)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c27bebdc-be71-4130-ab9d-42f09f87658b",
+   "metadata": {},
+   "source": [
+    "## Retriever\n",
+    " \n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "\n",
+    "urls = [\n",
+    "    \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/\",\n",
+    "    \"https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/\",\n",
+    "]\n",
+    "\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=250, chunk_overlap=0\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "276001c5-c079-4e5b-9f42-81a06704d200",
+   "metadata": {},
+   "source": [
+    "## State\n",
+    " \n",
+    "We will define a graph.\n",
+    "\n",
+    "Our state will be a `dict`.\n",
+    "\n",
+    "We can access this from any graph node as `state['keys']`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Dict, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        keys: A dictionary where each key is a string.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    keys: Dict[str, any]"
+   ]
+  },
+  {
+   "attachments": {
+    "e61fbd0c-e667-4160-a96c-82f95a560b44.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "251feeea-c9a0-404a-8b55-bef3020bb5e2",
+   "metadata": {},
+   "source": [
+    "## Nodes and Edges\n",
+    "\n",
+    "Each `node` will simply modify the `state`.\n",
+    "\n",
+    "Each `edge` will choose which `node` to call next.\n",
+    "\n",
+    "We can lay out `self-RAG` as a graph.\n",
+    "\n",
+    "Here is our graph flow:\n",
+    "\n",
+    "![Screenshot 2024-02-02 at 9.01.01 PM.png](attachment:e61fbd0c-e667-4160-a96c-82f95a560b44.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "add509d8-6682-4127-8d95-13dd37d79702",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.messages import BaseMessage, FunctionMessage\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_core.utils.function_calling import convert_to_openai_tool\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
+    "\n",
+    "### Nodes ###\n",
+    "\n",
+    "\n",
+    "def retrieve(state):\n",
+    "    \"\"\"\n",
+    "    Retrieve documents\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, documents, that contains retrieved documents\n",
+    "    \"\"\"\n",
+    "    print(\"---RETRIEVE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"keys\": {\"documents\": documents, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation, that contains LLM generation\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "    # LLM\n",
+    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "\n",
+    "    # Post-processing\n",
+    "    def format_docs(docs):\n",
+    "        return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "    # Chain\n",
+    "    rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "    # Run\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\n",
+    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def grade_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates documents key with relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK RELEVANCE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Data model\n",
+    "    class grade(BaseModel):\n",
+    "        \"\"\"Binary score for relevance check.\"\"\"\n",
+    "\n",
+    "        binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
+    "\n",
+    "    # LLM\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Tool\n",
+    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
+    "\n",
+    "    # LLM with tool and enforce invocation\n",
+    "    llm_with_tool = model.bind(\n",
+    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
+    "    )\n",
+    "\n",
+    "    # Parser\n",
+    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "        Here is the retrieved document: \\n\\n {context} \\n\\n\n",
+    "        Here is the user question: {question} \\n\n",
+    "        If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \\n\n",
+    "        Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\"\"\",\n",
+    "        input_variables=[\"context\", \"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Chain\n",
+    "    chain = prompt | llm_with_tool | parser_tool\n",
+    "\n",
+    "    # Score\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = chain.invoke({\"question\": question, \"context\": d.page_content})\n",
+    "        grade = score[0].binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---GRADE: DOCUMENT RELEVANT---\")\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            print(\"---GRADE: DOCUMENT NOT RELEVANT---\")\n",
+    "            continue\n",
+    "\n",
+    "    return {\"keys\": {\"documents\": filtered_docs, \"question\": question}}\n",
+    "\n",
+    "\n",
+    "def transform_query(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): Updates question key with a re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    # Create a prompt template with format instructions and the query\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are generating questions that is well optimized for retrieval. \\n \n",
+    "        Look at the input and try to reason about the underlying sematic intent / meaning. \\n \n",
+    "        Here is the initial question:\n",
+    "        \\n ------- \\n\n",
+    "        {question} \n",
+    "        \\n ------- \\n\n",
+    "        Formulate an improved question: \"\"\",\n",
+    "        input_variables=[\"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Grader\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Prompt\n",
+    "    chain = prompt | model | StrOutputParser()\n",
+    "    better_question = chain.invoke({\"question\": question})\n",
+    "\n",
+    "    return {\"keys\": {\"documents\": documents, \"question\": better_question}}\n",
+    "\n",
+    "\n",
+    "def prepare_for_final_grade(state):\n",
+    "    \"\"\"\n",
+    "    Passthrough state for final grade.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): The current graph state\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---FINAL GRADE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    generation = state_dict[\"generation\"]\n",
+    "\n",
+    "    return {\n",
+    "        \"keys\": {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "### Edges ###\n",
+    "\n",
+    "\n",
+    "def decide_to_generate(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether to generate an answer, or re-generate a question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current state of the agent, including all keys.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---DECIDE TO GENERATE---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    filtered_documents = state_dict[\"documents\"]\n",
+    "\n",
+    "    if not filtered_documents:\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\"---DECISION: TRANSFORM QUERY---\")\n",
+    "        return \"transform_query\"\n",
+    "    else:\n",
+    "        # We have relevant documents, so generate answer\n",
+    "        print(\"---DECISION: GENERATE---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "def grade_generation_v_documents(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current state of the agent, including all keys.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---GRADE GENERATION vs DOCUMENTS---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    generation = state_dict[\"generation\"]\n",
+    "\n",
+    "    # Data model\n",
+    "    class grade(BaseModel):\n",
+    "        \"\"\"Binary score for relevance check.\"\"\"\n",
+    "\n",
+    "        binary_score: str = Field(description=\"Supported score 'yes' or 'no'\")\n",
+    "\n",
+    "    # LLM\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Tool\n",
+    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
+    "\n",
+    "    # LLM with tool and enforce invocation\n",
+    "    llm_with_tool = model.bind(\n",
+    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
+    "    )\n",
+    "\n",
+    "    # Parser\n",
+    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing whether an answer is grounded in / supported by a set of facts. \\n \n",
+    "        Here are the facts:\n",
+    "        \\n ------- \\n\n",
+    "        {documents} \n",
+    "        \\n ------- \\n\n",
+    "        Here is the answer: {generation}\n",
+    "        Give a binary score 'yes' or 'no' to indicate whether the answer is grounded in / supported by a set of facts.\"\"\",\n",
+    "        input_variables=[\"generation\", \"documents\"],\n",
+    "    )\n",
+    "\n",
+    "    # Chain\n",
+    "    chain = prompt | llm_with_tool | parser_tool\n",
+    "\n",
+    "    score = chain.invoke({\"generation\": generation, \"documents\": documents})\n",
+    "    grade = score[0].binary_score\n",
+    "\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\")\n",
+    "        return \"supported\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: NOT SUPPORTED, GENERATE AGAIN---\")\n",
+    "        return \"not supported\"\n",
+    "\n",
+    "\n",
+    "def grade_generation_v_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation addresses the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current state of the agent, including all keys.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "    state_dict = state[\"keys\"]\n",
+    "    question = state_dict[\"question\"]\n",
+    "    documents = state_dict[\"documents\"]\n",
+    "    generation = state_dict[\"generation\"]\n",
+    "\n",
+    "    # Data model\n",
+    "    class grade(BaseModel):\n",
+    "        \"\"\"Binary score for relevance check.\"\"\"\n",
+    "\n",
+    "        binary_score: str = Field(description=\"Useful score 'yes' or 'no'\")\n",
+    "\n",
+    "    # LLM\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "\n",
+    "    # Tool\n",
+    "    grade_tool_oai = convert_to_openai_tool(grade)\n",
+    "\n",
+    "    # LLM with tool and enforce invocation\n",
+    "    llm_with_tool = model.bind(\n",
+    "        tools=[convert_to_openai_tool(grade_tool_oai)],\n",
+    "        tool_choice={\"type\": \"function\", \"function\": {\"name\": \"grade\"}},\n",
+    "    )\n",
+    "\n",
+    "    # Parser\n",
+    "    parser_tool = PydanticToolsParser(tools=[grade])\n",
+    "\n",
+    "    # Prompt\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=\"\"\"You are a grader assessing whether an answer is useful to resolve a question. \\n \n",
+    "        Here is the answer:\n",
+    "        \\n ------- \\n\n",
+    "        {generation} \n",
+    "        \\n ------- \\n\n",
+    "        Here is the question: {question}\n",
+    "        Give a binary score 'yes' or 'no' to indicate whether the answer is useful to resolve a question.\"\"\",\n",
+    "        input_variables=[\"generation\", \"question\"],\n",
+    "    )\n",
+    "\n",
+    "    # Prompt\n",
+    "    chain = prompt | llm_with_tool | parser_tool\n",
+    "\n",
+    "    score = chain.invoke({\"generation\": generation, \"question\": question})\n",
+    "    grade = score[0].binary_score\n",
+    "\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: USEFUL---\")\n",
+    "        return \"useful\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: NOT USEFUL---\")\n",
+    "        return \"not useful\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61cd5797-1782-4d78-a277-8196d13f3e1b",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "The just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generatae\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "workflow.add_node(\"prepare_for_final_grade\", prepare_for_final_grade)  # passthrough\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.set_entry_point(\"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"transform_query\": \"transform_query\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"generate\",\n",
+    "    grade_generation_v_documents,\n",
+    "    {\n",
+    "        \"supported\": \"prepare_for_final_grade\",\n",
+    "        \"not supported\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"prepare_for_final_grade\",\n",
+    "    grade_generation_v_question,\n",
+    "    {\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---GRADE GENERATION vs DOCUMENTS---\n",
+      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
+      "---FINAL GRADE---\n",
+      "\"Node 'prepare_for_final_grade':\"\n",
+      "'\\n---\\n'\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: USEFUL---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('Short-term memory is the stage of memory that stores information that we are '\n",
+      " 'currently aware of and needed to carry out complex cognitive tasks. It has a '\n",
+      " 'limited capacity and lasts for a short duration. Long-term memory, on the '\n",
+      " 'other hand, can store information for a long time and has unlimited storage '\n",
+      " 'capacity. It includes explicit/declarative memory for facts and events, and '\n",
+      " 'implicit/procedural memory for unconscious skills and routines.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "inputs = {\"keys\": {\"question\": \"Explain how the different types of agent memory work?\"}}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---DECIDE TO GENERATE---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---GRADE GENERATION vs DOCUMENTS---\n",
+      "---DECISION: SUPPORTED, MOVE TO FINAL GRADE---\n",
+      "---FINAL GRADE---\n",
+      "\"Node 'prepare_for_final_grade':\"\n",
+      "'\\n---\\n'\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: USEFUL---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('Chain of thought prompting involves guiding the behavior of autoregressive '\n",
+      " 'language models by providing prompts or demonstrations that contain '\n",
+      " 'high-quality reasoning chains. This can be done through methods such as '\n",
+      " 'self-asking, interleaving retrieval with chain-of-thought reasoning, and '\n",
+      " 'complexity-based prompting for multi-step reasoning. These techniques aim to '\n",
+      " \"improve the model's ability to generate coherent and logical responses \"\n",
+      " 'without updating its weights.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"keys\": {\"question\": \"Explain how chain of thought prompting works?\"}}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint.pprint(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint.pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint.pprint(value['keys']['generation'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "548f1c5b-4108-4aae-8abb-ec171b511b92",
+   "metadata": {},
+   "source": [
+    "LangSmith Traces - \n",
+    " \n",
+    "* https://smith.langchain.com/public/55d6180f-aab8-42bc-8799-dadce6247d9b/r\n",
+    "\n",
+    "* https://smith.langchain.com/public/f85ebc95-81d9-47fc-91c6-b54e5b78f359/r"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/streaming-tokens.ipynb b/examples/streaming-tokens.ipynb
index d1dfed602..3b19a50e3 100644
--- a/examples/streaming-tokens.ipynb
+++ b/examples/streaming-tokens.ipynb
@@ -198,40 +198,6 @@
     "model = model.bind_functions(functions)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff",
-   "metadata": {},
-   "source": [
-    "## Define the agent state\n",
-    "\n",
-    "The main type of graph in `langgraph` is the `StatefulGraph`.\n",
-    "This graph is parameterized by a state object that it passes around to each node.\n",
-    "Each node then returns operations to update that state.\n",
-    "These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n",
-    "Whether to set or add is denoted by annotating the state object you construct the graph with.\n",
-    "\n",
-    "For this example, the state we will track will just be a list of messages.\n",
-    "We want each node to just add messages to that list.\n",
-    "Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is always added to.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ea793afa-2eab-4901-910d-6eed90cd6564",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import TypedDict, Annotated, Sequence\n",
-    "import operator\n",
-    "from langchain_core.messages import BaseMessage\n",
-    "\n",
-    "\n",
-    "class AgentState(TypedDict):\n",
-    "    messages: Annotated[Sequence[BaseMessage], operator.add]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
@@ -265,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
    "metadata": {},
    "outputs": [],
@@ -275,8 +241,7 @@
     "from langchain_core.messages import FunctionMessage\n",
     "\n",
     "# Define the function that determines whether to continue or not\n",
-    "def should_continue(state):\n",
-    "    messages = state['messages']\n",
+    "def should_continue(messages):\n",
     "    last_message = messages[-1]\n",
     "    # If there is no function call, then we finish\n",
     "    if \"function_call\" not in last_message.additional_kwargs:\n",
@@ -286,15 +251,13 @@
     "        return \"continue\"\n",
     "\n",
     "# Define the function that calls the model\n",
-    "async def call_model(state):\n",
-    "    messages = state['messages']\n",
+    "async def call_model(messages):\n",
     "    response = await model.ainvoke(messages)\n",
     "    # We return a list, because this will get added to the existing list\n",
-    "    return {\"messages\": [response]}\n",
+    "    return response\n",
     "\n",
     "# Define the function to execute tools\n",
-    "async def call_tool(state):\n",
-    "    messages = state['messages']\n",
+    "async def call_tool(messages):\n",
     "    # Based on the continue condition\n",
     "    # we know the last message involves a function call\n",
     "    last_message = messages[-1]\n",
@@ -308,7 +271,7 @@
     "    # We use the response to create a FunctionMessage\n",
     "    function_message = FunctionMessage(content=str(response), name=action.tool)\n",
     "    # We return a list, because this will get added to the existing list\n",
-    "    return {\"messages\": [function_message]}"
+    "    return function_message"
    ]
   },
   {
@@ -323,14 +286,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langgraph.graph import StateGraph, END\n",
+    "from langgraph.graph import MessageGraph, END\n",
     "# Define a new graph\n",
-    "workflow = StateGraph(AgentState)\n",
+    "workflow = MessageGraph()\n",
     "\n",
     "# Define the two nodes we will cycle between\n",
     "workflow.add_node(\"agent\", call_model)\n",
@@ -385,84 +348,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "cfd140f0-a5a6-4697-8115-322242f197b5",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n",
+      "  warn_beta(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "content='' additional_kwargs={'function_call': {'arguments': '', 'name': 'tavily_search_results_json'}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': '{\\n', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' ', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' \"', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': 'query', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': '\":', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' \"', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': 'weather', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' in', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' San', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': ' Francisco', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': '\"\\n', 'name': ''}}\n",
-      "content='' additional_kwargs={'function_call': {'arguments': '}', 'name': ''}}\n",
-      "content=''\n",
-      "content=''\n",
-      "content='I'\n",
-      "content=\"'m\"\n",
-      "content=' sorry'\n",
-      "content=','\n",
-      "content=' but'\n",
-      "content=' I'\n",
-      "content=' couldn'\n",
-      "content=\"'t\"\n",
-      "content=' find'\n",
-      "content=' the'\n",
-      "content=' current'\n",
-      "content=' weather'\n",
-      "content=' in'\n",
-      "content=' San'\n",
-      "content=' Francisco'\n",
-      "content='.'\n",
-      "content=' However'\n",
-      "content=','\n",
-      "content=' you'\n",
-      "content=' can'\n",
-      "content=' check'\n",
-      "content=' the'\n",
-      "content=' weather'\n",
-      "content=' forecast'\n",
-      "content=' for'\n",
-      "content=' San'\n",
-      "content=' Francisco'\n",
-      "content=' on'\n",
-      "content=' websites'\n",
-      "content=' like'\n",
-      "content=' Weather'\n",
-      "content='.com'\n",
-      "content=' or'\n",
-      "content=' Acc'\n",
-      "content='u'\n",
-      "content='Weather'\n",
-      "content='.'\n",
-      "content=''\n"
+      "--\n",
+      "Starting tool: tavily_search_results_json with inputs: {'query': 'weather in San Francisco'}\n",
+      "Done tool: tavily_search_results_json\n",
+      "Tool output was: [{'url': 'https://www.whereandwhen.net/when/north-america/california/san-francisco-ca/january/', 'content': 'Best time to go to San Francisco? Weather in San Francisco in january 2024  How was the weather last january? Here is the day by day recorded weather in San Francisco in january 2023:  Seasonal average climate and temperature of San Francisco in january  8% 46% 29% 12% 8% Evolution of daily average temperature and precipitation in San Francisco in januaryWeather in San Francisco in january 2024. The weather in San Francisco in january comes from statistical datas on the past years. You can view the weather statistics the entire month, but also by using the tabs for the beginning, the middle and the end of the month. ... 23-01-2023 47°F to 61°F. 24-01-2023 43°F to 58°F. 25-01-2023 47°F to ...'}]\n",
+      "--\n",
+      "I|'m| sorry|,| but| I| couldn|'t| find| the| current| weather| in| San| Francisco|.| However|,| you| can| check| the| weather| in| San| Francisco| for| the| month| of| January| on| this| website|:| [|San| Francisco| Weather| in| January|](|https|://|www|.where|and|when|.net|/|when|/n|orth|-|amer|ica|/cal|ifornia|/s|an|-fr|anc|isco|-ca|/j|an|uary|/|).|"
      ]
     }
    ],
    "source": [
     "from langchain_core.messages import HumanMessage\n",
-    "inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
-    "async for output in app.astream_log(inputs, include_types=[\"llm\"]):\n",
-    "    # astream_log() yields the requested logs (here LLMs) in JSONPatch format\n",
-    "    for op in output.ops:\n",
-    "        if op[\"path\"] == \"/streamed_output/-\":\n",
-    "            # this is the output from .stream()\n",
-    "            ...\n",
-    "        elif op[\"path\"].startswith(\"/logs/\") and op[\"path\"].endswith(\n",
-    "            \"/streamed_output/-\"\n",
-    "        ):\n",
-    "            # because we chose to only include LLMs, these are LLM tokens\n",
-    "            print(op[\"value\"])"
+    "inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
+    "async for event in app.astream_events(inputs, version=\"v1\"):\n",
+    "    kind = event[\"event\"]\n",
+    "    if kind == \"on_chat_model_stream\":\n",
+    "        content = event[\"data\"][\"chunk\"].content\n",
+    "        if content:\n",
+    "            # Empty content in the context of OpenAI means\n",
+    "            # that the model is asking for a tool to be invoked.\n",
+    "            # So we only print non-empty content\n",
+    "            print(content, end=\"|\")\n",
+    "    elif kind == \"on_tool_start\":\n",
+    "        print(\"--\")\n",
+    "        print(\n",
+    "            f\"Starting tool: {event['name']} with inputs: {event['data'].get('input')}\"\n",
+    "        )\n",
+    "    elif kind == \"on_tool_end\":\n",
+    "        print(f\"Done tool: {event['name']}\")\n",
+    "        print(f\"Tool output was: {event['data'].get('output')}\")\n",
+    "        print(\"--\")"
    ]
   },
   {
diff --git a/examples/web-navigation/img/web-voyager.excalidraw.png b/examples/web-navigation/img/web-voyager.excalidraw.png
new file mode 100644
index 000000000..54563b056
Binary files /dev/null and b/examples/web-navigation/img/web-voyager.excalidraw.png differ
diff --git a/examples/web-navigation/mark_page.js b/examples/web-navigation/mark_page.js
new file mode 100644
index 000000000..e42270b75
--- /dev/null
+++ b/examples/web-navigation/mark_page.js
@@ -0,0 +1,157 @@
+const customCSS = `
+    ::-webkit-scrollbar {
+        width: 10px;
+    }
+    ::-webkit-scrollbar-track {
+        background: #27272a;
+    }
+    ::-webkit-scrollbar-thumb {
+        background: #888;
+        border-radius: 0.375rem;
+    }
+    ::-webkit-scrollbar-thumb:hover {
+        background: #555;
+    }
+`;
+
+const styleTag = document.createElement("style");
+styleTag.textContent = customCSS;
+document.head.append(styleTag);
+
+let labels = [];
+
+function unmarkPage() {
+  // Unmark page logic
+  for (const label of labels) {
+    document.body.removeChild(label);
+  }
+  labels = [];
+}
+
+function markPage() {
+  unmarkPage();
+
+  var bodyRect = document.body.getBoundingClientRect();
+
+  var items = Array.prototype.slice
+    .call(document.querySelectorAll("*"))
+    .map(function (element) {
+      var vw = Math.max(
+        document.documentElement.clientWidth || 0,
+        window.innerWidth || 0
+      );
+      var vh = Math.max(
+        document.documentElement.clientHeight || 0,
+        window.innerHeight || 0
+      );
+      var textualContent = element.textContent.trim().replace(/\s{2,}/g, " ");
+      var elementType = element.tagName.toLowerCase();
+      var ariaLabel = element.getAttribute("aria-label") || "";
+
+      var rects = [...element.getClientRects()]
+        .filter((bb) => {
+          var center_x = bb.left + bb.width / 2;
+          var center_y = bb.top + bb.height / 2;
+          var elAtCenter = document.elementFromPoint(center_x, center_y);
+
+          return elAtCenter === element || element.contains(elAtCenter);
+        })
+        .map((bb) => {
+          const rect = {
+            left: Math.max(0, bb.left),
+            top: Math.max(0, bb.top),
+            right: Math.min(vw, bb.right),
+            bottom: Math.min(vh, bb.bottom),
+          };
+          return {
+            ...rect,
+            width: rect.right - rect.left,
+            height: rect.bottom - rect.top,
+          };
+        });
+
+      var area = rects.reduce((acc, rect) => acc + rect.width * rect.height, 0);
+
+      return {
+        element: element,
+        include:
+          element.tagName === "INPUT" ||
+          element.tagName === "TEXTAREA" ||
+          element.tagName === "SELECT" ||
+          element.tagName === "BUTTON" ||
+          element.tagName === "A" ||
+          element.onclick != null ||
+          window.getComputedStyle(element).cursor == "pointer" ||
+          element.tagName === "IFRAME" ||
+          element.tagName === "VIDEO",
+        area,
+        rects,
+        text: textualContent,
+        type: elementType,
+        ariaLabel: ariaLabel,
+      };
+    })
+    .filter((item) => item.include && item.area >= 20);
+
+  // Only keep inner clickable items
+  items = items.filter(
+    (x) => !items.some((y) => x.element.contains(y.element) && !(x == y))
+  );
+
+  // Function to generate random colors
+  function getRandomColor() {
+    var letters = "0123456789ABCDEF";
+    var color = "#";
+    for (var i = 0; i < 6; i++) {
+      color += letters[Math.floor(Math.random() * 16)];
+    }
+    return color;
+  }
+
+  // Lets create a floating border on top of these elements that will always be visible
+  items.forEach(function (item, index) {
+    item.rects.forEach((bbox) => {
+      newElement = document.createElement("div");
+      var borderColor = getRandomColor();
+      newElement.style.outline = `2px dashed ${borderColor}`;
+      newElement.style.position = "fixed";
+      newElement.style.left = bbox.left + "px";
+      newElement.style.top = bbox.top + "px";
+      newElement.style.width = bbox.width + "px";
+      newElement.style.height = bbox.height + "px";
+      newElement.style.pointerEvents = "none";
+      newElement.style.boxSizing = "border-box";
+      newElement.style.zIndex = 2147483647;
+      // newElement.style.background = `${borderColor}80`;
+
+      // Add floating label at the corner
+      var label = document.createElement("span");
+      label.textContent = index;
+      label.style.position = "absolute";
+      // These we can tweak if we want
+      label.style.top = "-19px";
+      label.style.left = "0px";
+      label.style.background = borderColor;
+      // label.style.background = "black";
+      label.style.color = "white";
+      label.style.padding = "2px 4px";
+      label.style.fontSize = "12px";
+      label.style.borderRadius = "2px";
+      newElement.appendChild(label);
+
+      document.body.appendChild(newElement);
+      labels.push(newElement);
+      // item.element.setAttribute("-ai-label", label.textContent);
+    });
+  });
+  const coordinates = items.flatMap((item) =>
+    item.rects.map(({ left, top, width, height }) => ({
+      x: (left + left + width) / 2,
+      y: (top + top + height) / 2,
+      type: item.type,
+      text: item.text,
+      ariaLabel: item.ariaLabel,
+    }))
+  );
+  return coordinates;
+}
diff --git a/examples/web-navigation/web_voyager.ipynb b/examples/web-navigation/web_voyager.ipynb
new file mode 100644
index 000000000..904d3d27d
--- /dev/null
+++ b/examples/web-navigation/web_voyager.ipynb
@@ -0,0 +1,799 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f1a7d688-561c-4175-acfc-a6537f6dd042",
+   "metadata": {},
+   "source": [
+    "## Web Voyager\n",
+    "\n",
+    "[WebVoyager](https://arxiv.org/abs/2401.13919) by He, et. al., is a vision-enabled web-browsing agent capable of controlling the mouse and keyboard.\n",
+    "\n",
+    "It works by viewing annotated browser screenshots for each turn, then choosing the next step to take. The agent architecture is a basic reasoning and action (ReAct) loop. \n",
+    "The unique aspects of this agent are:\n",
+    "- It's usage of [Set-of-Marks](https://som-gpt4v.github.io/)-like image annotations to serve as UI affordances for the agent\n",
+    "- It's application in the browser by using tools to control both the mouse and keyboard\n",
+    "\n",
+    "The overall design looks like the following:\n",
+    "\n",
+    "<!-- ![Voyager Image](./img/web-voyager.excalidraw.png) -->\n",
+    "<img src=\"./img/web-voyager.excalidraw.png\" width=\"50%\">\n",
+    "\n",
+    "\n",
+    "## Configure environment\n",
+    "\n",
+    "We will first set up LangSmith tracing. Though optional, this lets us inspect and debug agent's trajectory for a given input.\n",
+    "\n",
+    "You can sign up at [smith.langchain.com](https://smith.langchain.com/) to get an API key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "af83b042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "langchain-experimental 0.0.47 requires langchain<0.1,>=0.0.350, but you have langchain 0.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install -U --quiet langgraph langsmith langchain_openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "02f6e962-c6a5-43fa-b069-616c78c86c26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optional: add tracing to visualize the agent trajectories\n",
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "\n",
+    "def _getpass(env_var: str):\n",
+    "    if not os.environ.get(env_var):\n",
+    "        os.environ[env_var] = getpass(f\"{env_var}=\")\n",
+    "\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Web-Voyager\"\n",
+    "_getpass(\"LANGCHAIN_API_KEY\")\n",
+    "_getpass(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15d2e932-e1ce-4f2e-93e9-c8caf44b2afc",
+   "metadata": {},
+   "source": [
+    "#### Install Agent requirements\n",
+    "\n",
+    "The only additional requirement we have is the [playwright](https://playwright.dev/) browser. Uncomment and install below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "50b70dbe-ea14-440c-99ab-9cd171d78742",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install --upgrade --quiet  playwright > /dev/null\n",
+    "# !playwright install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2a14fa9-8ca7-4a7a-9827-8fbd465b6959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "\n",
+    "# This is just required for running async playwright in a Jupyter notebook\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0ee0f97-eb4e-4a13-b4f4-fc6439eec6a6",
+   "metadata": {},
+   "source": [
+    "## Define Graph State\n",
+    "\n",
+    "The state provides the inputs to each node in the graph.\n",
+    "\n",
+    "In our case, the agent will track the webpage object (within the browser), annotated images + bounding boxes, the user's initial request, and the messages containing the agent scratchpad, system prompt, and other information.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "51467b74-1be9-46fc-a3c0-0051eec62ed4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage, SystemMessage\n",
+    "from playwright.async_api import Page\n",
+    "\n",
+    "\n",
+    "class BBox(TypedDict):\n",
+    "    x: float\n",
+    "    y: float\n",
+    "    text: str\n",
+    "    type: str\n",
+    "    ariaLabel: str\n",
+    "\n",
+    "\n",
+    "class Prediction(TypedDict):\n",
+    "    action: str\n",
+    "    args: Optional[List[str]]\n",
+    "\n",
+    "\n",
+    "# This represents the state of the agent\n",
+    "# as it proceeds through execution\n",
+    "class AgentState(TypedDict):\n",
+    "    page: Page  # The Playwright web page lets us interact with the web environment\n",
+    "    input: str  # User request\n",
+    "    img: str  # b64 encoded screenshot\n",
+    "    bboxes: List[BBox]  # The bounding boxes from the browser annotation function\n",
+    "    prediction: Prediction  # The Agent's output\n",
+    "    # A system message (or messages) containing the intermediate steps\n",
+    "    scratchpad: List[BaseMessage]\n",
+    "    observation: str  # The most recent response from a tool"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8016a06a-3a90-46a4-85d3-510b83dfcef4",
+   "metadata": {},
+   "source": [
+    "## Define tools\n",
+    "\n",
+    "The agent has 6 simple tools:\n",
+    "\n",
+    "1. Click (at labeled box)\n",
+    "2. Type\n",
+    "3. Scroll\n",
+    "4. Wait\n",
+    "5. Go back\n",
+    "6. Go to search engine (Google)\n",
+    "\n",
+    "\n",
+    "We define them below here as functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b77978f0-c20f-495f-8026-8e1ee0b56e0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "import platform\n",
+    "\n",
+    "\n",
+    "async def click(state: AgentState):\n",
+    "    # - Click [Numerical_Label]\n",
+    "    page = state[\"page\"]\n",
+    "    click_args = state[\"prediction\"][\"args\"]\n",
+    "    if click_args is None or len(click_args) != 1:\n",
+    "        return f\"Failed to click bounding box labeled as number {click_args}\"\n",
+    "    bbox_id = click_args[0]\n",
+    "    bbox_id = int(bbox_id)\n",
+    "    try:\n",
+    "        bbox = state[\"bboxes\"][bbox_id]\n",
+    "    except:\n",
+    "        return f\"Error: no bbox for : {bbox_id}\"\n",
+    "    x, y = bbox[\"x\"], bbox[\"y\"]\n",
+    "    res = await page.mouse.click(x, y)\n",
+    "    # TODO: In the paper, they automatically parse any downloaded PDFs\n",
+    "    # We could add something similar here as well and generally\n",
+    "    # improve response format.\n",
+    "    return f\"Clicked {bbox_id}\"\n",
+    "\n",
+    "\n",
+    "async def type_text(state: AgentState):\n",
+    "    page = state[\"page\"]\n",
+    "    type_args = state[\"prediction\"][\"args\"]\n",
+    "    if type_args is None or len(type_args) != 2:\n",
+    "        return (\n",
+    "            f\"Failed to type in element from bounding box labeled as number {type_args}\"\n",
+    "        )\n",
+    "    bbox_id = type_args[0]\n",
+    "    bbox_id = int(bbox_id)\n",
+    "    bbox = state[\"bboxes\"][bbox_id]\n",
+    "    x, y = bbox[\"x\"], bbox[\"y\"]\n",
+    "    text_content = type_args[1]\n",
+    "    await page.mouse.click(x, y)\n",
+    "    # Check if MacOS\n",
+    "    select_all = \"Meta+A\" if platform.system() == \"Darwin\" else \"Control+A\"\n",
+    "    await page.keyboard.press(select_all)\n",
+    "    await page.keyboard.press(\"Backspace\")\n",
+    "    await page.keyboard.type(text_content)\n",
+    "    await page.keyboard.press(\"Enter\")\n",
+    "    return f\"Typed {text_content} and submitted\"\n",
+    "\n",
+    "\n",
+    "async def scroll(state: AgentState):\n",
+    "    page = state[\"page\"]\n",
+    "    scroll_args = state[\"prediction\"][\"args\"]\n",
+    "    if scroll_args is None or len(scroll_args) != 2:\n",
+    "        return \"Failed to scroll due to incorrect arguments.\"\n",
+    "\n",
+    "    target, direction = scroll_args\n",
+    "\n",
+    "    if target.upper() == \"WINDOW\":\n",
+    "        # Not sure the best value for this:\n",
+    "        scroll_amount = 500\n",
+    "        scroll_direction = (\n",
+    "            -scroll_amount if direction.lower() == \"up\" else scroll_amount\n",
+    "        )\n",
+    "        await page.evaluate(f\"window.scrollBy(0, {scroll_direction})\")\n",
+    "    else:\n",
+    "        # Scrolling within a specific element\n",
+    "        scroll_amount = 200\n",
+    "        target_id = int(target)\n",
+    "        bbox = state[\"bboxes\"][target_id]\n",
+    "        x, y = bbox[\"x\"], bbox[\"y\"]\n",
+    "        scroll_direction = (\n",
+    "            -scroll_amount if direction.lower() == \"up\" else scroll_amount\n",
+    "        )\n",
+    "        await page.mouse.move(x, y)\n",
+    "        await page.mouse.wheel(0, scroll_direction)\n",
+    "\n",
+    "    return f\"Scrolled {direction} in {'window' if target.upper() == 'WINDOW' else 'element'}\"\n",
+    "\n",
+    "\n",
+    "async def wait(state: AgentState):\n",
+    "    sleep_time = 5\n",
+    "    await asyncio.sleep(sleep_time)\n",
+    "    return f\"Waited for {sleep_time}s.\"\n",
+    "\n",
+    "\n",
+    "async def go_back(state: AgentState):\n",
+    "    page = state[\"page\"]\n",
+    "    await page.go_back()\n",
+    "    return f\"Navigated back a page to {page.url}.\"\n",
+    "\n",
+    "\n",
+    "async def to_google(state: AgentState):\n",
+    "    page = state[\"page\"]\n",
+    "    await page.goto(\"https://www.google.com/\")\n",
+    "    return \"Navigated to google.com.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed4d4d9f-9971-477c-b391-1a73dee34573",
+   "metadata": {},
+   "source": [
+    "## Define Agent\n",
+    "\n",
+    "The agent is driven by a multi-modal model and decides the action to take for each step. It is composed of a few runnable objects:\n",
+    "\n",
+    "1. A `mark_page` function to annotate the current page with bounding boxes\n",
+    "2. A prompt to hold the user question, annotated image, and agent scratchpad\n",
+    "3. GPT-4V to decide the next steps\n",
+    "4. Parsing logic to extract the action\n",
+    "\n",
+    "\n",
+    "Let's first define the annotation step:\n",
+    "#### Browser Annotations\n",
+    "\n",
+    "This function annotates all buttons, inputs, text areas, etc. with numbered bounding boxes. GPT-4V then just has to refer to a bounding box\n",
+    "when taking actions, reducing the complexity of the overall task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "03604500-a16c-4ea0-b6a9-ab81d2ecc00a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "import base64\n",
+    "\n",
+    "from langchain_core.runnables import chain as chain_decorator\n",
+    "\n",
+    "# Some javascript we will run on each step\n",
+    "# to take a screenshot of the page, select the\n",
+    "# elements to annotate, and add bounding boxes\n",
+    "with open(\"mark_page.js\") as f:\n",
+    "    mark_page_script = f.read()\n",
+    "\n",
+    "\n",
+    "@chain_decorator\n",
+    "async def mark_page(page):\n",
+    "    await page.evaluate(mark_page_script)\n",
+    "    for _ in range(10):\n",
+    "        try:\n",
+    "            bboxes = await page.evaluate(\"markPage()\")\n",
+    "            break\n",
+    "        except:\n",
+    "            # May be loading...\n",
+    "            asyncio.sleep(3)\n",
+    "    screenshot = await page.screenshot()\n",
+    "    # Ensure the bboxes don't follow us around\n",
+    "    await page.evaluate(\"unmarkPage()\")\n",
+    "    return {\n",
+    "        \"img\": base64.b64encode(screenshot).decode(),\n",
+    "        \"bboxes\": bboxes,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e407ca7-1a8a-49ca-bec0-402529f60fe5",
+   "metadata": {},
+   "source": [
+    "#### Agent definition\n",
+    "\n",
+    "Now we'll compose this function with the prompt, llm and output parser to complete our agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d5e47466-93fc-4b84-9334-c39ebe2682d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "async def annotate(state):\n",
+    "    marked_page = await mark_page.with_retry().ainvoke(state[\"page\"])\n",
+    "    return {**state, **marked_page}\n",
+    "\n",
+    "\n",
+    "def format_descriptions(state):\n",
+    "    labels = []\n",
+    "    for i, bbox in enumerate(state[\"bboxes\"]):\n",
+    "        text = bbox.get(\"ariaLabel\") or \"\"\n",
+    "        if not text.strip():\n",
+    "            text = bbox[\"text\"]\n",
+    "        el_type = bbox.get(\"type\")\n",
+    "        labels.append(f'{i} (<{el_type}/>): \"{text}\"')\n",
+    "    bbox_descriptions = \"\\nValid Bounding Boxes:\\n\" + \"\\n\".join(labels)\n",
+    "    return {**state, \"bbox_descriptions\": bbox_descriptions}\n",
+    "\n",
+    "\n",
+    "def parse(text: str) -> dict:\n",
+    "    action_prefix = \"Action: \"\n",
+    "    if not text.strip().split(\"\\n\")[-1].startswith(action_prefix):\n",
+    "        return {\"action\": \"retry\", \"args\": f\"Could not parse LLM Output: {text}\"}\n",
+    "    action_block = text.strip().split(\"\\n\")[-1]\n",
+    "\n",
+    "    action_str = action_block[len(action_prefix) :]\n",
+    "    split_output = action_str.split(\" \", 1)\n",
+    "    if len(split_output) == 1:\n",
+    "        action, action_input = split_output[0], None\n",
+    "    else:\n",
+    "        action, action_input = split_output\n",
+    "    action = action.strip()\n",
+    "    if action_input is not None:\n",
+    "        action_input = [\n",
+    "            inp.strip().strip(\"[]\") for inp in action_input.strip().split(\";\")\n",
+    "        ]\n",
+    "    return {\"action\": action, \"args\": action_input}\n",
+    "\n",
+    "\n",
+    "# Will need a later version of langchain to pull\n",
+    "# this image prompt template\n",
+    "prompt = hub.pull(\"wfh/web-voyager\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "833f1708-bdaf-4cea-8c26-ed6f09eb8c90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=4096)\n",
+    "agent = annotate | RunnablePassthrough.assign(\n",
+    "    prediction=format_descriptions | prompt | llm | StrOutputParser() | parse\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7802b9fe-e75b-4779-b45d-003c218dba48",
+   "metadata": {},
+   "source": [
+    "## Define graph\n",
+    "\n",
+    "We've created most of the important logic. We have one more function to define that will help us update the graph state after a tool is called."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e2b68881-1c40-48f4-b046-07bf226ceb60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "\n",
+    "def update_scratchpad(state: AgentState):\n",
+    "    \"\"\"After a tool is invoked, we want to update\n",
+    "    the scratchpad so the agent is aware of its previous steps\"\"\"\n",
+    "    old = state.get(\"scratchpad\")\n",
+    "    if old:\n",
+    "        txt = old[0].content\n",
+    "        last_line = txt.rsplit(\"\\n\", 1)[-1]\n",
+    "        step = int(re.match(r\"\\d+\", last_line).group()) + 1\n",
+    "    else:\n",
+    "        txt = \"Previous action observations:\\n\"\n",
+    "        step = 1\n",
+    "    txt += f\"\\n{step}. {state['observation']}\"\n",
+    "\n",
+    "    return {**state, \"scratchpad\": [SystemMessage(content=txt)]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eed8f97-a535-4416-ac60-c06b357aecf5",
+   "metadata": {},
+   "source": [
+    "Now we can compose everything into a graph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c8aa7106-cc79-49d9-8f9d-c9c13019ef5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langgraph.graph import END, StateGraph\n",
+    "\n",
+    "graph_builder = StateGraph(AgentState)\n",
+    "\n",
+    "\n",
+    "graph_builder.add_node(\"agent\", agent)\n",
+    "graph_builder.set_entry_point(\"agent\")\n",
+    "\n",
+    "graph_builder.add_node(\"update_scratchpad\", update_scratchpad)\n",
+    "graph_builder.add_edge(\"update_scratchpad\", \"agent\")\n",
+    "\n",
+    "tools = {\n",
+    "    \"Click\": click,\n",
+    "    \"Type\": type_text,\n",
+    "    \"Scroll\": scroll,\n",
+    "    \"Wait\": wait,\n",
+    "    \"GoBack\": go_back,\n",
+    "    \"Google\": to_google,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "for node_name, tool in tools.items():\n",
+    "    graph_builder.add_node(\n",
+    "        node_name,\n",
+    "        # The lambda ensures the function's string output is mapped to the \"observation\"\n",
+    "        # key in the AgentState\n",
+    "        RunnableLambda(tool) | (lambda observation: {\"observation\": observation}),\n",
+    "    )\n",
+    "    # Always return to the agent (by means of the update-scratchpad node)\n",
+    "    graph_builder.add_edge(node_name, \"update_scratchpad\")\n",
+    "\n",
+    "\n",
+    "def select_tool(state: AgentState):\n",
+    "    # Any time the agent completes, this function\n",
+    "    # is called to route the output to a tool or\n",
+    "    # to the end user.\n",
+    "    action = state[\"prediction\"][\"action\"]\n",
+    "    if action == \"ANSWER\":\n",
+    "        return END\n",
+    "    if action == \"retry\":\n",
+    "        return \"agent\"\n",
+    "    return action\n",
+    "\n",
+    "\n",
+    "graph_builder.add_conditional_edges(\"agent\", select_tool)\n",
+    "\n",
+    "graph = graph_builder.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d11071f-f7ad-434d-99b7-14ebbbc92506",
+   "metadata": {},
+   "source": [
+    "## Run agent\n",
+    "\n",
+    "Now that we've created the whole agent executor, we can run it on a few questions! We'll start our browser at \"google.com\" and then let it control the rest.\n",
+    "\n",
+    "Below is a helper function to help print out the steps to the notebook (and display the intermediate screenshots)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5d0fb86f-6923-4eb7-a61a-567498c0eba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import playwright\n",
+    "from IPython import display\n",
+    "from playwright.async_api import async_playwright\n",
+    "\n",
+    "browser = await async_playwright().start()\n",
+    "# We will set headless=False so we can watch the agent navigate the web.\n",
+    "browser = await browser.chromium.launch(headless=False, args=None)\n",
+    "page = await browser.new_page()\n",
+    "_ = await page.goto(\"https://www.google.com\")\n",
+    "\n",
+    "\n",
+    "async def call_agent(question: str, page, max_steps: int = 150):\n",
+    "    event_stream = graph.astream(\n",
+    "        {\n",
+    "            \"page\": page,\n",
+    "            \"input\": question,\n",
+    "            \"scratchpad\": [],\n",
+    "        },\n",
+    "        {\n",
+    "            \"recursion_limit\": max_steps,\n",
+    "        },\n",
+    "    )\n",
+    "    final_answer = None\n",
+    "    steps = []\n",
+    "    async for event in event_stream:\n",
+    "        # We'll display an event stream here\n",
+    "        if \"agent\" not in event:\n",
+    "            continue\n",
+    "        pred = event[\"agent\"].get(\"prediction\") or {}\n",
+    "        action = pred.get(\"action\")\n",
+    "        action_input = pred.get(\"args\")\n",
+    "        display.clear_output(wait=False)\n",
+    "        steps.append(f\"{len(steps) + 1}. {action}: {action_input}\")\n",
+    "        print(\"\\n\".join(steps))\n",
+    "        display.display(display.Image(base64.b64decode(event[\"agent\"][\"img\"])))\n",
+    "        if \"ANSWER\" in action:\n",
+    "            final_answer = action_input[0]\n",
+    "            break\n",
+    "    return final_answer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c5e4da43-4f99-4c3a-9873-4dc5e76a9098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Type: ['7', 'WebVoyager paper arXiv']\n",
+      "2. Click: ['32']\n",
+      "3. Click: ['3']\n",
+      "4. ANSWER;: ['The \"WebVoyager\" paper discusses the development of an end-to-end web agent that leverages large multimodal models. The abstract highlights the importance of such agents in automating complex tasks on the web, which remains a challenging domain due to the heterogeneity in structure and the semantic gap between humans and machines. The paper proposes a solution that combines neural symbolic models and multimodal web environments, aiming to advance the capabilities of these agents to perform web browsing tasks effectively. Further details would require a more in-depth analysis of the paper\\'s content beyond the abstract.']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final response: The \"WebVoyager\" paper discusses the development of an end-to-end web agent that leverages large multimodal models. The abstract highlights the importance of such agents in automating complex tasks on the web, which remains a challenging domain due to the heterogeneity in structure and the semantic gap between humans and machines. The paper proposes a solution that combines neural symbolic models and multimodal web environments, aiming to advance the capabilities of these agents to perform web browsing tasks effectively. Further details would require a more in-depth analysis of the paper's content beyond the abstract.\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = await call_agent(\"Could you explain the WebVoyager paper (on arxiv)?\", page)\n",
+    "print(f\"Final response: {res}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d24da6b5-044e-474f-a664-875f760916ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. retry: Could not parse LLM Output: I'm sorry, but the image provided does not contain an XKCD comic. The image shows a page from a scientific paper titled \"WebVoyager 2: Building an End-to-End Web Agent with Large Multimodal Models.\" If you provide the XKCD comic you're referring to, I'd be happy to explain the humor in it.\n",
+      "2. retry: Could not parse LLM Output: I'm sorry, but I cannot assist with that request.\n",
+      "3. Google: None\n",
+      "4. Type: ['6', 'xkcd.com']\n",
+      "5. Click: ['25']\n",
+      "6. ANSWER;: ['The XKCD comic titled \"Relationship Advice\" pokes fun at the sometimes exaggerated way people talk about the challenges of relationships. It starts with one character stating that relationships require constant work and are like a job, which is a common sentiment. However, the other character takes this comparison to an extreme, calling it a \"grueling ordeal\" and a \"crushing burden,\" which humorously exaggerates the difficulties of maintaining a relationship. The punchline comes when, after this escalation, the second character insists they\\'re fine and that it\\'s all normal, which satirizes how people might downplay their struggles to appear in control or deny the extent of their challenges. The humor lies in the hyperbole and the relatable nature of discussing relationship difficulties, as well as the contrast between the characters\\' statements and the insistence that everything is okay.']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final response: The XKCD comic titled \"Relationship Advice\" pokes fun at the sometimes exaggerated way people talk about the challenges of relationships. It starts with one character stating that relationships require constant work and are like a job, which is a common sentiment. However, the other character takes this comparison to an extreme, calling it a \"grueling ordeal\" and a \"crushing burden,\" which humorously exaggerates the difficulties of maintaining a relationship. The punchline comes when, after this escalation, the second character insists they're fine and that it's all normal, which satirizes how people might downplay their struggles to appear in control or deny the extent of their challenges. The humor lies in the hyperbole and the relatable nature of discussing relationship difficulties, as well as the contrast between the characters' statements and the insistence that everything is okay.\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = await call_agent(\n",
+    "    \"Please explain the today's XKCD comic for me. Why is it funny?\", page\n",
+    ")\n",
+    "print(f\"Final response: {res}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a3386b5c-802e-4e95-8c20-1222388476be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Google: None\n",
+      "2. Type: ['6', 'latest blog posts from langchain']\n",
+      "3. Click: ['27']\n",
+      "4. Click: ['14']\n",
+      "5. Click: ['0']\n",
+      "6. retry: Could not parse LLM Output: Thought: The latest blog posts from Langchain are displayed on the right side of the screen with titles and reading time. I will provide the titles of the featured blog posts as seen on the screen.\n",
+      "\n",
+      "Action: ANSWER; The latest blog posts from Langchain are:\n",
+      "1. OpenGPTs - 7 min read\n",
+      "2. LangGraph: Multi-Agent Workflows - 6 min read\n",
+      "3. LangGraph - 7 min read\n",
+      "4. LangChain v0.1.0 - 10 min read\n",
+      "7. ANSWER;: ['The latest blog posts from Langchain are \"OpenGPTs,\" \"LangGraph: Multi-Agent Workflows,\" and \"LangGraph.\"']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final response: The latest blog posts from Langchain are \"OpenGPTs,\" \"LangGraph: Multi-Agent Workflows,\" and \"LangGraph.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = await call_agent(\"What are the latest blog posts from langchain?\", page)\n",
+    "print(f\"Final response: {res}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "319e95d3-4362-45f4-9c83-540913b23f41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. Google: None\n",
+      "2. Type: ['6', 'Google Maps']\n",
+      "3. Click: ['0']\n",
+      "4. Click: ['0']\n",
+      "5. Wait: None\n",
+      "6. Click: ['22']\n",
+      "7. Click: ['0']\n",
+      "8. Click: ['2']\n",
+      "9. Type: ['0', 'San Francisco downtown to SFO']\n",
+      "10. Click: ['1']\n",
+      "11. Click: ['2']\n",
+      "12. Type: ['8', 'San Francisco International Airport SFO']\n",
+      "13. Click: ['14']\n",
+      "14. Click: ['28']\n",
+      "15. Scroll: ['WINDOW', 'up']\n",
+      "16. Scroll: ['WINDOW', 'up']\n",
+      "17. Click: ['10']\n",
+      "18. Click: ['28']\n",
+      "19. ANSWER;: ['To arrive at San Francisco International Airport (SFO) by 7:00 AM starting from downtown San Francisco, you should leave by 6:46 AM according to the current Google Maps information, which estimates a 44-minute travel time.']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final response: To arrive at San Francisco International Airport (SFO) by 7:00 AM starting from downtown San Francisco, you should leave by 6:46 AM according to the current Google Maps information, which estimates a 44-minute travel time.\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = await call_agent(\n",
+    "    \"Could you check google maps to see when i should leave to get to SFO by 7 o'clock? starting from SF downtown.\",\n",
+    "    page,\n",
+    ")\n",
+    "print(f\"Final response: {res}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcb656d9-fff5-42b0-9b16-60716a51ca0b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/langgraph/channels/last_value.py b/langgraph/channels/last_value.py
index c585ceb86..858943c91 100644
--- a/langgraph/channels/last_value.py
+++ b/langgraph/channels/last_value.py
@@ -44,7 +44,7 @@ class LastValue(Generic[Value], BaseChannel[Value, Value, Value]):
         if len(values) == 0:
             return
         if len(values) != 1:
-            raise InvalidUpdateError()
+            raise InvalidUpdateError("LastValue can only receive one value per step.")
 
         self.value = values[-1]
 
diff --git a/langgraph/checkpoint/__init__.py b/langgraph/checkpoint/__init__.py
index 7a52e0c6f..3d8a1ab76 100644
--- a/langgraph/checkpoint/__init__.py
+++ b/langgraph/checkpoint/__init__.py
@@ -1,7 +1,8 @@
-from langgraph.checkpoint.base import BaseCheckpointSaver, CheckpointAt
+from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint, CheckpointAt
 from langgraph.checkpoint.memory import MemorySaver
 
 __all__ = [
+    "Checkpoint",
     "CheckpointAt",
     "BaseCheckpointSaver",
     "MemorySaver",
diff --git a/langgraph/checkpoint/aiosqlite.py b/langgraph/checkpoint/aiosqlite.py
new file mode 100644
index 000000000..580a3a102
--- /dev/null
+++ b/langgraph/checkpoint/aiosqlite.py
@@ -0,0 +1,85 @@
+import pickle
+from typing import Optional
+
+import aiosqlite
+from langchain_core.pydantic_v1 import Field
+from langchain_core.runnables import RunnableConfig
+from langchain_core.runnables.utils import ConfigurableFieldSpec
+
+from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint
+
+
+class AsyncSqliteSaver(BaseCheckpointSaver):
+    conn: aiosqlite.Connection
+
+    is_setup: bool = Field(False, init=False, repr=False)
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @classmethod
+    def from_conn_string(cls, conn_string: str) -> "AsyncSqliteSaver":
+        return AsyncSqliteSaver(conn=aiosqlite.connect(conn_string))
+
+    @property
+    def config_specs(self) -> list[ConfigurableFieldSpec]:
+        return [
+            ConfigurableFieldSpec(
+                id="thread_id",
+                annotation=str,
+                name="Thread ID",
+                description=None,
+                default="",
+                is_shared=True,
+            ),
+        ]
+
+    async def setup(self) -> None:
+        print("hello")
+        if self.is_setup:
+            return
+
+        try:
+            await self.conn
+            await self.conn.executescript(
+                """
+                CREATE TABLE IF NOT EXISTS checkpoints (
+                    thread_id TEXT PRIMARY KEY,
+                    checkpoint BLOB
+                );
+                """
+            )
+            await self.conn.commit()
+
+            print("good bye")
+
+            self.is_setup = True
+        except BaseException as e:
+            print(e)
+            raise e
+
+    def get(self, config: RunnableConfig) -> Optional[Checkpoint]:
+        raise NotImplementedError
+
+    def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None:
+        raise NotImplementedError
+
+    async def aget(self, config: RunnableConfig) -> Optional[Checkpoint]:
+        await self.setup()
+        async with self.conn.execute(
+            "SELECT checkpoint FROM checkpoints WHERE thread_id = ?",
+            (config["configurable"]["thread_id"],),
+        ) as cursor:
+            if value := await cursor.fetchone():
+                return pickle.loads(value[0])
+
+    async def aput(self, config: RunnableConfig, checkpoint: Checkpoint) -> None:
+        await self.setup()
+        await self.conn.execute(
+            "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint) VALUES (?, ?)",
+            (
+                config["configurable"]["thread_id"],
+                pickle.dumps(checkpoint),
+            ),
+        )
+        await self.conn.commit()
diff --git a/langgraph/checkpoint/base.py b/langgraph/checkpoint/base.py
index e40f3fcfc..699dc9dd8 100644
--- a/langgraph/checkpoint/base.py
+++ b/langgraph/checkpoint/base.py
@@ -19,13 +19,17 @@ class Checkpoint(TypedDict):
     versions_seen: defaultdict[str, defaultdict[str, int]]
 
 
+def _seen_dict():
+    return defaultdict(int)
+
+
 def empty_checkpoint() -> Checkpoint:
     return Checkpoint(
         v=1,
         ts=datetime.now(timezone.utc).isoformat(),
         channel_values={},
         channel_versions=defaultdict(int),
-        versions_seen=defaultdict(lambda: defaultdict(int)),
+        versions_seen=defaultdict(_seen_dict),
     )
 
 
diff --git a/langgraph/checkpoint/sqlite.py b/langgraph/checkpoint/sqlite.py
new file mode 100644
index 000000000..75958708e
--- /dev/null
+++ b/langgraph/checkpoint/sqlite.py
@@ -0,0 +1,87 @@
+import pickle
+import sqlite3
+from contextlib import contextmanager
+from typing import Optional
+
+from langchain_core.pydantic_v1 import Field
+from langchain_core.runnables import RunnableConfig
+from langchain_core.runnables.utils import ConfigurableFieldSpec
+
+from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint
+
+
+class SqliteSaver(BaseCheckpointSaver):
+    conn: sqlite3.Connection
+
+    is_setup: bool = Field(False, init=False, repr=False)
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @classmethod
+    def from_conn_string(cls, conn_string: str) -> "SqliteSaver":
+        return SqliteSaver(conn=sqlite3.connect(conn_string))
+
+    @property
+    def config_specs(self) -> list[ConfigurableFieldSpec]:
+        return [
+            ConfigurableFieldSpec(
+                id="thread_id",
+                annotation=str,
+                name="Thread ID",
+                description=None,
+                default="",
+                is_shared=True,
+            ),
+        ]
+
+    def setup(self) -> None:
+        if self.is_setup:
+            return
+
+        self.conn.executescript(
+            """
+            CREATE TABLE IF NOT EXISTS checkpoints (
+                thread_id TEXT PRIMARY KEY,
+                checkpoint BLOB
+            );
+            """
+        )
+
+        self.is_setup = True
+
+    @contextmanager
+    def cursor(self, transaction: bool = True):
+        self.setup()
+        cur = self.conn.cursor()
+        try:
+            yield cur
+        finally:
+            if transaction:
+                self.conn.commit()
+            cur.close()
+
+    def get(self, config: RunnableConfig) -> Optional[Checkpoint]:
+        with self.cursor(transaction=False) as cur:
+            cur.execute(
+                "SELECT checkpoint FROM checkpoints WHERE thread_id = ?",
+                (config["configurable"]["thread_id"],),
+            )
+            if value := cur.fetchone():
+                return pickle.loads(value[0])
+
+    def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None:
+        with self.cursor() as cur:
+            cur.execute(
+                "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint) VALUES (?, ?)",
+                (
+                    config["configurable"]["thread_id"],
+                    pickle.dumps(checkpoint),
+                ),
+            )
+
+    async def aget(self, config: RunnableConfig) -> Optional[Checkpoint]:
+        raise NotImplementedError
+
+    async def aput(self, config: RunnableConfig, checkpoint: Checkpoint) -> None:
+        raise NotImplementedError
diff --git a/langgraph/graph/__init__.py b/langgraph/graph/__init__.py
index 1173035b5..8fac44cfa 100644
--- a/langgraph/graph/__init__.py
+++ b/langgraph/graph/__init__.py
@@ -1,4 +1,5 @@
 from langgraph.graph.graph import END, Graph
+from langgraph.graph.message import MessageGraph
 from langgraph.graph.state import StateGraph
 
-__all__ = ["END", "Graph", "StateGraph"]
+__all__ = ["END", "Graph", "StateGraph", "MessageGraph"]
diff --git a/langgraph/graph/graph.py b/langgraph/graph/graph.py
index 4ebd8144e..a0ac4a172 100644
--- a/langgraph/graph/graph.py
+++ b/langgraph/graph/graph.py
@@ -17,11 +17,14 @@ END = "__end__"
 
 class Branch(NamedTuple):
     condition: Callable[..., str]
-    ends: dict[str, str]
+    ends: Optional[dict[str, str]]
 
     def runnable(self, input: Any) -> Runnable:
         result = self.condition(input)
-        destination = self.ends[result]
+        if self.ends:
+            destination = self.ends[result]
+        else:
+            destination = result
         return Channel.write_to(f"{destination}:inbox" if destination != END else END)
 
 
@@ -59,15 +62,21 @@ class Graph:
         self,
         start_key: str,
         condition: Callable[..., str],
-        conditional_edge_mapping: Dict[str, str],
+        conditional_edge_mapping: Optional[Dict[str, str]] = None,
     ) -> None:
         if start_key not in self.nodes:
             raise ValueError(f"Need to add_node `{start_key}` first")
         if iscoroutinefunction(condition):
             raise ValueError("Condition cannot be a coroutine function")
-        for destination in conditional_edge_mapping.values():
-            if destination not in self.nodes and destination != END:
-                raise ValueError(f"Need to add_node `{destination}` first")
+        if conditional_edge_mapping and set(
+            conditional_edge_mapping.values()
+        ).difference([END]).difference(self.nodes):
+            raise ValueError(
+                f"Missing nodes which are in conditional edge mapping. Mapping "
+                f"contains possible destinations: "
+                f"{list(conditional_edge_mapping.values())}. Possible nodes are "
+                f"{list(self.nodes.keys())}."
+            )
 
         self.branches[start_key].append(Branch(condition, conditional_edge_mapping))
 
@@ -81,23 +90,30 @@ class Graph:
 
     def validate(self) -> None:
         all_starts = {src for src, _ in self.edges} | {src for src in self.branches}
-        all_ends = (
-            {end for _, end in self.edges}
-            | {
-                end
-                for branch_list in self.branches.values()
-                for branch in branch_list
-                for end in branch.ends.values()
-            }
-            | {self.entry_point}
-        )
-
         for node in self.nodes:
-            if node not in all_ends:
-                raise ValueError(f"Node `{node}` is not reachable")
             if node not in all_starts:
                 raise ValueError(f"Node `{node}` is a dead-end")
 
+        if all(
+            branch.ends is not None
+            for branch_list in self.branches.values()
+            for branch in branch_list
+        ):
+            all_ends = (
+                {end for _, end in self.edges}
+                | {
+                    end
+                    for branch_list in self.branches.values()
+                    for branch in branch_list
+                    for end in branch.ends.values()
+                }
+                | {self.entry_point}
+            )
+
+            for node in self.nodes:
+                if node not in all_ends:
+                    raise ValueError(f"Node `{node}` is not reachable")
+
     def compile(self, checkpointer: Optional[BaseCheckpointSaver] = None) -> Pregel:
         self.validate()
 
diff --git a/langgraph/graph/message.py b/langgraph/graph/message.py
new file mode 100644
index 000000000..d41a2e747
--- /dev/null
+++ b/langgraph/graph/message.py
@@ -0,0 +1,24 @@
+from typing import Annotated, Union
+
+from langchain_core.messages import AnyMessage
+
+from langgraph.graph.state import StateGraph
+
+Messages = Union[list[AnyMessage], AnyMessage]
+
+
+def add_messages(left: Messages, right: Messages) -> Messages:
+    if not isinstance(left, list):
+        left = [left]
+    if not isinstance(right, list):
+        right = [right]
+    return left + right
+
+
+class MessageGraph(StateGraph):
+    """A StateGraph where every node
+    - receives a list of messages as input
+    - returns one or more messages as output."""
+
+    def __init__(self) -> None:
+        super().__init__(Annotated[list[AnyMessage], add_messages])
diff --git a/langgraph/graph/state.py b/langgraph/graph/state.py
index 45fb5ec65..88c526e2f 100644
--- a/langgraph/graph/state.py
+++ b/langgraph/graph/state.py
@@ -3,16 +3,17 @@ from functools import partial
 from inspect import signature
 from typing import Any, Optional, Type
 
-from langchain_core.runnables import RunnableConfig, RunnableLambda
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_core.runnables.base import RunnableLike
 
-from langgraph.channels.base import BaseChannel
+from langgraph.channels.base import BaseChannel, InvalidUpdateError
 from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.last_value import LastValue
 from langgraph.checkpoint import BaseCheckpointSaver
 from langgraph.graph.graph import END, Graph
 from langgraph.pregel import Channel, Pregel
 from langgraph.pregel.read import ChannelRead
-from langgraph.pregel.write import ChannelWrite
+from langgraph.pregel.write import SKIP_WRITE, ChannelWrite
 
 START = "__start__"
 
@@ -25,13 +26,32 @@ class StateGraph(Graph):
         if any(isinstance(c, BinaryOperatorAggregate) for c in self.channels.values()):
             self.support_multiple_edges = True
 
+    def add_node(self, key: str, action: RunnableLike) -> None:
+        if key in self.channels:
+            raise ValueError(
+                f"'{key}' is already being used as a state attribute "
+                "(a.k.a. a channel), cannot also be used as a node name."
+            )
+        return super().add_node(key, action)
+
     def compile(self, checkpointer: Optional[BaseCheckpointSaver] = None) -> Pregel:
         self.validate()
 
-        if any(key in self.nodes for key in self.channels):
-            raise ValueError("Cannot use channel names as node names")
-
         state_keys = list(self.channels)
+        state_keys_read = state_keys[0] if state_keys == ["__root__"] else state_keys
+        update_channels = (
+            [("__root__", None, True)]
+            if not isinstance(state_keys_read, list)
+            else [
+                (key, RunnableLambda(partial(_dict_getter, state_keys, key)), False)
+                for key in state_keys_read
+            ]
+        )
+        coerce_state = (
+            partial(_coerce_state, self.schema)
+            if isinstance(state_keys_read, list)
+            else RunnablePassthrough()
+        )
 
         outgoing_edges = defaultdict(list)
         for start, end in self.edges:
@@ -40,10 +60,9 @@ class StateGraph(Graph):
         nodes = {
             key: (
                 Channel.subscribe_to(f"{key}:inbox")
-                | partial(_coerce_state, self.schema)  # coerce/validate using schema
+                | coerce_state  # coerce/validate using schema
                 | node
-                | _update_state
-                | Channel.write_to(key)
+                | ChannelWrite(channels=[(key, None, False)] + update_channels)
             )
             for key, node in self.nodes.items()
         }
@@ -54,7 +73,7 @@ class StateGraph(Graph):
             if outgoing or key in self.branches:
                 nodes[edges_key] = Channel.subscribe_to(
                     key, tags=["langsmith:hidden"]
-                ) | ChannelRead(state_keys)
+                ) | ChannelRead(state_keys_read)
             if outgoing:
                 nodes[edges_key] |= Channel.write_to(*[dest for dest in outgoing])
             if key in self.branches:
@@ -63,14 +82,12 @@ class StateGraph(Graph):
                         branch.runnable, name=f"{key}_condition"
                     )
 
-        nodes[START] = (
-            Channel.subscribe_to(f"{START}:inbox", tags=["langsmith:hidden"])
-            | _update_state
-            | Channel.write_to(START)
-        )
+        nodes[START] = Channel.subscribe_to(
+            f"{START}:inbox", tags=["langsmith:hidden"]
+        ) | ChannelWrite(channels=[(START, None, False)] + update_channels)
         nodes[f"{START}:edges"] = (
             Channel.subscribe_to(START, tags=["langsmith:hidden"])
-            | ChannelRead(state_keys)
+            | ChannelRead(state_keys_read)
             | Channel.write_to(f"{self.entry_point}:inbox")
         )
 
@@ -88,26 +105,37 @@ def _coerce_state(schema: Type[Any], input: dict[str, Any]) -> dict[str, Any]:
     return schema(**input)
 
 
-def _update_state(input: dict[str, Any], config: RunnableConfig) -> dict[str, Any]:
+def _dict_getter(allowed_keys: str, key: str, input: dict) -> Any:
     if input is not None:
-        ChannelWrite.do_write(config, **input)
-    return input
+        if not isinstance(input, dict) or any(key not in allowed_keys for key in input):
+            raise InvalidUpdateError(
+                f"Invalid state update,"
+                f" expected dict with one or more of {allowed_keys}, got {input}"
+            )
+        return input.get(key, SKIP_WRITE)
+    else:
+        return SKIP_WRITE
 
 
 def _get_channels(schema: Type[dict]) -> dict[str, BaseChannel]:
     if not hasattr(schema, "__annotations__"):
-        raise ValueError("Schema must be a class with type annotations")
+        return {
+            "__root__": _get_channel(schema),
+        }
 
     channels: dict[str, BaseChannel] = {}
     for name, typ in schema.__annotations__.items():
-        if channel := _is_field_binop(typ):
-            channels[name] = channel
-        else:
-            channels[name] = LastValue(typ)
+        channels[name] = _get_channel(typ)
 
     return channels
 
 
+def _get_channel(annotation: Any) -> Optional[BaseChannel]:
+    if channel := _is_field_binop(annotation):
+        return channel
+    return LastValue(annotation)
+
+
 def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]:
     if hasattr(typ, "__metadata__"):
         meta = typ.__metadata__
@@ -122,3 +150,4 @@ def _is_field_binop(typ: Type[Any]) -> Optional[BinaryOperatorAggregate]:
                 ]
             ):
                 return BinaryOperatorAggregate(typ, meta[0])
+    return None
diff --git a/langgraph/prebuilt/chat_agent_executor.py b/langgraph/prebuilt/chat_agent_executor.py
index 253473416..bc7208107 100644
--- a/langgraph/prebuilt/chat_agent_executor.py
+++ b/langgraph/prebuilt/chat_agent_executor.py
@@ -2,10 +2,10 @@ import json
 import operator
 from typing import Annotated, Sequence, TypedDict
 
-from langchain.tools.render import format_tool_to_openai_function
 from langchain_core.agents import AgentAction
 from langchain_core.messages import BaseMessage, FunctionMessage
 from langchain_core.runnables import RunnableLambda
+from langchain_core.utils.function_calling import convert_to_openai_function
 
 from langgraph.graph import END, StateGraph
 from langgraph.prebuilt.tool_executor import ToolExecutor
@@ -18,9 +18,7 @@ def create_function_calling_executor(model, tools):
     else:
         tool_executor = ToolExecutor(tools)
         tool_classes = tools
-    model = model.bind_functions(
-        [format_tool_to_openai_function(t) for t in tool_classes]
-    )
+    model = model.bind(functions=[convert_to_openai_function(t) for t in tool_classes])
 
     # Define the function that determines whether to continue or not
     def should_continue(state):
diff --git a/langgraph/prebuilt/tool_executor.py b/langgraph/prebuilt/tool_executor.py
index 3c1bb20d9..b4d86240e 100644
--- a/langgraph/prebuilt/tool_executor.py
+++ b/langgraph/prebuilt/tool_executor.py
@@ -1,7 +1,7 @@
 from typing import Any, Sequence, Union
 
 from langchain_core.load.serializable import Serializable
-from langchain_core.runnables import RunnableBinding, RunnableLambda
+from langchain_core.runnables import RunnableBinding, RunnableConfig, RunnableLambda
 from langchain_core.tools import BaseTool
 
 INVALID_TOOL_MSG_TEMPLATE = (
@@ -47,7 +47,9 @@ class ToolExecutor(RunnableBinding):
             **kwargs,
         )
 
-    def _execute(self, tool_invocation: ToolInvocationInterface) -> Any:
+    def _execute(
+        self, tool_invocation: ToolInvocationInterface, *, config: RunnableConfig
+    ) -> Any:
         if tool_invocation.tool not in self.tool_map:
             return self.invalid_tool_msg_template.format(
                 requested_tool_name=tool_invocation.tool,
@@ -55,10 +57,12 @@ class ToolExecutor(RunnableBinding):
             )
         else:
             tool = self.tool_map[tool_invocation.tool]
-            output = tool.invoke(tool_invocation.tool_input)
+            output = tool.invoke(tool_invocation.tool_input, config=config)
             return output
 
-    async def _aexecute(self, tool_invocation: ToolInvocationInterface) -> Any:
+    async def _aexecute(
+        self, tool_invocation: ToolInvocationInterface, *, config: RunnableConfig
+    ) -> Any:
         if tool_invocation.tool not in self.tool_map:
             return self.invalid_tool_msg_template.format(
                 requested_tool_name=tool_invocation.tool,
@@ -66,5 +70,5 @@ class ToolExecutor(RunnableBinding):
             )
         else:
             tool = self.tool_map[tool_invocation.tool]
-            output = await tool.ainvoke(tool_invocation.tool_input)
+            output = await tool.ainvoke(tool_invocation.tool_input, config=config)
             return output
diff --git a/langgraph/pregel/__init__.py b/langgraph/pregel/__init__.py
index b557b9c5d..016f3a35b 100644
--- a/langgraph/pregel/__init__.py
+++ b/langgraph/pregel/__init__.py
@@ -46,6 +46,7 @@ from langgraph.channels.base import (
     BaseChannel,
     ChannelsManager,
     EmptyChannelError,
+    InvalidUpdateError,
     create_checkpoint,
 )
 from langgraph.channels.last_value import LastValue
@@ -148,8 +149,8 @@ class Channel:
         """Writes to channels the result of the lambda, or None to skip writing."""
         return ChannelWrite(
             channels=(
-                [(c, None) for c in channels]
-                + [(k, _coerce_write_value(v)) for k, v in kwargs.items()]
+                [(c, None, False) for c in channels]
+                + [(k, _coerce_write_value(v), True) for k, v in kwargs.items()]
             )
         )
 
@@ -194,10 +195,19 @@ class Pregel(
 
     @property
     def config_specs(self) -> list[ConfigurableFieldSpec]:
-        return get_unique_config_specs(
-            [spec for node in self.nodes.values() for spec in node.config_specs]
-            + (self.checkpointer.config_specs if self.checkpointer is not None else [])
-        )
+        return [
+            spec
+            for spec in get_unique_config_specs(
+                [spec for node in self.nodes.values() for spec in node.config_specs]
+                + (
+                    self.checkpointer.config_specs
+                    if self.checkpointer is not None
+                    else []
+                )
+            )
+            # these are provided by the Pregel class
+            if spec.id not in [CONFIG_KEY_READ, CONFIG_KEY_SEND]
+        ]
 
     @property
     def InputType(self) -> Any:
@@ -242,128 +252,150 @@ class Pregel(
         *,
         input_keys: Optional[Union[str, Sequence[str]]] = None,
         output_keys: Optional[Union[str, Sequence[str]]] = None,
+        interrupt: Optional[Sequence[str]] = None,
     ) -> Iterator[Union[dict[str, Any], Any]]:
-        if config["recursion_limit"] < 1:
-            raise ValueError("recursion_limit must be at least 1")
-        # assign defaults
-        if output_keys is None:
-            output_keys = [chan for chan in self.channels if chan not in self.hidden]
-        else:
-            validate_keys(output_keys, self.channels)
-        if input_keys is None:
-            input_keys = self.input
-        else:
-            validate_keys(input_keys, self.channels)
-        # copy nodes to ignore mutations during execution
-        processes = {**self.nodes}
-        # get checkpoint from saver, or create an empty one
-        checkpoint = self.checkpointer.get(config) if self.checkpointer else None
-        checkpoint = checkpoint or empty_checkpoint()
-        # create channels from checkpoint
-        with ChannelsManager(
-            self.channels, checkpoint
-        ) as channels, get_executor_for_config(config) as executor:
-            # map inputs to channel updates
-            _apply_writes(
-                checkpoint,
-                channels,
-                deque(w for c in input for w in map_input(input_keys, c)),
-                config,
-                0,
-            )
-
-            read = partial(_read_channel, channels)
-
-            # Similarly to Bulk Synchronous Parallel / Pregel model
-            # computation proceeds in steps, while there are channel updates
-            # channel updates from step N are only visible in step N+1
-            # channels are guaranteed to be immutable for the duration of the step,
-            # with channel updates applied only at the transition between steps
-            for step in range(config["recursion_limit"] + 1):
-                next_tasks = _prepare_next_tasks(checkpoint, processes, channels)
-
-                # if no more tasks, we're done
-                if not next_tasks:
-                    break
-                elif step == config["recursion_limit"]:
-                    raise GraphRecursionError(
-                        f"Recursion limit of {config['recursion_limit']} reached"
-                        "without hitting a stop condition. You can increase the limit"
-                        "by setting the `recursion_limit` config key."
-                    )
-
-                if self.debug:
-                    print_step_start(step, next_tasks)
-
-                # collect all writes to channels, without applying them yet
-                pending_writes = deque[tuple[str, Any]]()
-
-                # prepare tasks with config
-                tasks_w_config = [
-                    (
-                        proc,
-                        input,
-                        patch_config(
-                            config,
-                            run_name=name,
-                            callbacks=run_manager.get_child(f"graph:step:{step}"),
-                            configurable={
-                                # deque.extend is thread-safe
-                                CONFIG_KEY_SEND: pending_writes.extend,
-                                CONFIG_KEY_READ: read,
-                            },
-                        ),
-                    )
-                    for proc, input, name in next_tasks
+        try:
+            if config["recursion_limit"] < 1:
+                raise ValueError("recursion_limit must be at least 1")
+            # assign defaults
+            if output_keys is None:
+                output_keys = [
+                    chan for chan in self.channels if chan not in self.hidden
                 ]
+            else:
+                validate_keys(output_keys, self.channels)
+            if input_keys is None:
+                input_keys = self.input
+            else:
+                validate_keys(input_keys, self.channels)
+            interrupt = interrupt or self.interrupt
+            # copy nodes to ignore mutations during execution
+            processes = {**self.nodes}
+            # get checkpoint from saver, or create an empty one
+            checkpoint = self.checkpointer.get(config) if self.checkpointer else None
+            checkpoint = checkpoint or empty_checkpoint()
+            # create channels from checkpoint
+            with ChannelsManager(
+                self.channels, checkpoint
+            ) as channels, get_executor_for_config(config) as executor:
+                # map inputs to channel updates
+                if input_writes := deque(
+                    w for c in input for w in map_input(input_keys, c)
+                ):
+                    # discard any unfinished tasks from previous checkpoint
+                    _prepare_next_tasks(checkpoint, processes, channels)
+                    # apply input writes
+                    _apply_writes(
+                        checkpoint,
+                        channels,
+                        input_writes,
+                        config,
+                        0,
+                    )
 
-                # execute tasks, and wait for one to fail or all to finish.
-                # each task is independent from all other concurrent tasks
-                done, inflight = concurrent.futures.wait(
-                    [
+                read = partial(_read_channel, channels)
+
+                # Similarly to Bulk Synchronous Parallel / Pregel model
+                # computation proceeds in steps, while there are channel updates
+                # channel updates from step N are only visible in step N+1
+                # channels are guaranteed to be immutable for the duration of the step,
+                # with channel updates applied only at the transition between steps
+                for step in range(config["recursion_limit"] + 1):
+                    next_tasks = _prepare_next_tasks(checkpoint, processes, channels)
+
+                    # if no more tasks, we're done
+                    if not next_tasks:
+                        break
+                    elif step == config["recursion_limit"]:
+                        raise GraphRecursionError(
+                            f"Recursion limit of {config['recursion_limit']} reached"
+                            "without hitting a stop condition. You can increase the limit"
+                            "by setting the `recursion_limit` config key."
+                        )
+
+                    if self.debug:
+                        print_step_start(step, next_tasks)
+
+                    # collect all writes to channels, without applying them yet
+                    pending_writes = deque[tuple[str, Any]]()
+
+                    # prepare tasks with config
+                    tasks_w_config = [
+                        (
+                            proc,
+                            input,
+                            patch_config(
+                                config,
+                                run_name=name,
+                                callbacks=run_manager.get_child(f"graph:step:{step}"),
+                                configurable={
+                                    # deque.extend is thread-safe
+                                    CONFIG_KEY_SEND: pending_writes.extend,
+                                    CONFIG_KEY_READ: read,
+                                },
+                            ),
+                        )
+                        for proc, input, name in next_tasks
+                    ]
+
+                    futures = [
                         executor.submit(proc.invoke, input, config)
                         for proc, input, config in tasks_w_config
-                    ],
-                    return_when=concurrent.futures.FIRST_EXCEPTION,
-                    timeout=self.step_timeout,
-                )
+                    ]
 
-                # interrupt on failure or timeout
-                _interrupt_or_proceed(done, inflight, step)
+                    # execute tasks, and wait for one to fail or all to finish.
+                    # each task is independent from all other concurrent tasks
+                    done, inflight = concurrent.futures.wait(
+                        futures,
+                        return_when=concurrent.futures.FIRST_EXCEPTION,
+                        timeout=self.step_timeout,
+                    )
 
-                # apply writes to channels
-                _apply_writes(checkpoint, channels, pending_writes, config, step + 1)
+                    # interrupt on failure or timeout
+                    _interrupt_or_proceed(done, inflight, step)
 
-                if self.debug:
-                    print_checkpoint(step, channels)
+                    # apply writes to channels
+                    _apply_writes(
+                        checkpoint, channels, pending_writes, config, step + 1
+                    )
 
-                # yield current value and checkpoint view
-                if step_output := map_output(output_keys, pending_writes, channels):
-                    yield step_output
-                    # we can detect updates when output is multiple channels (ie. dict)
-                    if not isinstance(output_keys, str):
-                        # if view was updated, apply writes to channels
-                        _apply_writes_from_view(checkpoint, channels, step_output)
+                    if self.debug:
+                        print_checkpoint(step, channels)
 
-                # save end of step checkpoint
+                    # yield current value and checkpoint view
+                    if step_output := map_output(output_keys, pending_writes, channels):
+                        yield step_output
+                        # we can detect updates when output is multiple channels (ie. dict)
+                        if not isinstance(output_keys, str):
+                            # if view was updated, apply writes to channels
+                            _apply_writes_from_view(checkpoint, channels, step_output)
+
+                    # save end of step checkpoint
+                    if (
+                        self.checkpointer is not None
+                        and self.checkpointer.at == CheckpointAt.END_OF_STEP
+                    ):
+                        checkpoint = create_checkpoint(checkpoint, channels)
+                        self.checkpointer.put(config, checkpoint)
+
+                    # interrupt if any channel written to is in interrupt list
+                    if any(chan for chan, _ in pending_writes if chan in interrupt):
+                        break
+
+                # save end of run checkpoint
                 if (
                     self.checkpointer is not None
-                    and self.checkpointer.at == CheckpointAt.END_OF_STEP
+                    and self.checkpointer.at == CheckpointAt.END_OF_RUN
                 ):
                     checkpoint = create_checkpoint(checkpoint, channels)
                     self.checkpointer.put(config, checkpoint)
-
-                # interrupt if any channel written to is in interrupt list
-                if any(chan for chan, _ in pending_writes if chan in self.interrupt):
-                    break
-
-            # save end of run checkpoint
-            if (
-                self.checkpointer is not None
-                and self.checkpointer.at == CheckpointAt.END_OF_RUN
-            ):
-                checkpoint = create_checkpoint(checkpoint, channels)
-                self.checkpointer.put(config, checkpoint)
+        finally:
+            # cancel any pending tasks when generator is interrupted
+            try:
+                for task in futures:
+                    task.cancel()
+            except NameError:
+                pass
 
     async def _atransform(
         self,
@@ -373,140 +405,166 @@ class Pregel(
         *,
         input_keys: Optional[Union[str, Sequence[str]]] = None,
         output_keys: Optional[Union[str, Sequence[str]]] = None,
+        interrupt: Optional[Sequence[str]] = None,
     ) -> AsyncIterator[Union[dict[str, Any], Any]]:
-        if config["recursion_limit"] < 1:
-            raise ValueError("recursion_limit must be at least 1")
-        # if running from astream_log() run each proc with streaming
-        do_stream = next(
-            (
-                h
-                for h in run_manager.handlers
-                if isinstance(h, LogStreamCallbackHandler)
-            ),
-            None,
-        )
-        # assign defaults
-        if output_keys is None:
-            output_keys = [chan for chan in self.channels if chan not in self.hidden]
-        else:
-            validate_keys(output_keys, self.channels)
-        if input_keys is None:
-            input_keys = self.input
-        else:
-            validate_keys(input_keys, self.channels)
-        # copy nodes to ignore mutations during execution
-        processes = {**self.nodes}
-        # get checkpoint from saver, or create an empty one
-        checkpoint = await self.checkpointer.aget(config) if self.checkpointer else None
-        checkpoint = checkpoint or empty_checkpoint()
-        # create channels from checkpoint
-        async with AsyncChannelsManager(self.channels, checkpoint) as channels:
-            # map inputs to channel updates
-            _apply_writes(
-                checkpoint,
-                channels,
-                deque([w async for c in input for w in map_input(input_keys, c)]),
-                config,
-                0,
+        try:
+            if config["recursion_limit"] < 1:
+                raise ValueError("recursion_limit must be at least 1")
+            # if running from astream_log() run each proc with streaming
+            do_stream = next(
+                (
+                    h
+                    for h in run_manager.handlers
+                    if isinstance(h, LogStreamCallbackHandler)
+                ),
+                None,
             )
-
-            read = partial(_read_channel, channels)
-
-            # Similarly to Bulk Synchronous Parallel / Pregel model
-            # computation proceeds in steps, while there are channel updates
-            # channel updates from step N are only visible in step N+1,
-            # channels are guaranteed to be immutable for the duration of the step,
-            # channel updates being applied only at the transition between steps
-            for step in range(config["recursion_limit"] + 1):
-                next_tasks = _prepare_next_tasks(checkpoint, processes, channels)
-
-                # if no more tasks, we're done
-                if not next_tasks:
-                    break
-                elif step == config["recursion_limit"]:
-                    raise GraphRecursionError(
-                        f"Recursion limit of {config['recursion_limit']} reached"
-                        "without hitting a stop condition. You can increase the limit"
-                        "by setting the `recursion_limit` config key."
-                    )
-
-                if self.debug:
-                    print_step_start(step, next_tasks)
-
-                # collect all writes to channels, without applying them yet
-                pending_writes = deque[tuple[str, Any]]()
-
-                # prepare tasks with config
-                tasks_w_config = [
-                    (
-                        proc,
-                        input,
-                        patch_config(
-                            config,
-                            run_name=name,
-                            callbacks=run_manager.get_child(f"graph:step:{step}"),
-                            configurable={
-                                # deque.extend is thread-safe
-                                CONFIG_KEY_SEND: pending_writes.extend,
-                                CONFIG_KEY_READ: read,
-                            },
-                        ),
-                    )
-                    for proc, input, name in next_tasks
+            # assign defaults
+            if output_keys is None:
+                output_keys = [
+                    chan for chan in self.channels if chan not in self.hidden
                 ]
+            else:
+                validate_keys(output_keys, self.channels)
+            if input_keys is None:
+                input_keys = self.input
+            else:
+                validate_keys(input_keys, self.channels)
+            interrupt = interrupt or self.interrupt
+            # copy nodes to ignore mutations during execution
+            processes = {**self.nodes}
+            # get checkpoint from saver, or create an empty one
+            checkpoint = (
+                await self.checkpointer.aget(config) if self.checkpointer else None
+            )
+            checkpoint = checkpoint or empty_checkpoint()
+            # create channels from checkpoint
+            async with AsyncChannelsManager(self.channels, checkpoint) as channels:
+                # map inputs to channel updates
+                if input_writes := deque(
+                    [w async for c in input for w in map_input(input_keys, c)]
+                ):
+                    # discard any unfinished tasks from previous checkpoint
+                    _prepare_next_tasks(checkpoint, processes, channels)
+                    # apply input writes
+                    _apply_writes(
+                        checkpoint,
+                        channels,
+                        input_writes,
+                        config,
+                        0,
+                    )
 
-                # execute tasks, and wait for one to fail or all to finish.
-                # each task is independent from all other concurrent tasks
-                done, inflight = await asyncio.wait(
-                    [
-                        asyncio.create_task(_aconsume(proc.astream(input, config)))
-                        for proc, input, config in tasks_w_config
+                read = partial(_read_channel, channels)
+
+                # Similarly to Bulk Synchronous Parallel / Pregel model
+                # computation proceeds in steps, while there are channel updates
+                # channel updates from step N are only visible in step N+1,
+                # channels are guaranteed to be immutable for the duration of the step,
+                # channel updates being applied only at the transition between steps
+                for step in range(config["recursion_limit"] + 1):
+                    next_tasks = _prepare_next_tasks(checkpoint, processes, channels)
+
+                    # if no more tasks, we're done
+                    if not next_tasks:
+                        break
+                    elif step == config["recursion_limit"]:
+                        raise GraphRecursionError(
+                            f"Recursion limit of {config['recursion_limit']} reached"
+                            "without hitting a stop condition. You can increase the limit"
+                            "by setting the `recursion_limit` config key."
+                        )
+
+                    if self.debug:
+                        print_step_start(step, next_tasks)
+
+                    # collect all writes to channels, without applying them yet
+                    pending_writes = deque[tuple[str, Any]]()
+
+                    # prepare tasks with config
+                    tasks_w_config = [
+                        (
+                            proc,
+                            input,
+                            patch_config(
+                                config,
+                                run_name=name,
+                                callbacks=run_manager.get_child(f"graph:step:{step}"),
+                                configurable={
+                                    # deque.extend is thread-safe
+                                    CONFIG_KEY_SEND: pending_writes.extend,
+                                    CONFIG_KEY_READ: read,
+                                },
+                            ),
+                        )
+                        for proc, input, name in next_tasks
                     ]
-                    if do_stream
-                    else [
-                        asyncio.create_task(proc.ainvoke(input, config))
-                        for proc, input, config in tasks_w_config
-                    ],
-                    return_when=asyncio.FIRST_EXCEPTION,
-                    timeout=self.step_timeout,
-                )
 
-                # interrupt on failure or timeout
-                _interrupt_or_proceed(done, inflight, step)
+                    futures = (
+                        [
+                            asyncio.create_task(_aconsume(proc.astream(input, config)))
+                            for proc, input, config in tasks_w_config
+                        ]
+                        if do_stream
+                        else [
+                            asyncio.create_task(proc.ainvoke(input, config))
+                            for proc, input, config in tasks_w_config
+                        ]
+                    )
 
-                # apply writes to channels
-                _apply_writes(checkpoint, channels, pending_writes, config, step + 1)
+                    # execute tasks, and wait for one to fail or all to finish.
+                    # each task is independent from all other concurrent tasks
+                    done, inflight = await asyncio.wait(
+                        futures,
+                        return_when=asyncio.FIRST_EXCEPTION,
+                        timeout=self.step_timeout,
+                    )
 
-                if self.debug:
-                    print_checkpoint(step, channels)
+                    # interrupt on failure or timeout
+                    _interrupt_or_proceed(done, inflight, step)
 
-                # yield current value and checkpoint view
-                if step_output := map_output(output_keys, pending_writes, channels):
-                    yield step_output
-                    # we can detect updates when output is multiple channels (ie. dict)
-                    if not isinstance(output_keys, str):
-                        # if view was updated, apply writes to channels
-                        _apply_writes_from_view(checkpoint, channels, step_output)
+                    # apply writes to channels
+                    _apply_writes(
+                        checkpoint, channels, pending_writes, config, step + 1
+                    )
 
-                # save end of step checkpoint
+                    if self.debug:
+                        print_checkpoint(step, channels)
+
+                    # yield current value and checkpoint view
+                    if step_output := map_output(output_keys, pending_writes, channels):
+                        yield step_output
+                        # we can detect updates when output is multiple channels (ie. dict)
+                        if not isinstance(output_keys, str):
+                            # if view was updated, apply writes to channels
+                            _apply_writes_from_view(checkpoint, channels, step_output)
+
+                    # save end of step checkpoint
+                    if (
+                        self.checkpointer is not None
+                        and self.checkpointer.at == CheckpointAt.END_OF_STEP
+                    ):
+                        checkpoint = create_checkpoint(checkpoint, channels)
+                        await self.checkpointer.aput(config, checkpoint)
+
+                    # interrupt if any channel written to is in interrupt list
+                    if any(chan for chan, _ in pending_writes if chan in interrupt):
+                        break
+
+                # save end of run checkpoint
                 if (
                     self.checkpointer is not None
-                    and self.checkpointer.at == CheckpointAt.END_OF_STEP
+                    and self.checkpointer.at == CheckpointAt.END_OF_RUN
                 ):
                     checkpoint = create_checkpoint(checkpoint, channels)
                     await self.checkpointer.aput(config, checkpoint)
-
-                # interrupt if any channel written to is in interrupt list
-                if any(chan for chan, _ in pending_writes if chan in self.interrupt):
-                    break
-
-            # save end of run checkpoint
-            if (
-                self.checkpointer is not None
-                and self.checkpointer.at == CheckpointAt.END_OF_RUN
-            ):
-                checkpoint = create_checkpoint(checkpoint, channels)
-                await self.checkpointer.aput(config, checkpoint)
+        finally:
+            # cancel any pending tasks when generator is interrupted
+            try:
+                for task in futures:
+                    task.cancel()
+            except NameError:
+                pass
 
     def invoke(
         self,
@@ -684,7 +742,12 @@ def _apply_writes(
     # Apply writes to channels
     for chan, vals in pending_writes_by_channel.items():
         if chan in channels:
-            channels[chan].update(vals)
+            try:
+                channels[chan].update(vals)
+            except InvalidUpdateError as e:
+                raise InvalidUpdateError(
+                    f"Invalid update for channel {chan}: {e}"
+                ) from e
             checkpoint["channel_versions"][chan] += 1
             updated_channels.add(chan)
         else:
diff --git a/langgraph/pregel/read.py b/langgraph/pregel/read.py
index 2baea3fa9..08903082e 100644
--- a/langgraph/pregel/read.py
+++ b/langgraph/pregel/read.py
@@ -16,9 +16,9 @@ from langchain_core.runnables.base import (
     RunnableEach,
     coerce_to_runnable,
 )
+from langchain_core.runnables.config import merge_configs
 from langchain_core.runnables.utils import ConfigurableFieldSpec
 
-from langgraph.channels.base import BaseChannel
 from langgraph.constants import CONFIG_KEY_READ
 
 
@@ -33,7 +33,7 @@ class ChannelRead(RunnableLambda):
                 name=CONFIG_KEY_READ,
                 description=None,
                 default=None,
-                annotation=Callable[[BaseChannel], Any],
+                annotation=None,
             ),
         ]
 
@@ -103,7 +103,7 @@ class ChannelInvoke(RunnableBindingBase):
             when=when,
             bound=bound or default_bound,
             kwargs=kwargs or {},
-            config={**(config or {}), "tags": tags or []},
+            config=merge_configs(config, {"tags": tags or []}),
             **other_kwargs,
         )
 
diff --git a/langgraph/pregel/write.py b/langgraph/pregel/write.py
index e44b3e3db..3a050925f 100644
--- a/langgraph/pregel/write.py
+++ b/langgraph/pregel/write.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 from typing import Any, Callable, Optional, Sequence
 
 from langchain_core.runnables import (
@@ -14,8 +15,11 @@ from langgraph.constants import CONFIG_KEY_SEND
 TYPE_SEND = Callable[[Sequence[tuple[str, Any]]], None]
 
 
+SKIP_WRITE = object()
+
+
 class ChannelWrite(RunnablePassthrough):
-    channels: Sequence[tuple[str, Optional[Runnable]]]
+    channels: Sequence[tuple[str, Optional[Runnable], bool]]
     """
     Mapping of write channels to Runnables that return the value to be written,
     or None to skip writing.
@@ -27,10 +31,10 @@ class ChannelWrite(RunnablePassthrough):
     def __init__(
         self,
         *,
-        channels: Sequence[tuple[str, Optional[Runnable]]],
+        channels: Sequence[tuple[str, Optional[Runnable], bool]],
     ):
         super().__init__(func=self._write, afunc=self._awrite, channels=channels)
-        self.name = f"ChannelWrite<{','.join(chan for chan, _ in self.channels)}>"
+        self.name = f"ChannelWrite<{','.join(chan for chan, _, _ in self.channels)}>"
 
     def __repr_args__(self) -> Any:
         return [("channels", self.channels)]
@@ -43,31 +47,34 @@ class ChannelWrite(RunnablePassthrough):
                 name=CONFIG_KEY_SEND,
                 description=None,
                 default=None,
-                annotation=TYPE_SEND,
+                annotation=None,
             ),
         ]
 
     def _write(self, input: Any, config: RunnableConfig) -> None:
         values = [
-            (chan, r.invoke(input, config) if r else input) for chan, r in self.channels
+            (chan, r.invoke(input, config) if r else input)
+            for chan, r, _ in self.channels
         ]
         values = [
             write
-            for write, chan in zip(values, self.channels)
-            if chan[1] is None or write[1] is not None
+            for write, (_, _, skip_none) in zip(values, self.channels)
+            if not skip_none or write[1] is not None
         ]
 
         self.do_write(config, **dict(values))
 
     async def _awrite(self, input: Any, config: RunnableConfig) -> None:
+        values = await asyncio.gather(
+            *(
+                r.ainvoke(input, config) if r else _mk_future(input)
+                for _, r, _ in self.channels
+            )
+        )
         values = [
-            (chan, await r.ainvoke(input, config) if r else input)
-            for chan, r in self.channels
-        ]
-        values = [
-            write
-            for write, chan in zip(values, self.channels)
-            if chan[1] is None or write[1] is not None
+            (chan, val)
+            for val, (chan, _, skip_none) in zip(values, self.channels)
+            if not skip_none or val is not None
         ]
 
         self.do_write(config, **dict(values))
@@ -75,4 +82,10 @@ class ChannelWrite(RunnablePassthrough):
     @staticmethod
     def do_write(config: RunnableConfig, **values: Any) -> None:
         write: TYPE_SEND = config["configurable"][CONFIG_KEY_SEND]
-        write([(chan, val) for chan, val in values.items()])
+        write([(chan, val) for chan, val in values.items() if val is not SKIP_WRITE])
+
+
+def _mk_future(val: Any) -> asyncio.Future:
+    fut = asyncio.Future()
+    fut.set_result(val)
+    return fut
diff --git a/poetry.lock b/poetry.lock
index 163943b30..462f4e42e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,87 +2,87 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.9.1"
+version = "3.9.3"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -110,6 +110,21 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
+[[package]]
+name = "aiosqlite"
+version = "0.19.0"
+description = "asyncio bridge to the standard sqlite3 module"
+optional = false
+python-versions = ">=3.7"
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+]
+
+[package.extras]
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+docs = ["sphinx (==6.1.3)", "sphinx-mdinclude (==0.5.3)"]
+
 [[package]]
 name = "annotated-types"
 version = "0.6.0"
@@ -306,32 +321,24 @@ files = [
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
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 python-versions = ">=3.6.0"
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 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
@@ -557,63 +564,63 @@ test = ["pytest"]
 
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 ]
 
 [package.dependencies]
@@ -1006,13 +1013,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.28.0"
+version = "6.29.0"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1035,46 +1042,44 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "ipywidgets"
@@ -1132,13 +1137,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1188,13 +1193,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.20.0"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
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 ]
 
 [package.dependencies]
@@ -1298,13 +1303,13 @@ test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.6.1"
+version = "5.7.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"},
 ]
 
 [package.dependencies]
@@ -1343,13 +1348,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.1"
+version = "2.2.2"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1358,13 +1363,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.1"
+version = "2.12.5"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
+    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
 ]
 
 [package.dependencies]
@@ -1394,13 +1399,13 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.1"
+version = "0.5.2"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server_terminals-0.5.1-py3-none-any.whl", hash = "sha256:5e63e947ddd97bb2832db5ef837a258d9ccd4192cd608c1270850ad947ae5dd7"},
-    {file = "jupyter_server_terminals-0.5.1.tar.gz", hash = "sha256:16d3be9cf48be6a1f943f3a6c93c033be259cf4779184c66421709cf63dccfea"},
+    {file = "jupyter_server_terminals-0.5.2-py3-none-any.whl", hash = "sha256:1b80c12765da979513c42c90215481bbc39bd8ae7c0350b4f85bc3eb58d0fa80"},
+    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
 ]
 
 [package.dependencies]
@@ -1413,13 +1418,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.10"
+version = "4.0.12"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.0.10-py3-none-any.whl", hash = "sha256:fe010ad9e37017488b468632ef2ead255fc7c671c5b64d9ca13e1f7b7e665c37"},
-    {file = "jupyterlab-4.0.10.tar.gz", hash = "sha256:46177eb8ede70dc73be922ac99f8ef943bdc2dfbc6a31b353c4bde848a35dee1"},
+    {file = "jupyterlab-4.0.12-py3-none-any.whl", hash = "sha256:53f132480e5f6564f4e20d1b5ed4e8b7945952a2decd5bdfa43760b1b536c99d"},
+    {file = "jupyterlab-4.0.12.tar.gz", hash = "sha256:965d92efa82a538ed70ccb3968d9aabba788840da882e13d7b061780cdedc3b7"},
 ]
 
 [package.dependencies]
@@ -1493,13 +1498,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.1.0"
+version = "0.1.4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.1.0-py3-none-any.whl", hash = "sha256:8652e74b039333a55c79faff4400b077ba1bd0ddce5255574e42d301c05c1733"},
-    {file = "langchain-0.1.0.tar.gz", hash = "sha256:d43119f8d3fda2c8ddf8c3a19bd5b94b347e27d1867ff14a921b90bdbed0668a"},
+    {file = "langchain-0.1.4-py3-none-any.whl", hash = "sha256:6befdd6221f5f326092e31a3c19efdc7ce3d7d1f2e2cab065141071451730ed7"},
+    {file = "langchain-0.1.4.tar.gz", hash = "sha256:8767a9461e2b717ce9a35b1fa20659de89ea86ba9c2a4ff516e05d47ab2d195d"},
 ]
 
 [package.dependencies]
@@ -1507,9 +1512,9 @@ aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.9,<0.1"
-langchain-core = ">=0.1.7,<0.2"
-langsmith = ">=0.0.77,<0.1.0"
+langchain-community = ">=0.0.14,<0.1"
+langchain-core = ">=0.1.16,<0.2"
+langsmith = ">=0.0.83,<0.1"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -1533,20 +1538,20 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.9"
+version = "0.0.16"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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+    {file = "langchain_community-0.0.16-py3-none-any.whl", hash = "sha256:0f1dfc1a6205ce8d39931d3515974a208a9f69c16157c649f83490a7cc830b73"},
+    {file = "langchain_community-0.0.16.tar.gz", hash = "sha256:c06512a93013a06fba7679cd5a1254ff8b927cddd2d1fbe0cc444bf7bbdf0b8c"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.7,<0.2"
-langsmith = ">=0.0.63,<0.1.0"
+langchain-core = ">=0.1.16,<0.2"
+langsmith = ">=0.0.83,<0.1"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
 requests = ">=2,<3"
@@ -1555,23 +1560,23 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
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 [[package]]
 name = "langchain-core"
-version = "0.1.8"
+version = "0.1.17"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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-    {file = "langchain_core-0.1.8.tar.gz", hash = "sha256:93ab72f5ab202526310fad389a45626501fd76ecf56d451111c0d4abe8183407"},
+    {file = "langchain_core-0.1.17-py3-none-any.whl", hash = "sha256:026155cf97867bde410ab1834799ab4c5ba64c39380f2a4328bcf9c78623ca64"},
+    {file = "langchain_core-0.1.17.tar.gz", hash = "sha256:59016e457cd6a1708d83a3a454acc97cf02c2a2c3af95626d13f83894fd4e777"},
 ]
 
 [package.dependencies]
 anyio = ">=3,<5"
 jsonpatch = ">=1.33,<2.0"
-langsmith = ">=0.0.63,<0.1.0"
+langsmith = ">=0.0.83,<0.1"
 packaging = ">=23.2,<24.0"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -1583,13 +1588,13 @@ extended-testing = ["jinja2 (>=3,<4)"]
 
 [[package]]
 name = "langchain-openai"
-version = "0.0.2"
+version = "0.0.2.post1"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1615,13 +1620,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
 
 [[package]]
 name = "langsmith"
-version = "0.0.77"
+version = "0.0.85"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1630,91 +1635,91 @@ requests = ">=2,<3"
 
 [[package]]
 name = "markupsafe"
-version = "2.1.3"
+version = "2.1.4"
 description = "Safely add untrusted strings to HTML/XML markup."
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 python-versions = ">=3.7"
 files = [
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 python-versions = ">=3.8"
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 packaging = ">=17.0"
 
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+lint = ["pre-commit (>=2.4,<4.0)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
@@ -1907,13 +1912,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.14.0"
+version = "7.14.2"
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 [package.dependencies]
@@ -1966,24 +1971,24 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
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+version = "7.0.7"
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 [package.dependencies]
@@ -2017,50 +2022,58 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"
 
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+version = "1.10.0"
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 python-versions = ">=3.7.1"
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@@ -2077,13 +2090,13 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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 [package.extras]
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 ]
 
 [package.dependencies]
@@ -2974,136 +2951,136 @@ files = [
 
 [[package]]
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 optional = false
 python-versions = ">=3.8"
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+    {file = "SQLAlchemy-2.0.25.tar.gz", hash = "sha256:a2c69a7664fb2d54b8682dd774c3b54f67f84fa123cf84dda2a5f40dcaa04e08"},
 ]
 
 [package.dependencies]
 greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
-typing-extensions = ">=4.2.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
@@ -3448,24 +3425,24 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.8.19.14"
+version = "2.8.19.20240106"
 description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
-    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
+    {file = "types-python-dateutil-2.8.19.20240106.tar.gz", hash = "sha256:1f8db221c3b98e6ca02ea83a58371b22c374f42ae5bbdf186db9c9a76581459f"},
+    {file = "types_python_dateutil-2.8.19.20240106-py3-none-any.whl", hash = "sha256:efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2"},
 ]
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20231231"
+version = "2.31.0.20240125"
 description = "Typing stubs for requests"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20231231.tar.gz", hash = "sha256:0f8c0c9764773384122813548d9eea92a5c4e1f33ed54556b508968ec5065cee"},
-    {file = "types_requests-2.31.0.20231231-py3-none-any.whl", hash = "sha256:2e2230c7bc8dd63fa3153c1c0ae335f8a368447f0582fc332f17d54f88e69027"},
+    {file = "types-requests-2.31.0.20240125.tar.gz", hash = "sha256:03a28ce1d7cd54199148e043b2079cdded22d6795d19a2c2a6791a4b2b5e2eb5"},
+    {file = "types_requests-2.31.0.20240125-py3-none-any.whl", hash = "sha256:9592a9a4cb92d6d75d9b491a41477272b710e021011a2a3061157e2fb1f1a5d1"},
 ]
 
 [package.dependencies]
@@ -3513,17 +3490,18 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.1.0"
+version = "2.2.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
+    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
+    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
@@ -3568,13 +3546,13 @@ watchmedo = ["PyYAML (>=3.10)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.12"
+version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.12-py2.py3-none-any.whl", hash = "sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c"},
-    {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"},
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
 ]
 
 [[package]]
@@ -3751,4 +3729,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<4.0"
-content-hash = "3329b683659a6d0f15ce64a0e7135cfc26affe4c8d19c32dd481e1398cc41b3f"
+content-hash = "f489f2e9159e8db255a43027617c41a389afb5fe84ff94bf8521c0f98de8cd38"
diff --git a/pyproject.toml b/pyproject.toml
index 97c8bc544..7d19d2096 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langgraph"
-version = "0.0.12"
+version = "0.0.23"
 description = "langgraph"
 authors = []
 license = "LangGraph License"
@@ -9,7 +9,7 @@ repository = "https://www.github.com/langchain-ai/langgraph"
 
 [tool.poetry.dependencies]
 python = ">=3.9.0,<4.0"
-langchain-core = "^0.1.8"
+langchain-core = "^0.1.16"
 
 
 [tool.poetry.group.test.dependencies]
@@ -24,6 +24,8 @@ pytest-mock  = "^3.10.0"
 syrupy = "^4.0.2"
 httpx = "^0.26.0"
 pytest-watcher = "^0.3.4"
+langchain = "^0.1.0"
+aiosqlite = "^0.19.0"
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.1.4"
diff --git a/tests/test_pregel.py b/tests/test_pregel.py
index fd4a986b5..f9fef277b 100644
--- a/tests/test_pregel.py
+++ b/tests/test_pregel.py
@@ -1,5 +1,7 @@
+import json
 import operator
 import time
+import warnings
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import contextmanager
 from typing import Annotated, Generator, Optional, TypedDict, Union
@@ -14,8 +16,12 @@ from langgraph.channels.context import Context
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
 from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.sqlite import SqliteSaver
 from langgraph.graph import END, Graph
+from langgraph.graph.message import MessageGraph
 from langgraph.graph.state import StateGraph
+from langgraph.prebuilt.chat_agent_executor import create_function_calling_executor
+from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.pregel import Channel, GraphRecursionError, Pregel
 from langgraph.pregel.reserved import ReservedChannels
 
@@ -43,6 +49,13 @@ def test_invoke_single_process_in_out(mocker: MockerFixture) -> None:
 
     assert app.input_schema.schema() == {"title": "LangGraphInput", "type": "integer"}
     assert app.output_schema.schema() == {"title": "LangGraphOutput", "type": "integer"}
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")  # raise warnings as errors
+        assert app.config_schema().schema() == {
+            "properties": {},
+            "title": "LangGraphConfig",
+            "type": "object",
+        }
     assert app.invoke(2) == 3
     assert app.invoke(2, output_keys=["output"]) == {"output": 3}
     assert repr(app), "does not raise recursion error"
@@ -232,6 +245,40 @@ def test_invoke_two_processes_in_out(mocker: MockerFixture) -> None:
     assert step == 3
 
 
+def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None:
+    add_one = mocker.Mock(side_effect=lambda x: x + 1)
+    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
+    two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output")
+
+    memory = MemorySaver()
+    app = Pregel(
+        nodes={"one": one, "two": two}, checkpointer=memory, interrupt=["inbox"]
+    )
+
+    # start execution, stop at inbox
+    assert app.invoke(2, {"configurable": {"thread_id": 1}}) is None
+
+    # inbox == 3
+    checkpoint = memory.get({"configurable": {"thread_id": 1}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"]["inbox"] == 3
+
+    # resume execution, finish
+    assert app.invoke(None, {"configurable": {"thread_id": 1}}) == 4
+
+    # start execution again, stop at inbox
+    assert app.invoke(20, {"configurable": {"thread_id": 1}}) is None
+
+    # inbox == 21
+    checkpoint = memory.get({"configurable": {"thread_id": 1}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"]["inbox"] == 21
+
+    # send a new value in, interrupting the previous execution
+    assert app.invoke(3, {"configurable": {"thread_id": 1}}) is None
+    assert app.invoke(None, {"configurable": {"thread_id": 1}}) == 5
+
+
 def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
     add_one = mocker.Mock(side_effect=lambda x: x + 1)
     one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
@@ -415,6 +462,56 @@ def test_invoke_checkpoint(mocker: MockerFixture) -> None:
     assert checkpoint["channel_values"].get("total") == 5
 
 
+def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None:
+    add_one = mocker.Mock(side_effect=lambda x: x["total"] + x["input"])
+
+    def raise_if_above_10(input: int) -> int:
+        if input > 10:
+            raise ValueError("Input is too large")
+        return input
+
+    one = (
+        Channel.subscribe_to(["input"]).join(["total"])
+        | add_one
+        | Channel.write_to("output", "total")
+        | raise_if_above_10
+    )
+
+    memory = SqliteSaver.from_conn_string(":memory:")
+
+    app = Pregel(
+        nodes={"one": one},
+        channels={"total": BinaryOperatorAggregate(int, operator.add)},
+        checkpointer=memory,
+    )
+
+    # total starts out as 0, so output is 0+2=2
+    assert app.invoke(2, {"configurable": {"thread_id": "1"}}) == 2
+    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 2
+    # total is now 2, so output is 2+3=5
+    assert app.invoke(3, {"configurable": {"thread_id": "1"}}) == 5
+    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    # total is now 2+5=7, so output would be 7+4=11, but raises ValueError
+    with pytest.raises(ValueError):
+        app.invoke(4, {"configurable": {"thread_id": "1"}})
+    # checkpoint is not updated
+    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    # on a new thread, total starts out as 0, so output is 0+5=5
+    assert app.invoke(5, {"configurable": {"thread_id": "2"}}) == 5
+    checkpoint = memory.get({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    checkpoint = memory.get({"configurable": {"thread_id": "2"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 5
+
+
 def test_invoke_two_processes_two_in_join_two_out(mocker: MockerFixture) -> None:
     add_one = mocker.Mock(side_effect=lambda x: x + 1)
     add_10_each = mocker.Mock(side_effect=lambda x: sorted(y + 10 for y in x))
@@ -780,8 +877,6 @@ def test_conditional_graph() -> None:
 
 
 def test_conditional_graph_state() -> None:
-    from copy import deepcopy
-
     from langchain.llms.fake import FakeStreamingListLLM
     from langchain_community.tools import tool
     from langchain_core.agents import AgentAction, AgentFinish
@@ -811,7 +906,7 @@ def test_conditional_graph_state() -> None:
         ]
     )
 
-    def agent_parser(input: str) -> Union[AgentAction, AgentFinish]:
+    def agent_parser(input: str) -> dict[str, Union[AgentAction, AgentFinish]]:
         if input.startswith("finish"):
             _, answer = input.split(":")
             return {
@@ -886,7 +981,7 @@ def test_conditional_graph_state() -> None:
         ),
     }
 
-    assert [deepcopy(c) for c in app.stream({"input": "what is weather in sf"})] == [
+    assert [*app.stream({"input": "what is weather in sf"})] == [
         {
             "agent": {
                 "agent_outcome": AgentAction(
@@ -965,3 +1060,324 @@ def test_conditional_graph_state() -> None:
             }
         },
     ]
+
+
+def test_prebuilt_chat() -> None:
+    from langchain.chat_models.fake import FakeMessagesListChatModel
+    from langchain_community.tools import tool
+    from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
+
+    class FakeFuntionChatModel(FakeMessagesListChatModel):
+        def bind_functions(self, functions: list):
+            return self
+
+    @tool()
+    def search_api(query: str) -> str:
+        """Searches the API for the query."""
+        return f"result for {query}"
+
+    tools = [search_api]
+
+    app = create_function_calling_executor(
+        FakeFuntionChatModel(
+            responses=[
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": json.dumps("query"),
+                        }
+                    },
+                ),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": json.dumps("another"),
+                        }
+                    },
+                ),
+                AIMessage(content="answer"),
+            ]
+        ),
+        tools,
+    )
+
+    assert app.invoke(
+        {"messages": [HumanMessage(content="what is weather in sf")]}
+    ) == {
+        "messages": [
+            HumanMessage(content="what is weather in sf"),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"query"'}
+                },
+            ),
+            FunctionMessage(content="result for query", name="search_api"),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"another"'}
+                },
+            ),
+            FunctionMessage(content="result for another", name="search_api"),
+            AIMessage(content="answer"),
+        ]
+    }
+
+    assert [
+        *app.stream({"messages": [HumanMessage(content="what is weather in sf")]})
+    ] == [
+        {
+            "agent": {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"query"',
+                            }
+                        },
+                    )
+                ]
+            }
+        },
+        {
+            "action": {
+                "messages": [
+                    FunctionMessage(content="result for query", name="search_api")
+                ]
+            }
+        },
+        {
+            "agent": {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"another"',
+                            }
+                        },
+                    )
+                ]
+            }
+        },
+        {
+            "action": {
+                "messages": [
+                    FunctionMessage(content="result for another", name="search_api")
+                ]
+            }
+        },
+        {"agent": {"messages": [AIMessage(content="answer")]}},
+        {
+            "__end__": {
+                "messages": [
+                    HumanMessage(content="what is weather in sf"),
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"query"',
+                            }
+                        },
+                    ),
+                    FunctionMessage(content="result for query", name="search_api"),
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"another"',
+                            }
+                        },
+                    ),
+                    FunctionMessage(content="result for another", name="search_api"),
+                    AIMessage(content="answer"),
+                ]
+            }
+        },
+    ]
+
+
+def test_message_graph() -> None:
+    from langchain.chat_models.fake import FakeMessagesListChatModel
+    from langchain_community.tools import tool
+    from langchain_core.agents import AgentAction
+    from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
+
+    class FakeFuntionChatModel(FakeMessagesListChatModel):
+        def bind_functions(self, functions: list):
+            return self
+
+    @tool()
+    def search_api(query: str) -> str:
+        """Searches the API for the query."""
+        return f"result for {query}"
+
+    tools = [search_api]
+
+    model = FakeFuntionChatModel(
+        responses=[
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "search_api",
+                        "arguments": json.dumps("query"),
+                    }
+                },
+            ),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "search_api",
+                        "arguments": json.dumps("another"),
+                    }
+                },
+            ),
+            AIMessage(content="answer"),
+        ]
+    )
+
+    tool_executor = ToolExecutor(tools)
+
+    # Define the function that determines whether to continue or not
+    def should_continue(messages):
+        last_message = messages[-1]
+        # If there is no function call, then we finish
+        if "function_call" not in last_message.additional_kwargs:
+            return "end"
+        # Otherwise if there is, we continue
+        else:
+            return "continue"
+
+    def call_tool(messages):
+        # Based on the continue condition
+        # we know the last message involves a function call
+        last_message = messages[-1]
+        # We construct an AgentAction from the function_call
+        action = AgentAction(
+            tool=last_message.additional_kwargs["function_call"]["name"],
+            tool_input=json.loads(
+                last_message.additional_kwargs["function_call"]["arguments"]
+            ),
+            log="",
+        )
+        # We call the tool_executor and get back a response
+        response = tool_executor.invoke(action)
+        # We use the response to create a FunctionMessage
+        return FunctionMessage(content=str(response), name=action.tool)
+
+    # Define a new graph
+    workflow = MessageGraph()
+
+    # Define the two nodes we will cycle between
+    workflow.add_node("agent", model)
+    workflow.add_node("action", call_tool)
+
+    # Set the entrypoint as `agent`
+    # This means that this node is the first one called
+    workflow.set_entry_point("agent")
+
+    # We now add a conditional edge
+    workflow.add_conditional_edges(
+        # First, we define the start node. We use `agent`.
+        # This means these are the edges taken after the `agent` node is called.
+        "agent",
+        # Next, we pass in the function that will determine which node is called next.
+        should_continue,
+        # Finally we pass in a mapping.
+        # The keys are strings, and the values are other nodes.
+        # END is a special node marking that the graph should finish.
+        # What will happen is we will call `should_continue`, and then the output of that
+        # will be matched against the keys in this mapping.
+        # Based on which one it matches, that node will then be called.
+        {
+            # If `tools`, then we call the tool node.
+            "continue": "action",
+            # Otherwise we finish.
+            "end": END,
+        },
+    )
+
+    # We now add a normal edge from `tools` to `agent`.
+    # This means that after `tools` is called, `agent` node is called next.
+    workflow.add_edge("action", "agent")
+
+    # Finally, we compile it!
+    # This compiles it into a LangChain Runnable,
+    # meaning you can use it as you would any other runnable
+    app = workflow.compile()
+
+    assert app.invoke(HumanMessage(content="what is weather in sf")) == [
+        HumanMessage(content="what is weather in sf"),
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "function_call": {"name": "search_api", "arguments": '"query"'}
+            },
+        ),
+        FunctionMessage(content="result for query", name="search_api"),
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "function_call": {"name": "search_api", "arguments": '"another"'}
+            },
+        ),
+        FunctionMessage(content="result for another", name="search_api"),
+        AIMessage(content="answer"),
+    ]
+
+    assert [*app.stream([HumanMessage(content="what is weather in sf")])] == [
+        {
+            "agent": AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"query"'}
+                },
+            )
+        },
+        {"action": FunctionMessage(content="result for query", name="search_api")},
+        {
+            "agent": AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"another"'}
+                },
+            )
+        },
+        {"action": FunctionMessage(content="result for another", name="search_api")},
+        {"agent": AIMessage(content="answer")},
+        {
+            "__end__": [
+                HumanMessage(content="what is weather in sf"),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {"name": "search_api", "arguments": '"query"'}
+                    },
+                ),
+                FunctionMessage(content="result for query", name="search_api"),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": '"another"',
+                        }
+                    },
+                ),
+                FunctionMessage(content="result for another", name="search_api"),
+                AIMessage(content="answer"),
+            ]
+        },
+    ]
diff --git a/tests/test_pregel_async.py b/tests/test_pregel_async.py
index 6799bdd73..db281e7c6 100644
--- a/tests/test_pregel_async.py
+++ b/tests/test_pregel_async.py
@@ -1,4 +1,5 @@
 import asyncio
+import json
 import operator
 from contextlib import asynccontextmanager, contextmanager
 from typing import (
@@ -21,8 +22,12 @@ from langgraph.channels.binop import BinaryOperatorAggregate
 from langgraph.channels.context import Context
 from langgraph.channels.last_value import LastValue
 from langgraph.channels.topic import Topic
+from langgraph.checkpoint.aiosqlite import AsyncSqliteSaver
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, Graph, StateGraph
+from langgraph.graph.message import MessageGraph
+from langgraph.prebuilt.chat_agent_executor import create_function_calling_executor
+from langgraph.prebuilt.tool_executor import ToolExecutor
 from langgraph.pregel import Channel, GraphRecursionError, Pregel
 from langgraph.pregel.reserved import ReservedChannels
 
@@ -248,6 +253,40 @@ async def test_invoke_two_processes_in_out(mocker: MockerFixture) -> None:
     assert step == 3
 
 
+async def test_invoke_two_processes_in_out_interrupt(mocker: MockerFixture) -> None:
+    add_one = mocker.Mock(side_effect=lambda x: x + 1)
+    one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
+    two = Channel.subscribe_to("inbox") | add_one | Channel.write_to("output")
+
+    memory = MemorySaver()
+    app = Pregel(
+        nodes={"one": one, "two": two}, checkpointer=memory, interrupt=["inbox"]
+    )
+
+    # start execution, stop at inbox
+    assert await app.ainvoke(2, {"configurable": {"thread_id": 1}}) is None
+
+    # inbox == 3
+    checkpoint = await memory.aget({"configurable": {"thread_id": 1}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"]["inbox"] == 3
+
+    # resume execution, finish
+    assert await app.ainvoke(None, {"configurable": {"thread_id": 1}}) == 4
+
+    # start execution again, stop at inbox
+    assert await app.ainvoke(20, {"configurable": {"thread_id": 1}}) is None
+
+    # inbox == 21
+    checkpoint = await memory.aget({"configurable": {"thread_id": 1}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"]["inbox"] == 21
+
+    # send a new value in, interrupting the previous execution
+    assert await app.ainvoke(3, {"configurable": {"thread_id": 1}}) is None
+    assert await app.ainvoke(None, {"configurable": {"thread_id": 1}}) == 5
+
+
 async def test_invoke_two_processes_in_dict_out(mocker: MockerFixture) -> None:
     add_one = mocker.Mock(side_effect=lambda x: x + 1)
     one = Channel.subscribe_to("input") | add_one | Channel.write_to("inbox")
@@ -438,6 +477,59 @@ async def test_invoke_checkpoint(mocker: MockerFixture) -> None:
     assert checkpoint["channel_values"].get("total") == 5
 
 
+async def test_invoke_checkpoint_sqlite(mocker: MockerFixture) -> None:
+    add_one = mocker.Mock(side_effect=lambda x: x["total"] + x["input"])
+
+    def raise_if_above_10(input: int) -> int:
+        if input > 10:
+            raise ValueError("Input is too large")
+        return input
+
+    one = (
+        Channel.subscribe_to(["input"]).join(["total"])
+        | add_one
+        | Channel.write_to("output", "total")
+        | raise_if_above_10
+    )
+
+    memory = AsyncSqliteSaver.from_conn_string(":memory:")
+
+    app = Pregel(
+        nodes={"one": one},
+        channels={"total": BinaryOperatorAggregate(int, operator.add)},
+        checkpointer=memory,
+        debug=True,
+    )
+
+    # total starts out as 0, so output is 0+2=2
+    assert await app.ainvoke(2, {"configurable": {"thread_id": "1"}}) == 2
+    checkpoint = await memory.aget({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 2
+    # total is now 2, so output is 2+3=5
+    assert await app.ainvoke(3, {"configurable": {"thread_id": "1"}}) == 5
+    checkpoint = await memory.aget({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    # total is now 2+5=7, so output would be 7+4=11, but raises ValueError
+    with pytest.raises(ValueError):
+        await app.ainvoke(4, {"configurable": {"thread_id": "1"}})
+    # checkpoint is not updated
+    checkpoint = await memory.aget({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    # on a new thread, total starts out as 0, so output is 0+5=5
+    assert await app.ainvoke(5, {"configurable": {"thread_id": "2"}}) == 5
+    checkpoint = await memory.aget({"configurable": {"thread_id": "1"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 7
+    checkpoint = await memory.aget({"configurable": {"thread_id": "2"}})
+    assert checkpoint is not None
+    assert checkpoint["channel_values"].get("total") == 5
+
+    await memory.conn.close()
+
+
 async def test_invoke_two_processes_two_in_join_two_out(mocker: MockerFixture) -> None:
     add_one = mocker.Mock(side_effect=lambda x: x + 1)
     add_10_each = mocker.Mock(side_effect=lambda x: sorted(y + 10 for y in x))
@@ -834,8 +926,6 @@ async def test_conditional_graph() -> None:
 
 
 async def test_conditional_graph_state() -> None:
-    from copy import deepcopy
-
     from langchain.llms.fake import FakeStreamingListLLM
     from langchain_community.tools import tool
     from langchain_core.agents import AgentAction, AgentFinish
@@ -865,7 +955,7 @@ async def test_conditional_graph_state() -> None:
         ]
     )
 
-    def agent_parser(input: str) -> Union[AgentAction, AgentFinish]:
+    def agent_parser(input: str) -> dict[str, Union[AgentAction, AgentFinish]]:
         if input.startswith("finish"):
             _, answer = input.split(":")
             return {
@@ -940,9 +1030,7 @@ async def test_conditional_graph_state() -> None:
         ),
     }
 
-    assert [
-        deepcopy(c) async for c in app.astream({"input": "what is weather in sf"})
-    ] == [
+    assert [c async for c in app.astream({"input": "what is weather in sf"})] == [
         {
             "agent": {
                 "agent_outcome": AgentAction(
@@ -1021,3 +1109,329 @@ async def test_conditional_graph_state() -> None:
             }
         },
     ]
+
+
+async def test_prebuilt_chat() -> None:
+    from langchain.chat_models.fake import FakeMessagesListChatModel
+    from langchain_community.tools import tool
+    from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
+
+    class FakeFuntionChatModel(FakeMessagesListChatModel):
+        def bind_functions(self, functions: list):
+            return self
+
+    @tool()
+    def search_api(query: str) -> str:
+        """Searches the API for the query."""
+        return f"result for {query}"
+
+    tools = [search_api]
+
+    app = create_function_calling_executor(
+        FakeFuntionChatModel(
+            responses=[
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": json.dumps("query"),
+                        }
+                    },
+                ),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": json.dumps("another"),
+                        }
+                    },
+                ),
+                AIMessage(content="answer"),
+            ]
+        ),
+        tools,
+    )
+
+    assert await app.ainvoke(
+        {"messages": [HumanMessage(content="what is weather in sf")]}
+    ) == {
+        "messages": [
+            HumanMessage(content="what is weather in sf"),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"query"'}
+                },
+            ),
+            FunctionMessage(content="result for query", name="search_api"),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"another"'}
+                },
+            ),
+            FunctionMessage(content="result for another", name="search_api"),
+            AIMessage(content="answer"),
+        ]
+    }
+
+    assert [
+        c
+        async for c in app.astream(
+            {"messages": [HumanMessage(content="what is weather in sf")]}
+        )
+    ] == [
+        {
+            "agent": {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"query"',
+                            }
+                        },
+                    )
+                ]
+            }
+        },
+        {
+            "action": {
+                "messages": [
+                    FunctionMessage(content="result for query", name="search_api")
+                ]
+            }
+        },
+        {
+            "agent": {
+                "messages": [
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"another"',
+                            }
+                        },
+                    )
+                ]
+            }
+        },
+        {
+            "action": {
+                "messages": [
+                    FunctionMessage(content="result for another", name="search_api")
+                ]
+            }
+        },
+        {"agent": {"messages": [AIMessage(content="answer")]}},
+        {
+            "__end__": {
+                "messages": [
+                    HumanMessage(content="what is weather in sf"),
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"query"',
+                            }
+                        },
+                    ),
+                    FunctionMessage(content="result for query", name="search_api"),
+                    AIMessage(
+                        content="",
+                        additional_kwargs={
+                            "function_call": {
+                                "name": "search_api",
+                                "arguments": '"another"',
+                            }
+                        },
+                    ),
+                    FunctionMessage(content="result for another", name="search_api"),
+                    AIMessage(content="answer"),
+                ]
+            }
+        },
+    ]
+
+
+async def test_message_graph() -> None:
+    from langchain.chat_models.fake import FakeMessagesListChatModel
+    from langchain_community.tools import tool
+    from langchain_core.agents import AgentAction
+    from langchain_core.messages import AIMessage, FunctionMessage, HumanMessage
+
+    class FakeFuntionChatModel(FakeMessagesListChatModel):
+        def bind_functions(self, functions: list):
+            return self
+
+    @tool()
+    def search_api(query: str) -> str:
+        """Searches the API for the query."""
+        return f"result for {query}"
+
+    tools = [search_api]
+
+    model = FakeFuntionChatModel(
+        responses=[
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "search_api",
+                        "arguments": json.dumps("query"),
+                    }
+                },
+            ),
+            AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "search_api",
+                        "arguments": json.dumps("another"),
+                    }
+                },
+            ),
+            AIMessage(content="answer"),
+        ]
+    )
+
+    tool_executor = ToolExecutor(tools)
+
+    # Define the function that determines whether to continue or not
+    def should_continue(messages):
+        last_message = messages[-1]
+        # If there is no function call, then we finish
+        if "function_call" not in last_message.additional_kwargs:
+            return "end"
+        # Otherwise if there is, we continue
+        else:
+            return "continue"
+
+    async def call_tool(messages):
+        # Based on the continue condition
+        # we know the last message involves a function call
+        last_message = messages[-1]
+        # We construct an AgentAction from the function_call
+        action = AgentAction(
+            tool=last_message.additional_kwargs["function_call"]["name"],
+            tool_input=json.loads(
+                last_message.additional_kwargs["function_call"]["arguments"]
+            ),
+            log="",
+        )
+        # We call the tool_executor and get back a response
+        response = await tool_executor.ainvoke(action)
+        # We use the response to create a FunctionMessage
+        return FunctionMessage(content=str(response), name=action.tool)
+
+    # Define a new graph
+    workflow = MessageGraph()
+
+    # Define the two nodes we will cycle between
+    workflow.add_node("agent", model)
+    workflow.add_node("action", call_tool)
+
+    # Set the entrypoint as `agent`
+    # This means that this node is the first one called
+    workflow.set_entry_point("agent")
+
+    # We now add a conditional edge
+    workflow.add_conditional_edges(
+        # First, we define the start node. We use `agent`.
+        # This means these are the edges taken after the `agent` node is called.
+        "agent",
+        # Next, we pass in the function that will determine which node is called next.
+        should_continue,
+        # Finally we pass in a mapping.
+        # The keys are strings, and the values are other nodes.
+        # END is a special node marking that the graph should finish.
+        # What will happen is we will call `should_continue`, and then the output of that
+        # will be matched against the keys in this mapping.
+        # Based on which one it matches, that node will then be called.
+        {
+            # If `tools`, then we call the tool node.
+            "continue": "action",
+            # Otherwise we finish.
+            "end": END,
+        },
+    )
+
+    # We now add a normal edge from `tools` to `agent`.
+    # This means that after `tools` is called, `agent` node is called next.
+    workflow.add_edge("action", "agent")
+
+    # Finally, we compile it!
+    # This compiles it into a LangChain Runnable,
+    # meaning you can use it as you would any other runnable
+    app = workflow.compile()
+
+    assert await app.ainvoke(HumanMessage(content="what is weather in sf")) == [
+        HumanMessage(content="what is weather in sf"),
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "function_call": {"name": "search_api", "arguments": '"query"'}
+            },
+        ),
+        FunctionMessage(content="result for query", name="search_api"),
+        AIMessage(
+            content="",
+            additional_kwargs={
+                "function_call": {"name": "search_api", "arguments": '"another"'}
+            },
+        ),
+        FunctionMessage(content="result for another", name="search_api"),
+        AIMessage(content="answer"),
+    ]
+
+    assert [
+        c async for c in app.astream([HumanMessage(content="what is weather in sf")])
+    ] == [
+        {
+            "agent": AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"query"'}
+                },
+            )
+        },
+        {"action": FunctionMessage(content="result for query", name="search_api")},
+        {
+            "agent": AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {"name": "search_api", "arguments": '"another"'}
+                },
+            )
+        },
+        {"action": FunctionMessage(content="result for another", name="search_api")},
+        {"agent": AIMessage(content="answer")},
+        {
+            "__end__": [
+                HumanMessage(content="what is weather in sf"),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {"name": "search_api", "arguments": '"query"'}
+                    },
+                ),
+                FunctionMessage(content="result for query", name="search_api"),
+                AIMessage(
+                    content="",
+                    additional_kwargs={
+                        "function_call": {
+                            "name": "search_api",
+                            "arguments": '"another"',
+                        }
+                    },
+                ),
+                FunctionMessage(content="result for another", name="search_api"),
+                AIMessage(content="answer"),
+            ]
+        },
+    ]