locking

exciting!

relnotes preview:

# LangGraph 1.1.0 Release Notes

## Type-Safe Streaming & Invoke

LangGraph 1.1 introduces `version="v2"` — a new opt-in streaming format
that brings full type safety to `stream()`, `astream()`, `invoke()`, and
`ainvoke()`.

### What's changing

**v1 (default, unchanged):** `stream()` yields bare tuples like
`(stream_mode, data)` or just `data`. `invoke()` returns a plain `dict`.
Interrupts are mixed into the output dict under `"__interrupt__"`.

**v2 (opt-in):** `stream()` yields strongly-typed `StreamPart` dicts
with `type`, `ns`, `data`, and (for values) `interrupts` fields.
`invoke()` returns a `GraphOutput` object with `.value` and
`.interrupts` attributes. When your state schema is a Pydantic model or
dataclass, outputs are automatically coerced to the correct type.

### `invoke()` / `ainvoke()` with `version="v2"`

```python
from langgraph.types import GraphOutput

result = graph.invoke({"input": "hello"}, version="v2")

# result is a GraphOutput, not a dict
assert isinstance(result, GraphOutput)
result.value       # your output — dict, Pydantic model, or dataclass
result.interrupts  # tuple[Interrupt, ...], empty if none occurred
```

With a non-`"values"` stream mode, `invoke(..., stream_mode="updates",
version="v2")` returns `list[StreamPart]` instead of `list[tuple]`.

### `stream()` / `astream()` with `version="v2"`

```python
for part in graph.stream({"input": "hello"}, version="v2"):
    if part["type"] == "values":
        part["data"]        # OutputT — full state
        part["interrupts"]  # tuple[Interrupt, ...]
    elif part["type"] == "updates":
        part["data"]        # dict[str, Any]
    elif part["type"] == "messages":
        part["data"]        # tuple[BaseMessage, dict]
    elif part["type"] == "custom":
        part["data"]        # Any
    elif part["type"] == "tasks":
        part["data"]        # TaskPayload | TaskResultPayload
    elif part["type"] == "debug":
        part["data"]        # DebugPayload
```

Each stream mode has its own `TypedDict` — `ValuesStreamPart`,
`UpdatesStreamPart`, `MessagesStreamPart`, `CustomStreamPart`,
`CheckpointStreamPart`, `TasksStreamPart`, `DebugStreamPart` — all
importable from `langgraph.types`. The union type `StreamPart` is a
discriminated union on `part["type"]`, enabling full type narrowing in
editors and type checkers.

### Pydantic & dataclass output coercion

When your graph's state schema is a Pydantic model or dataclass,
`version="v2"` automatically coerces outputs to the declared type:

```python
from pydantic import BaseModel

class MyState(BaseModel):
    answer: str
    count: int

graph = StateGraph(MyState)
# ... build graph ...
compiled = graph.compile()

result = compiled.invoke({"answer": "", "count": 0}, version="v2")
assert isinstance(result.value, MyState)  # not a dict!
```

### Backward compatibility

- **Default is still `version="v1"`** — existing code works without
changes.
- To make migration easier, `GraphOutput` supports old-style best-effort
access to graph values and interrupts. Dict-style access
(`result["key"]`, `"key" in result`, `result["__interrupt__"]`) still
works and delegates to `result.value` / `result.interrupts` under the
hood. However, this is **deprecated** and emits a
`LangGraphDeprecatedSinceV11` warning. It will be removed in v3.0 —
migrate to `result.value` and `result.interrupts` at your convenience.

```python
result = graph.invoke({"input": "hello"}, version="v2")

# Old style — still works, but deprecated
result["input"]          # delegates to result.value["input"]
result["__interrupt__"]  # delegates to result.interrupts
"input" in result        # delegates to "input" in result.value

# New style — preferred
result.value["input"]
result.interrupts
```

## Migration Guide

1. **No action required** — `version="v1"` remains the default. All
existing code continues to work.
2. **Adopt v2 incrementally** — Add `version="v2"` to individual
`invoke()`/`stream()` calls to get typed outputs.
3. **Use typed imports** — Import `GraphOutput`, `StreamPart`, and
individual part types from `langgraph.types` for type-safe code.

.github/CONTRIBUTING.md

@@ -1,6 +0,0 @@
-# Contributing to LangGraph
-
-Hi there! Thank you for even being interested in contributing to LangGraph.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
-
-To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,43 +1,60 @@
 name: "\U0001F41B Bug Report"
-description: Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
-labels: [pending, bug]
+description: Report a bug in LangGraph. To report a security issue, please instead use the security option (below). For questions, please use the LangChain forum (below).
+labels: ["bug"]
+type: bug
 body:
   - type: markdown
     attributes:
       value: |
-        Thank you for taking the time to file a bug report. 
+        Thank you for taking the time to file a bug report.
 
-        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
+        For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
 
-        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:
+        Check these before submitting to see if your issue has already been reported, fixed or if there's another way to solve your problem:
 
-        * [LangChain Forum](https://forum.langchain.com/),
-        * [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        * [LangChain documentation with the integrated search](https://docs.langchain.com/),
+        * [Documentation](https://docs.langchain.com/oss/python/langgraph/overview),
+        * [API Reference Documentation](https://reference.langchain.com/python/),
+        * [LangChain ChatBot](https://chat.langchain.com/)
         * [GitHub search](https://github.com/langchain-ai/langgraph),
+        * [LangChain Forum](https://forum.langchain.com/),
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
+      description: Please confirm and check all the following options.
       options:
-        - label: This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
+        - label: This is a bug, not a usage question.
           required: true
-        - label: I added a clear and detailed title that summarizes the issue.
+        - label: I added a clear and descriptive title that summarizes this issue.
           required: true
-        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
+        - label: I used the GitHub search to find a similar question and didn't find it.
           required: true
-        - label: I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
+        - label: I am sure that this is a bug in LangGraph rather than my code.
+          required: true
+        - label: The bug is not resolved by updating to the latest stable version of LangGraph (or the specific integration package).
+          required: true
+        - label: This is not related to the langchain-community package.
+          required: true
+        - label: I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.
           required: true
   - type: textarea
     id: reproduction
     validations:
       required: true
     attributes:
-      label: Example Code
+      label: Reproduction Steps / Example Code (Python)
       description: |
-        Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
+        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!**
+
+        * Avoid screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+        * Reduce your code to the minimum required to reproduce the issue if possible.
+
+        (This will be automatically formatted into code, so no need for backticks.)
+      render: python
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -46,17 +63,13 @@ body:
           
         chain = StateGraph(list)
         chain.invoke('Hello!')
-      render: python
   - 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
+        If you are reporting an error, please copy and paste the full error message and
+        stack trace.
+        (This will be automatically formatted into code, so no need for backticks.)
       render: shell
   - type: textarea
     id: description
@@ -77,7 +90,18 @@ body:
     attributes:
       label: System Info
       description: |
-        Run on your machine: `python -m langchain_core.sys_info`
+        Please share your system info with us.
+
+        Run the following command in your terminal and paste the output here:
+
+        `python -m langchain_core.sys_info`
+
+        or if you have an existing python interpreter running:
+
+        ```python
+        from langchain_core import sys_info
+        sys_info.print_sys_info()
+        ```
       placeholder: |
         python -m langchain_core.sys_info
     validations:

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +1,15 @@
 blank_issues_enabled: false
 version: 2.1
 contact_links:
-  - name: Documentation
-    url: https://github.com/langchain-ai/docs/issues/new?template=langgraph.yml
-    about: Report an issue related to the LangGraph documentation
-  - name: LangChain Forum
+  - name: 💬 LangChain Forum
     url:  https://forum.langchain.com/
     about: General community discussions and support
+  - name: 📚 LangGraph Documentation
+    url: https://docs.langchain.com/oss/python/langgraph/overview
+    about: View the official LangGraph documentation
+  - name: 📚 API Reference Documentation
+    url: https://reference.langchain.com/python/
+    about: View the official LangGraph API reference documentation
+  - name: 📚 Documentation issue
+    url: https://github.com/langchain-ai/docs/issues/new?template=02-langgraph.yml
+    about: Report an issue related to the LangGraph documentation

.github/PULL_REQUEST_TEMPLATE.md

@@ -21,7 +21,7 @@ Thank you for contributing to LangGraph! Follow these steps to mark your pull re
   1. A test for the integration, preferably unit tests that do not rely on network access,
   2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
 
-- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://docs.langchain.com/oss/python/contributing/overview) for more.
 
 Additional guidelines:
 

.github/dependabot.yml

@@ -4,15 +4,108 @@ updates:
     directory: "/"
     schedule:
       interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
 
-  - package-ecosystem: "pip"
-    directories:
-      - "libs/checkpoint"
-      - "libs/checkpoint-postgres"
-      - "libs/checkpoint-sqlite"
-      - "libs/cli"
-      - "libs/langgraph"
-      - "libs/prebuilt"
-      - "libs/sdk-py"
+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint"
     schedule:
       interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
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+  - package-ecosystem: "uv"
+    directory: "/libs/checkpoint-conformance"
+    schedule:
+      interval: "weekly"
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+    groups:
+      all-dependencies:
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+
+  - package-ecosystem: "uv"
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+    schedule:
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+    groups:
+      all-dependencies:
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+  - package-ecosystem: "uv"
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+    schedule:
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+    groups:
+      all-dependencies:
+        patterns:
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+
+  - package-ecosystem: "uv"
+    directory: "/libs/cli"
+    schedule:
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+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/langgraph"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/prebuilt"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "uv"
+    directory: "/libs/sdk-py"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "npm"
+    directory: "/libs/cli/js-examples"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"
+
+  - package-ecosystem: "npm"
+    directory: "/libs/cli/js-monorepo-example"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+    groups:
+      all-dependencies:
+        patterns:
+          - "*"

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+</svg>

.github/scripts/run_langgraph_cli_test.py

@@ -63,7 +63,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
             try:
                 sys.stderr.write("\n== docker compose ps ==\n")
                 runner.run(
-                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=False)
+                    subp_exec(*compose_cmd, *args, "ps", input=stdin, verbose=True)
                 )
             except Exception:
                 pass
@@ -76,7 +76,7 @@ def test(config: pathlib.Path, port: int, tag: str, verbose: bool):
                         "logs",
                         "langgraph-api",
                         input=stdin,
-                        verbose=False,
+                        verbose=True,
                     )
                 )
             except Exception:

.github/workflows/_integration_test.yml

@@ -2,6 +2,9 @@ name: CLI integration test
 
 on:
   workflow_call:
+    secrets:
+      LANGSMITH_API_KEY:
+        required: false
 
 permissions:
   contents: read
@@ -28,6 +31,8 @@ jobs:
             workdir: libs/cli/examples/graphs_reqs_b
             tag: langgraph-test-d
     name: "CLI integration test"
+    env:
+      HAS_LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY != '' }}
     defaults:
       run:
         working-directory: libs/cli
@@ -35,11 +40,12 @@ jobs:
       - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
+        if: github.event_name != 'workflow_dispatch'
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "libs/cli/**"
       - name: Set up Python ${{ matrix.python-version }}
-        if: steps.changed-files.outputs.all
+        if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
         uses: astral-sh/setup-uv@v7
         with:
           python-version: ${{ matrix.python-version }}
@@ -47,57 +53,72 @@ jobs:
           cache-suffix: "cli-integration-test"
           ignore-nothing-to-cache: true
       - name: Install cli globally
-        if: steps.changed-files.outputs.all
+        if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
         run: pip install -e .
-      - name: Build and test service ${{ matrix.example.name }}
-        if: steps.changed-files.outputs.all
+      - name: Build service ${{ matrix.example.name }}
+        if: (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch')
+        working-directory: ${{ matrix.example.workdir }}
+        run: |
+          langgraph build -t ${{ matrix.example.tag }}
+      - name: Test service ${{ matrix.example.name }}
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&env.HAS_LANGSMITH_API_KEY == 'true' }}
         working-directory: ${{ matrix.example.workdir }}
         env:
           LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
         run: |
-          # Build the image for this example
-          langgraph build -t ${{ matrix.example.tag }}
           # Prepare environment file from local or parent example directory
           if [ -f .env.example ]; then cp .env.example .env; elif [ -f ../.env.example ]; then cp ../.env.example .env && cp ../.env.example ../.env; fi
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
+          if [ -f ../.env ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> ../.env; fi
           # Run the integration test using the built tag
-          # Compute repo root to reference the shared script robustly
           REPO_ROOT=$(git rev-parse --show-toplevel)
           timeout 60 python "$REPO_ROOT/.github/scripts/run_langgraph_cli_test.py" -t ${{ matrix.example.tag }}
 
       - name: Build JS service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
         working-directory: libs/cli/js-examples
         run: |
           langgraph build -t langgraph-test-e
 
       - name: Build JS monorepo service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
         working-directory: libs/cli/js-monorepo-example
         run: |
           langgraph build -t langgraph-test-f -c apps/agent/langgraph.json --build-command "yarn run turbo build" --install-command "yarn install"
 
       - name: Build Python monorepo service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
         working-directory: libs/cli/python-monorepo-example
         run: |
           langgraph build -t langgraph-test-g -c apps/agent/langgraph.json
+      - name: Test Python monorepo service
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
+        working-directory: libs/cli/python-monorepo-example
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
           cp apps/agent/.env.example apps/agent/.env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> apps/agent/.env
           timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-g -c apps/agent/langgraph.json
 
-      - name: Build and test prerelease reqs service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+      - name: Build prerelease reqs service
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
         working-directory: libs/cli/examples/graph_prerelease_reqs
         run: |
           langgraph build -t langgraph-test-h
+      - name: Test prerelease reqs service
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' && env.HAS_LANGSMITH_API_KEY == 'true' }}
+        working-directory: libs/cli/examples/graph_prerelease_reqs
+        env:
+          LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_API_KEY }}
+        run: |
           cp ../.env.example .env
-          if [ -n "${{ secrets.LANGSMITH_API_KEY }}" ]; then echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env; fi
+          echo "LANGSMITH_API_KEY=${{ secrets.LANGSMITH_API_KEY }}" >> .env
           timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-h
           echo "Finished starting up langgraph-test-h"
           LANGGRAPH_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langgraph'); print(v);")
-          if [ "$LANGGRAPH_VERSION" != "1.0.2" ]; then
-            echo "LANGGRAPH_VERSION != 1.0.2; $LANGGRAPH_VERSION"
+          if [ "$LANGGRAPH_VERSION" != "1.0.8" ]; then
+            echo "LANGGRAPH_VERSION != 1.0.8; $LANGGRAPH_VERSION"
             exit 1
           fi
           LANGCHAIN_OPENAI_VERSION=$(docker run --rm --entrypoint "" langgraph-test-h python -c "import sys; from importlib.metadata import version; v = version('langchain-openai'); print(v);")
@@ -112,7 +133,7 @@ jobs:
           fi
 
       - name: Build and test prerelease reqs fail service
-        if: ${{ steps.changed-files.outputs.all && matrix.example.name == 'A' }}
+        if: ${{ (steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch') &&matrix.example.name == 'A' }}
         working-directory: libs/cli/examples/graph_prerelease_reqs_fail
         run: |
           langgraph build -t langgraph-test-i || [ $? -eq 1 ]

.github/workflows/_lint.yml

@@ -34,11 +34,12 @@ jobs:
       - uses: actions/checkout@v6
       - name: Get changed files
         id: changed-files
+        if: github.event_name != 'workflow_dispatch'
         uses: Ana06/get-changed-files@v2.3.0
         with:
           filter: "${{ inputs.working-directory }}/**"
       - name: Set up Python ${{ matrix.python-version }}
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         uses: astral-sh/setup-uv@v7
         with:
           python-version: ${{ matrix.python-version }}
@@ -46,12 +47,12 @@ jobs:
           cache-suffix: lint-${{ inputs.working-directory }}
 
       - name: Install dependencies
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         working-directory: ${{ inputs.working-directory }}
         run: uv sync --frozen --group lint
 
       - name: Get .mypy_cache to speed up mypy
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         uses: actions/cache@v5
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
@@ -61,7 +62,7 @@ jobs:
           key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
 
       - name: Analysing package code with our lint
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         working-directory: ${{ inputs.working-directory }}
         run: |
           if make lint_package > /dev/null 2>&1; then
@@ -72,12 +73,12 @@ jobs:
           fi
 
       - name: Install test dependencies
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         working-directory: ${{ inputs.working-directory }}
         run: uv sync --group lint
 
       - name: Get .mypy_cache_test to speed up mypy
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         uses: actions/cache@v5
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
@@ -87,7 +88,7 @@ jobs:
           key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/uv.lock', inputs.working-directory)) }}
 
       - name: Analysing tests with our lint
-        if: steps.changed-files.outputs.all
+        if: steps.changed-files.outputs.all || github.event_name == 'workflow_dispatch'
         working-directory: ${{ inputs.working-directory }}
         run: |
           if make lint_tests > /dev/null 2>&1; then

.github/workflows/_test.yml

@@ -33,7 +33,7 @@ jobs:
           enable-cache: true
           cache-suffix: test-${{ inputs.working-directory }}
       - name: Login to Docker Hub
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
         if: ${{ !github.event.pull_request.head.repo.fork }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}

.github/workflows/_test_langgraph.yml

@@ -31,7 +31,7 @@ jobs:
           enable-cache: true
           cache-suffix: "test-langgraph"
       - name: Login to Docker Hub
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
         if: ${{ !github.event.pull_request.head.repo.fork }}
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
@@ -45,6 +45,13 @@ jobs:
         shell: bash
         run: make test_parallel
 
+      - name: Run strict msgpack pregel tests
+        if: ${{ matrix.python-version == '3.13' }}
+        shell: bash
+        env:
+          LANGGRAPH_STRICT_MSGPACK: "true"
+        run: make test TEST="tests/test_pregel.py tests/test_pregel_async.py"
+
       - name: Ensure the tests did not create any additional files
         shell: bash
         run: |

.github/workflows/_test_release.yml

@@ -48,7 +48,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         with:
           name: test-dist
           path: ${{ inputs.working-directory }}/dist/
@@ -76,7 +76,7 @@ jobs:
     steps:
       - uses: actions/checkout@v6
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v8
         with:
           name: test-dist
           path: ${{ inputs.working-directory }}/dist/

.github/workflows/ci.yml

@@ -2,10 +2,12 @@
 name: CI
 
 on:
+  workflow_dispatch:
   push:
     branches:
       - main
   pull_request:
+  
 
 permissions:
   contents: read
@@ -24,11 +26,12 @@ jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
-      python: ${{ steps.filter.outputs.python }}
-      deps: ${{ steps.filter.outputs.deps }}
+      python: ${{ steps.filter.outputs.python || 'true' }}
+      deps: ${{ steps.filter.outputs.deps || 'true' }}
     steps:
       - uses: actions/checkout@v6
       - uses: dorny/paths-filter@v3
+        if: github.event_name != 'workflow_dispatch'
         id: filter
         with:
           filters: |
@@ -39,6 +42,7 @@ jobs:
               - 'libs/checkpoint/**'
               - 'libs/checkpoint-sqlite/**'
               - 'libs/checkpoint-postgres/**'
+              - 'libs/checkpoint-conformance/**'
               - 'libs/prebuilt/**'
             deps:
               - '**/pyproject.toml'
@@ -57,7 +61,7 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
-
+            "libs/checkpoint-conformance",
             "libs/prebuilt",
           ]
     if: needs.changes.outputs.python == 'true' || needs.changes.outputs.deps == 'true'
@@ -77,6 +81,7 @@ jobs:
             "libs/checkpoint",
             "libs/checkpoint-sqlite",
             "libs/checkpoint-postgres",
+            "libs/checkpoint-conformance",
             "libs/prebuilt",
             "libs/sdk-py",
           ]

.github/workflows/deploy-redirects.yml

@@ -0,0 +1,49 @@
+name: Deploy Redirects to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'docs/**'
+      - '.github/workflows/deploy-redirects.yml'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  deploy:
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Setup Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: '3.11'
+
+      - name: Generate redirect files
+        run: python docs/generate_redirects.py
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v5
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v4
+        with:
+          path: 'docs/_site'
+
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

.github/workflows/release.yml

@@ -50,7 +50,7 @@ jobs:
         working-directory: ${{ inputs.working-directory }}
 
       - name: Upload build
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -269,7 +269,7 @@ jobs:
           enable-cache: true
           cache-suffix: "release"
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v8
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/
@@ -310,7 +310,7 @@ jobs:
           enable-cache: true
           cache-suffix: "release"
 
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v8
         with:
           name: dist
           path: ${{ inputs.working-directory }}/dist/

.gitignore

@@ -100,3 +100,10 @@ dmypy.json
 .turbo
 .editorconfig
 .scratch
+
+# macOS debug symbol bundles generated during local Rust builds
+saf-python-sdk/python/saf_python_sdk/*.dSYM/
+
+# Local PyO3 extension artifacts for saf-python-sdk
+saf-python-sdk/python/saf_python_sdk/langgraph_rust_core*.so
+saf-python-sdk/python/saf_python_sdk/langgraph_rust_core*.pyd

AGENTS.md

@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```
 
 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

CLAUDE.md

@@ -53,3 +53,5 @@ sdk-js (standalone)
 ```
 
 Changes to a library may impact all of its dependents shown above.
+
+- Do NOT use Sphinx-style double backtick formatting (` ``code`` `). Use single backticks (`` `code` ``) for inline code references in docstrings and comments.

README.md

@@ -1,7 +1,7 @@
 <picture class="github-only">
-  <source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
-  <source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
-  <img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
+  <source media="(prefers-color-scheme: light)" srcset=".github/images/logo-light.svg">
+  <source media="(prefers-color-scheme: dark)" srcset=".github/images/logo-dark.svg">
+  <img alt="LangGraph Logo" src=".github/images/logo-dark.svg" width="50%">
 </picture>
 
 <div>
@@ -56,6 +56,9 @@ Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/pytho
 
 To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
 
+> [!TIP]
+> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
+
 ## Core benefits
 
 LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
@@ -79,7 +82,7 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 
 ## Additional resources
 
-- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
 - [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
 - [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
 - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.

advanced-graph-examples/Makefile

@@ -0,0 +1,6 @@
+PYTHON_VERSION ?= 3.13
+
+.PHONY: run-basic
+run-basic:
+	uv run --python $(PYTHON_VERSION) python examples/basic_run.py
+

advanced-graph-examples/README.md

@@ -0,0 +1,14 @@
+# advanced-graph-examples
+
+Examples for the published `saf-python-sdk` package.
+
+## Quick start
+
+```bash
+cd advanced-graph-examples
+uv sync
+uv run python examples/basic_run.py
+```
+
+> Note: current published wheel is for Python 3.13 on macOS arm64.
+

advanced-graph-examples/examples/basic_run.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import asyncio
+from typing import TypedDict
+
+from saf_python_sdk import Command, Send
+from saf_python_sdk.advanced_graph import AdvancedStateGraph
+
+
+class MyState(TypedDict):
+    count: int
+    logs: list[str]
+
+
+async def start_node(state: MyState) -> Command:
+    state["logs"].append("start")
+    return Command(update=state, goto=Send("finish_node", "hello"))
+
+
+async def finish_node(input: str, state: MyState) -> Command:
+    state["logs"].append(f"finish:{input}")
+    state["count"] += 1
+    return Command(update=state)
+
+
+async def main() -> None:
+    graph = AdvancedStateGraph(MyState)
+    graph.add_entry_node(start_node)
+    graph.add_finish_node(finish_node)
+    result = await graph.compile().ainvoke({"count": 0, "logs": []})
+    print(result)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

advanced-graph-examples/pyproject.toml

@@ -0,0 +1,12 @@
+[project]
+name = "advanced-graph-examples"
+version = "0.1.0"
+requires-python = ">=3.13,<3.14"
+dependencies = [
+    "saf-python-sdk>=0.1.1",
+]
+
+[tool.uv]
+dev-dependencies = []
+
+

advanced-graph-examples/uv.lock

@@ -0,0 +1,25 @@
+version = 1
+revision = 3
+requires-python = "==3.13.*"
+
+[[package]]
+name = "advanced-graph-examples"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "saf-python-sdk" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "saf-python-sdk", specifier = ">=0.1.1" }]
+
+[package.metadata.requires-dev]
+dev = []
+
+[[package]]
+name = "saf-python-sdk"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c5/43/2a86cdc8a1fbb185b9ce4642f0e3676acc168d48ed2e68d4ead6ad57e95e/saf_python_sdk-0.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9035ab6ede9884b5588925c47d5b8be2c1c107becf5d6f88e0ebb39f4e8fac3b", size = 517053, upload-time = "2026-03-16T23:49:25.661Z" },
+]

docs/.gitignore

@@ -0,0 +1 @@
+_site/

docs/generate_redirects.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+"""
+Generate HTML redirect files from redirects.json.
+
+Usage:
+    python generate_redirects.py
+
+This script reads redirects.json and generates individual HTML files
+for each redirect path. Each HTML file uses meta refresh (0 delay)
+which is SEO-friendly and treated similarly to 301 redirects by Google.
+
+To add new redirects, simply edit redirects.json and re-run this script.
+"""
+
+import json
+import os
+from pathlib import Path
+
+# Default fallback URL for any path not in the redirect map
+DEFAULT_REDIRECT = "https://docs.langchain.com/oss/python/langgraph/overview"
+
+HTML_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting...</title>
+    <link rel="canonical" href="{url}">
+    <meta name="robots" content="noindex">
+    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
+    <meta http-equiv="refresh" content="0; url={url}">
+</head>
+<body>
+Redirecting...
+</body>
+</html>
+"""
+
+ROOT_HTML_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting to LangGraph Documentation</title>
+    <link rel="canonical" href="{url}">
+    <meta name="robots" content="noindex">
+    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
+    <meta http-equiv="refresh" content="0; url={url}">
+</head>
+<body>
+<h1>Documentation has moved</h1>
+<p>The LangGraph documentation has moved to <a href="{url}">docs.langchain.com</a>.</p>
+<p>Redirecting you now...</p>
+</body>
+</html>
+"""
+
+CATCHALL_404_TEMPLATE = """<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <title>Redirecting to LangGraph Documentation</title>
+    <link rel="canonical" href="{default_url}">
+    <meta name="robots" content="noindex">
+    <script>
+        // Catchall redirect for any unmapped paths
+        window.location.replace("{default_url}");
+    </script>
+    <meta http-equiv="refresh" content="0; url={default_url}">
+</head>
+<body>
+<h1>Documentation has moved</h1>
+<p>The LangGraph documentation has moved to <a href="{default_url}">docs.langchain.com</a>.</p>
+<p>Redirecting you now...</p>
+</body>
+</html>
+"""
+
+
+def generate_redirects():
+    script_dir = Path(__file__).parent
+    output_dir = script_dir / "_site"
+
+    # Load redirects
+    with open(script_dir / "redirects.json") as f:
+        redirects = json.load(f)
+
+    # Clean output directory
+    if output_dir.exists():
+        import shutil
+        shutil.rmtree(output_dir)
+    output_dir.mkdir(parents=True)
+
+    # Generate individual HTML files for each redirect
+    for old_path, new_url in redirects.items():
+        # Remove leading slash and create directory structure
+        path = old_path.lstrip("/")
+
+        # Check if path has a file extension (e.g., .txt, .xml)
+        # If so, create the file directly instead of a directory with index.html
+        path_obj = Path(path)
+        has_extension = path_obj.suffix and len(path_obj.suffix) <= 5
+
+        if not path:
+            html_path = output_dir / "index.html"
+        elif has_extension:
+            # For files with extensions, create the file directly
+            html_path = output_dir / path
+        else:
+            # For directory-style URLs, create index.html inside
+            html_path = output_dir / path / "index.html"
+
+        # Create parent directories
+        html_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Write the redirect HTML
+        html_path.write_text(HTML_TEMPLATE.format(url=new_url))
+        print(f"Created: {html_path}")
+
+    # Create root index.html
+    root_index = output_dir / "index.html"
+    if not root_index.exists():
+        root_index.write_text(ROOT_HTML_TEMPLATE.format(url=DEFAULT_REDIRECT))
+        print(f"Created: {root_index}")
+
+    # Create 404.html for catchall
+    catchall_404 = output_dir / "404.html"
+    catchall_404.write_text(CATCHALL_404_TEMPLATE.format(default_url=DEFAULT_REDIRECT))
+    print(f"Created: {catchall_404}")
+
+    # Copy static files (like llms.txt) that can't be redirected via HTML
+    static_files = ["llms.txt"]
+    for static_file in static_files:
+        src = script_dir / static_file
+        if src.exists():
+            dst = output_dir / static_file
+            dst.write_text(src.read_text())
+            print(f"Copied: {dst}")
+
+    print(f"\nGenerated {len(redirects)} redirect files in {output_dir}")
+
+
+if __name__ == "__main__":
+    generate_redirects()

docs/llms.txt

@@ -0,0 +1,35 @@
+# LangGraph
+
+LangGraph documentation has moved to docs.langchain.com.
+
+## Overview
+
+- [LangGraph Overview](https://docs.langchain.com/oss/python/langgraph/overview): Introduction to LangGraph, a library for building stateful, multi-actor applications with LLMs.
+- [Why LangGraph?](https://docs.langchain.com/oss/python/langgraph/why-langgraph): Motivation for LangGraph and its key features.
+
+## Core Concepts
+
+- [Graph API](https://docs.langchain.com/oss/python/langgraph/graph-api): Learn how to define state, create nodes, and connect them with edges.
+- [Streaming](https://docs.langchain.com/oss/python/langgraph/streaming): Stream outputs from your graph for better UX.
+- [Persistence](https://docs.langchain.com/oss/python/langgraph/persistence): Add memory and checkpointing to your graphs.
+- [Add Memory](https://docs.langchain.com/oss/python/langgraph/add-memory): Implement short-term and long-term memory.
+- [Workflows & Agents](https://docs.langchain.com/oss/python/langgraph/workflows-agents): Build agents and workflows with LangGraph.
+
+## How-To Guides
+
+- [Use Subgraphs](https://docs.langchain.com/oss/python/langgraph/use-subgraphs): Compose graphs using subgraphs.
+- [Observability](https://docs.langchain.com/oss/python/langgraph/observability): Add tracing and debugging to your graphs.
+- [Common Errors](https://docs.langchain.com/oss/python/langgraph/common-errors): Troubleshoot common LangGraph errors.
+
+## Tutorials
+
+- [Agentic RAG](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Build an agentic RAG system with LangGraph.
+- [SQL Agent](https://docs.langchain.com/oss/python/langgraph/sql-agent): Create a SQL agent with LangGraph.
+
+## Reference
+
+- [API Reference](https://reference.langchain.com/python/langgraph/): Complete API documentation for LangGraph.
+
+## LangGraph Platform
+
+For deploying LangGraph applications in production, see the [LangSmith documentation](https://docs.langchain.com/langsmith/agent-server).

docs/redirects.json

@@ -0,0 +1,296 @@
+{
+  "/how-tos/stream-values": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/stream-updates": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-content": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/stream-multiple": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-tokens-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-from-final-node": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming-events-from-within-tools-without-langchain": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/state-reducers": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-and-update-state",
+  "/how-tos/sequence": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-a-sequence-of-steps",
+  "/how-tos/branching": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-branches",
+  "/how-tos/recursion-limit": "https://docs.langchain.com/oss/python/langgraph/graph-api#create-and-control-loops",
+  "/how-tos/visualization": "https://docs.langchain.com/oss/python/langgraph/graph-api#visualize-your-graph",
+  "/how-tos/input_output_schema": "https://docs.langchain.com/oss/python/langgraph/graph-api#define-input-and-output-schemas",
+  "/how-tos/pass_private_state": "https://docs.langchain.com/oss/python/langgraph/graph-api#pass-private-state-between-nodes",
+  "/how-tos/state-model": "https://docs.langchain.com/oss/python/langgraph/graph-api#use-pydantic-models-for-graph-state",
+  "/how-tos/map-reduce": "https://docs.langchain.com/oss/python/langgraph/graph-api#map-reduce-and-the-send-api",
+  "/how-tos/command": "https://docs.langchain.com/oss/python/langgraph/graph-api#combine-control-flow-and-state-updates-with-command",
+  "/how-tos/configuration": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-runtime-configuration",
+  "/how-tos/node-retries": "https://docs.langchain.com/oss/python/langgraph/graph-api#add-retry-policies",
+  "/how-tos/return-when-recursion-limit-hits": "https://docs.langchain.com/oss/python/langgraph/graph-api#impose-a-recursion-limit",
+  "/how-tos/async": "https://docs.langchain.com/oss/python/langgraph/graph-api#async",
+  "/how-tos/memory/manage-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/memory/delete-messages": "https://docs.langchain.com/oss/python/langgraph/add-memory#delete-messages",
+  "/how-tos/memory/add-summary-conversation-history": "https://docs.langchain.com/oss/python/langgraph/add-memory#summarize-messages",
+  "/how-tos/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/agents/memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/subgraph-transform-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#different-state-schemas",
+  "/how-tos/subgraphs-manage-state": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs#add-persistence",
+  "/how-tos/persistence_postgres": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/persistence_mongodb": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/persistence_redis": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-in-production",
+  "/how-tos/subgraph-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#use-with-subgraphs",
+  "/how-tos/cross-thread-persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
+  "/cloud/how-tos/copy_threads": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/how-tos/check-thread-status": "https://docs.langchain.com/langsmith/use-threads",
+  "/cloud/concepts/threads": "https://docs.langchain.com/oss/python/langgraph/persistence#threads",
+  "/how-tos/persistence": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/tool-calling-errors": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/pass-config-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/pass-run-time-values-to-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/update-state-from-tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/agents/tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/agent-handoffs": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-network": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-multi-turn-convo": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/cloud/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/cloud/how-tos/index": "https://docs.langchain.com/langsmith/home",
+  "/cloud/concepts/api": "https://docs.langchain.com/langsmith/agent-server",
+  "/cloud/concepts/cloud": "https://docs.langchain.com/langsmith/cloud",
+  "/cloud/faq/studio": "https://docs.langchain.com/langsmith/studio",
+  "/cloud/how-tos/human_in_the_loop_edit_state": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/cloud/how-tos/human_in_the_loop_user_input": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/concepts/platform_architecture": "https://docs.langchain.com/langsmith/cloud#architecture",
+  "/cloud/how-tos/stream_values": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_updates": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_messages": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_events": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_debug": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/how-tos/stream_multiple": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/concepts/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/agents/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/create-react-agent": "https://docs.langchain.com/oss/python/langchain/agents#basic-configuration",
+  "/how-tos/create-react-agent-memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/create-react-agent-system-prompt": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/create-react-agent-structured-output": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+  "/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/reference/prebuilt": "https://reference.langchain.com/python/langgraph/agents/",
+  "/concepts/high_level": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/v0-human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/introduction": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/deployment": "https://docs.langchain.com/oss/python/langgraph/local-server",
+  "/how-tos/deploy-self-hosted": "https://docs.langchain.com/langsmith/platform-setup",
+  "/concepts/self_hosted": "https://docs.langchain.com/langsmith/platform-setup",
+  "/tutorials/deployment": "https://docs.langchain.com/langsmith/deployments",
+  "/cloud/how-tos/assistant_versioning": "https://docs.langchain.com/langsmith/configuration-cloud",
+  "/cloud/concepts/runs": "https://docs.langchain.com/langsmith/assistants#execution",
+  "/how-tos/wait-user-input-functional": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/how-tos/review-tool-calls-functional": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/how-tos/create-react-agent-hitl": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/agents/human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/human_in_the_loop/dynamic_breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/concepts/breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/human_in_the_loop/breakpoints": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/cloud/how-tos/human_in_the_loop_breakpoint": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/how-tos/human_in_the_loop/edit-graph-state": "https://docs.langchain.com/oss/python/langgraph/use-time-travel",
+  "/examples/index": "https://docs.langchain.com/oss/python/langgraph/case-studies",
+  "/guides/index": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/tutorials/index": "https://docs.langchain.com/oss/python/learn",
+  "/llms-txt-overview": "https://docs.langchain.com/llms.txt",
+  "/tutorials/rag/langgraph_adaptive_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/multi-agent-collaboration": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+  "/how-tos/create-react-agent-manage-message-history": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/many-tools": "https://docs.langchain.com/oss/python/langchain/tools",
+  "/tutorials/customer-support/customer-support": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/how-tos/react-agent-structured-output": "https://docs.langchain.com/oss/python/langchain/agents#structured-output",
+  "/tutorials/code_assistant/langgraph_code_assistant": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/hierarchical_agent_teams": "https://docs.langchain.com/oss/python/langchain/supervisor",
+  "/tutorials/auth/getting_started": "https://docs.langchain.com/langsmith/auth",
+  "/tutorials/auth/resource_auth": "https://docs.langchain.com/langsmith/resource-auth",
+  "/tutorials/auth/add_auth_server": "https://docs.langchain.com/langsmith/add-auth-server",
+  "/how-tos/use-remote-graph": "https://docs.langchain.com/langsmith/use-remote-graph",
+  "/how-tos/autogen-integration": "https://docs.langchain.com/langsmith/autogen-integration",
+  "/how-tos/human_in_the_loop/wait-user-input": "https://docs.langchain.com/oss/python/langgraph/interrupts",
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+  "/cloud/deployment/standalone_container": "https://docs.langchain.com/langsmith/docker",
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+  "/cloud/how-tos/human_in_the_loop_time_travel": "https://docs.langchain.com/langsmith/human-in-the-loop-time-travel",
+  "/cloud/how-tos/add-human-in-the-loop": "https://docs.langchain.com/langsmith/add-human-in-the-loop",
+  "/cloud/deployment/egress": "https://docs.langchain.com/langsmith/env-var",
+  "/cloud/how-tos/streaming": "https://docs.langchain.com/langsmith/streaming",
+  "/cloud/reference/api/api_ref": "https://docs.langchain.com/langsmith/server-api-ref",
+  "/cloud/reference/langgraph_server_changelog": "https://docs.langchain.com/langsmith/agent-server-changelog",
+  "/cloud/reference/api/api_ref_control_plane": "https://docs.langchain.com/langsmith/api-ref-control-plane",
+  "/cloud/reference/cli": "https://docs.langchain.com/langsmith/cli",
+  "/cloud/reference/env_var": "https://docs.langchain.com/langsmith/env-var",
+  "/troubleshooting/studio": "https://docs.langchain.com/langsmith/troubleshooting-studio",
+  "/index": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/agents": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/concepts/why-langgraph": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/get-started/1-build-basic-chatbot": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/2-add-tools": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/3-add-memory": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/4-human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/5-customize-state": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/get-started/6-time-travel": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/tutorials/langsmith/local-server": "https://docs.langchain.com/oss/python/langgraph/local-server",
+  "/tutorials/workflows": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/tutorials/plan-and-execute/plan-and-execute": "https://docs.langchain.com/oss/python/langchain/middleware/built-in#to-do-list",
+  "/tutorials/langgraph-platform/local-server/local-server": "https://docs.langchain.com/langsmith/local-server",
+  "/concepts/agentic_concepts": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/agents/overview": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/agents/run_agents": "https://docs.langchain.com/oss/python/langgraph/quickstart",
+  "/concepts/low_level": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/graph-api": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/react-agent-from-scratch": "https://docs.langchain.com/oss/python/langchain/quickstart",
+  "/concepts/functional_api": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/how-tos/use-functional-api": "https://docs.langchain.com/oss/python/langgraph/functional-api",
+  "/concepts/pregel": "https://docs.langchain.com/oss/python/langgraph/pregel",
+  "/concepts/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/concepts/persistence": "https://docs.langchain.com/oss/python/langgraph/persistence",
+  "/concepts/durable_execution": "https://docs.langchain.com/oss/python/langgraph/durable-execution",
+  "/concepts/memory": "https://docs.langchain.com/oss/python/langgraph/memory",
+  "/how-tos/memory/add-memory": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/agents/context": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/agents/models": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/tools": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/how-tos/tool-calling": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/concepts/human_in_the_loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/how-tos/human_in_the_loop/add-human-in-the-loop": "https://docs.langchain.com/oss/python/langgraph/interrupts",
+  "/concepts/time-travel": "https://docs.langchain.com/oss/python/langgraph/persistence",
+  "/how-tos/human_in_the_loop/time-travel": "https://docs.langchain.com/oss/python/langgraph/use-time-travel",
+  "/concepts/subgraphs": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
+  "/how-tos/subgraph": "https://docs.langchain.com/oss/python/langgraph/use-subgraphs",
+  "/concepts/multi_agent": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/agents/multi-agent": "https://docs.langchain.com/oss/python/langchain/multi-agent",
+  "/how-tos/multi_agent": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/concepts/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/mcp": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/how-tos/enable-tracing": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/agents/evals": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/concepts/template_applications": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rag/langgraph_agentic_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/multi_agent/agent_supervisor": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/tutorials/sql/sql-agent": "https://docs.langchain.com/oss/python/langgraph/sql-agent",
+  "/agents/ui": "https://docs.langchain.com/oss/python/langgraph/ui",
+  "/how-tos/run-id-langsmith": "https://docs.langchain.com/oss/python/langgraph/observability",
+  "/troubleshooting/errors/index": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/troubleshooting/errors/INVALID_CHAT_HISTORY": "https://docs.langchain.com/oss/python/langgraph/INVALID_CHAT_HISTORY",
+  "/troubleshooting/errors/INVALID_LICENSE": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/adopters": "https://docs.langchain.com/oss/python/langgraph/case-studies",
+  "/concepts/faq": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/agents/prebuilt": "https://docs.langchain.com/oss/python/langchain/agents",
+  "/reference/index": "https://reference.langchain.com/python/langgraph/",
+  "/reference/graphs": "https://reference.langchain.com/python/langgraph/graphs/",
+  "/reference/func": "https://reference.langchain.com/python/langgraph/func/",
+  "/reference/pregel": "https://reference.langchain.com/python/langgraph/pregel/",
+  "/reference/checkpoints": "https://reference.langchain.com/python/langgraph/checkpoints/",
+  "/reference/store": "https://reference.langchain.com/python/langgraph/store/",
+  "/reference/cache": "https://reference.langchain.com/python/langgraph/cache/",
+  "/reference/types": "https://reference.langchain.com/python/langgraph/types/",
+  "/reference/runtime": "https://reference.langchain.com/python/langgraph/runtime/",
+  "/reference/config": "https://reference.langchain.com/python/langgraph/config/",
+  "/reference/errors": "https://reference.langchain.com/python/langgraph/errors/",
+  "/reference/constants": "https://reference.langchain.com/python/langgraph/constants/",
+  "/reference/channels": "https://reference.langchain.com/python/langgraph/channels/",
+  "/reference/agents": "https://reference.langchain.com/python/langgraph/agents/",
+  "/reference/supervisor": "https://reference.langchain.com/python/langgraph/supervisor/",
+  "/reference/swarm": "https://reference.langchain.com/python/langgraph/swarm/",
+  "/reference/mcp": "https://reference.langchain.com/python/langgraph/mcp/",
+  "/cloud/reference/sdk/python_sdk_ref": "https://reference.langchain.com/python/langsmith/deployment/sdk/",
+  "/reference/remote_graph": "https://reference.langchain.com/python/langsmith/deployment/remote_graph/",
+  "/additional-resources/index": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/cloud/reference/sdk/js_ts_sdk_ref": "https://reference.langchain.com/javascript/modules/langsmith.html",
+  "/snippets/chat_model_tabs": "https://docs.langchain.com/oss/python/langchain/overview",
+  "/troubleshooting/errors/GRAPH_RECURSION_LIMIT": "https://docs.langchain.com/oss/python/langgraph/GRAPH_RECURSION_LIMIT",
+  "/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE": "https://docs.langchain.com/oss/python/langgraph/INVALID_CONCURRENT_GRAPH_UPDATE",
+  "/troubleshooting/errors/INVALID_GRAPH_NODE_RETURN_VALUE": "https://docs.langchain.com/oss/python/langgraph/INVALID_GRAPH_NODE_RETURN_VALUE",
+  "/troubleshooting/errors/MULTIPLE_SUBGRAPHS": "https://docs.langchain.com/oss/python/langgraph/MULTIPLE_SUBGRAPHS",
+  "/tutorials/rag/langgraph_self_rag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/additional-resources": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/examples": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/guides": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/how-tos/autogen-integration-functional": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/how-tos/cross-thread-persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory#add-long-term-memory",
+  "/how-tos/disable-streaming": "https://docs.langchain.com/oss/python/langgraph/streaming",
+  "/how-tos/memory/semantic-search": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/multi-agent-multi-turn-convo-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/multi-agent-network-functional": "https://docs.langchain.com/oss/python/langgraph/graph-api",
+  "/how-tos/persistence-functional": "https://docs.langchain.com/oss/python/langgraph/add-memory",
+  "/how-tos/react-agent-from-scratch-functional": "https://docs.langchain.com/oss/python/langgraph/workflows-agents",
+  "/reference": "https://reference.langchain.com/python/langgraph/",
+  "/troubleshooting/errors": "https://docs.langchain.com/oss/python/langgraph/common-errors",
+  "/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/chatbots/information-gather-prompting": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/extraction/retries": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/langgraph-platform/local-server": "https://docs.langchain.com/langsmith/agent-server",
+  "/tutorials/lats/lats": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/llm-compiler/LLMCompiler": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rag/langgraph_adaptive_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_crag": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_crag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/rag/langgraph_self_rag_local": "https://docs.langchain.com/oss/python/langgraph/agentic-rag",
+  "/tutorials/reflection/reflection": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/reflexion/reflexion": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/rewoo/rewoo": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/self-discover/self-discover": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/tnt-llm/tnt-llm": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/tot/tot": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/usaco/usaco": "https://docs.langchain.com/oss/python/langgraph/overview",
+  "/tutorials/web-navigation/web_voyager": "https://docs.langchain.com/oss/python/langgraph/overview"
+}

examples/README.md

@@ -0,0 +1,3 @@
+# LangGraph examples
+
+This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview). Please refer to the LangChain docs for the most up-to-date examples and usage guidelines for LangGraph.

examples/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "10251c1c",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fc63df",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a4351a24",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cc9af1e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/chatbot-simulation-evaluation/simulation_utils.py

@@ -0,0 +1,203 @@
+import functools
+from typing import Annotated, Any, Callable, Dict, List, Optional, Union
+
+from langchain_community.adapters.openai import convert_message_to_dict
+from langchain_core.messages import AIMessage, AnyMessage, BaseMessage, HumanMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import Runnable, RunnableLambda
+from langchain_core.runnables import chain as as_runnable
+from langchain_openai import ChatOpenAI
+from typing_extensions import TypedDict
+
+from langgraph.graph import END, StateGraph, START
+
+
+def langchain_to_openai_messages(messages: List[BaseMessage]):
+    """
+    Convert a list of langchain base messages to a list of openai messages.
+
+    Parameters:
+        messages (List[BaseMessage]): A list of langchain base messages.
+
+    Returns:
+        List[dict]: A list of openai messages.
+    """
+
+    return [
+        convert_message_to_dict(m) if isinstance(m, BaseMessage) else m
+        for m in messages
+    ]
+
+
+def create_simulated_user(
+    system_prompt: str, llm: Runnable | None = None
+) -> Runnable[Dict, AIMessage]:
+    """
+    Creates a simulated user for chatbot simulation.
+
+    Args:
+        system_prompt (str): The system prompt to be used by the simulated user.
+        llm (Runnable | None, optional): The language model to be used for the simulation.
+            Defaults to gpt-3.5-turbo.
+
+    Returns:
+        Runnable[Dict, AIMessage]: The simulated user for chatbot simulation.
+    """
+    return ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            MessagesPlaceholder(variable_name="messages"),
+        ]
+    ) | (llm or ChatOpenAI(model="gpt-3.5-turbo")).with_config(
+        run_name="simulated_user"
+    )
+
+
+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 SimulationState(TypedDict):
+    """
+    Represents the state of a simulation.
+
+    Attributes:
+        messages (List[AnyMessage]): A list of messages in the simulation.
+        inputs (Optional[dict[str, Any]]): Optional inputs for the simulation.
+    """
+
+    messages: Annotated[List[AnyMessage], add_messages]
+    inputs: Optional[dict[str, Any]]
+
+
+def create_chat_simulator(
+    assistant: (
+        Callable[[List[AnyMessage]], str | AIMessage]
+        | Runnable[List[AnyMessage], str | AIMessage]
+    ),
+    simulated_user: Runnable[Dict, AIMessage],
+    *,
+    input_key: str,
+    max_turns: int = 6,
+    should_continue: Optional[Callable[[SimulationState], str]] = None,
+):
+    """Creates a chat simulator for evaluating a chatbot.
+
+    Args:
+        assistant: The chatbot assistant function or runnable object.
+        simulated_user: The simulated user object.
+        input_key: The key for the input to the chat simulation.
+        max_turns: The maximum number of turns in the chat simulation. Default is 6.
+        should_continue: Optional function to determine if the simulation should continue.
+            If not provided, a default function will be used.
+
+    Returns:
+        The compiled chat simulation graph.
+
+    """
+    graph_builder = StateGraph(SimulationState)
+    graph_builder.add_node(
+        "user",
+        _create_simulated_user_node(simulated_user),
+    )
+    graph_builder.add_node(
+        "assistant", _fetch_messages | assistant | _coerce_to_message
+    )
+    graph_builder.add_edge("assistant", "user")
+    graph_builder.add_conditional_edges(
+        "user",
+        should_continue or functools.partial(_should_continue, max_turns=max_turns),
+    )
+    # If your dataset has a 'leading question/input', then we route first to the assistant, otherwise, we let the user take the lead.
+    graph_builder.add_edge(START, "assistant" if input_key is not None else "user")
+
+    return (
+        RunnableLambda(_prepare_example).bind(input_key=input_key)
+        | graph_builder.compile()
+    )
+
+
+## Private methods
+
+
+def _prepare_example(inputs: dict[str, Any], input_key: Optional[str] = None):
+    if input_key is not None:
+        if input_key not in inputs:
+            raise ValueError(
+                f"Dataset's example input must contain the provided input key: '{input_key}'.\nFound: {list(inputs.keys())}"
+            )
+        messages = [HumanMessage(content=inputs[input_key])]
+        return {
+            "inputs": {k: v for k, v in inputs.items() if k != input_key},
+            "messages": messages,
+        }
+    return {"inputs": inputs, "messages": []}
+
+
+def _invoke_simulated_user(state: SimulationState, simulated_user: Runnable):
+    """Invoke the simulated user node."""
+    runnable = (
+        simulated_user
+        if isinstance(simulated_user, Runnable)
+        else RunnableLambda(simulated_user)
+    )
+    inputs = state.get("inputs", {})
+    inputs["messages"] = state["messages"]
+    return runnable.invoke(inputs)
+
+
+def _swap_roles(state: SimulationState):
+    new_messages = []
+    for m in state["messages"]:
+        if isinstance(m, AIMessage):
+            new_messages.append(HumanMessage(content=m.content))
+        else:
+            new_messages.append(AIMessage(content=m.content))
+    return {
+        "inputs": state.get("inputs", {}),
+        "messages": new_messages,
+    }
+
+
+@as_runnable
+def _fetch_messages(state: SimulationState):
+    """Invoke the simulated user node."""
+    return state["messages"]
+
+
+def _convert_to_human_message(message: BaseMessage):
+    return {"messages": [HumanMessage(content=message.content)]}
+
+
+def _create_simulated_user_node(simulated_user: Runnable):
+    """Simulated user accepts a {"messages": [...]} argument and returns a single message."""
+    return (
+        _swap_roles
+        | RunnableLambda(_invoke_simulated_user).bind(simulated_user=simulated_user)
+        | _convert_to_human_message
+    )
+
+
+def _coerce_to_message(assistant_output: str | BaseMessage):
+    if isinstance(assistant_output, str):
+        return {"messages": [AIMessage(content=assistant_output)]}
+    else:
+        return {"messages": [assistant_output]}
+
+
+def _should_continue(state: SimulationState, max_turns: int = 6):
+    messages = state["messages"]
+    # TODO support other stop criteria
+    if len(messages) > max_turns:
+        return END
+    elif messages[-1].content.strip() == "FINISHED":
+        return END
+    else:
+        return "assistant"

examples/chatbots/information-gather-prompting.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a9014f94",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f47ce992",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/code_assistant/langgraph_code_assistant.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f2f13ca",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4c9bfe",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/code_assistant/langgraph_code_assistant_mistral.ipynb

@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d38cbab",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "15d3ac32-cdf3-4800-a30c-f26d828d69c8.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6101c9d6-b7d1-46af-afab-47d05bfadeae",
+   "metadata": {},
+   "source": [
+    "# Code generation with self-correction\n",
+    "\n",
+    "AlphaCodium presented an approach for code generation that uses control flow.\n",
+    "\n",
+    "Main idea: [construct an answer to a coding question iteratively.](https://x.com/karpathy/status/1748043513156272416?s=20). \n",
+    "\n",
+    "[AlphaCodium](https://github.com/Codium-ai/AlphaCodium) iteravely tests and improves an answer on public and AI-generated tests for a particular question. \n",
+    "\n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\n",
+    "\n",
+    "1. We show how to route user questions to different types of documentation\n",
+    "2. We we will show how to perform inline unit tests to confirm imports and code execution work\n",
+    "3. We will show how to use LangGraph to orchestrate this\n",
+    "\n",
+    "![Screenshot 2024-05-23 at 2.17.51 PM.png](attachment:15d3ac32-cdf3-4800-a30c-f26d828d69c8.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e501686f-323f-4b87-8f9c-8ba89133078b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -U langchain_community langchain-mistralai langchain langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ef4fb67-113a-4b88-9f93-7e3a95cee035",
+   "metadata": {},
+   "source": [
+    "### LLM\n",
+    "\n",
+    "We'll use the Mistral API and `Codestral` instruct model, which support tool use!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "982e4609-86e4-4934-828f-e03d89c20393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
+    "mistral_api_key = os.getenv(\"MISTRAL_API_KEY\")  # Ensure this is set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a20a3eb-6dc5-4a61-9705-9daf68172c0b",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "Optionally, we'll use LangSmith for tracing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37b172d2-3a9d-49a8-898c-22ed0cb45c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\"\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Mistral-code-gen-testing\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3a3dca9-4485-4ae5-aa87-cd1f02bad8b9",
+   "metadata": {},
+   "source": [
+    "## Code Generation\n",
+    "\n",
+    "Test with structured output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a188c8ca-c053-4e6d-b7af-38a3b6b371c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select LLM\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_mistralai import ChatMistralAI\n",
+    "\n",
+    "mistral_model = \"mistral-large-latest\"\n",
+    "llm = ChatMistralAI(model=mistral_model, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "code_gen_prompt_claude = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"\"\"You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables \\n\n",
+    "            defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.\n",
+    "            \\n Here is the user question:\"\"\",\n",
+    "        ),\n",
+    "        (\"placeholder\", \"{messages}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class code(BaseModel):\n",
+    "    \"\"\"Code output\"\"\"\n",
+    "\n",
+    "    prefix: str = Field(description=\"Description of the problem and approach\")\n",
+    "    imports: str = Field(description=\"Code block import statements\")\n",
+    "    code: str = Field(description=\"Code block not including import statements\")\n",
+    "    description = \"Schema for code solutions to questions about LCEL.\"\n",
+    "\n",
+    "\n",
+    "# LLM\n",
+    "code_gen_chain = llm.with_structured_output(code, include_raw=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9fc0290d-5a04-4514-8664-91f9dbf2da7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"Write a function for fibonacci.\"\n",
+    "messages = [(\"user\", question)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "973281bd-e74b-4386-98c6-210af5e31982",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "code(prefix='A function to calculate the nth Fibonacci number.', imports='', code='def fibonacci(n):\\n    if n <= 0:\\n        return \"Input should be positive integer\"\\n    elif n == 1:\\n        return 0\\n    elif n == 2:\\n        return 1\\n    else:\\n        a, b = 0, 1\\n        for _ in range(2, n):\\n            a, b = b, a + b\\n        return b', description='Schema for code solutions to questions about LCEL.')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test\n",
+    "result = code_gen_chain.invoke(messages)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4235eb2d-f5b3-4cd0-bbb1-a889eb5564d7",
+   "metadata": {},
+   "source": [
+    "## State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "183d77b8-f180-4815-b39f-8ef507ec0534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated, TypedDict\n",
+    "\n",
+    "from langgraph.graph.message import AnyMessage, add_messages\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        error : Binary flag for control flow to indicate whether test error was tripped\n",
+    "        messages : With user question, error messages, reasoning\n",
+    "        generation : Code solution\n",
+    "        iterations : Number of tries\n",
+    "    \"\"\"\n",
+    "\n",
+    "    error: str\n",
+    "    messages: Annotated[list[AnyMessage], add_messages]\n",
+    "    generation: str\n",
+    "    iterations: int"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55043d78-c012-4280-bc8b-259f04a29cb4",
+   "metadata": {},
+   "source": [
+    "## Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "14bc89d1-3ca6-4847-a048-1803e0e4600e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "\n",
+    "### Parameters\n",
+    "max_iterations = 3\n",
+    "\n",
+    "\n",
+    "### Nodes\n",
+    "def generate(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Generate a code solution\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, generation\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---GENERATING CODE SOLUTION---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Solution\n",
+    "    code_solution = code_gen_chain.invoke(messages)\n",
+    "    messages += [\n",
+    "        (\n",
+    "            \"assistant\",\n",
+    "            f\"Here is my attempt to solve the problem: {code_solution.prefix} \\n Imports: {code_solution.imports} \\n Code: {code_solution.code}\",\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    # Increment\n",
+    "    iterations = iterations + 1\n",
+    "    return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n",
+    "\n",
+    "\n",
+    "def code_check(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Check code\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        state (dict): New key added to state, error\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECKING CODE---\")\n",
+    "\n",
+    "    # State\n",
+    "    messages = state[\"messages\"]\n",
+    "    code_solution = state[\"generation\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    # Get solution components\n",
+    "    imports = code_solution.imports\n",
+    "    code = code_solution.code\n",
+    "\n",
+    "    # Check imports\n",
+    "    try:\n",
+    "        exec(imports)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE IMPORT CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # Check execution\n",
+    "    try:\n",
+    "        combined_code = f\"{imports}\\n{code}\"\n",
+    "        print(f\"CODE TO TEST: {combined_code}\")\n",
+    "        # Use a shared scope for exec\n",
+    "        global_scope = {}\n",
+    "        exec(combined_code, global_scope)\n",
+    "    except Exception as e:\n",
+    "        print(\"---CODE BLOCK CHECK: FAILED---\")\n",
+    "        error_message = [\n",
+    "            (\n",
+    "                \"user\",\n",
+    "                f\"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:\",\n",
+    "            )\n",
+    "        ]\n",
+    "        messages += error_message\n",
+    "        return {\n",
+    "            \"generation\": code_solution,\n",
+    "            \"messages\": messages,\n",
+    "            \"iterations\": iterations,\n",
+    "            \"error\": \"yes\",\n",
+    "        }\n",
+    "\n",
+    "    # No errors\n",
+    "    print(\"---NO CODE TEST FAILURES---\")\n",
+    "    return {\n",
+    "        \"generation\": code_solution,\n",
+    "        \"messages\": messages,\n",
+    "        \"iterations\": iterations,\n",
+    "        \"error\": \"no\",\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "### Conditional edges\n",
+    "\n",
+    "\n",
+    "def decide_to_finish(state: GraphState):\n",
+    "    \"\"\"\n",
+    "    Determines whether to finish.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "    error = state[\"error\"]\n",
+    "    iterations = state[\"iterations\"]\n",
+    "\n",
+    "    if error == \"no\" or iterations == max_iterations:\n",
+    "        print(\"---DECISION: FINISH---\")\n",
+    "        return \"end\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: RE-TRY SOLUTION---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "### Utilities\n",
+    "\n",
+    "\n",
+    "def _print_event(event: dict, _printed: set, max_length=1500):\n",
+    "    current_state = event.get(\"dialog_state\")\n",
+    "    if current_state:\n",
+    "        print(\"Currently in: \", current_state[-1])\n",
+    "    message = event.get(\"messages\")\n",
+    "    if message:\n",
+    "        if isinstance(message, list):\n",
+    "            message = message[-1]\n",
+    "        if message.id not in _printed:\n",
+    "            msg_repr = message.pretty_repr(html=True)\n",
+    "            if len(msg_repr) > max_length:\n",
+    "                msg_repr = msg_repr[:max_length] + \" ... (truncated)\"\n",
+    "            print(msg_repr)\n",
+    "            _printed.add(message.id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2dff2209-44c7-4e2c-b607-ba6675f9e45f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.checkpoint.memory import InMemorySaver\n",
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "builder = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "builder.add_node(\"generate\", generate)  # generation solution\n",
+    "builder.add_node(\"check_code\", code_check)  # check code\n",
+    "\n",
+    "# Build graph\n",
+    "builder.add_edge(START, \"generate\")\n",
+    "builder.add_edge(\"generate\", \"check_code\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"check_code\",\n",
+    "    decide_to_finish,\n",
+    "    {\n",
+    "        \"end\": END,\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "memory = InMemorySaver()\n",
+    "graph = builder.compile(checkpointer=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d4bb21cd-af20-4d4d-89ff-384db034b7c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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truncated
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "try:\n",
+    "    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
+    "except Exception:\n",
+    "    # This requires some extra dependencies and is optional\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "242aa2f0-2c31-462f-a958-ff9ae0cf7c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"Write a Python program that prints 'Hello, World!' to the console.\"\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6924e707-5970-4254-a748-fa75628916f2",
+   "metadata": {},
+   "source": [
+    "`Trace:`\n",
+    "\n",
+    "https://smith.langchain.com/public/53bcdaab-e3c5-4423-9908-c44595325c38/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "390b2768-f395-4aea-8b0e-9d36212a31ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that checks if a given string is a palindrome. A palindrome is a word, phrase, number, or other sequence of characters that reads the same forward and backward (ignoring spaces, punctuation, and capitalization).\n",
+    "\n",
+    "Requirements:\n",
+    "The program should define a function is_palindrome(s) that takes a string s as input.\n",
+    "The function should return True if the string is a palindrome and False otherwise.\n",
+    "Ignore spaces, punctuation, and case differences when checking for palindromes.\n",
+    "\n",
+    "Give an example of it working on an example input word.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f96e0137-6df3-4a2a-8711-c9cb7dc66831",
+   "metadata": {},
+   "source": [
+    "Trace:\n",
+    "\n",
+    "https://smith.langchain.com/public/e749936d-7746-49de-b980-c41b17986e79/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a3f946b-e2f2-44d9-905b-09f36980cf9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Write a program that prints the numbers from 1 to 100. \n",
+    "But for multiples of three, print \"Fizz\" instead of the number, and for the multiples of five, print \"Buzz\". \n",
+    "For numbers which are multiples of both three and five, print \"FizzBuzz\".\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f3ef03b-0a07-49f5-9cbf-15e2503d020e",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/f5c19708-7592-4512-9f00-9696ab34a9eb/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb883df-540b-46ab-9415-fe27db68456f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "\n",
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"I want to vectorize a function\n",
+    "\n",
+    "        frame = np.zeros((out_h, out_w, 3), dtype=np.uint8)\n",
+    "        for i, val1 in enumerate(rows):\n",
+    "            for j, val2 in enumerate(cols):\n",
+    "                for j, val3 in enumerate(ch):\n",
+    "                    # Assuming you want to store the pair as tuples in the matrix\n",
+    "                    frame[i, j, k] = image[val1, val2, val3]\n",
+    "\n",
+    "        out.write(np.array(frame))\n",
+    "\n",
+    "with a simple numpy function that does something like this what is it called. Show me a test case with this working.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750a3292-1e0e-49cf-8b28-bef179afe6a2",
+   "metadata": {},
+   "source": [
+    "Trace w/ good example of self-correction:\n",
+    "\n",
+    "https://smith.langchain.com/public/b54778a0-d267-4f09-bc28-71761201c522/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee05da1f-c272-405d-8a7b-552cfc3106e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_printed = set()\n",
+    "thread_id = str(uuid.uuid4())\n",
+    "config = {\n",
+    "    \"configurable\": {\n",
+    "        # Checkpoints are accessed by thread_id\n",
+    "        \"thread_id\": thread_id,\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "question = \"\"\"Create a Python program that allows two players to play a game of Tic-Tac-Toe. The game should be played on a 3x3 grid. The program should:\n",
+    "\n",
+    "- Allow players to take turns to input their moves.\n",
+    "- Check for invalid moves (e.g., placing a marker on an already occupied space).\n",
+    "- Determine and announce the winner or if the game ends in a draw.\n",
+    "\n",
+    "Requirements:\n",
+    "- Use a 2D list to represent the Tic-Tac-Toe board.\n",
+    "- Use functions to modularize the code.\n",
+    "- Validate player input.\n",
+    "- Check for win conditions and draw conditions after each move.\"\"\"\n",
+    "\n",
+    "events = graph.stream(\n",
+    "    {\"messages\": [(\"user\", question)], \"iterations\": 0}, config, stream_mode=\"values\"\n",
+    ")\n",
+    "for event in events:\n",
+    "    _print_event(event, _printed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d900cd6-2df9-467d-8e74-803527269008",
+   "metadata": {},
+   "source": [
+    "Trace w/ good example of failure to correct:\n",
+    "\n",
+    "https://smith.langchain.com/public/871ae736-2f77-44d4-b0da-a600d8f5377d/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "814fc2a4-8e5b-4faa-8f52-3977226bd09a",
+   "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/customer-support/customer-support.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8232bc9",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/customer-support/customer-support.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63da8671",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/extraction/retries.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8dbdba5b",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/extraction/retries.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d444b7f",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/human_in_the_loop/wait-user-input.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3ecab357",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f2866bd",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/lats/lats.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "09038b53",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/lats/lats.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1669748",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/llm-compiler/LLMCompiler.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "85205e97",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fdab366",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/multi_agent/hierarchical_agent_teams.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5cc8a2ad",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9f3508a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/multi_agent/multi-agent-collaboration.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d2b507b9",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41a8f10a",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/plan-and-execute/plan-and-execute.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9138f92e",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "093678ba",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/rag/langgraph_adaptive_rag.ipynb

@@ -0,0 +1,938 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fedd6d23",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "36fa621a-9d3d-4860-a17c-5d20e6987481.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "5afcaed0-3d55-4e1f-95d3-c32c751c29d8",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "# Adaptive RAG\n",
+    "\n",
+    "Adaptive RAG is a strategy for RAG that unites (1) [query analysis](https://blog.langchain.dev/query-construction/) with (2) [active / self-corrective RAG](https://blog.langchain.dev/agentic-rag-with-langgraph/).\n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2403.14403), they report query analysis to route across:\n",
+    "\n",
+    "* No Retrieval\n",
+    "* Single-shot RAG\n",
+    "* Iterative RAG\n",
+    "\n",
+    "Let's build on this using LangGraph. \n",
+    "\n",
+    "In our implementation, we will route between:\n",
+    "\n",
+    "* Web search: for questions related to recent events\n",
+    "* Self-corrective RAG: for questions related to our index\n",
+    "\n",
+    "![Screenshot 2024-03-26 at 1.36.03 PM.png](attachment:36fa621a-9d3d-4860-a17c-5d20e6987481.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a85501ca-eb89-4795-aeab-cdab050ead6b",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53d1a740-9fea-4a6e-8f95-fb9dbf1c80a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "! pip install -U langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph  tavily-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "222f204d-956f-4128-b597-2c698120edda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
+    "_set_env(\"COHERE_API_KEY\")\n",
+    "_set_env(\"TAVILY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47e04b18",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ac1c2cd-81fb-40eb-8ba1-e9197800cba6",
+   "metadata": {},
+   "source": [
+    "## Create Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b224e5ba-50ca-495a-a7fa-0f75a080e03c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Build Index\n",
+    "\n",
+    "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",
+    "### from langchain_cohere import CohereEmbeddings\n",
+    "\n",
+    "# Set embeddings\n",
+    "embd = OpenAIEmbeddings()\n",
+    "\n",
+    "# Docs to index\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",
+    "# Load\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=500, chunk_overlap=0\n",
+    ")\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Add to vectorstore\n",
+    "vectorstore = Chroma.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    collection_name=\"rag-chroma\",\n",
+    "    embedding=embd,\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f52b427-750c-40f8-8893-e9caab3afd8d",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4dec9d98-f3dc-4b7f-abc0-9d01c754f2be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "datasource='web_search'\n",
+      "datasource='vectorstore'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Router\n",
+    "\n",
+    "from typing import Literal\n",
+    "\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class RouteQuery(BaseModel):\n",
+    "    \"\"\"Route a user query to the most relevant datasource.\"\"\"\n",
+    "\n",
+    "    datasource: Literal[\"vectorstore\", \"web_search\"] = Field(\n",
+    "        ...,\n",
+    "        description=\"Given a user question choose to route it to web search or a vectorstore.\",\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_router = llm.with_structured_output(RouteQuery)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are an expert at routing a user question to a vectorstore or web search.\n",
+    "The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.\n",
+    "Use the vectorstore for questions on these topics. Otherwise, use web-search.\"\"\"\n",
+    "route_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_router = route_prompt | structured_llm_router\n",
+    "print(\n",
+    "    question_router.invoke(\n",
+    "        {\"question\": \"Who will the Bears draft first in the NFL draft?\"}\n",
+    "    )\n",
+    ")\n",
+    "print(question_router.invoke({\"question\": \"What are the types of agent memory?\"}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "856801cb-f42a-44e7-956f-47845e3664ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_score='no'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    If the document contains keyword(s) or semantic meaning 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",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2272333e-50b2-42ab-b472-e1055a3b94a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave based on past experience and interact with other agents. Memory stream is a long-term memory module that records agents' experiences in natural language. The retrieval model surfaces context to inform the agent's behavior based on relevance, recency, and importance.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\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",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f0c08d14-77a0-4eed-b882-2d636abb22a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeHallucinations(binary_score='yes')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeHallucinations(BaseModel):\n",
+    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
+    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
+    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ded99680-437a-4c9d-b860-619c88949d84",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeAnswer(binary_score='yes')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeAnswer(BaseModel):\n",
+    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer addresses the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
+    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
+    "answer_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9d75f1d7-a47a-4577-bb0d-84b504b0867e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"What is the role of memory in an agent's functioning?\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\",\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d07c0b31-b919-4498-869f-9673125c2473",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "01d829bb-1074-4976-b650-ead41dcb9788",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efbbff0e-8843-45bb-b2ff-137bef707ef4",
+   "metadata": {},
+   "source": [
+    "## Construct the Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "### Define Graph State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e723fcdb-06e6-402d-912e-899795b78408",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e2d6c0d-42e8-4399-9751-e315be16607a",
+   "metadata": {},
+   "source": [
+    "### Define Graph Flow "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b76b5ec3-0720-443d-85b1-c0e79659ca0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.invoke(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.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",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Web search\n",
+    "    docs = web_search_tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "\n",
+    "    return {\"documents\": web_results, \"question\": question}\n",
+    "\n",
+    "\n",
+    "### Edges ###\n",
+    "\n",
+    "\n",
+    "def route_question(state):\n",
+    "    \"\"\"\n",
+    "    Route question to web search or RAG.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ROUTE QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    source = question_router.invoke({\"question\": question})\n",
+    "    if source.datasource == \"web_search\":\n",
+    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
+    "        return \"web_search\"\n",
+    "    elif source.datasource == \"vectorstore\":\n",
+    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
+    "        return \"vectorstore\"\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 graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score.binary_score\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ab01f36-5628-49ab-bfd3-84bb6f1a1b0f",
+   "metadata": {},
+   "source": [
+    "### Compile Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "67854e07-9293-4c3c-bf9a-bc9a605570ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_conditional_edges(\n",
+    "    START,\n",
+    "    route_question,\n",
+    "    {\n",
+    "        \"web_search\": \"web_search\",\n",
+    "        \"vectorstore\": \"retrieve\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85bce541",
+   "metadata": {},
+   "source": [
+    "## Use Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "29acc541-d726-4b75-84d1-a215845fe88a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---ROUTE QUESTION---\n",
+      "---ROUTE QUESTION TO WEB SEARCH---\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "('It is expected that the Chicago Bears could have the opportunity to draft '\n",
+      " 'the first defensive player in the 2024 NFL draft. The Bears have the first '\n",
+      " 'overall pick in the draft, giving them a prime position to select top '\n",
+      " 'talent. The top wide receiver Marvin Harrison Jr. from Ohio State is also '\n",
+      " 'mentioned as a potential pick for the Cardinals.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\n",
+    "    \"question\": \"What player at the Bears expected to draft first in the 2024 NFL draft?\"\n",
+    "}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        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(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11fddd00-58bf-4910-bf36-be9e5bfba778",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/7e3aa7e5-c51f-45c2-bc66-b34f17ff2263/r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "69a985dd-03c6-45af-a67b-b15746a2cb5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---ROUTE QUESTION---\n",
+      "---ROUTE QUESTION TO RAG---\n",
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "('The types of agent memory include Sensory Memory, Short-Term Memory (STM) or '\n",
+      " 'Working Memory, and Long-Term Memory (LTM) with subtypes of Explicit / '\n",
+      " 'declarative memory and Implicit / procedural memory. Sensory memory retains '\n",
+      " 'sensory information briefly, STM stores information for cognitive tasks, and '\n",
+      " 'LTM stores information for a long time with different types of memories.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run\n",
+    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        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(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebf41097-fc4c-4072-95b3-e7e07731ada1",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/fdf0a180-6d15-4d09-bb92-f84f2105ca51/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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_adaptive_rag_local.ipynb

@@ -0,0 +1,846 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "39b26b09",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "3755396d-c4a8-45bd-87d4-00cb56339fe5.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "bb89d3f0-7ade-43a8-a527-4bec45971cf6",
+   "metadata": {},
+   "source": [
+    "# Adaptive RAG using local LLMs\n",
+    "\n",
+    "Adaptive RAG is a strategy for RAG that unites (1) [query analysis](https://blog.langchain.dev/query-construction/) with (2) [active / self-corrective RAG](https://blog.langchain.dev/agentic-rag-with-langgraph/).\n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2403.14403), they report query analysis to route across:\n",
+    "\n",
+    "* No Retrieval\n",
+    "* Single-shot RAG\n",
+    "* Iterative RAG\n",
+    "\n",
+    "Let's build on this using LangGraph. \n",
+    "\n",
+    "In our implementation, we will route between:\n",
+    "\n",
+    "* Web search: for questions related to recent events\n",
+    "* Self-corrective RAG: for questions related to our index\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 1.29.15 PM.png](attachment:3755396d-c4a8-45bd-87d4-00cb56339fe5.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cece98f-a3ed-417e-8b6a-1754e8f9c42a",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88debf5c-6972-415c-b8fb-f65eab203b7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%capture --no-stderr\n",
+    "%pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python nomic[local]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2369652a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(var: str):\n",
+    "    if not os.environ.get(var):\n",
+    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+    "\n",
+    "\n",
+    "_set_env(\"TAVILY_API_KEY\")\n",
+    "_set_env(\"NOMIC_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aea269f6",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a5d4a26-249b-4551-aa13-6c373429618e",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available. Also, try one of the [quantized command-R models](https://ollama.com/library/command-r).\n",
+    "\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "af8379bd-7eae-4ba6-b632-12e89eab9920",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ollama model name\n",
+    "local_llm = \"mistral\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04718a0c-7a48-4243-97a2-940a0239cc12",
+   "metadata": {},
+   "source": [
+    "## Create Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f9ff6b99-080d-4827-b2cb-f775543d76f5",
+   "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_nomic.embeddings import NomicEmbeddings\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=NomicEmbeddings(model=\"nomic-embed-text-v1.5\", inference_mode=\"local\"),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3eb922-27a1-4a72-a727-85fbf5b3daf1",
+   "metadata": {},
+   "source": [
+    "## LLMs\n",
+    "\n",
+    "Note: tested cmd-R on Mac M2 32GB and [latency is ~52 sec for RAG generation](https://smith.langchain.com/public/3998fe48-efc2-4d18-9069-972643d0982d/r)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7045e064-e666-4aea-9111-6e9d2007f27e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'datasource': 'vectorstore'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Router\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are an expert at routing a user question to a vectorstore or web search. \\n\n",
+    "    Use the vectorstore for questions on LLM  agents, prompt engineering, and adversarial attacks. \\n\n",
+    "    You do not need to be stringent with the keywords in the question related to these topics. \\n\n",
+    "    Otherwise, use web-search. Give a binary choice 'web_search' or 'vectorstore' based on the question. \\n\n",
+    "    Return the a JSON with a single key 'datasource' and no premable or explanation. \\n\n",
+    "    Question to route: {question}\"\"\",\n",
+    "    input_variables=[\"question\"],\n",
+    ")\n",
+    "\n",
+    "question_router = prompt | llm | JsonOutputParser()\n",
+    "question = \"llm agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(question_router.invoke({\"question\": question}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "813cdcef-8b75-4214-a2ed-b89077b3d287",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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 {document} \\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 score as a JSON with a single key 'score' and no premable or explanation.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aeb8b373-0289-4dec-bd4b-8b2701200301",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " In an LLM-powered autonomous agent system, the Large Language Model (LLM) functions as the agent's brain. The agent has key components including memory, planning, and reflection mechanisms. The memory component is a long-term memory module that records a comprehensive list of agents’ experience in natural language. It includes a memory stream, which is an external database for storing past experiences. The reflection mechanism synthesizes memories into higher-level inferences over time and guides the agent's future behavior.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "question = \"agent memory\"\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38345cff-e2d0-436e-aa09-599522a61eed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = prompt | llm | JsonOutputParser()\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9771caa1-5542-47c3-8354-aeeafcf51964",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "answer_grader = prompt | llm | JsonOutputParser()\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "830ba5f7-9c8d-4c01-83b1-e4d51d40d48f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "686c9bb1-5069-45f9-8a7e-cba34fe07dd9",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6c3c1c70-ff84-41e8-bf72-738ed52f2dde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "630d1751-a20b-4858-b3fd-0312de4f3ad7",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6e09087e-b2a9-437a-abee-129e426df799",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7c5fa507-77ae-426a-a65f-f518b9525bd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Nodes\n",
+    "\n",
+    "from langchain.schema import Document\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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\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",
+    "            continue\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Web search\n",
+    "    docs = web_search_tool.invoke({\"query\": question})\n",
+    "    web_results = \"\\n\".join([d[\"content\"] for d in docs])\n",
+    "    web_results = Document(page_content=web_results)\n",
+    "\n",
+    "    return {\"documents\": web_results, \"question\": question}\n",
+    "\n",
+    "\n",
+    "### Edges ###\n",
+    "\n",
+    "\n",
+    "def route_question(state):\n",
+    "    \"\"\"\n",
+    "    Route question to web search or RAG.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ROUTE QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    print(question)\n",
+    "    source = question_router.invoke({\"question\": question})\n",
+    "    print(source)\n",
+    "    print(source[\"datasource\"])\n",
+    "    if source[\"datasource\"] == \"web_search\":\n",
+    "        print(\"---ROUTE QUESTION TO WEB SEARCH---\")\n",
+    "        return \"web_search\"\n",
+    "    elif source[\"datasource\"] == \"vectorstore\":\n",
+    "        print(\"---ROUTE QUESTION TO RAG---\")\n",
+    "        return \"vectorstore\"\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 graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score[\"score\"]\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score[\"score\"]\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed7d8eb6-31d7-4ab5-8a88-7081b64582bb",
+   "metadata": {},
+   "source": [
+    "## Build Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "450eb313-ca75-4a43-b57e-7034bd3f40bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "workflow = StateGraph(GraphState)\n",
+    "\n",
+    "# Define the nodes\n",
+    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieve\n",
+    "workflow.add_node(\"grade_documents\", grade_documents)  # grade documents\n",
+    "workflow.add_node(\"generate\", generate)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_conditional_edges(\n",
+    "    START,\n",
+    "    route_question,\n",
+    "    {\n",
+    "        \"web_search\": \"web_search\",\n",
+    "        \"vectorstore\": \"retrieve\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b095c1db-8bd1-4a34-937c-1a9b74ae74ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---ROUTE QUESTION---\n",
+      "What is the AlphaCodium paper about?\n",
+      "{'datasource': 'web_search'}\n",
+      "web_search\n",
+      "---ROUTE QUESTION TO WEB SEARCH---\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "(' The AlphaCodium paper introduces a new approach for code generation by '\n",
+      " 'Large Language Models (LLMs). It presents AlphaCodium, an iterative process '\n",
+      " 'that involves generating additional data to aid the flow, and testing it on '\n",
+      " 'the CodeContests dataset. The results show that AlphaCodium outperforms '\n",
+      " \"DeepMind's AlphaCode and AlphaCode2 without fine-tuning a model. The \"\n",
+      " 'approach includes a pre-processing phase for problem reasoning in natural '\n",
+      " 'language and an iterative code generation phase with runs and fixes against '\n",
+      " 'tests.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"What is the AlphaCodium paper about?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        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(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "644c7293-9cb5-4236-ba08-1e63b0309cb7",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/81813813-be53-403c-9877-afcd5786ca2e/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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_agentic_rag.ipynb

@@ -0,0 +1,547 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "47e3b43b",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "425fb020-e864-40ce-a31f-8da40c73d14b",
+   "metadata": {},
+   "source": [
+    "# Agentic RAG\n",
+    "\n",
+    "[Retrieval Agents](https://python.langchain.com/v0.2/docs/tutorials/qa_chat_history/#agents) are useful when we want to make decisions about whether to retrieve from an index.\n",
+    "\n",
+    "To implement a retrieval agent, we simple need to give an LLM access to a retriever tool.\n",
+    "\n",
+    "We can incorporate this into [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, let's download the required packages and set our API keys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "969fb438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph langchain-text-splitters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e4958a8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d07e8d4",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74e4532",
+   "metadata": {},
+   "source": [
+    "## Retriever\n",
+    "\n",
+    "First, we index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e50c9efe-4abe-42fa-b35a-05eeeede9ec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter\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": "markdown",
+   "id": "225d2277-45b2-4ae8-a7d6-62b07fb4a002",
+   "metadata": {},
+   "source": [
+    "Then we create a retriever tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b97bdd8-d7e3-444d-ac96-5ef4725f9048",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.tools.retriever import create_retriever_tool\n",
+    "\n",
+    "retriever_tool = create_retriever_tool(\n",
+    "    retriever,\n",
+    "    \"retrieve_blog_posts\",\n",
+    "    \"Search and return information about Lilian Weng blog posts on LLM agents, prompt engineering, and adversarial attacks on LLMs.\",\n",
+    ")\n",
+    "\n",
+    "tools = [retriever_tool]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe6e8f78-1ef7-42ad-b2bf-835ed5850553",
+   "metadata": {},
+   "source": [
+    "## Agent State\n",
+    " \n",
+    "We will define 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": 4,
+   "id": "0e378706-47d5-425a-8ba0-57b9acffbd0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "from langgraph.graph.message import add_messages\n",
+    "\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    # The add_messages function defines how an update should be processed\n",
+    "    # Default is to replace. add_messages says \"append\"\n",
+    "    messages: Annotated[Sequence[BaseMessage], add_messages]"
+   ]
+  },
+  {
+   "attachments": {
+    "7ad1a116-28d7-473f-8cff-5f2efd0bf118.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dc949d42-8a34-4231-bff0-b8198975e2ce",
+   "metadata": {},
+   "source": [
+    "## Nodes and Edges\n",
+    "\n",
+    "We can lay out an agentic RAG graph like this:\n",
+    "\n",
+    "* The state is a set of messages\n",
+    "* Each node will update (append to) state\n",
+    "* Conditional edges decide which node to visit next\n",
+    "\n",
+    "![Screenshot 2024-02-14 at 3.43.58 PM.png](attachment:7ad1a116-28d7-473f-8cff-5f2efd0bf118.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "278d1d83-dda6-4de4-bf8b-be9965c227fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "********************Prompt[rlm/rag-prompt]********************\n",
+      "================================\u001b[1m Human Message \u001b[0m=================================\n",
+      "\n",
+      "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
+      "Question: \u001b[33;1m\u001b[1;3m{question}\u001b[0m \n",
+      "Context: \u001b[33;1m\u001b[1;3m{context}\u001b[0m \n",
+      "Answer:\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import Annotated, Literal, Sequence, TypedDict\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.messages import BaseMessage, HumanMessage\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "from langgraph.prebuilt import tools_condition\n",
+    "\n",
+    "### Edges\n",
+    "\n",
+    "\n",
+    "def grade_documents(state) -> Literal[\"generate\", \"rewrite\"]:\n",
+    "    \"\"\"\n",
+    "    Determines whether the retrieved documents are relevant to the question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: A decision for whether the documents are relevant or not\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK RELEVANCE---\")\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-4o\", streaming=True)\n",
+    "\n",
+    "    # LLM with tool and validation\n",
+    "    llm_with_tool = model.with_structured_output(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\n",
+    "\n",
+    "    messages = state[\"messages\"]\n",
+    "    last_message = messages[-1]\n",
+    "\n",
+    "    question = messages[0].content\n",
+    "    docs = last_message.content\n",
+    "\n",
+    "    scored_result = chain.invoke({\"question\": question, \"context\": docs})\n",
+    "\n",
+    "    score = scored_result.binary_score\n",
+    "\n",
+    "    if score == \"yes\":\n",
+    "        print(\"---DECISION: DOCS RELEVANT---\")\n",
+    "        return \"generate\"\n",
+    "\n",
+    "    else:\n",
+    "        print(\"---DECISION: DOCS NOT RELEVANT---\")\n",
+    "        print(score)\n",
+    "        return \"rewrite\"\n",
+    "\n",
+    "\n",
+    "### Nodes\n",
+    "\n",
+    "\n",
+    "def agent(state):\n",
+    "    \"\"\"\n",
+    "    Invokes the agent model to generate a response based on the current state. Given\n",
+    "    the question, it will decide to retrieve using the retriever tool, or simply end.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: The updated state with the agent response appended to messages\n",
+    "    \"\"\"\n",
+    "    print(\"---CALL AGENT---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    model = ChatOpenAI(temperature=0, streaming=True, model=\"gpt-4-turbo\")\n",
+    "    model = model.bind_tools(tools)\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",
+    "def rewrite(state):\n",
+    "    \"\"\"\n",
+    "    Transform the query to produce a better question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state\n",
+    "\n",
+    "    Returns:\n",
+    "        dict: The updated state with re-phrased question\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---TRANSFORM QUERY---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    question = messages[0].content\n",
+    "\n",
+    "    msg = [\n",
+    "        HumanMessage(\n",
+    "            content=f\"\"\" \\n \n",
+    "    Look at the input and try to reason about the underlying semantic intent / meaning. \\n \n",
+    "    Here is the initial question:\n",
+    "    \\n ------- \\n\n",
+    "    {question} \n",
+    "    \\n ------- \\n\n",
+    "    Formulate an improved question: \"\"\",\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    # Grader\n",
+    "    model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
+    "    response = model.invoke(msg)\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "def generate(state):\n",
+    "    \"\"\"\n",
+    "    Generate answer\n",
+    "\n",
+    "    Args:\n",
+    "        state (messages): The current state\n",
+    "\n",
+    "    Returns:\n",
+    "         dict: The updated state with re-phrased question\n",
+    "    \"\"\"\n",
+    "    print(\"---GENERATE---\")\n",
+    "    messages = state[\"messages\"]\n",
+    "    question = messages[0].content\n",
+    "    last_message = messages[-1]\n",
+    "\n",
+    "    docs = last_message.content\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",
+    "    response = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "\n",
+    "print(\"*\" * 20 + \"Prompt[rlm/rag-prompt]\" + \"*\" * 20)\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\").pretty_print()  # Show what the prompt looks like"
+   ]
+  },
+  {
+   "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": 18,
+   "id": "8718a37f-83c2-4f16-9850-e61e0f49c3d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\n",
+    "from langgraph.prebuilt import ToolNode\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(AgentState)\n",
+    "\n",
+    "# Define the nodes we will cycle between\n",
+    "workflow.add_node(\"agent\", agent)  # agent\n",
+    "retrieve = ToolNode([retriever_tool])\n",
+    "workflow.add_node(\"retrieve\", retrieve)  # retrieval\n",
+    "workflow.add_node(\"rewrite\", rewrite)  # Re-writing the question\n",
+    "workflow.add_node(\n",
+    "    \"generate\", generate\n",
+    ")  # Generating a response after we know the documents are relevant\n",
+    "# Call agent node to decide to retrieve or not\n",
+    "workflow.add_edge(START, \"agent\")\n",
+    "\n",
+    "# Decide whether to retrieve\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"agent\",\n",
+    "    # Assess agent decision\n",
+    "    tools_condition,\n",
+    "    {\n",
+    "        # Translate the condition outputs to nodes in our graph\n",
+    "        \"tools\": \"retrieve\",\n",
+    "        END: END,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Edges taken after the `action` node is called.\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"retrieve\",\n",
+    "    # Assess agent decision\n",
+    "    grade_documents,\n",
+    ")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "workflow.add_edge(\"rewrite\", \"agent\")\n",
+    "\n",
+    "# Compile\n",
+    "graph = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7b5a1d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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truncated
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "try:\n",
+    "    display(Image(graph.get_graph(xray=True).draw_mermaid_png()))\n",
+    "except Exception:\n",
+    "    # This requires some extra dependencies and is optional\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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={'tool_calls': [{'index': 0, 'id': 'call_z36oPZN8l1UC6raxrebqc1bH', 'function': {'arguments': '{\"query\":\"types of agent memory\"}', 'name': 'retrieve_blog_posts'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-2bad2518-8187-4d8f-8e23-2b9501becb6f-0', tool_calls=[{'name': 'retrieve_blog_posts', 'args': {'query': 'types of agent memory'}, 'id': 'call_z36oPZN8l1UC6raxrebqc1bH'}])]}\n",
+      "'\\n---\\n'\n",
+      "---CHECK RELEVANCE---\n",
+      "---DECISION: DOCS RELEVANT---\n",
+      "\"Output from node 'retrieve':\"\n",
+      "'---'\n",
+      "{ 'messages': [ ToolMessage(content='Table 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\\n\\nComponent Three: Tool Use\\n\\nCase Studies\\n\\nScientific Discovery Agent\\n\\nGenerative Agents Simulation\\n\\nProof-of-Concept Examples\\n\\n\\nChallenges\\n\\nCitation\\n\\nReferences\\n\\nPlanning\\n\\nSubgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.\\nReflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.\\n\\n\\nMemory\\n\\nMemory\\n\\nShort-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.\\nLong-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.\\n\\n\\nTool use\\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.', name='retrieve_blog_posts', id='d815f283-868c-4660-a1c6-5f6e5373ca06', tool_call_id='call_z36oPZN8l1UC6raxrebqc1bH')]}\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Output from node 'generate':\"\n",
+      "'---'\n",
+      "{ 'messages': [ 'Lilian Weng discusses short-term and long-term memory in '\n",
+      "                'agent systems. Short-term memory is used for in-context '\n",
+      "                'learning, while long-term memory allows agents to retain and '\n",
+      "                'recall information over extended periods.']}\n",
+      "'\\n---\\n'\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pprint\n",
+    "\n",
+    "inputs = {\n",
+    "    \"messages\": [\n",
+    "        (\"user\", \"What does Lilian Weng say about the types of agent memory?\"),\n",
+    "    ]\n",
+    "}\n",
+    "for output in graph.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\")"
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_crag.ipynb

@@ -0,0 +1,709 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c71da2ea",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "683fae34-980f-43f0-a9c2-9894bebd9157.png": {
+     "image/png": "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 truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8889a307-fa3f-4d38-9127-d41e4686ae47",
+   "metadata": {},
+   "source": [
+    "# Corrective RAG (CRAG)\n",
+    "\n",
+    "Corrective-RAG (CRAG) is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents. \n",
+    "\n",
+    "In the paper [here](https://arxiv.org/pdf/2401.15884.pdf), a few steps are taken:\n",
+    "\n",
+    "* If at least one document exceeds the threshold for relevance, then it proceeds to generation\n",
+    "* Before generation, it performs knowledge refinement\n",
+    "* This partitions the document into \"knowledge strips\"\n",
+    "* It grades each strip, and filters our irrelevant ones\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",
+    " \n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\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* documents are irrelevant, let's opt to supplement retrieval with web search. \n",
+    "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n",
+    "* Let's use query re-writing to optimize the query for web search.\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 9.28.30 AM.png](attachment:683fae34-980f-43f0-a9c2-9894bebd9157.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4931ac25-99f9-4f04-b3d1-4683f7853667",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First, let's download our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "568c84d6-9df6-4b7b-b50d-476c0a64a04b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph tavily-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74710419-158d-4270-931c-de83db7b580d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
+    "_set_env(\"TAVILY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3adde047",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21f32d2-92ce-4995-b309-99347bafe3be",
+   "metadata": {},
+   "source": [
+    "## Create Index\n",
+    " \n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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": "6fca2db8-8d68-42b0-981d-4be5ccdbe293",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7ece414c-2df5-4ffd-aa82-550a65775261",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_score='yes'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    If the document contains keyword(s) or semantic meaning related to the 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",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a207c85f-e414-46b7-8999-4c0ead1493da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience. Memory stream is a long-term memory module that records a comprehensive list of agents' experience in natural language. Short-term memory is utilized for in-context learning, while long-term memory allows agents to retain and recall information over extended periods.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\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",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "30d0a69b-9087-4f85-af26-cab55b567872",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'What is the role of memory in artificial intelligence agents?'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for web search. Look at the input and try to reason about the underlying semantic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\",\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4538467-4a15-4733-b93c-2b79d4d6bf25",
+   "metadata": {},
+   "source": [
+    "## Web Search Tool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "46d51b53-54a9-4e0a-9f14-e39998f5b340",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87194a1b-535a-4593-ab95-5736fae176d1",
+   "metadata": {},
+   "source": [
+    "## Create Graph \n",
+    "\n",
+    "Now let's create our graph that will use CRAG\n",
+    "\n",
+    "### Define Graph State"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "94b3945f-ef0f-458d-a443-f763903550b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        web_search: whether to add search\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    web_search: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "efd639c5-82e2-45e6-a94a-6a4039646ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    web_search = \"No\"\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.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",
+    "            web_search = \"Yes\"\n",
+    "            continue\n",
+    "    return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Web search\n",
+    "    docs = web_search_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 {\"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.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    web_search = state[\"web_search\"]\n",
+    "    state[\"documents\"]\n",
+    "\n",
+    "    if web_search == \"Yes\":\n",
+    "        # All documents have been filtered check_relevance\n",
+    "        # We will re-generate a new query\n",
+    "        print(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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": [
+    "### Compile Graph\n",
+    "\n",
+    "The just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "dedae17a-98c6-474d-90a7-9234b7c8cea0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\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)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "workflow.add_node(\"web_search_node\", web_search)  # web search\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"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_node\")\n",
+    "workflow.add_edge(\"web_search_node\", \"generate\")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27ba16a8",
+   "metadata": {},
+   "source": [
+    "## Use the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "f5b7c2fe-1fc7-4b76-bf93-ba701a40aa6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\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",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search_node':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('Agents possess short-term memory, which is utilized for in-context learning, '\n",
+      " 'and long-term memory, allowing them to retain and recall vast amounts of '\n",
+      " 'information over extended periods. Some experts also classify working memory '\n",
+      " 'as a distinct type, although it can be considered a part of short-term '\n",
+      " 'memory in many cases.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"What are the types of agent memory?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        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(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "41ea1108-f385-4774-962d-db157922e231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\n",
+      "---TRANSFORM QUERY---\n",
+      "\"Node 'transform_query':\"\n",
+      "'\\n---\\n'\n",
+      "---WEB SEARCH---\n",
+      "\"Node 'web_search_node':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "('The AlphaCodium paper functions by proposing a code-oriented iterative flow '\n",
+      " 'that involves repeatedly running and fixing generated code against '\n",
+      " 'input-output tests. Its key mechanisms include generating additional data '\n",
+      " 'like problem reflection and test reasoning to aid the iterative process, as '\n",
+      " 'well as enriching the code generation process. AlphaCodium aims to improve '\n",
+      " 'the performance of Large Language Models on code problems by following a '\n",
+      " 'test-based, multi-stage approach.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"How does the AlphaCodium paper work?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        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(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e44593-1959-4abf-8405-5e23aa9398f5",
+   "metadata": {},
+   "source": [
+    "LangSmith Traces - \n",
+    " \n",
+    "* https://smith.langchain.com/public/f6b1716c-e842-4282-9112-1026b93e246b/r\n",
+    "\n",
+    "* https://smith.langchain.com/public/497c8ed9-d9e2-429e-8ada-e64de3ec26c9/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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_crag_local.ipynb

@@ -0,0 +1,861 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ac7db067",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "b77a7d3b-b28a-4dcf-9f1a-861f2f2c5f6c.png": {
+     "image/png": "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 truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "92ddc4f4-f7bf-4e0e-b5a5-5abd8a008b21",
+   "metadata": {},
+   "source": [
+    "# Corrective RAG (CRAG) using local LLMs\n",
+    "\n",
+    "[Corrective-RAG (CRAG)](https://arxiv.org/abs/2401.15884) is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents. \n",
+    "\n",
+    "The paper follows this general flow:\n",
+    "\n",
+    "* If at least one document exceeds the threshold for `relevance`, then it proceeds to generation\n",
+    "* If all documents fall below the `relevance` threshold or if the grader is unsure, then it uses web search to supplement retrieval\n",
+    "* Before generation, it performs knowledge refinement of the search or retrieved documents\n",
+    "* This partitions the document into `knowledge strips`\n",
+    "* It grades each strip, and filters out irrelevant ones\n",
+    "\n",
+    "We will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/):\n",
+    "\n",
+    "* If *any* documents are irrelevant, we'll supplement retrieval with web search. \n",
+    "* We'll skip the knowledge refinement, but this can be added back as a node if desired. \n",
+    "* We'll use [Tavily Search](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n",
+    "\n",
+    "![Screenshot 2024-06-24 at 3.03.16 PM.png](attachment:b77a7d3b-b28a-4dcf-9f1a-861f2f2c5f6c.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba4302f-09d9-4d2a-a18d-a6fd23704850",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We'll use [Ollama](https://ollama.ai/) to access a local LLM:\n",
+    "\n",
+    "* Download [Ollama app](https://ollama.ai/).\n",
+    "* Pull your model of choice, e.g.: `ollama pull llama3`\n",
+    "\n",
+    "We'll use [Tavily](https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/) for web search.\n",
+    "\n",
+    "We'll use a vectorstore with [Nomic local embeddings](https://blog.nomic.ai/posts/nomic-embed-text-v1) or, optionally, OpenAI embeddings.\n",
+    "\n",
+    "\n",
+    "Let's install our required packages and set our API keys:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a660963-bd3d-4c87-b2e4-b6e432055211",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture --no-stderr\n",
+    "%pip install -U langchain_community tiktoken langchainhub scikit-learn langchain langgraph tavily-python  nomic[local] langchain-nomic langchain_openai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68316ba0-854b-41e1-9af5-1f9e965946e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")\n",
+    "_set_env(\"TAVILY_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98f863ea",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c059c3a3-7f01-4d46-8289-fde4c1b4155f",
+   "metadata": {},
+   "source": [
+    "### LLM\n",
+    "\n",
+    "You can select from [Ollama LLMs](https://ollama.com/library)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f4db331-c4d0-4c7c-a9a5-0bebc8a89c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_llm = \"llama3\"\n",
+    "model_tested = \"llama3-8b\"\n",
+    "metadata = f\"CRAG, {model_tested}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e2b6eed-3b3f-44b5-a34a-4ade1e94caf0",
+   "metadata": {},
+   "source": [
+    "## Create Index\n",
+    "\n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bb8b789b-475b-4e1b-9c66-03504c837830",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain_community.vectorstores import SKLearnVectorStore\n",
+    "from langchain_nomic.embeddings import NomicEmbeddings  # local\n",
+    "from langchain_openai import OpenAIEmbeddings  # api\n",
+    "\n",
+    "# List of URLs to load documents from\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",
+    "# Load documents from the URLs\n",
+    "docs = [WebBaseLoader(url).load() for url in urls]\n",
+    "docs_list = [item for sublist in docs for item in sublist]\n",
+    "\n",
+    "# Initialize a text splitter with specified chunk size and overlap\n",
+    "text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
+    "    chunk_size=250, chunk_overlap=0\n",
+    ")\n",
+    "\n",
+    "# Split the documents into chunks\n",
+    "doc_splits = text_splitter.split_documents(docs_list)\n",
+    "\n",
+    "# Embedding\n",
+    "\"\"\"\n",
+    "embedding=NomicEmbeddings(\n",
+    "    model=\"nomic-embed-text-v1.5\",\n",
+    "    inference_mode=\"local\",\n",
+    ")\n",
+    "\"\"\"\n",
+    "embedding = OpenAIEmbeddings()\n",
+    "\n",
+    "# Add the document chunks to the \"vector store\"\n",
+    "vectorstore = SKLearnVectorStore.from_documents(\n",
+    "    documents=doc_splits,\n",
+    "    embedding=embedding,\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever(k=4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "fe7fd10a-f64a-48de-a116-6d5890def1af",
+   "metadata": {},
+   "source": [
+    "## Define Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0e75c029-6c10-47c7-871c-1f4932b25309",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': '1'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "from langchain_mistralai.chat_models import ChatMistralAI\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are a teacher grading a quiz. You will be given: \n",
+    "    1/ a QUESTION\n",
+    "    2/ A FACT provided by the student\n",
+    "    \n",
+    "    You are grading RELEVANCE RECALL:\n",
+    "    A score of 1 means that ANY of the statements in the FACT are relevant to the QUESTION. \n",
+    "    A score of 0 means that NONE of the statements in the FACT are relevant to the QUESTION. \n",
+    "    1 is the highest (best) score. 0 is the lowest score you can give. \n",
+    "    \n",
+    "    Explain your reasoning in a step-by-step manner. Ensure your reasoning and conclusion are correct. \n",
+    "    \n",
+    "    Avoid simply stating the correct answer at the outset.\n",
+    "    \n",
+    "    Question: {question} \\n\n",
+    "    Fact: \\n\\n {documents} \\n\\n\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 score as a JSON with a single key 'score' and no premable or explanation.\n",
+    "    \"\"\",\n",
+    "    input_variables=[\"question\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.invoke(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"documents\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dad03302-bd93-43fc-949e-af51a3298cfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The document mentions \"memory stream\" which is a long-term memory module that records a comprehensive list of agents' experience in natural language. It also discusses short-term memory and long-term memory, with the latter providing the agent with the capability to retain and recall information over extended periods. Additionally, it mentions planning and reflection mechanisms that enable agents to behave conditioned on past experience.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = PromptTemplate(\n",
+    "    template=\"\"\"You are an assistant for question-answering tasks. \n",
+    "    \n",
+    "    Use the following documents to answer the question. \n",
+    "    \n",
+    "    If you don't know the answer, just say that you don't know. \n",
+    "    \n",
+    "    Use three sentences maximum and keep the answer concise:\n",
+    "    Question: {question} \n",
+    "    Documents: {documents} \n",
+    "    Answer: \n",
+    "    \"\"\",\n",
+    "    input_variables=[\"question\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"documents\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b36a2f36-bc5f-408d-a5e8-3fa203c233f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Search\n",
+    "\n",
+    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
+    "\n",
+    "web_search_tool = TavilySearchResults(k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3421cf0-9067-43fe-8681-0d3189d15dd3",
+   "metadata": {},
+   "source": [
+    "## Create the Graph \n",
+    "\n",
+    "Here we'll explicitly define the majority of the control flow, only using an LLM to define a single branch point following grading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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truncated
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from typing import List\n",
+    "from typing_extensions import TypedDict\n",
+    "from IPython.display import Image, display\n",
+    "from langchain.schema import Document\n",
+    "from langgraph.graph import START, END, StateGraph\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        search: whether to add search\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    search: str\n",
+    "    documents: List[str]\n",
+    "    steps: List[str]\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",
+    "    question = state[\"question\"]\n",
+    "    documents = retriever.invoke(question)\n",
+    "    steps = state[\"steps\"]\n",
+    "    steps.append(\"retrieve_documents\")\n",
+    "    return {\"documents\": documents, \"question\": question, \"steps\": steps}\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",
+    "\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = rag_chain.invoke({\"documents\": documents, \"question\": question})\n",
+    "    steps = state[\"steps\"]\n",
+    "    steps.append(\"generate_answer\")\n",
+    "    return {\n",
+    "        \"documents\": documents,\n",
+    "        \"question\": question,\n",
+    "        \"generation\": generation,\n",
+    "        \"steps\": steps,\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    steps = state[\"steps\"]\n",
+    "    steps.append(\"grade_document_retrieval\")\n",
+    "    filtered_docs = []\n",
+    "    search = \"No\"\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"documents\": d.page_content}\n",
+    "        )\n",
+    "        grade = score[\"score\"]\n",
+    "        if grade == \"yes\":\n",
+    "            filtered_docs.append(d)\n",
+    "        else:\n",
+    "            search = \"Yes\"\n",
+    "            continue\n",
+    "    return {\n",
+    "        \"documents\": filtered_docs,\n",
+    "        \"question\": question,\n",
+    "        \"search\": search,\n",
+    "        \"steps\": steps,\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def web_search(state):\n",
+    "    \"\"\"\n",
+    "    Web search based on the re-phrased question.\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",
+    "    question = state[\"question\"]\n",
+    "    documents = state.get(\"documents\", [])\n",
+    "    steps = state[\"steps\"]\n",
+    "    steps.append(\"web_search\")\n",
+    "    web_results = web_search_tool.invoke({\"query\": question})\n",
+    "    documents.extend(\n",
+    "        [\n",
+    "            Document(page_content=d[\"content\"], metadata={\"url\": d[\"url\"]})\n",
+    "            for d in web_results\n",
+    "        ]\n",
+    "    )\n",
+    "    return {\"documents\": documents, \"question\": question, \"steps\": steps}\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 graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "    search = state[\"search\"]\n",
+    "    if search == \"Yes\":\n",
+    "        return \"search\"\n",
+    "    else:\n",
+    "        return \"generate\"\n",
+    "\n",
+    "\n",
+    "# Graph\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)  # generate\n",
+    "workflow.add_node(\"web_search\", web_search)  # web search\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"retrieve\")\n",
+    "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
+    "workflow.add_conditional_edges(\n",
+    "    \"grade_documents\",\n",
+    "    decide_to_generate,\n",
+    "    {\n",
+    "        \"search\": \"web_search\",\n",
+    "        \"generate\": \"generate\",\n",
+    "    },\n",
+    ")\n",
+    "workflow.add_edge(\"web_search\", \"generate\")\n",
+    "workflow.add_edge(\"generate\", END)\n",
+    "\n",
+    "custom_graph = workflow.compile()\n",
+    "\n",
+    "display(Image(custom_graph.get_graph(xray=True).draw_mermaid_png()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'response': 'According to the documents, there are two types of agent memory:\\n\\n* Short-term memory (STM): This is a data structure that holds information temporarily and allows the agent to process it when needed.\\n* Long-term memory (LTM): This provides the agent with the capability to retain and recall information over extended periods.\\n\\nThese types of memories allow the agent to learn, reason, and make decisions.',\n",
+       " 'steps': ['retrieve_documents',\n",
+       "  'grade_document_retrieval',\n",
+       "  'web_search',\n",
+       "  'generate_answer']}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import uuid\n",
+    "\n",
+    "\n",
+    "def predict_custom_agent_local_answer(example: dict):\n",
+    "    config = {\"configurable\": {\"thread_id\": str(uuid.uuid4())}}\n",
+    "    state_dict = custom_graph.invoke(\n",
+    "        {\"question\": example[\"input\"], \"steps\": []}, config\n",
+    "    )\n",
+    "    return {\"response\": state_dict[\"generation\"], \"steps\": state_dict[\"steps\"]}\n",
+    "\n",
+    "\n",
+    "example = {\"input\": \"What are the types of agent memory?\"}\n",
+    "response = predict_custom_agent_local_answer(example)\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91325c88-ec77-4c79-8a77-cb2e2842bcd4",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/88e7579e-2571-4cf6-98d2-1f9ce3359967/r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b80d5da-f698-40d2-a2fb-4eac89e35350",
+   "metadata": {},
+   "source": [
+    "## Evaluation\n",
+    "\n",
+    "Now we've defined two different agent architectures that do roughly the same thing!\n",
+    "\n",
+    "We can evaluate them. See our [conceptual guide](https://docs.smith.langchain.com/concepts/evaluation#agents) for context on agent evaluation.\n",
+    "\n",
+    "### Response\n",
+    "\n",
+    "First, we can assess how well [our agent performs on a set of question-answer pairs](https://docs.smith.langchain.com/tutorials/Developers/agents#response-evaluation).\n",
+    "\n",
+    "We'll create a dataset and save it in LangSmith."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b83706ac-724b-46b1-9f08-66e6c4fac742",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langsmith import Client\n",
+    "\n",
+    "client = Client()\n",
+    "\n",
+    "# Create a dataset\n",
+    "examples = [\n",
+    "    (\n",
+    "        \"How does the ReAct agent use self-reflection? \",\n",
+    "        \"ReAct integrates reasoning and acting, performing actions - such tools like Wikipedia search API - and then observing / reasoning about the tool outputs.\",\n",
+    "    ),\n",
+    "    (\n",
+    "        \"What are the types of biases that can arise with few-shot prompting?\",\n",
+    "        \"The biases that can arise with few-shot prompting include (1) Majority label bias, (2) Recency bias, and (3) Common token bias.\",\n",
+    "    ),\n",
+    "    (\n",
+    "        \"What are five types of adversarial attacks?\",\n",
+    "        \"Five types of adversarial attacks are (1) Token manipulation, (2) Gradient based attack, (3) Jailbreak prompting, (4) Human red-teaming, (5) Model red-teaming.\",\n",
+    "    ),\n",
+    "    (\n",
+    "        \"Who did the Chicago Bears draft first in the 2024 NFL draft”?\",\n",
+    "        \"The Chicago Bears drafted Caleb Williams first in the 2024 NFL draft.\",\n",
+    "    ),\n",
+    "    (\"Who won the 2024 NBA finals?\", \"The Boston Celtics on the 2024 NBA finals\"),\n",
+    "]\n",
+    "\n",
+    "# Save it\n",
+    "dataset_name = \"Corrective RAG Agent Testing\"\n",
+    "if not client.has_dataset(dataset_name=dataset_name):\n",
+    "    dataset = client.create_dataset(dataset_name=dataset_name)\n",
+    "    inputs, outputs = zip(\n",
+    "        *[({\"input\": text}, {\"output\": label}) for text, label in examples]\n",
+    "    )\n",
+    "    client.create_examples(inputs=inputs, outputs=outputs, dataset_id=dataset.id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a23f6bc0-2d03-488c-8f4b-747c93876788",
+   "metadata": {},
+   "source": [
+    "Now, we'll use an `LLM as a grader` to compare both agent responses to our ground truth reference answer.\n",
+    "\n",
+    "[Here](https://smith.langchain.com/hub/rlm/rag-answer-vs-reference) is the default prompt that we can use.\n",
+    "\n",
+    "We'll use `gpt-4o` as our LLM grader.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "# Grade prompt\n",
+    "grade_prompt_answer_accuracy = hub.pull(\"langchain-ai/rag-answer-vs-reference\")\n",
+    "\n",
+    "\n",
+    "def answer_evaluator(run, example) -> dict:\n",
+    "    \"\"\"\n",
+    "    A simple evaluator for RAG answer accuracy\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Get the question, the ground truth reference answer, RAG chain answer prediction\n",
+    "    input_question = example.inputs[\"input\"]\n",
+    "    reference = example.outputs[\"output\"]\n",
+    "    prediction = run.outputs[\"response\"]\n",
+    "\n",
+    "    # Define an LLM grader\n",
+    "    llm = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
+    "    answer_grader = grade_prompt_answer_accuracy | llm\n",
+    "\n",
+    "    # Run evaluator\n",
+    "    score = answer_grader.invoke(\n",
+    "        {\n",
+    "            \"question\": input_question,\n",
+    "            \"correct_answer\": reference,\n",
+    "            \"student_answer\": prediction,\n",
+    "        }\n",
+    "    )\n",
+    "    score = score[\"Score\"]\n",
+    "    return {\"key\": \"answer_v_reference_score\", \"score\": score}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "960f1a01-7f8c-429f-83d0-052cea47b32b",
+   "metadata": {},
+   "source": [
+    "### Trajectory\n",
+    "\n",
+    "Second, [we can assess the list of tool calls](https://docs.smith.langchain.com/tutorials/Developers/agents#trajectory) that each agent makes relative to expected trajectories.\n",
+    "\n",
+    "This evaluates the specific reasoning traces taken by our agents!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "deb28175-27a1-4afc-9747-2983e87fc881",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langsmith.schemas import Example, Run\n",
+    "\n",
+    "# Reasoning traces that we expect the agents to take\n",
+    "expected_trajectory_1 = [\n",
+    "    \"retrieve_documents\",\n",
+    "    \"grade_document_retrieval\",\n",
+    "    \"web_search\",\n",
+    "    \"generate_answer\",\n",
+    "]\n",
+    "expected_trajectory_2 = [\n",
+    "    \"retrieve_documents\",\n",
+    "    \"grade_document_retrieval\",\n",
+    "    \"generate_answer\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def find_tool_calls_react(messages):\n",
+    "    \"\"\"\n",
+    "    Find all tool calls in the messages returned\n",
+    "    \"\"\"\n",
+    "    tool_calls = [\n",
+    "        tc[\"name\"] for m in messages[\"messages\"] for tc in getattr(m, \"tool_calls\", [])\n",
+    "    ]\n",
+    "    return tool_calls\n",
+    "\n",
+    "\n",
+    "def check_trajectory_react(root_run: Run, example: Example) -> dict:\n",
+    "    \"\"\"\n",
+    "    Check if all expected tools are called in exact order and without any additional tool calls.\n",
+    "    \"\"\"\n",
+    "    messages = root_run.outputs[\"messages\"]\n",
+    "    tool_calls = find_tool_calls_react(messages)\n",
+    "    print(f\"Tool calls ReAct agent: {tool_calls}\")\n",
+    "    if tool_calls == expected_trajectory_1 or tool_calls == expected_trajectory_2:\n",
+    "        score = 1\n",
+    "    else:\n",
+    "        score = 0\n",
+    "\n",
+    "    return {\"score\": int(score), \"key\": \"tool_calls_in_exact_order\"}\n",
+    "\n",
+    "\n",
+    "def check_trajectory_custom(root_run: Run, example: Example) -> dict:\n",
+    "    \"\"\"\n",
+    "    Check if all expected tools are called in exact order and without any additional tool calls.\n",
+    "    \"\"\"\n",
+    "    tool_calls = root_run.outputs[\"steps\"]\n",
+    "    print(f\"Tool calls custom agent: {tool_calls}\")\n",
+    "    if tool_calls == expected_trajectory_1 or tool_calls == expected_trajectory_2:\n",
+    "        score = 1\n",
+    "    else:\n",
+    "        score = 0\n",
+    "\n",
+    "    return {\"score\": int(score), \"key\": \"tool_calls_in_exact_order\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "909b097d-cda1-45ff-8210-afeb2d18b8ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "View the evaluation results for experiment: 'custom-agent-llama3-8b-answer-and-tool-use-d6006159' at:\n",
+      "https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/datasets/a8b9273b-ca33-4e2f-9f69-9bbc37f6f51b/compare?selectedSessions=83c60822-ef22-43e8-ac85-4488af279c6f\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "529952314cd34ac1bb115840536921c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n",
+      "Tool calls custom agent: ['retrieve_documents', 'grade_document_retrieval', 'web_search', 'generate_answer']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langsmith.evaluation import evaluate\n",
+    "\n",
+    "experiment_prefix = f\"custom-agent-{model_tested}\"\n",
+    "experiment_results = evaluate(\n",
+    "    predict_custom_agent_local_answer,\n",
+    "    data=dataset_name,\n",
+    "    evaluators=[answer_evaluator, check_trajectory_custom],\n",
+    "    experiment_prefix=experiment_prefix + \"-answer-and-tool-use\",\n",
+    "    num_repetitions=3,\n",
+    "    max_concurrency=1,  # Use when running locally\n",
+    "    metadata={\"version\": metadata},\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {
+    "80e86604-7734-4aeb-a200-d1413870c3cb.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "47c38cd3-2c31-48c4-8281-bd9e1f7b2830",
+   "metadata": {},
+   "source": [
+    "We can see the results benchmarked against `GPT-4o` and `Llama-3-70b` using `Custom` agent (as shown here) and ReAct.\n",
+    "\n",
+    "![Screenshot 2024-06-24 at 4.14.04 PM.png](attachment:80e86604-7734-4aeb-a200-d1413870c3cb.png)\n",
+    "\n",
+    "The `local custom agent` performs well in terms of tool calling reliability: it follows the expected reasoning traces.\n",
+    "\n",
+    "However, the answer accuracy performance lags the larger models with `custom agent` implementations."
+   ]
+  }
+ ],
+ "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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_self_rag.ipynb

@@ -0,0 +1,800 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3d959ff",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "15cba0ab-a549-4909-8373-fb761e384eff.png": {
+     "image/png": 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truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "919fe33c-0149-4f7d-b200-544a18986c9a",
+   "metadata": {},
+   "source": [
+    "# Self-RAG\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2310.11511), a few decisions are made:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\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",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\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",
+    "* 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 will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.41.50 PM.png](attachment:15cba0ab-a549-4909-8373-fb761e384eff.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72f3ee57-68ab-4040-bd36-4014e2a23d96",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a384cc48-0425-4e8f-aafc-cfb8e56025c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -U langchain_community tiktoken langchain-openai langchainhub chromadb langchain langgraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de4ee2a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"OPENAI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25d16369",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "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": "29c12f74-53e2-43cc-896f-875d1c5d9d93",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/rlm/miniforge3/envs/llama2/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `BaseRetriever.get_relevant_documents` was deprecated in langchain-core 0.1.46 and will be removed in 0.3.0. Use invoke instead.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_score='yes'\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
+    "    It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\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",
+    "grade_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Retrieved document: \\n\\n {document} \\n\\n User question: {question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dcd77cc1-4587-40ec-b633-5364eab9e1ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experience and interact with other agents. Long-term memory provides the agent with the capability to retain and recall infinite information over extended periods. Short-term memory is utilized for in-context learning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\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",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e78931ec-940c-46ad-a0b2-f43f953f1fd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeHallucinations(binary_score='yes')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeHallucinations(BaseModel):\n",
+    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \\n \n",
+    "     Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts.\"\"\"\n",
+    "hallucination_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"Set of facts: \\n\\n {documents} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bd62276f-bf26-40d0-8cff-e07b10e00321",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GradeAnswer(binary_score='yes')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeAnswer(BaseModel):\n",
+    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer addresses the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You are a grader assessing whether an answer addresses / resolves a question \\n \n",
+    "     Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question.\"\"\"\n",
+    "answer_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\"human\", \"User question: \\n\\n {question} \\n\\n LLM generation: {generation}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c6f4c70e-1660-4149-82c0-837f19fc9fb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"What is the role of memory in an agent's functioning?\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning.\"\"\"\n",
+    "re_write_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", system),\n",
+    "        (\n",
+    "            \"human\",\n",
+    "            \"Here is the initial question: \\n\\n {question} \\n Formulate an improved question.\",\n",
+    "        ),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "276001c5-c079-4e5b-9f42-81a06704d200",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "add509d8-6682-4127-8d95-13dd37d79702",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.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",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_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.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score.binary_score\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "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": 10,
+   "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\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)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "('Short-term memory is used for in-context learning in agents, allowing them '\n",
+      " 'to learn quickly. Long-term memory enables agents to retain and recall vast '\n",
+      " 'amounts of information over extended periods. Agents can also utilize '\n",
+      " 'external tools like APIs to access additional information beyond what is '\n",
+      " 'stored in their memory.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"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(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "('Chain of thought prompting works by repeatedly prompting the model to ask '\n",
+      " 'follow-up questions to construct the thought process iteratively. This '\n",
+      " 'method can be combined with queries to search for relevant entities and '\n",
+      " 'content to add back into the context. It extends the thought process by '\n",
+      " 'exploring multiple reasoning possibilities at each step, creating a tree '\n",
+      " 'structure of thoughts.')\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {\"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(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"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/1c6bf654-61b2-4fc5-9889-054b020c78aa/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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_self_rag_local.ipynb

@@ -0,0 +1,755 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "345488d8",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png": {
+     "image/png": "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 truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "848ba742-7443-4123-8115-061da9823309",
+   "metadata": {},
+   "source": [
+    "# Self-RAG using local LLMs\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "In the [paper](https://arxiv.org/abs/2310.11511), a few decisions are made:\n",
+    "\n",
+    "1. Should I retrieve from retriever, `R` -\n",
+    "\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",
+    "* * Input: (`x (question)`, `d (chunk)`) for `d` in `D`\n",
+    "* `d` provides useful information to solve `x`\n",
+    "* Output: `relevant, irrelevant`\n",
+    "\n",
+    "3. Are the LLM generation from each chunk in `D` is relevant to the chunk (hallucinations, etc)  -\n",
+    "\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",
+    "* 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 will implement some of these ideas from scratch using [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.42.59 PM.png](attachment:5fca0a3e-d13d-4bfa-95ea-58203640cc7a.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ed0a85a-a33b-40a6-99fa-2444bf57a6cc",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "First let's install our required packages and set our API keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7f9cc6d-a70c-433a-b0ad-ea47c5a0717e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%capture --no-stderr\n",
+    "%pip install -U langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph nomic[local]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71c540ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "def _set_env(key: str):\n",
+    "    if key not in os.environ:\n",
+    "        os.environ[key] = getpass.getpass(f\"{key}:\")\n",
+    "\n",
+    "\n",
+    "_set_env(\"NOMIC_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e8cf60",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition tip\">\n",
+    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
+    "    <p style=\"padding-top: 5px;\">\n",
+    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
+    "    </p>\n",
+    "</div>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc6b6bd-a2fa-4a43-83b7-704b8b6fb855",
+   "metadata": {},
+   "source": [
+    "### LLMs\n",
+    "\n",
+    "#### Local Embeddings\n",
+    "\n",
+    "You can use `GPT4AllEmbeddings()` from Nomic, which can access use Nomic's recently released [v1](https://blog.nomic.ai/posts/nomic-embed-text-v1) and [v1.5](https://blog.nomic.ai/posts/nomic-embed-matryoshka) embeddings.\n",
+    "\n",
+    "\n",
+    "Follow the documentation [here](https://docs.gpt4all.io/gpt4all_python_embedding.html#supported-embedding-models).\n",
+    "\n",
+    "#### Local LLM\n",
+    "\n",
+    "(1) Download [Ollama app](https://ollama.ai/).\n",
+    "\n",
+    "(2) Download a `Mistral` model from various Mistral versions [here](https://ollama.ai/library/mistral) and Mixtral versions [here](https://ollama.ai/library/mixtral) available.\n",
+    "```\n",
+    "ollama pull mistral\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bedffc73-6b10-42c8-8768-2085c8ed3398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ollama model name\n",
+    "local_llm = \"mistral\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba68a46d-b617-4fdc-9113-fabdcf736feb",
+   "metadata": {},
+   "source": [
+    "## Create Index\n",
+    "\n",
+    "Let's index 3 blog posts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3bb9060-ad74-4470-9991-2ba167b6b8d8",
+   "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_nomic.embeddings import NomicEmbeddings\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=NomicEmbeddings(model=\"nomic-embed-text-v1.5\", inference_mode=\"local\"),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc60ff95-7e12-4004-b18e-a067a2dcc201",
+   "metadata": {},
+   "source": [
+    "## LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3aad0c60-3208-48fb-af82-0024630b4da1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'score': 'yes'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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 {document} \\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 score as a JSON with a single key 'score' and no premable or explanation.\"\"\",\n",
+    "    input_variables=[\"question\", \"document\"],\n",
+    ")\n",
+    "\n",
+    "retrieval_grader = prompt | llm | JsonOutputParser()\n",
+    "question = \"agent memory\"\n",
+    "docs = retriever.get_relevant_documents(question)\n",
+    "doc_txt = docs[1].page_content\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e5e45953-248d-492f-af28-d5e80c664c95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " In an LLM-powered autonomous agent system, the Large Language Model (LLM) functions as the agent's brain. The agent has key components including memory, planning, and reflection mechanisms. The memory component is a long-term memory module that records a comprehensive list of agents’ experience in natural language. It includes a memory stream, which is an external database for storing past experiences. The reflection mechanism synthesizes memories into higher-level inferences over time and guides the agent's future behavior.\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "\n",
+    "# Post-processing\n",
+    "def format_docs(docs):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "56297862-df87-42a7-ba9d-310926dfb328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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' score to indicate whether the answer is grounded in / supported by a set of facts. \\n\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"documents\"],\n",
+    ")\n",
+    "\n",
+    "hallucination_grader = prompt | llm | JsonOutputParser()\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dd9174-2df6-45b1-8e69-13381f579c39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'score': 'yes'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\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\n",
+    "    Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "answer_grader = prompt | llm | JsonOutputParser()\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5216d92b-1ca1-4bcf-a34f-c99ec2766b54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' What is agent memory and how can it be effectively utilized in vector database retrieval?'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOllama(model=local_llm, temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = PromptTemplate(\n",
+    "    template=\"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
+    "     for vectorstore retrieval. Look at the initial and formulate an improved question. \\n\n",
+    "     Here is the initial question: \\n\\n {question}. Improved question with no preamble: \\n \"\"\",\n",
+    "    input_variables=[\"generation\", \"question\"],\n",
+    ")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3339b9-5f30-4d54-bfc9-9091c6035955",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "90fb1dc6-c482-483a-8441-39965c401beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5324ea49-5745-47b5-a0a5-bf58c8babe46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.get_relevant_documents(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\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",
+    "            continue\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_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.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score[\"score\"]\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score[\"score\"]\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        print(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdf3826c-b668-4f0b-bf83-81c40baaaf02",
+   "metadata": {},
+   "source": [
+    "## Build Graph\n",
+    "\n",
+    "This just follows the flow we outlined in the figure above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5605dee4-b2df-46ae-a640-cc2ed90c21a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\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)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "105ae1b5-6963-4186-bb83-6d6cb96d095f",
+   "metadata": {},
+   "source": [
+    "## Run\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26a64f7d-0c14-4e31-a67f-63021dee626e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "---GENERATE---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node '__end__':\"\n",
+      "'\\n---\\n'\n",
+      "(' In a LLM-powered autonomous agent system, memory is a key component that '\n",
+      " 'enables agents to store and retrieve information. There are different types '\n",
+      " 'of memory in human brains, such as sensory memory which retains impressions '\n",
+      " 'of sensory information for a few seconds, and long-term memory which records '\n",
+      " \"experiences for extended periods (Lil'Log, 2023). In the context of LLM \"\n",
+      " 'agents, memory is often implemented as an external database or memory stream '\n",
+      " \"(Lil'Log, 2023). The agent can consult this memory to inform its behavior \"\n",
+      " 'based on relevance, recency, and importance. Additionally, reflection '\n",
+      " 'mechanisms synthesize memories into higher-level inferences over time and '\n",
+      " \"guide the agent's future behavior (Lil'Log, 2023).\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"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(f\"Node '{key}':\")\n",
+    "        # Optional: print full state at each node\n",
+    "        # pprint.pprint(value[\"keys\"], indent=2, width=80, depth=None)\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a27907-2611-4791-910b-0d66c59f5cf5",
+   "metadata": {},
+   "source": [
+    "Trace: \n",
+    "\n",
+    "https://smith.langchain.com/public/4163a342-5260-4852-8602-bda3f95177e7/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": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rag/langgraph_self_rag_pinecone_movies.ipynb

@@ -0,0 +1,758 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "403aeb6e",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. Please see the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview) for the most current information and resources."
+   ]
+  },
+  {
+   "attachments": {
+    "15cba0ab-a549-4909-8373-fb761e384eff.png": {
+     "image/png": "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 truncated
+    }
+   },
+   "cell_type": "markdown",
+   "id": "919fe33c-0149-4f7d-b200-544a18986c9a",
+   "metadata": {},
+   "source": [
+    "# Self RAG\n",
+    "\n",
+    "Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations. \n",
+    "\n",
+    "[Paper](https://arxiv.org/abs/2310.11511)\n",
+    "\n",
+    "![Screenshot 2024-04-01 at 12.41.50 PM.png](attachment:15cba0ab-a549-4909-8373-fb761e384eff.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72f3ee57-68ab-4040-bd36-4014e2a23d96",
+   "metadata": {},
+   "source": [
+    "# Environment "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a384cc48-0425-4e8f-aafc-cfb8e56025c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-pinecone langchain-openai langchainhub langgraph"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "532d91fb-381e-4e11-b3b1-254321351773",
+   "metadata": {},
+   "source": [
+    "### Tracing\n",
+    "\n",
+    "Use [LangSmith](https://docs.smith.langchain.com/) for tracing (shown at bottom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccc3dae5-1df6-48ca-af8a-50f0e6128876",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
+    "os.environ[\"LANGCHAIN_API_KEY\"] = \"<your-api-key>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "88637820",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"LANGCHAIN_PROJECT\"] = \"pinecone-devconnect\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c27bebdc-be71-4130-ab9d-42f09f87658b",
+   "metadata": {},
+   "source": [
+    "## Retriever\n",
+    " \n",
+    "Let's use Pinecone's sample movies database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "565a6d44-2c9f-4fff-b1ec-eea05df9350d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_pinecone import PineconeVectorStore\n",
+    "\n",
+    "# use pinecone movies database\n",
+    "\n",
+    "# Add to vectorDB\n",
+    "vectorstore = PineconeVectorStore(\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    "    index_name=\"sample-movies\",\n",
+    "    text_key=\"summary\",\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1aeb4373",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Avatar\n",
+      "On the alien world of Pandora, paraplegic Marine Jake Sully uses an avatar to walk again and becomes torn between his mission and protecting the planet's indigenous Na'vi people. The film stars Sam Worthington, Zoe Saldana, and Sigourney Weaver.\n",
+      "\n",
+      "# Top Gun: Maverick\n",
+      "Capt. Pete \"Maverick\" Mitchell, after decades of service as one of the Navy's top aviators, confronts his past while training a new squad for a dangerous mission. Tom Cruise reprises his iconic role, showcasing thrilling aerial stunts.\n",
+      "\n",
+      "# Jurassic World Dominion\n",
+      "The film concludes the story of Jurassic World, with humanity now living alongside dinosaurs. It follows Owen Grady and Claire Dearing as they navigate this new world.\n",
+      "\n",
+      "# Aquaman\n",
+      "Arthur Curry learns he is the heir to the underwater kingdom of Atlantis and must step forward to lead his people and be a hero to the world. Stars Jason Momoa, Amber Heard, and Willem Dafoe.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "docs = retriever.invoke(\"James Cameron\")\n",
+    "for doc in docs:\n",
+    "    print(\"# \" + doc.metadata[\"title\"])\n",
+    "    print(doc.page_content)\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29c12f74-53e2-43cc-896f-875d1c5d9d93",
+   "metadata": {},
+   "source": [
+    "## Structured Output - Retrieval Grader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1fafad21-60cc-483e-92a3-6a7edb1838e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Retrieval Grader\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeDocuments(BaseModel):\n",
+    "    \"\"\"Binary score for relevance check on retrieved documents.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Documents are relevant to the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# https://smith.langchain.com/hub/efriis/self-rag-retrieval-grader\n",
+    "grade_prompt = hub.pull(\"efriis/self-rag-retrieval-grader\")\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
+    "\n",
+    "retrieval_grader = grade_prompt | structured_llm_grader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2e79eed8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arthur Curry learns he is the heir to the underwater kingdom of Atlantis and must step forward to lead his people and be a hero to the world. Stars Jason Momoa, Amber Heard, and Willem Dafoe.\n",
+      "binary_score='yes'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test the retrieval grader\n",
+    "question = \"movies starring jason momoa\"\n",
+    "docs = retriever.invoke(question)\n",
+    "doc_txt = docs[0].page_content\n",
+    "print(doc_txt)\n",
+    "print(retrieval_grader.invoke({\"question\": question, \"document\": doc_txt}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c06abc65",
+   "metadata": {},
+   "source": [
+    "# Generation Step\n",
+    "\n",
+    "Standard RAG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dcd77cc1-4587-40ec-b633-5364eab9e1ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jason Momoa stars in the movies \"Aquaman\" and \"Furious 7.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Generate\n",
+    "\n",
+    "from langchain import hub\n",
+    "from langchain_core.output_parsers import StrOutputParser\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",
+    "# Chain\n",
+    "rag_chain = prompt | llm | StrOutputParser()\n",
+    "\n",
+    "# Run\n",
+    "generation = rag_chain.invoke({\"context\": docs, \"question\": question})\n",
+    "print(generation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e78931ec-940c-46ad-a0b2-f43f953f1fd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jason Momoa stars in the movies \"Aquaman\" and \"Furious 7.\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GradeHallucinations(binary_score='yes')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Hallucination Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeHallucinations(BaseModel):\n",
+    "    \"\"\"Binary score for hallucination present in generation answer.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer is grounded in the facts, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
+    "\n",
+    "# https://smith.langchain.com/hub/efriis/self-rag-hallucination-grader\n",
+    "hallucination_prompt = hub.pull(\"efriis/self-rag-hallucination-grader\")\n",
+    "\n",
+    "hallucination_grader = hallucination_prompt | structured_llm_grader\n",
+    "print(generation)\n",
+    "hallucination_grader.invoke({\"documents\": docs, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bd62276f-bf26-40d0-8cff-e07b10e00321",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "movies starring jason momoa\n",
+      "Jason Momoa stars in the movies \"Aquaman\" and \"Furious 7.\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GradeAnswer(binary_score='yes')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Answer Grader\n",
+    "\n",
+    "\n",
+    "# Data model\n",
+    "class GradeAnswer(BaseModel):\n",
+    "    \"\"\"Binary score to assess answer addresses question.\"\"\"\n",
+    "\n",
+    "    binary_score: str = Field(\n",
+    "        description=\"Answer addresses the question, 'yes' or 'no'\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# LLM with function call\n",
+    "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
+    "structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
+    "\n",
+    "# Prompt\n",
+    "answer_prompt = hub.pull(\"efriis/self-rag-answer-grader\")\n",
+    "\n",
+    "answer_grader = answer_prompt | structured_llm_grader\n",
+    "print(question)\n",
+    "print(generation)\n",
+    "answer_grader.invoke({\"question\": question, \"generation\": generation})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c6f4c70e-1660-4149-82c0-837f19fc9fb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "movies starring jason momoa\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Which movies feature Jason Momoa in a leading role?'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Question Re-writer\n",
+    "\n",
+    "# LLM\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
+    "\n",
+    "# Prompt\n",
+    "re_write_prompt = hub.pull(\"efriis/self-rag-question-rewriter\")\n",
+    "\n",
+    "question_rewriter = re_write_prompt | llm | StrOutputParser()\n",
+    "print(question)\n",
+    "question_rewriter.invoke({\"question\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "276001c5-c079-4e5b-9f42-81a06704d200",
+   "metadata": {},
+   "source": [
+    "# Graph \n",
+    "\n",
+    "Capture the flow in as a graph.\n",
+    "\n",
+    "## Graph state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f1617e9e-66a8-4c1a-a1fe-cc936284c085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "\n",
+    "class GraphState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our graph.\n",
+    "\n",
+    "    Attributes:\n",
+    "        question: question\n",
+    "        generation: LLM generation\n",
+    "        documents: list of documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    question: str\n",
+    "    generation: str\n",
+    "    documents: List[str]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "add509d8-6682-4127-8d95-13dd37d79702",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 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",
+    "    question = state[\"question\"]\n",
+    "\n",
+    "    # Retrieval\n",
+    "    documents = retriever.invoke(question)\n",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # RAG generation\n",
+    "    generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": question, \"generation\": generation}\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 only filtered relevant documents\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Score each doc\n",
+    "    filtered_docs = []\n",
+    "    for d in documents:\n",
+    "        score = retrieval_grader.invoke(\n",
+    "            {\"question\": question, \"document\": d.page_content}\n",
+    "        )\n",
+    "        grade = score.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",
+    "    return {\"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",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "\n",
+    "    # Re-write question\n",
+    "    better_question = question_rewriter.invoke({\"question\": question})\n",
+    "    return {\"documents\": documents, \"question\": better_question}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "09fc91b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### 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 graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Binary decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---ASSESS GRADED DOCUMENTS---\")\n",
+    "    state[\"question\"]\n",
+    "    filtered_documents = state[\"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(\n",
+    "            \"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---\"\n",
+    "        )\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_and_question(state):\n",
+    "    \"\"\"\n",
+    "    Determines whether the generation is grounded in the document and answers question.\n",
+    "\n",
+    "    Args:\n",
+    "        state (dict): The current graph state\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Decision for next node to call\n",
+    "    \"\"\"\n",
+    "\n",
+    "    print(\"---CHECK HALLUCINATIONS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = state[\"generation\"]\n",
+    "\n",
+    "    score = hallucination_grader.invoke(\n",
+    "        {\"documents\": documents, \"generation\": generation}\n",
+    "    )\n",
+    "    grade = score.binary_score\n",
+    "\n",
+    "    # Check hallucination\n",
+    "    if grade == \"yes\":\n",
+    "        print(\"---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\")\n",
+    "        # Check question-answering\n",
+    "        print(\"---GRADE GENERATION vs QUESTION---\")\n",
+    "        score = answer_grader.invoke({\"question\": question, \"generation\": generation})\n",
+    "        grade = score.binary_score\n",
+    "        if grade == \"yes\":\n",
+    "            print(\"---DECISION: GENERATION ADDRESSES QUESTION---\")\n",
+    "            return \"useful\"\n",
+    "        else:\n",
+    "            print(\"---DECISION: GENERATION DOES NOT ADDRESS QUESTION---\")\n",
+    "            return \"not useful\"\n",
+    "    else:\n",
+    "        pprint(\"---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---\")\n",
+    "        return \"not supported\""
+   ]
+  },
+  {
+   "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": 13,
+   "id": "0e09ca9f-e36d-4ef4-a0d5-79fdbada9fe0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.graph import END, StateGraph, START\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)  # generate\n",
+    "workflow.add_node(\"transform_query\", transform_query)  # transform_query\n",
+    "\n",
+    "# Build graph\n",
+    "workflow.add_edge(START, \"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_and_question,\n",
+    "    {\n",
+    "        \"not supported\": \"generate\",\n",
+    "        \"useful\": END,\n",
+    "        \"not useful\": \"transform_query\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Compile\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fb69dbb9-91ee-4868-8c3c-93af3cd885be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---RETRIEVE---\n",
+      "\"Node 'retrieve':\"\n",
+      "'\\n---\\n'\n",
+      "---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---GRADE: DOCUMENT NOT RELEVANT---\n",
+      "---ASSESS GRADED DOCUMENTS---\n",
+      "---DECISION: GENERATE---\n",
+      "\"Node 'grade_documents':\"\n",
+      "'\\n---\\n'\n",
+      "---GENERATE---\n",
+      "---CHECK HALLUCINATIONS---\n",
+      "---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---\n",
+      "---GRADE GENERATION vs QUESTION---\n",
+      "---DECISION: GENERATION ADDRESSES QUESTION---\n",
+      "\"Node 'generate':\"\n",
+      "'\\n---\\n'\n",
+      "'Daniel Craig stars as 007 in \"Skyfall\" (2012) and \"Spectre\" (2015).'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "# Run\n",
+    "inputs = {\"question\": \"Movies that star Daniel Craig\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4138bc51-8c84-4b8a-8d24-f7f470721f6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = {\"question\": \"Which movies are about aliens?\"}\n",
+    "for output in app.stream(inputs):\n",
+    "    for key, value in output.items():\n",
+    "        # Node\n",
+    "        pprint(f\"Node '{key}':\")\n",
+    "    pprint(\"\\n---\\n\")\n",
+    "\n",
+    "# Final generation\n",
+    "pprint(value[\"generation\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42369ab8-322d-434a-b5dd-2266e4cb2903",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/react-agent-from-scratch.ipynb

@@ -0,0 +1,40 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "294995c4",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-from-scratch.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/react-agent-structured-output.ipynb

@@ -0,0 +1,40 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "40f0d107",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/react-agent-structured-output.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/reflection/reflection.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "658773a2",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflection/reflection.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb60657",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/reflexion/reflexion.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "caf07859",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/reflexion/reflexion.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd1df0e0",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/rewoo/rewoo.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "961f43ec",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/rewoo/rewoo.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f00c427",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/run-id-langsmith.ipynb

@@ -0,0 +1,40 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbd6e9b8",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/run-id-langsmith.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/self-discover/self-discover.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f6db1873",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/self-discover/self-discover.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "219a78f9",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/subgraph.ipynb

@@ -0,0 +1,40 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f49876e1",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/subgraph.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/tool-calling.ipynb

@@ -0,0 +1,40 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7fd8bd65",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/how-tos/tool-calling.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/tutorials/sql-agent.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "83c2223f",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/sql/sql-agent.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57f924b1",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/tutorials/tnt-llm/tnt-llm.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11140167",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/tnt-llm/tnt-llm.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a2ba3e6",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/usaco/usaco.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9dffdb54",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/usaco/usaco.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579c9959",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

examples/web-navigation/web_voyager.ipynb

@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "007ea2e9",
+   "metadata": {},
+   "source": [
+    "[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/web-navigation/web_voyager.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0d7b895",
+   "metadata": {},
+   "source": [
+    "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."
+   ]
+  }
+ ],
+ "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
+}

langgraph-go/advancedgraph/graph.go

@@ -0,0 +1,581 @@
+package advancedgraph
+
+import (
+	"encoding/json"
+	"fmt"
+	"reflect"
+	"runtime"
+	"strings"
+)
+
+type nodeExecutor func(ctx *Context, input any, state map[string]any) (Command, error)
+
+type nodeConfig struct {
+	lockedFields []string
+}
+
+type NodeOption interface {
+	applyToNodeConfig(*nodeConfig)
+}
+
+type NodeStateOption struct {
+	LockedFields []string
+}
+
+func (o NodeStateOption) applyToNodeConfig(cfg *nodeConfig) {
+	if cfg == nil {
+		return
+	}
+	cfg.lockedFields = append(cfg.lockedFields[:0], o.LockedFields...)
+}
+
+type AdvancedStateGraph[StateT any] struct {
+	nodes         map[string]nodeExecutor
+	nodeOptions   map[string]nodeConfig
+	asyncChannels []string
+	customStreams []string
+	entryPoint    string
+	finishPoint   string
+	stateType     reflect.Type
+}
+
+func NewAdvancedStateGraph[StateT any]() *AdvancedStateGraph[StateT] {
+	stateType := mustTypeOf[StateT]()
+	if stateType.Kind() != reflect.Struct {
+		panic(fmt.Sprintf("StateT must be a struct, got %s", stateType.String()))
+	}
+	return &AdvancedStateGraph[StateT]{
+		nodes:       make(map[string]nodeExecutor),
+		nodeOptions: make(map[string]nodeConfig),
+		stateType:   stateType,
+	}
+}
+
+// AddNode keeps `fn` as `any` because advanced graph nodes can have different
+// input argument types per node, while only `StateT` is globally constrained.
+// We validate and adapt node signatures at runtime in compileNodeExecutor.
+func (g *AdvancedStateGraph[StateT]) AddNode(fn any, nodeOption ...NodeOption) string {
+	name := NodeName(fn)
+	return g.AddNodeAs(name, fn, nodeOption...)
+}
+
+func (g *AdvancedStateGraph[StateT]) AddNodeAs(name string, fn any, nodeOption ...NodeOption) string {
+	if _, exists := g.nodes[name]; exists {
+		panic(fmt.Sprintf("node `%s` already exists", name))
+	}
+	exec, err := compileNodeExecutor(fn, g.stateType)
+	if err != nil {
+		panic(err)
+	}
+	g.nodes[name] = exec
+	g.nodeOptions[name] = resolveNodeConfig(nodeOption...)
+	return name
+}
+
+func (g *AdvancedStateGraph[StateT]) AddAsyncChannel(name string) {
+	g.asyncChannels = append(g.asyncChannels, name)
+}
+
+func (g *AdvancedStateGraph[StateT]) AddCustomOutputStream(name string) {
+	g.customStreams = append(g.customStreams, name)
+}
+
+func (g *AdvancedStateGraph[StateT]) AddEntryNode(fn any, nodeOption ...NodeOption) string {
+	name := NodeName(fn)
+	return g.AddEntryNodeAs(name, fn, nodeOption...)
+}
+
+func (g *AdvancedStateGraph[StateT]) AddEntryNodeAs(name string, fn any, nodeOption ...NodeOption) string {
+	name = g.AddNodeAs(name, fn, nodeOption...)
+	g.entryPoint = name
+	return name
+}
+
+func (g *AdvancedStateGraph[StateT]) AddFinishNode(fn any, nodeOption ...NodeOption) string {
+	name := NodeName(fn)
+	return g.AddFinishNodeAs(name, fn, nodeOption...)
+}
+
+func (g *AdvancedStateGraph[StateT]) AddFinishNodeAs(name string, fn any, nodeOption ...NodeOption) string {
+	name = g.AddNodeAs(name, fn, nodeOption...)
+	g.finishPoint = name
+	return name
+}
+
+func (g *AdvancedStateGraph[StateT]) Compile() *CompiledGraph[StateT] {
+	return &CompiledGraph[StateT]{
+		nodes:         g.nodes,
+		nodeOptions:   g.nodeOptions,
+		asyncChannels: g.asyncChannels,
+		customStreams: g.customStreams,
+		entryPoint:    g.entryPoint,
+		finishPoint:   g.finishPoint,
+		stateType:     g.stateType,
+	}
+}
+
+type CompiledGraph[StateT any] struct {
+	nodes         map[string]nodeExecutor
+	nodeOptions   map[string]nodeConfig
+	asyncChannels []string
+	customStreams []string
+	entryPoint    string
+	finishPoint   string
+	stateType     reflect.Type
+}
+
+type Context struct {
+	engine      *RustEngine
+	resumeEvent *WaitEvent
+	isResume    bool
+}
+
+func (c *Context) WaitFor(target WaitTarget) (WaitForResult, error) {
+	if target == nil {
+		return WaitForResult{}, fmt.Errorf("wait target cannot be nil")
+	}
+	if c.resumeEvent != nil {
+		event := *c.resumeEvent
+		c.resumeEvent = nil
+		return waitForResultFromRaw(target, event), nil
+	}
+	return WaitForResult{}, ErrWaitRequested{Target: target}
+}
+
+func (c *Context) IsResume() bool {
+	return c.isResume
+}
+
+func (c *Context) PublishToChannel(channel string, value any) error {
+	return c.engine.Publish(channel, value)
+}
+
+func (c *Context) SendCustomStreamEvent(streamName string, value any) error {
+	return c.engine.SendCustomStreamEvent(streamName, value)
+}
+
+type Handler[StateT any] struct {
+	engine       *RustEngine
+	done         chan resultOrErr[StateT]
+	streamReadyC chan struct{}
+}
+
+type resultOrErr[StateT any] struct {
+	state StateT
+	err   error
+}
+
+func (h *Handler[StateT]) PublishToChannel(channel string, value any) error {
+	return h.engine.Publish(channel, value)
+}
+
+func (h *Handler[StateT]) WaitForResult() (StateT, error) {
+	res := <-h.done
+	return res.state, res.err
+}
+
+func (h *Handler[StateT]) ReceiveStream(streamName string) (any, error) {
+	<-h.streamReadyC
+	event, hasEvent, err := h.engine.ReceiveStream(streamName)
+	if err != nil {
+		return nil, err
+	}
+	if !hasEvent {
+		return nil, nil
+	}
+	return event, nil
+}
+
+func (h *Handler[StateT]) CloseAllStreams() error {
+	<-h.streamReadyC
+	return h.engine.CloseAllStreams()
+}
+
+func (g *CompiledGraph[StateT]) Start(initialInput any, initialState StateT, streamMode ...string) (*Handler[StateT], error) {
+	resolvedStreamMode := ""
+	if len(streamMode) > 1 {
+		return nil, fmt.Errorf("start accepts at most one stream mode")
+	}
+	if len(streamMode) == 1 {
+		resolvedStreamMode = streamMode[0]
+	}
+	engine := NewRustEngine()
+	for _, ch := range g.asyncChannels {
+		if err := engine.AddAsyncChannel(ch); err != nil {
+			return nil, err
+		}
+	}
+	for _, streamName := range g.customStreams {
+		if err := engine.AddCustomOutputStream(streamName); err != nil {
+			return nil, err
+		}
+	}
+
+	handler := &Handler[StateT]{
+		engine:       engine,
+		done:         make(chan resultOrErr[StateT], 1),
+		streamReadyC: make(chan struct{}),
+	}
+	go func() {
+		defer engine.Close()
+		streamModeForRun := resolvedStreamMode
+		if resolvedStreamMode != "" {
+			if err := engine.StartStream(resolvedStreamMode); err != nil {
+				close(handler.streamReadyC)
+				handler.done <- resultOrErr[StateT]{err: err}
+				close(handler.done)
+				return
+			}
+			streamModeForRun = ""
+		}
+		close(handler.streamReadyC)
+		rawState, err := engine.RunGraph(
+			g.entryPoint,
+			g.finishPoint,
+			streamModeForRun,
+			initialState,
+			initialInput,
+			g.nodeLockedFields(),
+			func(node string, nodeInput any, fallbackState map[string]any) (Command, error) {
+				fn, ok := g.nodes[node]
+				if !ok {
+					return Command{}, fmt.Errorf("unknown node `%s`", node)
+				}
+				if fallbackState == nil {
+					return Command{}, fmt.Errorf("node `%s` expected map state argument", node)
+				}
+				resolvedInput, resumeEvent := unwrapResumeInput(nodeInput)
+				return fn(
+					&Context{
+						engine:      engine,
+						resumeEvent: resumeEvent,
+						isResume:    resumeEvent != nil,
+					},
+					resolvedInput,
+					fallbackState,
+				)
+			},
+		)
+		if err != nil {
+			handler.done <- resultOrErr[StateT]{err: err}
+			close(handler.done)
+			return
+		}
+		state, err := mapToState[StateT](rawState)
+		handler.done <- resultOrErr[StateT]{state: state, err: err}
+		close(handler.done)
+	}()
+	return handler, nil
+}
+
+func (g *CompiledGraph[StateT]) nodeLockedFields() map[string][]string {
+	result := make(map[string][]string, len(g.nodeOptions))
+	for nodeName, option := range g.nodeOptions {
+		if len(option.lockedFields) == 0 {
+			continue
+		}
+		fields := make([]string, 0, len(option.lockedFields))
+		for _, field := range option.lockedFields {
+			if field == "" {
+				continue
+			}
+			fields = append(fields, field)
+		}
+		if len(fields) > 0 {
+			result[nodeName] = fields
+		}
+	}
+	return result
+}
+
+func resolveNodeConfig(nodeOption ...NodeOption) nodeConfig {
+	cfg := nodeConfig{}
+	for _, option := range nodeOption {
+		if option == nil {
+			panic("node option cannot be nil")
+		}
+		option.applyToNodeConfig(&cfg)
+	}
+	return cfg
+}
+
+func NodeName(fn any) string {
+	rv := reflect.ValueOf(fn)
+	if !rv.IsValid() || rv.Kind() != reflect.Func {
+		panic("cannot infer node name from non-function value")
+	}
+	pc := rv.Pointer()
+	f := runtime.FuncForPC(pc)
+	if f == nil {
+		panic("cannot infer node name from nil function")
+	}
+	full := f.Name()
+	if strings.Contains(full, ".func") {
+		panic("anonymous functions are not allowed as nodes")
+	}
+	short := full
+	if i := strings.LastIndex(short, "/"); i >= 0 {
+		short = short[i+1:]
+	}
+	if i := strings.LastIndex(short, "."); i >= 0 {
+		short = short[i+1:]
+	}
+	short = strings.TrimSuffix(short, "-fm")
+	if short == "" || strings.Contains(short, "func") {
+		panic(fmt.Sprintf("cannot infer stable node name from `%s`", full))
+	}
+	return short
+}
+
+func compileNodeExecutor(fn any, expectedStateType reflect.Type) (nodeExecutor, error) {
+	rv := reflect.ValueOf(fn)
+	if !rv.IsValid() || rv.Kind() != reflect.Func {
+		return nil, fmt.Errorf("node must be a function")
+	}
+	rt := rv.Type()
+	if rt.NumIn() != 3 {
+		return nil, fmt.Errorf("node `%s` must accept exactly 3 args: (*Context, input, state)", NodeName(fn))
+	}
+	ctxType := reflect.TypeOf((*Context)(nil))
+	if rt.In(0) != ctxType {
+		return nil, fmt.Errorf("node `%s` first arg must be *Context", NodeName(fn))
+	}
+	if rt.NumOut() != 2 {
+		return nil, fmt.Errorf("node `%s` must return (Command, error)", NodeName(fn))
+	}
+	cmdType := reflect.TypeOf(Command{})
+	if rt.Out(0) != cmdType {
+		return nil, fmt.Errorf("node `%s` first return must be Command", NodeName(fn))
+	}
+	errType := reflect.TypeOf((*error)(nil)).Elem()
+	if !rt.Out(1).Implements(errType) {
+		return nil, fmt.Errorf("node `%s` second return must be error", NodeName(fn))
+	}
+
+	inputType := rt.In(1)
+	stateType := rt.In(2)
+	if stateType != expectedStateType {
+		return nil, fmt.Errorf(
+			"node `%s` state type mismatch: got %s, graph expects %s",
+			NodeName(fn),
+			stateType.String(),
+			expectedStateType.String(),
+		)
+	}
+	return func(ctx *Context, input any, state map[string]any) (Command, error) {
+		stateArg, err := convertStateArg(state, stateType)
+		if err != nil {
+			return Command{}, fmt.Errorf("node `%s` state decode failed: %w", NodeName(fn), err)
+		}
+		args := []reflect.Value{
+			reflect.ValueOf(ctx),
+			reflect.Zero(inputType),
+			stateArg,
+		}
+		if input != nil {
+			inVal := reflect.ValueOf(input)
+			if inVal.Type().AssignableTo(inputType) {
+				args[1] = inVal
+			} else if inVal.Type().ConvertibleTo(inputType) {
+				args[1] = inVal.Convert(inputType)
+			} else {
+				return Command{}, fmt.Errorf(
+					"node `%s` input type mismatch: got %T, want %s",
+					NodeName(fn),
+					input,
+					inputType.String(),
+				)
+			}
+		}
+		out := rv.Call(args)
+		cmd := out[0].Interface().(Command)
+		if cmd.Update != nil {
+			updateType := reflect.TypeOf(cmd.Update)
+			if updateType != stateType {
+				return Command{}, fmt.Errorf(
+					"node `%s` update type mismatch: got %s, graph expects %s",
+					NodeName(fn),
+					updateType.String(),
+					stateType.String(),
+				)
+			}
+		}
+		if out[1].IsNil() {
+			return cmd, nil
+		}
+		return cmd, out[1].Interface().(error)
+	}, nil
+}
+
+func convertStateArg(state map[string]any, stateType reflect.Type) (reflect.Value, error) {
+	if stateType == reflect.TypeOf(map[string]any{}) {
+		return reflect.ValueOf(state), nil
+	}
+	raw, err := json.Marshal(state)
+	if err != nil {
+		return reflect.Value{}, fmt.Errorf("marshal state: %w", err)
+	}
+	if stateType.Kind() == reflect.Ptr {
+		target := reflect.New(stateType.Elem())
+		if err := json.Unmarshal(raw, target.Interface()); err != nil {
+			return reflect.Value{}, fmt.Errorf("unmarshal state into %s: %w", stateType.String(), err)
+		}
+		return target, nil
+	}
+	target := reflect.New(stateType)
+	if err := json.Unmarshal(raw, target.Interface()); err != nil {
+		return reflect.Value{}, fmt.Errorf("unmarshal state into %s: %w", stateType.String(), err)
+	}
+	return target.Elem(), nil
+}
+
+func mapToState[StateT any](raw map[string]any) (StateT, error) {
+	var out StateT
+	if anyVal, ok := any(raw).(StateT); ok {
+		return anyVal, nil
+	}
+	payload, err := json.Marshal(raw)
+	if err != nil {
+		return out, fmt.Errorf("marshal state: %w", err)
+	}
+	if err := json.Unmarshal(payload, &out); err != nil {
+		return out, fmt.Errorf("unmarshal state: %w", err)
+	}
+	return out, nil
+}
+
+func mustTypeOf[T any]() reflect.Type {
+	var zero T
+	t := reflect.TypeOf(zero)
+	if t != nil {
+		return t
+	}
+	// Handles nil-able types where zero value has no dynamic type.
+	return reflect.TypeOf((*T)(nil)).Elem()
+}
+
+func unwrapResumeInput(input any) (any, *WaitEvent) {
+	wrapper, ok := input.(map[string]any)
+	if !ok {
+		return input, nil
+	}
+	rawArg, hasArg := wrapper["__lg_resume_arg__"]
+	rawEvent, hasEvent := wrapper["__lg_resume_event__"]
+	if !hasArg || !hasEvent {
+		return input, nil
+	}
+	eventPayload, err := json.Marshal(rawEvent)
+	if err != nil {
+		return rawArg, nil
+	}
+	var event WaitEvent
+	if err := json.Unmarshal(eventPayload, &event); err != nil {
+		return rawArg, nil
+	}
+	return rawArg, &event
+}
+
+func waitForResultFromRaw(target WaitTarget, event WaitEvent) WaitForResult {
+	conditions := target.waitConditions()
+	result := WaitForResult{
+		Conditions: make([]ConditionResult, len(conditions)),
+	}
+	if target.waitKind() == "all_of" {
+		for i, cond := range conditions {
+			if isTimerCondition(cond) {
+				result.Conditions[i] = ConditionResult{Met: true}
+			}
+		}
+	}
+
+	if event.Condition == "timer" {
+		for i, cond := range conditions {
+			if isTimerCondition(cond) {
+				result.Conditions[i] = ConditionResult{Met: true}
+				if target.waitKind() == "any_of" {
+					break
+				}
+			}
+		}
+		return result
+	}
+
+	if event.Condition != "channel" {
+		return result
+	}
+
+	if event.Channel == "__any_of__" || event.Channel == "__all_of__" {
+		var matched []struct {
+			Channel string `json:"channel"`
+			Value   any    `json:"value"`
+		}
+		_ = json.Unmarshal(event.Value, &matched)
+		cursor := 0
+		for i, cond := range conditions {
+			channelName, ok := channelNameOfCondition(cond)
+			if !ok || cursor >= len(matched) {
+				continue
+			}
+			if channelName == matched[cursor].Channel {
+				result.Conditions[i] = ConditionResult{
+					Met:         true,
+					ChannelName: channelName,
+					Values:      toValues(matched[cursor].Value),
+				}
+				cursor++
+			}
+		}
+		return result
+	}
+
+	var value any
+	_ = json.Unmarshal(event.Value, &value)
+	for i, cond := range conditions {
+		channelName, ok := channelNameOfCondition(cond)
+		if !ok {
+			continue
+		}
+		if channelName == event.Channel {
+			result.Conditions[i] = ConditionResult{
+				Met:         true,
+				ChannelName: channelName,
+				Values:      toValues(value),
+			}
+			break
+		}
+	}
+	return result
+}
+
+func isTimerCondition(cond WaitCondition) bool {
+	switch cond.(type) {
+	case TimerCondition, *TimerCondition:
+		return true
+	default:
+		return false
+	}
+}
+
+func channelNameOfCondition(cond WaitCondition) (string, bool) {
+	switch c := cond.(type) {
+	case ChannelCondition:
+		return c.Channel, true
+	case *ChannelCondition:
+		if c == nil {
+			return "", false
+		}
+		return c.Channel, true
+	default:
+		return "", false
+	}
+}
+
+func toValues(value any) []any {
+	if value == nil {
+		return []any{}
+	}
+	if vals, ok := value.([]any); ok {
+		return vals
+	}
+	return []any{value}
+}

langgraph-go/advancedgraph/rust_engine.go

@@ -0,0 +1,456 @@
+package advancedgraph
+
+/*
+#cgo CFLAGS: -I${SRCDIR}/../../rust-core/include
+#cgo LDFLAGS: -L${SRCDIR}/../../rust-core/target/debug -llanggraph_rust_core
+#include "langgraph_rust_core.h"
+#include <stdlib.h>
+extern char* goNodeCallback(unsigned long user_data, char* node, char* arg_json, char* state_json);
+static inline char* rc_run_graph_json_with_go_callback(
+    Engine* ptr,
+    const char* entry_point,
+    const char* finish_point,
+    const char* initial_state_json,
+    const char* initial_input_json,
+    const char* stream_mode,
+    const char* node_locked_fields_json,
+    unsigned long user_data
+) {
+    return rc_run_graph_json(
+        ptr,
+        entry_point,
+        finish_point,
+        initial_state_json,
+        initial_input_json,
+        stream_mode,
+        node_locked_fields_json,
+        user_data,
+        goNodeCallback
+    );
+}
+*/
+import "C"
+
+import (
+	"encoding/json"
+	"fmt"
+	"reflect"
+	"sync"
+	"sync/atomic"
+	"unsafe"
+)
+
+type RustEngine struct {
+	ptr *C.Engine
+}
+
+type runGraphCallbackCtx struct {
+	exec func(node string, nodeInput any, state map[string]any) (Command, error)
+}
+
+var (
+	callbackRegistryMu sync.RWMutex
+	callbackRegistry   = map[uint64]*runGraphCallbackCtx{}
+	callbackNextID     uint64
+)
+
+func registerRunGraphCallbackCtx(ctx *runGraphCallbackCtx) uint64 {
+	id := atomic.AddUint64(&callbackNextID, 1)
+	callbackRegistryMu.Lock()
+	callbackRegistry[id] = ctx
+	callbackRegistryMu.Unlock()
+	return id
+}
+
+func unregisterRunGraphCallbackCtx(id uint64) {
+	callbackRegistryMu.Lock()
+	delete(callbackRegistry, id)
+	callbackRegistryMu.Unlock()
+}
+
+func getRunGraphCallbackCtx(id uint64) (*runGraphCallbackCtx, bool) {
+	callbackRegistryMu.RLock()
+	ctx, ok := callbackRegistry[id]
+	callbackRegistryMu.RUnlock()
+	return ctx, ok
+}
+
+//export goNodeCallback
+func goNodeCallback(userData C.ulong, node *C.char, argJSON *C.char, stateJSON *C.char) *C.char {
+	ctx, ok := getRunGraphCallbackCtx(uint64(userData))
+	if !ok {
+		return cCallbackEnvelopeError("invalid callback context (possibly stale callback)")
+	}
+
+	nodeName := C.GoString(node)
+
+	var nodeInput any
+	if err := json.Unmarshal([]byte(C.GoString(argJSON)), &nodeInput); err != nil {
+		return cCallbackEnvelopeError(fmt.Sprintf("decode arg failed for `%s`: %v", nodeName, err))
+	}
+	var state map[string]any
+	if err := json.Unmarshal([]byte(C.GoString(stateJSON)), &state); err != nil {
+		return cCallbackEnvelopeError(fmt.Sprintf("decode state failed for `%s`: %v", nodeName, err))
+	}
+	nodeInput = coerceJSONValue(nodeInput)
+	stateAny := coerceJSONValue(state)
+	state, ok = stateAny.(map[string]any)
+	if !ok {
+		return cCallbackEnvelopeError(fmt.Sprintf("decoded state has unexpected type for `%s`", nodeName))
+	}
+
+	cmd, err := ctx.exec(nodeName, nodeInput, state)
+	if err != nil {
+		if waitReq, ok := AsErrWaitRequested(err); ok {
+			return cCallbackEnvelopeSuspend(waitReq.Target)
+		}
+		return cCallbackEnvelopeError(err.Error())
+	}
+
+	sends := make([]map[string]any, 0, len(cmd.Goto))
+	for _, send := range cmd.Goto {
+		targetNode, err := resolveSendTarget(send.Node)
+		if err != nil {
+			return cCallbackEnvelopeError(err.Error())
+		}
+		sends = append(sends, map[string]any{
+			"node": targetNode,
+			"arg":  send.NodeInput,
+		})
+	}
+	payload := map[string]any{
+		"update": cmd.Update,
+		"sends":  sends,
+	}
+	raw, err := json.Marshal(map[string]any{
+		"ok":      true,
+		"payload": payload,
+	})
+	if err != nil {
+		return cCallbackEnvelopeError(fmt.Sprintf("encode callback payload failed: %v", err))
+	}
+	return C.CString(string(raw))
+}
+
+func NewRustEngine() *RustEngine {
+	return &RustEngine{ptr: C.rc_engine_new()}
+}
+
+func (e *RustEngine) Close() {
+	if e.ptr != nil {
+		C.rc_engine_free(e.ptr)
+		e.ptr = nil
+	}
+}
+
+func (e *RustEngine) AddAsyncChannel(channel string) error {
+	cch := C.CString(channel)
+	defer C.free(unsafe.Pointer(cch))
+	resp := C.rc_add_async_channel(e.ptr, cch)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) AddCustomOutputStream(streamName string) error {
+	cname := C.CString(streamName)
+	defer C.free(unsafe.Pointer(cname))
+	resp := C.rc_add_custom_output_stream(e.ptr, cname)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) StartStream(streamMode string) error {
+	var cmode *C.char
+	if streamMode != "" {
+		cmode = C.CString(streamMode)
+		defer C.free(unsafe.Pointer(cmode))
+	}
+	resp := C.rc_start_stream(e.ptr, cmode)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) ReceiveStream(streamName string) (any, bool, error) {
+	cname := C.CString(streamName)
+	defer C.free(unsafe.Pointer(cname))
+	resp := C.rc_receive_stream_json(e.ptr, cname)
+	defer C.rc_string_free(resp)
+
+	raw := C.GoString(resp)
+	var status struct {
+		OK       bool            `json:"ok"`
+		Error    string          `json:"error"`
+		HasEvent bool            `json:"has_event"`
+		Event    json.RawMessage `json:"event"`
+	}
+	if err := json.Unmarshal([]byte(raw), &status); err != nil {
+		return nil, false, fmt.Errorf("decode rust stream response: %w", err)
+	}
+	if !status.OK {
+		return nil, false, fmt.Errorf("rust stream failed: %s", status.Error)
+	}
+	if !status.HasEvent {
+		return nil, false, nil
+	}
+	var event any
+	if err := json.Unmarshal(status.Event, &event); err != nil {
+		return nil, false, fmt.Errorf("decode stream event: %w", err)
+	}
+	return coerceJSONValue(event), true, nil
+}
+
+func (e *RustEngine) SendCustomStreamEvent(streamName string, value any) error {
+	payload, err := json.Marshal(value)
+	if err != nil {
+		return fmt.Errorf("marshal stream event: %w", err)
+	}
+	cname := C.CString(streamName)
+	cval := C.CString(string(payload))
+	defer C.free(unsafe.Pointer(cname))
+	defer C.free(unsafe.Pointer(cval))
+	resp := C.rc_send_custom_stream_event(e.ptr, cname, cval)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) CloseAllStreams() error {
+	resp := C.rc_close_all_streams(e.ptr)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) Publish(channel string, value any) error {
+	payload, err := json.Marshal(value)
+	if err != nil {
+		return fmt.Errorf("marshal publish value: %w", err)
+	}
+	cch := C.CString(channel)
+	cval := C.CString(string(payload))
+	defer C.free(unsafe.Pointer(cch))
+	defer C.free(unsafe.Pointer(cval))
+	resp := C.rc_publish_json(e.ptr, cch, cval)
+	return parseRustStatus(resp)
+}
+
+func (e *RustEngine) WaitAnyOf(cond AnyOfCondition) (WaitEvent, error) {
+	payload, err := json.Marshal(cond)
+	if err != nil {
+		return WaitEvent{}, fmt.Errorf("marshal any_of: %w", err)
+	}
+	cpayload := C.CString(string(payload))
+	defer C.free(unsafe.Pointer(cpayload))
+	resp := C.rc_wait_any_of_json(e.ptr, cpayload)
+	defer C.rc_string_free(resp)
+
+	raw := C.GoString(resp)
+	var status struct {
+		OK    bool            `json:"ok"`
+		Error string          `json:"error"`
+		Event json.RawMessage `json:"event"`
+	}
+	if err := json.Unmarshal([]byte(raw), &status); err != nil {
+		return WaitEvent{}, fmt.Errorf("decode rust wait response: %w", err)
+	}
+	if !status.OK {
+		return WaitEvent{}, fmt.Errorf("rust wait failed: %s", status.Error)
+	}
+	var event WaitEvent
+	if err := json.Unmarshal(status.Event, &event); err != nil {
+		return WaitEvent{}, fmt.Errorf("decode wait event: %w", err)
+	}
+	return event, nil
+}
+
+func (e *RustEngine) WaitAllOf(cond AllOfCondition) (WaitEvent, error) {
+	payload, err := json.Marshal(cond)
+	if err != nil {
+		return WaitEvent{}, fmt.Errorf("marshal all_of: %w", err)
+	}
+	cpayload := C.CString(string(payload))
+	defer C.free(unsafe.Pointer(cpayload))
+	resp := C.rc_wait_all_of_json(e.ptr, cpayload)
+	defer C.rc_string_free(resp)
+
+	raw := C.GoString(resp)
+	var status struct {
+		OK    bool            `json:"ok"`
+		Error string          `json:"error"`
+		Event json.RawMessage `json:"event"`
+	}
+	if err := json.Unmarshal([]byte(raw), &status); err != nil {
+		return WaitEvent{}, fmt.Errorf("decode rust wait response: %w", err)
+	}
+	if !status.OK {
+		return WaitEvent{}, fmt.Errorf("rust wait failed: %s", status.Error)
+	}
+	var event WaitEvent
+	if err := json.Unmarshal(status.Event, &event); err != nil {
+		return WaitEvent{}, fmt.Errorf("decode wait event: %w", err)
+	}
+	return event, nil
+}
+
+func (e *RustEngine) RunGraph(
+	entryPoint string,
+	finishPoint string,
+	streamMode string,
+	initialState any,
+	initialInput any,
+	nodeLockedFields map[string][]string,
+	exec func(node string, nodeInput any, state map[string]any) (Command, error),
+) (map[string]any, error) {
+	initialJSON, err := json.Marshal(initialState)
+	if err != nil {
+		return nil, fmt.Errorf("marshal initial state: %w", err)
+	}
+	initialInputJSON, err := json.Marshal(initialInput)
+	if err != nil {
+		return nil, fmt.Errorf("marshal initial input: %w", err)
+	}
+	lockedFieldsJSON, err := json.Marshal(nodeLockedFields)
+	if err != nil {
+		return nil, fmt.Errorf("marshal node locked fields: %w", err)
+	}
+	centry := C.CString(entryPoint)
+	cfinish := C.CString(finishPoint)
+	cinitial := C.CString(string(initialJSON))
+	cinitialInput := C.CString(string(initialInputJSON))
+	clockedFields := C.CString(string(lockedFieldsJSON))
+	var cstreamMode *C.char
+	if streamMode != "" {
+		cstreamMode = C.CString(streamMode)
+	}
+	defer C.free(unsafe.Pointer(centry))
+	defer C.free(unsafe.Pointer(cfinish))
+	defer C.free(unsafe.Pointer(cinitial))
+	defer C.free(unsafe.Pointer(cinitialInput))
+	defer C.free(unsafe.Pointer(clockedFields))
+	if cstreamMode != nil {
+		defer C.free(unsafe.Pointer(cstreamMode))
+	}
+
+	callbackID := registerRunGraphCallbackCtx(&runGraphCallbackCtx{exec: exec})
+	defer unregisterRunGraphCallbackCtx(callbackID)
+
+	resp := C.rc_run_graph_json_with_go_callback(
+		e.ptr,
+		centry,
+		cfinish,
+		cinitial,
+		cinitialInput,
+		cstreamMode,
+		clockedFields,
+		C.ulong(callbackID),
+	)
+	defer C.rc_string_free(resp)
+
+	raw := C.GoString(resp)
+	var status struct {
+		OK    bool           `json:"ok"`
+		Error string         `json:"error"`
+		State map[string]any `json:"state"`
+	}
+	if err := json.Unmarshal([]byte(raw), &status); err != nil {
+		return nil, fmt.Errorf("decode rust run response: %w", err)
+	}
+	if !status.OK {
+		return nil, fmt.Errorf("rust run failed: %s", status.Error)
+	}
+	coerced := coerceJSONValue(status.State)
+	typed, ok := coerced.(map[string]any)
+	if !ok {
+		return nil, fmt.Errorf("unexpected state type from rust run")
+	}
+	return typed, nil
+}
+
+func cCallbackEnvelopeError(message string) *C.char {
+	raw, _ := json.Marshal(map[string]any{
+		"ok":    false,
+		"error": message,
+	})
+	return C.CString(string(raw))
+}
+
+func cCallbackEnvelopeSuspend(target WaitTarget) *C.char {
+	if target == nil {
+		return cCallbackEnvelopeError("wait requested with nil target")
+	}
+	kind := target.waitKind()
+	if kind != "any_of" && kind != "all_of" {
+		return cCallbackEnvelopeError(fmt.Sprintf("unsupported wait target kind `%s`", kind))
+	}
+	var payload map[string]any
+	if kind == "any_of" {
+		payload = map[string]any{
+			"kind":   "any_of",
+			"any_of": AnyOfCondition{Conditions: target.waitConditions()},
+		}
+	} else {
+		payload = map[string]any{
+			"kind":   "all_of",
+			"all_of": AllOfCondition{Conditions: target.waitConditions()},
+		}
+	}
+	raw, _ := json.Marshal(map[string]any{
+		"ok":      true,
+		"suspend": payload,
+	})
+	return C.CString(string(raw))
+}
+
+func resolveSendTarget(target any) (string, error) {
+	if name, ok := target.(string); ok {
+		if name == "" {
+			return "", fmt.Errorf("send target cannot be empty string")
+		}
+		return name, nil
+	}
+	rv := reflect.ValueOf(target)
+	if rv.IsValid() && rv.Kind() == reflect.Func {
+		return NodeName(target), nil
+	}
+	return "", fmt.Errorf("unsupported send target type %T", target)
+}
+
+func coerceJSONValue(v any) any {
+	switch t := v.(type) {
+	case map[string]any:
+		out := make(map[string]any, len(t))
+		for k, val := range t {
+			out[k] = coerceJSONValue(val)
+		}
+		return out
+	case []any:
+		coerced := make([]any, len(t))
+		allStrings := true
+		for i, val := range t {
+			cv := coerceJSONValue(val)
+			coerced[i] = cv
+			if _, ok := cv.(string); !ok {
+				allStrings = false
+			}
+		}
+		if allStrings {
+			out := make([]string, len(coerced))
+			for i, item := range coerced {
+				out[i] = item.(string)
+			}
+			return out
+		}
+		return coerced
+	default:
+		return v
+	}
+}
+
+func parseRustStatus(resp *C.char) error {
+	defer C.rc_string_free(resp)
+	raw := C.GoString(resp)
+	var status struct {
+		OK    bool   `json:"ok"`
+		Error string `json:"error"`
+	}
+	if err := json.Unmarshal([]byte(raw), &status); err != nil {
+		return fmt.Errorf("decode rust response: %w", err)
+	}
+	if !status.OK {
+		return fmt.Errorf("rust error: %s", status.Error)
+	}
+	return nil
+}

langgraph-go/advancedgraph/types.go

@@ -0,0 +1,191 @@
+package advancedgraph
+
+import (
+	"encoding/json"
+	"errors"
+	"fmt"
+	"reflect"
+)
+
+type WaitCondition interface {
+	json.Marshaler
+}
+
+type ChannelCondition struct {
+	Channel string
+	Min     int
+	Max     int
+}
+
+type channelConditionJSON struct {
+	Kind    string `json:"kind"`
+	Channel string `json:"channel"`
+	Min     int    `json:"min"`
+	Max     int    `json:"max"`
+}
+
+func (c ChannelCondition) MarshalJSON() ([]byte, error) {
+	min := c.Min
+	if min <= 0 {
+		min = 1
+	}
+	return json.Marshal(channelConditionJSON{
+		Kind:    "channel",
+		Channel: c.Channel,
+		Min:     min,
+		Max:     c.Max,
+	})
+}
+
+type TimerCondition struct {
+	Seconds float64
+}
+
+type timerConditionJSON struct {
+	Kind    string  `json:"kind"`
+	Seconds float64 `json:"seconds"`
+}
+
+func (t TimerCondition) MarshalJSON() ([]byte, error) {
+	return json.Marshal(timerConditionJSON{
+		Kind:    "timer",
+		Seconds: t.Seconds,
+	})
+}
+
+type AnyOfCondition struct {
+	Conditions []WaitCondition
+}
+
+type AllOfCondition struct {
+	Conditions []WaitCondition
+}
+
+type WaitTarget interface {
+	waitKind() string
+	waitConditions() []WaitCondition
+}
+
+func (a AnyOfCondition) waitKind() string {
+	return "any_of"
+}
+
+func (a AnyOfCondition) waitConditions() []WaitCondition {
+	return a.Conditions
+}
+
+func (a AllOfCondition) waitKind() string {
+	return "all_of"
+}
+
+func (a AllOfCondition) waitConditions() []WaitCondition {
+	return a.Conditions
+}
+
+func AnyOf(conditions ...WaitCondition) AnyOfCondition {
+	return AnyOfCondition{Conditions: append([]WaitCondition{}, conditions...)}
+}
+
+func AllOf(conditions ...WaitCondition) AllOfCondition {
+	return AllOfCondition{Conditions: append([]WaitCondition{}, conditions...)}
+}
+
+func (a AnyOfCondition) MarshalJSON() ([]byte, error) {
+	result := struct {
+		Conditions []json.RawMessage `json:"conditions"`
+	}{
+		Conditions: make([]json.RawMessage, 0, len(a.Conditions)),
+	}
+	for i, cond := range a.Conditions {
+		if isNilWaitCondition(cond) {
+			return nil, fmt.Errorf("any_of condition[%d] is nil", i)
+		}
+		raw, err := cond.MarshalJSON()
+		if err != nil {
+			return nil, fmt.Errorf("encode any_of condition[%d]: %w", i, err)
+		}
+		result.Conditions = append(result.Conditions, json.RawMessage(raw))
+	}
+	return json.Marshal(result)
+}
+
+func (a AllOfCondition) MarshalJSON() ([]byte, error) {
+	result := struct {
+		Conditions []json.RawMessage `json:"conditions"`
+	}{
+		Conditions: make([]json.RawMessage, 0, len(a.Conditions)),
+	}
+	for i, cond := range a.Conditions {
+		if isNilWaitCondition(cond) {
+			return nil, fmt.Errorf("all_of condition[%d] is nil", i)
+		}
+		raw, err := cond.MarshalJSON()
+		if err != nil {
+			return nil, fmt.Errorf("encode all_of condition[%d]: %w", i, err)
+		}
+		result.Conditions = append(result.Conditions, json.RawMessage(raw))
+	}
+	return json.Marshal(result)
+}
+
+func isNilWaitCondition(cond WaitCondition) bool {
+	if cond == nil {
+		return true
+	}
+	v := reflect.ValueOf(cond)
+	switch v.Kind() {
+	case reflect.Ptr, reflect.Interface, reflect.Slice, reflect.Map, reflect.Func:
+		return v.IsNil()
+	default:
+		return false
+	}
+}
+
+type WaitEvent struct {
+	Condition string          `json:"condition"`
+	Channel   string          `json:"channel,omitempty"`
+	Value     json.RawMessage `json:"value,omitempty"`
+	Seconds   float64         `json:"seconds,omitempty"`
+}
+
+type ConditionResult struct {
+	Met         bool   `json:"met"`
+	ChannelName string `json:"channel_name,omitempty"`
+	Values      []any  `json:"values,omitempty"`
+}
+
+type WaitForResult struct {
+	Conditions []ConditionResult `json:"conditions"`
+}
+
+type Send struct {
+	Node      any
+	NodeInput any
+}
+
+type Command struct {
+	Update any
+	Goto   []Send
+}
+
+type ErrWaitRequested struct {
+	Target WaitTarget
+}
+
+func (e ErrWaitRequested) Error() string {
+	return "wait requested"
+}
+
+func AsErrWaitRequested(err error) (ErrWaitRequested, bool) {
+	var target ErrWaitRequested
+	if !errors.As(err, &target) {
+		return target, false
+	}
+	return target, true
+}
+
+func DecodeString(raw json.RawMessage) string {
+	var s string
+	_ = json.Unmarshal(raw, &s)
+	return s
+}

langgraph-go/go.mod

@@ -0,0 +1,3 @@
+module github.com/langchain-ai/langgraph/langgraph-go
+
+go 1.25

langgraph-go/stategraph/state_graph.go

@@ -0,0 +1,329 @@
+package stategraph
+
+import (
+	"encoding/json"
+	"errors"
+	"fmt"
+	"slices"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+type StateNodeFunc[StateT any] func(ctx *Context, state StateT) (StateT, error)
+
+const (
+	internalBarrierChannel   = "__stategraph_barrier"
+	internalInterruptChannel = "__stategraph_interrupt"
+)
+
+type Context struct {
+	inner *ag.Context
+}
+
+func (c *Context) Interrupt(name string) (any, error) {
+	if name == "" {
+		return nil, fmt.Errorf("interrupt name cannot be empty")
+	}
+	event, err := c.inner.WaitFor(ag.AnyOf(ag.ChannelCondition{
+		Channel: internalInterruptChannel,
+		Min:     1,
+	}))
+	if err != nil {
+		if waitReq, ok := ag.AsErrWaitRequested(err); ok {
+			return nil, errInterruptRequested{
+				Name:      name,
+				Condition: waitReq.Target,
+			}
+		}
+		return nil, err
+	}
+	if len(event.Conditions) == 0 || !event.Conditions[0].Met {
+		return nil, nil
+	}
+	values := event.Conditions[0].Values
+	if len(values) == 0 {
+		return nil, nil
+	}
+	rawValue := values[0]
+	valueBytes, err := json.Marshal(rawValue)
+	if err != nil {
+		return nil, fmt.Errorf("encode interrupt `%s` value: %w", name, err)
+	}
+
+	var payload interruptPayload
+	if err := json.Unmarshal(valueBytes, &payload); err != nil {
+		var value any
+		if err := json.Unmarshal(valueBytes, &value); err != nil {
+			return nil, fmt.Errorf("decode interrupt `%s` value: %w", name, err)
+		}
+		return value, nil
+	}
+	if payload.Name != "" && payload.Name != name {
+		return nil, fmt.Errorf("interrupt name mismatch: expected `%s`, got `%s`", name, payload.Name)
+	}
+	if len(payload.Value) == 0 {
+		return nil, nil
+	}
+	var value any
+	if err := json.Unmarshal(payload.Value, &value); err != nil {
+		return nil, fmt.Errorf("decode interrupt `%s` payload: %w", name, err)
+	}
+	return value, nil
+}
+
+type errInterruptRequested struct {
+	Name      string
+	Condition ag.WaitTarget
+}
+
+func (e errInterruptRequested) Error() string {
+	if e.Name == "" {
+		return "interrupt requested"
+	}
+	return fmt.Sprintf("interrupt requested: %s", e.Name)
+}
+
+func asErrInterruptRequested(err error) (errInterruptRequested, bool) {
+	var target errInterruptRequested
+	if !errors.As(err, &target) {
+		return target, false
+	}
+	return target, true
+}
+
+type BasicStateGraph[StateT any] struct {
+	nodes map[string]StateNodeFunc[StateT]
+	edges map[string][]string
+}
+
+type interruptPayload struct {
+	Name  string          `json:"name"`
+	Value json.RawMessage `json:"value"`
+}
+
+func NewBasicStateGraph[StateT any]() *BasicStateGraph[StateT] {
+	return &BasicStateGraph[StateT]{
+		nodes: make(map[string]StateNodeFunc[StateT]),
+		edges: make(map[string][]string),
+	}
+}
+
+func (g *BasicStateGraph[StateT]) AddNode(fn StateNodeFunc[StateT]) string {
+	name := ag.NodeName(fn)
+	if _, exists := g.nodes[name]; exists {
+		panic(fmt.Sprintf("node `%s` already exists", name))
+	}
+	g.nodes[name] = fn
+	return name
+}
+
+func (g *BasicStateGraph[StateT]) AddEdge(from StateNodeFunc[StateT], to StateNodeFunc[StateT]) {
+	fromName := ag.NodeName(from)
+	toName := ag.NodeName(to)
+	if _, ok := g.nodes[fromName]; !ok {
+		panic(fmt.Sprintf("source node `%s` does not exist", fromName))
+	}
+	if _, ok := g.nodes[toName]; !ok {
+		panic(fmt.Sprintf("target node `%s` does not exist", toName))
+	}
+	g.edges[fromName] = append(g.edges[fromName], toName)
+}
+
+type CompiledBasicStateGraph[StateT any] struct {
+	inner *ag.CompiledGraph[StateT]
+}
+
+type Handler[StateT any] struct {
+	inner *ag.Handler[StateT]
+}
+
+func (h *Handler[StateT]) WaitForResult() (StateT, error) {
+	return h.inner.WaitForResult()
+}
+
+func (h *Handler[StateT]) Resume(name string, value any) error {
+	if name == "" {
+		return fmt.Errorf("interrupt name cannot be empty")
+	}
+	return h.inner.PublishToChannel(internalInterruptChannel, map[string]any{
+		"name":  name,
+		"value": value,
+	})
+}
+
+func (g *BasicStateGraph[StateT]) Compile() *CompiledBasicStateGraph[StateT] {
+	if len(g.nodes) == 0 {
+		panic("graph has no nodes")
+	}
+	levels, err := g.computeSupersteps()
+	if err != nil {
+		panic(err)
+	}
+	adv := ag.NewAdvancedStateGraph[StateT]()
+	adv.AddAsyncChannel(internalBarrierChannel)
+	adv.AddAsyncChannel(internalInterruptChannel)
+
+	const finalNodeName = "__stategraph_finish"
+	finalNode := func(_ *ag.Context, _ any, state StateT) (ag.Command, error) {
+		return ag.Command{Update: state}, nil
+	}
+	adv.AddFinishNodeAs(finalNodeName, finalNode)
+
+	for stepIdx, stepNodes := range levels {
+		for _, nodeName := range stepNodes {
+			userFn := g.nodes[nodeName]
+			nextBarrier := fmt.Sprintf("__stategraph_barrier_%d", stepIdx+1)
+			wrapper := func(ctx *ag.Context, _ any, state StateT) (ag.Command, error) {
+				updated, err := userFn(&Context{inner: ctx}, state)
+				if err != nil {
+					if interruptReq, ok := asErrInterruptRequested(err); ok {
+						cond := interruptReq.Condition
+						if cond == nil {
+							cond = ag.AnyOf(ag.ChannelCondition{
+								Channel: internalInterruptChannel,
+								Min:     1,
+							})
+						}
+						return ag.Command{}, ag.ErrWaitRequested{Target: cond}
+					}
+					return ag.Command{}, err
+				}
+				if err := ctx.PublishToChannel(internalBarrierChannel, map[string]any{
+					"step": stepIdx,
+				}); err != nil {
+					return ag.Command{}, err
+				}
+				return ag.Command{
+					Update: updated,
+					Goto:   []ag.Send{{Node: nextBarrier}},
+				}, nil
+			}
+			adv.AddNodeAs(fmt.Sprintf("__stategraph_node_%s", nodeName), wrapper)
+		}
+	}
+
+	lastBarrier := len(levels)
+	for barrierStep := 0; barrierStep <= lastBarrier; barrierStep++ {
+		barrierName := fmt.Sprintf("__stategraph_barrier_%d", barrierStep)
+		nextStep := barrierStep
+		barrier := func(ctx *ag.Context, _ any, state StateT) (ag.Command, error) {
+			if nextStep > 0 {
+				needed := len(levels[nextStep-1])
+				_, err := ctx.WaitFor(ag.AnyOf(ag.ChannelCondition{
+					Channel: internalBarrierChannel,
+					Min:     needed,
+				}))
+				if err != nil {
+					return ag.Command{}, err
+				}
+			}
+			if nextStep >= len(levels) {
+				return ag.Command{
+					Update: state,
+					Goto:   []ag.Send{{Node: finalNodeName}},
+				}, nil
+			}
+			sends := make([]ag.Send, 0, len(levels[nextStep]))
+			for _, nodeName := range levels[nextStep] {
+				sends = append(sends, ag.Send{
+					Node: fmt.Sprintf("__stategraph_node_%s", nodeName),
+				})
+			}
+			return ag.Command{
+				Update: state,
+				Goto:   sends,
+			}, nil
+		}
+		if barrierStep == 0 {
+			adv.AddEntryNodeAs(barrierName, barrier)
+		} else {
+			adv.AddNodeAs(barrierName, barrier)
+		}
+	}
+
+	return &CompiledBasicStateGraph[StateT]{
+		inner: adv.Compile(),
+	}
+}
+
+func (g *CompiledBasicStateGraph[StateT]) Start(initialState StateT) (*Handler[StateT], error) {
+	raw, err := g.inner.Start(nil, initialState)
+	if err != nil {
+		return nil, err
+	}
+	return &Handler[StateT]{inner: raw}, nil
+}
+
+func (g *CompiledBasicStateGraph[StateT]) Invoke(initialState StateT) (StateT, error) {
+	handler, err := g.Start(initialState)
+	if err != nil {
+		var zero StateT
+		return zero, err
+	}
+	return handler.WaitForResult()
+}
+
+func (g *BasicStateGraph[StateT]) computeSupersteps() ([][]string, error) {
+	indegree := make(map[string]int, len(g.nodes))
+	for name := range g.nodes {
+		indegree[name] = 0
+	}
+	for from, tos := range g.edges {
+		if _, ok := g.nodes[from]; !ok {
+			return nil, fmt.Errorf("edge source `%s` does not exist", from)
+		}
+		for _, to := range tos {
+			if _, ok := g.nodes[to]; !ok {
+				return nil, fmt.Errorf("edge target `%s` does not exist", to)
+			}
+			indegree[to]++
+		}
+	}
+
+	queue := make([]string, 0, len(g.nodes))
+	level := make(map[string]int, len(g.nodes))
+	for name, deg := range indegree {
+		if deg == 0 {
+			queue = append(queue, name)
+		}
+	}
+	if len(queue) == 0 {
+		return nil, fmt.Errorf("graph has no entry nodes (cycle suspected)")
+	}
+
+	processed := 0
+	for len(queue) > 0 {
+		curr := queue[0]
+		queue = queue[1:]
+		processed++
+		currLevel := level[curr]
+		for _, to := range g.edges[curr] {
+			if level[to] < currLevel+1 {
+				level[to] = currLevel + 1
+			}
+			indegree[to]--
+			if indegree[to] == 0 {
+				queue = append(queue, to)
+			}
+		}
+	}
+	if processed != len(g.nodes) {
+		return nil, fmt.Errorf("graph contains a cycle")
+	}
+
+	maxLevel := 0
+	for _, lv := range level {
+		if lv > maxLevel {
+			maxLevel = lv
+		}
+	}
+	levels := make([][]string, maxLevel+1)
+	for nodeName := range g.nodes {
+		lv := level[nodeName]
+		levels[lv] = append(levels[lv], nodeName)
+	}
+	for i := range levels {
+		slices.Sort(levels[i])
+	}
+	return levels, nil
+}

langgraph-go/stategraph/state_graph_test.go

@@ -0,0 +1,218 @@
+package stategraph_test
+
+import (
+	"fmt"
+	"sync"
+	"sync/atomic"
+	"testing"
+	"time"
+
+	sg "github.com/langchain-ai/langgraph/langgraph-go/stategraph"
+)
+
+type stateGraphState struct {
+	Noop bool `json:"noop"`
+}
+
+type orderRecorder struct {
+	mu     sync.Mutex
+	orders map[string]int32
+	seq    int32
+}
+
+func newOrderRecorder() *orderRecorder {
+	return &orderRecorder{orders: make(map[string]int32)}
+}
+
+func (f *orderRecorder) record(name string) {
+	idx := atomic.AddInt32(&f.seq, 1)
+	f.mu.Lock()
+	f.orders[name] = idx
+	f.mu.Unlock()
+}
+
+type orderGraph struct {
+	recorder *orderRecorder
+}
+
+func newOrderGraph() *orderGraph {
+	return &orderGraph{
+		recorder: newOrderRecorder(),
+	}
+}
+
+func (g *orderGraph) A(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("A")
+	return state, nil
+}
+
+func (g *orderGraph) B1(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("B1")
+	return state, nil
+}
+
+func (g *orderGraph) B2(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("B2")
+	return state, nil
+}
+
+func (g *orderGraph) C1(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("C1")
+	return state, nil
+}
+
+func (g *orderGraph) C2(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("C2")
+	return state, nil
+}
+
+func (g *orderGraph) C3(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("C3")
+	return state, nil
+}
+
+func (g *orderGraph) D(_ *sg.Context, state stateGraphState) (stateGraphState, error) {
+	g.recorder.record("D")
+	return state, nil
+}
+
+func TestBasicStateGraphWithoutInterrupt(t *testing.T) {
+	fixture := newOrderGraph()
+	graph := sg.NewBasicStateGraph[stateGraphState]()
+	graph.AddNode(fixture.A)
+	graph.AddNode(fixture.B1)
+	graph.AddNode(fixture.B2)
+	graph.AddNode(fixture.C1)
+	graph.AddNode(fixture.C2)
+	graph.AddNode(fixture.C3)
+	graph.AddNode(fixture.D)
+
+	graph.AddEdge(fixture.A, fixture.B1)
+	graph.AddEdge(fixture.A, fixture.B2)
+	graph.AddEdge(fixture.B1, fixture.C1)
+	graph.AddEdge(fixture.B1, fixture.C2)
+	graph.AddEdge(fixture.B2, fixture.C3)
+	graph.AddEdge(fixture.C1, fixture.D)
+	graph.AddEdge(fixture.C2, fixture.D)
+	graph.AddEdge(fixture.C3, fixture.D)
+
+	_, err := graph.Compile().Invoke(stateGraphState{})
+	if err != nil {
+		t.Fatalf("invoke failed: %v", err)
+	}
+
+	fixture.recorder.mu.Lock()
+	orders := make(map[string]int32, len(fixture.recorder.orders))
+	for k, v := range fixture.recorder.orders {
+		orders[k] = v
+	}
+	fixture.recorder.mu.Unlock()
+
+	for _, name := range []string{"A", "B1", "B2", "C1", "C2", "C3", "D"} {
+		if _, ok := orders[name]; !ok {
+			t.Fatalf("node %s did not execute; orders=%v", name, orders)
+		}
+	}
+	maxB := maxInt32(orders["B1"], orders["B2"])
+	minC := minInt32(orders["C1"], minInt32(orders["C2"], orders["C3"]))
+	maxC := maxInt32(orders["C1"], maxInt32(orders["C2"], orders["C3"]))
+	if !(orders["A"] < orders["B1"] && orders["A"] < orders["B2"]) {
+		t.Fatalf("A should run before B-step, orders=%v", orders)
+	}
+	if !(maxB < minC) {
+		t.Fatalf("B-step should finish before C-step, orders=%v", orders)
+	}
+	if !(maxC < orders["D"]) {
+		t.Fatalf("C-step should finish before D, orders=%v", orders)
+	}
+}
+
+type interruptState struct {
+	A bool `json:"a"`
+	B bool `json:"b"`
+}
+
+type interruptFixture struct{}
+
+func (f *interruptFixture) A(_ *sg.Context, state interruptState) (interruptState, error) {
+	state.A = true
+	return state, nil
+}
+
+func (f *interruptFixture) B(ctx *sg.Context, state interruptState) (interruptState, error) {
+	if !state.A {
+		return state, fmt.Errorf("B should observe A=true")
+	}
+	value, err := ctx.Interrupt("resume_channel")
+	if err != nil {
+		return state, err
+	}
+	s, ok := value.(string)
+	if !ok || s != "go" {
+		return state, fmt.Errorf("unexpected interrupt payload: %#v", value)
+	}
+	state.B = true
+	return state, nil
+}
+
+func TestBasicStateGraphWithInterrupt(t *testing.T) {
+	fixture := &interruptFixture{}
+	graph := sg.NewBasicStateGraph[interruptState]()
+	graph.AddNode(fixture.A)
+	graph.AddNode(fixture.B)
+	graph.AddEdge(fixture.A, fixture.B)
+
+	handler, err := graph.Compile().Start(interruptState{})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	doneCh := make(chan interruptState, 1)
+	errCh := make(chan error, 1)
+	go func() {
+		result, runErr := handler.WaitForResult()
+		if runErr != nil {
+			errCh <- runErr
+			return
+		}
+		doneCh <- result
+	}()
+
+	select {
+	case <-doneCh:
+		t.Fatalf("run should pause for interrupt, but completed early")
+	case err := <-errCh:
+		t.Fatalf("run should pause for interrupt, but failed early: %v", err)
+	case <-time.After(120 * time.Millisecond):
+		// expected: paused
+	}
+
+	if err := handler.Resume("resume_channel", "go"); err != nil {
+		t.Fatalf("resume interrupt failed: %v", err)
+	}
+
+	select {
+	case err := <-errCh:
+		t.Fatalf("run failed after interrupt: %v", err)
+	case result := <-doneCh:
+		if !(result.A && result.B) {
+			t.Fatalf("unexpected final state: %#v", result)
+		}
+	case <-time.After(2 * time.Second):
+		t.Fatalf("timeout waiting for resumed run completion")
+	}
+}
+
+func minInt32(a int32, b int32) int32 {
+	if a < b {
+		return a
+	}
+	return b
+}
+
+func maxInt32(a int32, b int32) int32 {
+	if a > b {
+		return a
+	}
+	return b
+}

langgraph-go/tests/sub_agents_test.go

@@ -0,0 +1,213 @@
+package tests
+
+import (
+	"slices"
+	"testing"
+	"time"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+type decision struct {
+	Type     string
+	SubAgent string
+	Tool     string
+	Complete string
+}
+
+type mockLLM struct {
+	responses [][]decision
+	i         int
+}
+
+func (m *mockLLM) invoke() []decision {
+	if m.i >= len(m.responses) {
+		return []decision{}
+	}
+	resp := m.responses[m.i]
+	m.i++
+	return resp
+}
+
+type lunchWorkflow struct {
+	planner *mockLLM
+}
+
+type lunchState struct {
+	Input  string   `json:"input"`
+	Output []string `json:"output"`
+	Done   string   `json:"done"`
+}
+
+func (w *lunchWorkflow) llmNode(ctx *ag.Context, _ any, _ lunchState) (ag.Command, error) {
+	decisions := w.planner.invoke()
+	sends := make([]ag.Send, 0, 4)
+	for _, d := range decisions {
+		if d.Type == "end" {
+			return ag.Command{
+				Goto: []ag.Send{
+					{Node: w.orderFoodNode, NodeInput: d.Complete},
+				},
+			}, nil
+		}
+		if d.Type == "sub_agent" {
+			sends = append(sends, ag.Send{Node: w.subAgentNode, NodeInput: d.SubAgent})
+		}
+		if d.Type == "tool" {
+			sends = append(sends, ag.Send{Node: w.toolNode, NodeInput: d.Tool})
+		}
+	}
+	sends = append(sends, ag.Send{Node: w.waitNode})
+	return ag.Command{Goto: sends}, nil
+}
+
+func (w *lunchWorkflow) waitNode(ctx *ag.Context, _ any, state lunchState) (ag.Command, error) {
+	event, err := ctx.WaitFor(
+		ag.AnyOf(
+			ag.ChannelCondition{Channel: "tool_completion_channel"},
+			ag.ChannelCondition{Channel: "subagent_completion_channel"},
+			ag.ChannelCondition{Channel: "user_input_channel"},
+			ag.TimerCondition{Seconds: 1},
+		),
+	)
+	if err != nil {
+		return ag.Command{}, err
+	}
+
+	output := append([]string(nil), state.Output...)
+	hadChannel := false
+	for _, cond := range event.Conditions {
+		if !cond.Met || cond.ChannelName == "" {
+			continue
+		}
+		for _, raw := range cond.Values {
+			payload, _ := raw.(string)
+			switch cond.ChannelName {
+			case "tool_completion_channel":
+				output = append(output, "tool: "+payload)
+			case "subagent_completion_channel":
+				output = append(output, "sub_agent: "+payload)
+			case "user_input_channel":
+				output = append(output, "user_input: "+payload)
+			}
+		}
+		hadChannel = true
+	}
+	if hadChannel {
+		state.Output = output
+		return ag.Command{Goto: []ag.Send{{Node: w.llmNode}}, Update: state}, nil
+	}
+
+	output = append(output, "timer: no updates yet")
+	state.Output = output
+	return ag.Command{Goto: []ag.Send{{Node: w.waitNode}}, Update: state}, nil
+}
+
+func (w *lunchWorkflow) toolNode(ctx *ag.Context, input any, _ lunchState) (ag.Command, error) {
+	toolInput, _ := input.(string)
+	time.Sleep(100 * time.Millisecond)
+	err := ctx.PublishToChannel("tool_completion_channel", "tool completed for: "+toolInput)
+	return ag.Command{}, err
+}
+
+func (w *lunchWorkflow) subAgentNode(ctx *ag.Context, input any, _ lunchState) (ag.Command, error) {
+	subInput, _ := input.(string)
+	time.Sleep(5 * time.Second)
+	err := ctx.PublishToChannel(
+		"subagent_completion_channel",
+		"research sub agent completed for: "+subInput,
+	)
+	return ag.Command{}, err
+}
+
+func (w *lunchWorkflow) orderFoodNode(ctx *ag.Context, input any, state lunchState) (ag.Command, error) {
+	complete, _ := input.(string)
+	output := append([]string(nil), state.Output...)
+	output = append(output, "order_food: "+complete)
+	state.Output = output
+	state.Done = complete
+	return ag.Command{Update: state}, nil
+}
+
+func TestSubAgentsEquivalentFlow(t *testing.T) {
+	planner := &mockLLM{
+		responses: [][]decision{
+			{
+				{Type: "sub_agent", SubAgent: "research lunch options"},
+				{Type: "tool", Tool: "slack_tool"},
+			},
+			{},
+			{},
+			{{Type: "sub_agent", SubAgent: "find vegetarian fallback"}},
+			{{Type: "end", Complete: "order submitted"}},
+		},
+	}
+	workflow := &lunchWorkflow{
+		planner: planner,
+	}
+
+	graph := ag.NewAdvancedStateGraph[lunchState]()
+	graph.AddAsyncChannel("tool_completion_channel")
+	graph.AddAsyncChannel("subagent_completion_channel")
+	graph.AddAsyncChannel("user_input_channel")
+
+	graph.AddEntryNode(workflow.llmNode)
+	graph.AddNode(workflow.waitNode)
+	graph.AddNode(workflow.toolNode)
+	graph.AddNode(workflow.subAgentNode)
+	graph.AddFinishNode(workflow.orderFoodNode)
+
+	handler, err := graph.Compile().Start(
+		nil,
+		lunchState{
+			Input:  "help me get something for lunch",
+			Output: []string{},
+			Done:   "",
+		},
+	)
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	time.Sleep(10 * time.Millisecond)
+	if err := handler.PublishToChannel("user_input_channel", "No spicy food please"); err != nil {
+		t.Fatalf("publish failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+
+	output := result.Output
+	if len(output) == 0 {
+		t.Fatalf("output is empty, full result=%#v", result)
+	}
+	if result.Done != "order submitted" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+	if !slices.Contains(output, "user_input: No spicy food please") {
+		t.Fatalf("missing user input output: %#v", output)
+	}
+	if !slices.Contains(output, "tool: tool completed for: slack_tool") {
+		t.Fatalf("missing tool output: %#v", output)
+	}
+	if !slices.Contains(output, "sub_agent: research sub agent completed for: research lunch options") {
+		t.Fatalf("missing first sub-agent output: %#v", output)
+	}
+	if !slices.Contains(output, "sub_agent: research sub agent completed for: find vegetarian fallback") {
+		t.Fatalf("missing second sub-agent output: %#v", output)
+	}
+	timerCount := 0
+	for _, line := range output {
+		if line == "timer: no updates yet" {
+			timerCount++
+		}
+	}
+	if timerCount < 3 {
+		t.Fatalf("expected >=3 timer outputs, got %d, output=%#v", timerCount, output)
+	}
+	if output[len(output)-1] != "order_food: order submitted" {
+		t.Fatalf("unexpected last output: %#v", output[len(output)-1])
+	}
+}

langgraph-go/tests/test_primitives_test.go

@@ -0,0 +1,589 @@
+package tests
+
+import (
+	"fmt"
+	"sync"
+	"testing"
+	"time"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+type primitiveWorkflow struct {
+	dbWriteCount int
+	intervalMu   sync.Mutex
+	intervals    map[string][2]time.Time
+}
+
+type primitiveState struct {
+	Count int      `json:"count"`
+	Logs  []string `json:"logs"`
+	Done  string   `json:"done"`
+}
+
+func (w *primitiveWorkflow) startNode(ctx *ag.Context, input int, state primitiveState) (ag.Command, error) {
+	state.Logs = append(state.Logs, fmt.Sprintf("start:%d", input))
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.middleNode, NodeInput: "from_start"},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) middleNode(ctx *ag.Context, input string, state primitiveState) (ag.Command, error) {
+	state.Logs = append(state.Logs, "middle:"+input)
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.finishNode, NodeInput: "from_middle"},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) finishNode(ctx *ag.Context, input string, state primitiveState) (ag.Command, error) {
+	state.Logs = append(state.Logs, "finish:"+input)
+	state.Done = input
+	return ag.Command{Update: state}, nil
+}
+
+func TestInputAndStatePrimitivesCompatible(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+
+	graph.AddEntryNode(workflow.startNode)
+	graph.AddNode(workflow.middleNode)
+	graph.AddFinishNode(workflow.finishNode)
+
+	handler, err := graph.Compile().Start(100, primitiveState{
+		Count: 1,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Done != "from_middle" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+	if result.Count != 1 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 3 || result.Logs[0] != "start:100" || result.Logs[1] != "middle:from_start" || result.Logs[2] != "finish:from_middle" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+}
+
+func (w *primitiveWorkflow) startNoFinishNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	state.Logs = append(state.Logs, "start")
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.middleNoFinishNode, NodeInput: "from_start"},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) middleNoFinishNode(ctx *ag.Context, input string, state primitiveState) (ag.Command, error) {
+	state.Logs = append(state.Logs, "middle:"+input)
+	state.Count += 1
+	state.Done = "stopped"
+	// No goto and no finish node configured: run should end automatically.
+	return ag.Command{Update: state}, nil
+}
+
+func TestRunEndsWithoutFinishNode(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+
+	graph.AddEntryNode(workflow.startNoFinishNode)
+	graph.AddNode(workflow.middleNoFinishNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 7,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Done != "stopped" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+	if result.Count != 8 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 2 || result.Logs[0] != "start" || result.Logs[1] != "middle:from_start" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+}
+
+func (w *primitiveWorkflow) startWaitBatchNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if err := ctx.PublishToChannel("events", "a"); err != nil {
+		return ag.Command{}, err
+	}
+	if err := ctx.PublishToChannel("events", "b"); err != nil {
+		return ag.Command{}, err
+	}
+	if err := ctx.PublishToChannel("events", "c"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.waitBatchNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) waitBatchNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	result, err := ctx.WaitFor(ag.AnyOf(ag.ChannelCondition{
+		Channel: "events",
+		Min:     2,
+		Max:     4,
+	}))
+	if err != nil {
+		return ag.Command{}, err
+	}
+	if len(result.Conditions) != 1 || !result.Conditions[0].Met {
+		return ag.Command{}, fmt.Errorf("expected one met condition")
+	}
+	values := make([]string, 0, len(result.Conditions[0].Values))
+	for _, v := range result.Conditions[0].Values {
+		s, _ := v.(string)
+		values = append(values, s)
+	}
+	state.Count = len(values)
+	state.Logs = values
+	state.Done = "ok"
+	return ag.Command{Update: state}, nil
+}
+
+func TestChannelWaitRespectsMaxM(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddAsyncChannel("events")
+	graph.AddEntryNode(workflow.startWaitBatchNode)
+	graph.AddFinishNode(workflow.waitBatchNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Count != 3 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 3 || result.Logs[0] != "a" || result.Logs[1] != "b" || result.Logs[2] != "c" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+	if result.Done != "ok" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+}
+
+func (w *primitiveWorkflow) startAnyOfTwoChannelsNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if err := ctx.PublishToChannel("alpha", "a1"); err != nil {
+		return ag.Command{}, err
+	}
+	if err := ctx.PublishToChannel("beta", "b1"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.waitAnyOfTwoChannelsNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) waitAnyOfTwoChannelsNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	first, err := ctx.WaitFor(ag.AnyOf(
+		ag.ChannelCondition{Channel: "alpha"},
+		ag.ChannelCondition{Channel: "beta"},
+	))
+	if err != nil {
+		return ag.Command{}, err
+	}
+	if len(first.Conditions) != 2 {
+		return ag.Command{}, fmt.Errorf("expected 2 condition results, got %d", len(first.Conditions))
+	}
+	if !first.Conditions[0].Met || first.Conditions[0].ChannelName != "alpha" || len(first.Conditions[0].Values) != 1 || first.Conditions[0].Values[0] != "a1" {
+		return ag.Command{}, fmt.Errorf("unexpected first condition result: %#v", first.Conditions[0])
+	}
+	if !first.Conditions[1].Met || first.Conditions[1].ChannelName != "beta" || len(first.Conditions[1].Values) != 1 || first.Conditions[1].Values[0] != "b1" {
+		return ag.Command{}, fmt.Errorf("unexpected second condition result: %#v", first.Conditions[1])
+	}
+	if err := ctx.PublishToChannel("beta", "b2"); err != nil {
+		return ag.Command{}, err
+	}
+	state.Count = 1
+	state.Logs = []string{
+		"matched=2",
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.verifyBetaAfterAnyOfNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) verifyBetaAfterAnyOfNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	second, err := ctx.WaitFor(ag.AnyOf(
+		ag.ChannelCondition{Channel: "beta"},
+	))
+	if err != nil {
+		return ag.Command{}, err
+	}
+	if len(second.Conditions) != 1 || !second.Conditions[0].Met || second.Conditions[0].ChannelName != "beta" || len(second.Conditions[0].Values) != 1 {
+		return ag.Command{}, fmt.Errorf("unexpected beta condition result: %#v", second.Conditions)
+	}
+	payload, _ := second.Conditions[0].Values[0].(string)
+	state.Count = 2
+	state.Logs = append(state.Logs, fmt.Sprintf("beta=%s", payload))
+	state.Done = "ok"
+	return ag.Command{Update: state}, nil
+}
+
+func TestAnyOfConsumesAllReadyChannels(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddAsyncChannel("alpha")
+	graph.AddAsyncChannel("beta")
+	graph.AddEntryNode(workflow.startAnyOfTwoChannelsNode)
+	graph.AddNode(workflow.waitAnyOfTwoChannelsNode)
+	graph.AddFinishNode(workflow.verifyBetaAfterAnyOfNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Count != 2 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 2 || result.Logs[0] != "matched=2" || result.Logs[1] != "beta=b2" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+	if result.Done != "ok" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+}
+
+func (w *primitiveWorkflow) startAllOfTwoChannelsNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if err := ctx.PublishToChannel("alpha", "a1"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.waitAllOfTwoChannelsNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) waitAllOfTwoChannelsNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	result, err := ctx.WaitFor(ag.AllOf(
+		ag.ChannelCondition{Channel: "alpha"},
+		ag.ChannelCondition{Channel: "beta"},
+	))
+	if err != nil {
+		return ag.Command{}, err
+	}
+	if len(result.Conditions) != 2 {
+		return ag.Command{}, fmt.Errorf("expected 2 condition results, got %d", len(result.Conditions))
+	}
+	if !result.Conditions[0].Met || result.Conditions[0].ChannelName != "alpha" || len(result.Conditions[0].Values) != 1 || result.Conditions[0].Values[0] != "a1" {
+		return ag.Command{}, fmt.Errorf("unexpected alpha condition result: %#v", result.Conditions[0])
+	}
+	if !result.Conditions[1].Met || result.Conditions[1].ChannelName != "beta" || len(result.Conditions[1].Values) != 1 || result.Conditions[1].Values[0] != "b1" {
+		return ag.Command{}, fmt.Errorf("unexpected beta condition result: %#v", result.Conditions[1])
+	}
+	state.Count = 2
+	state.Logs = []string{"all_of_channels_ok"}
+	state.Done = "ok"
+	return ag.Command{Update: state}, nil
+}
+
+func TestAllOfWaitsUntilAllChannelsAreReady(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddAsyncChannel("alpha")
+	graph.AddAsyncChannel("beta")
+	graph.AddEntryNode(workflow.startAllOfTwoChannelsNode)
+	graph.AddFinishNode(workflow.waitAllOfTwoChannelsNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	if err := handler.PublishToChannel("beta", "b1"); err != nil {
+		t.Fatalf("publish beta failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Count != 2 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 1 || result.Logs[0] != "all_of_channels_ok" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+	if result.Done != "ok" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+}
+
+func (w *primitiveWorkflow) startAllOfChannelTimerNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if err := ctx.PublishToChannel("alpha", "a1"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.waitAllOfChannelTimerNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) waitAllOfChannelTimerNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	result, err := ctx.WaitFor(ag.AllOf(
+		ag.ChannelCondition{Channel: "alpha"},
+		ag.TimerCondition{Seconds: 0.05},
+	))
+	if err != nil {
+		return ag.Command{}, err
+	}
+	if len(result.Conditions) != 2 {
+		return ag.Command{}, fmt.Errorf("expected 2 condition results, got %d", len(result.Conditions))
+	}
+	if !result.Conditions[0].Met || result.Conditions[0].ChannelName != "alpha" || len(result.Conditions[0].Values) != 1 || result.Conditions[0].Values[0] != "a1" {
+		return ag.Command{}, fmt.Errorf("unexpected channel condition result: %#v", result.Conditions[0])
+	}
+	if !result.Conditions[1].Met {
+		return ag.Command{}, fmt.Errorf("timer condition should be met: %#v", result.Conditions[1])
+	}
+	state.Count = 1
+	state.Logs = []string{"all_of_channel_timer_ok"}
+	state.Done = "ok"
+	return ag.Command{Update: state}, nil
+}
+
+func TestAllOfChannelAndTimerMarksBothConditions(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddAsyncChannel("alpha")
+	graph.AddEntryNode(workflow.startAllOfChannelTimerNode)
+	graph.AddFinishNode(workflow.waitAllOfChannelTimerNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if result.Count != 1 {
+		t.Fatalf("unexpected count: %v", result.Count)
+	}
+	if len(result.Logs) != 1 || result.Logs[0] != "all_of_channel_timer_ok" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+	if result.Done != "ok" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+}
+
+func (w *primitiveWorkflow) startResumeFlagNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	state.Logs = []string{}
+	state.Count = 0
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.resumeFlagNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) resumeFlagNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if !ctx.IsResume() {
+		// Simulate one-time side effect (e.g. DB write).
+		w.dbWriteCount += 1
+	}
+	if _, err := ctx.WaitFor(ag.AnyOf(ag.TimerCondition{Seconds: 0.02})); err != nil {
+		return ag.Command{}, err
+	}
+	state.Logs = append(state.Logs, fmt.Sprintf("resume=%v", ctx.IsResume()))
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.finishResumeFlagNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) finishResumeFlagNode(_ *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	state.Done = "ok"
+	return ag.Command{Update: state}, nil
+}
+
+func TestIsResumeAvoidsDuplicateSideEffects(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddEntryNode(workflow.startResumeFlagNode)
+	graph.AddNode(workflow.resumeFlagNode)
+	graph.AddFinishNode(workflow.finishResumeFlagNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if workflow.dbWriteCount != 1 {
+		t.Fatalf("db write should happen once, got=%v", workflow.dbWriteCount)
+	}
+	if len(result.Logs) != 1 || result.Logs[0] != "resume=true" {
+		t.Fatalf("unexpected logs: %#v", result.Logs)
+	}
+	if result.Done != "ok" {
+		t.Fatalf("unexpected done: %v", result.Done)
+	}
+}
+
+func (w *primitiveWorkflow) startLockedWorkersNode(_ *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	w.intervalMu.Lock()
+	w.intervals = map[string][2]time.Time{}
+	w.intervalMu.Unlock()
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.lockedWorkerANode, NodeInput: nil},
+			{Node: w.lockedWorkerBNode, NodeInput: nil},
+			{Node: w.waitLockedWorkersNode, NodeInput: nil},
+		},
+	}, nil
+}
+
+func (w *primitiveWorkflow) lockedWorkerANode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	start := time.Now()
+	time.Sleep(40 * time.Millisecond)
+	end := time.Now()
+	w.intervalMu.Lock()
+	w.intervals["a"] = [2]time.Time{start, end}
+	w.intervalMu.Unlock()
+	if err := ctx.PublishToChannel("done", "a"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{Update: state}, nil
+}
+
+func (w *primitiveWorkflow) lockedWorkerBNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	start := time.Now()
+	time.Sleep(40 * time.Millisecond)
+	end := time.Now()
+	w.intervalMu.Lock()
+	w.intervals["b"] = [2]time.Time{start, end}
+	w.intervalMu.Unlock()
+	if err := ctx.PublishToChannel("done", "b"); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{Update: state}, nil
+}
+
+func (w *primitiveWorkflow) waitLockedWorkersNode(ctx *ag.Context, _ any, state primitiveState) (ag.Command, error) {
+	if _, err := ctx.WaitFor(ag.AnyOf(ag.ChannelCondition{Channel: "done", Min: 2})); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto:   []ag.Send{{Node: w.finishResumeFlagNode, NodeInput: nil}},
+	}, nil
+}
+
+func TestStateFieldLockingSerializesConflictingNodes(t *testing.T) {
+	workflow := &primitiveWorkflow{}
+	graph := ag.NewAdvancedStateGraph[primitiveState]()
+	graph.AddAsyncChannel("done")
+	graph.AddEntryNode(workflow.startLockedWorkersNode)
+	graph.AddNode(
+		workflow.lockedWorkerANode,
+		ag.NodeStateOption{LockedFields: []string{"counter"}},
+	)
+	graph.AddNode(
+		workflow.lockedWorkerBNode,
+		ag.NodeStateOption{LockedFields: []string{"counter"}},
+	)
+	graph.AddNode(workflow.waitLockedWorkersNode)
+	graph.AddFinishNode(workflow.finishResumeFlagNode)
+
+	handler, err := graph.Compile().Start(nil, primitiveState{
+		Count: 0,
+		Logs:  []string{},
+		Done:  "",
+	})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	if _, err := handler.WaitForResult(); err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+
+	workflow.intervalMu.Lock()
+	ia, okA := workflow.intervals["a"]
+	ib, okB := workflow.intervals["b"]
+	workflow.intervalMu.Unlock()
+	if !okA || !okB {
+		t.Fatalf("missing worker intervals: %#v", workflow.intervals)
+	}
+	serialized := !ia[1].After(ib[0]) || !ib[1].After(ia[0])
+	if !serialized {
+		t.Fatalf("expected serialized execution, got overlap: a=%v..%v b=%v..%v", ia[0], ia[1], ib[0], ib[1])
+	}
+}

langgraph-go/tests/test_state_type_validation_test.go

@@ -0,0 +1,54 @@
+package tests
+
+import (
+	"strings"
+	"testing"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+func TestNewAdvancedStateGraphRejectsNonStructState(t *testing.T) {
+	defer func() {
+		if r := recover(); r == nil {
+			t.Fatalf("expected panic for non-struct StateT")
+		}
+	}()
+	_ = ag.NewAdvancedStateGraph[map[string]any]()
+}
+
+type stateTypeA struct {
+	X int `json:"x"`
+}
+
+type stateTypeB struct {
+	X int `json:"x"`
+}
+
+type wrongUpdateWorkflow struct{}
+
+func (w *wrongUpdateWorkflow) startNode(ctx *ag.Context, _ any, _ stateTypeA) (ag.Command, error) {
+	return ag.Command{Goto: []ag.Send{{Node: w.badNode}}}, nil
+}
+
+func (w *wrongUpdateWorkflow) badNode(ctx *ag.Context, _ any, _ stateTypeA) (ag.Command, error) {
+	return ag.Command{Update: stateTypeB{X: 1}}, nil
+}
+
+func TestNodeUpdateTypeMustMatchGraphStateType(t *testing.T) {
+	workflow := &wrongUpdateWorkflow{}
+	graph := ag.NewAdvancedStateGraph[stateTypeA]()
+	graph.AddEntryNode(workflow.startNode)
+	graph.AddFinishNode(workflow.badNode)
+
+	handler, err := graph.Compile().Start(nil, stateTypeA{X: 0})
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	_, err = handler.WaitForResult()
+	if err == nil {
+		t.Fatalf("expected runtime error for wrong update type")
+	}
+	if !strings.Contains(err.Error(), "update type mismatch") {
+		t.Fatalf("unexpected error: %v", err)
+	}
+}

langgraph-go/tests/test_streaming_test.go

@@ -0,0 +1,117 @@
+package tests
+
+import (
+	"strings"
+	"testing"
+	"time"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+type streamState struct {
+	Done bool `json:"done"`
+}
+
+type streamWorkflow struct{}
+
+func (w *streamWorkflow) startNode(ctx *ag.Context, _ any, state streamState) (ag.Command, error) {
+	if err := ctx.SendCustomStreamEvent("high", map[string]any{"step": "start", "value": 1}); err != nil {
+		return ag.Command{}, err
+	}
+	time.Sleep(80 * time.Millisecond)
+	if err := ctx.SendCustomStreamEvent("regular", map[string]any{"step": "start", "value": 2}); err != nil {
+		return ag.Command{}, err
+	}
+	return ag.Command{
+		Update: state,
+		Goto: []ag.Send{
+			{Node: w.finishNode},
+		},
+	}, nil
+}
+
+func (w *streamWorkflow) finishNode(ctx *ag.Context, _ any, state streamState) (ag.Command, error) {
+	state.Done = true
+	return ag.Command{Update: state}, nil
+}
+
+func TestCustomStreamReceiveAndClose(t *testing.T) {
+	workflow := &streamWorkflow{}
+	graph := ag.NewAdvancedStateGraph[streamState]()
+	graph.AddCustomOutputStream("high")
+	graph.AddCustomOutputStream("regular")
+	graph.AddEntryNode(workflow.startNode)
+	graph.AddFinishNode(workflow.finishNode)
+
+	handler, err := graph.Compile().Start(nil, streamState{Done: false}, "custom")
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+
+	event, err := handler.ReceiveStream("high")
+	if err != nil {
+		t.Fatalf("receive stream failed: %v", err)
+	}
+	if event == nil {
+		t.Fatalf("expected first stream event, got nil")
+	}
+	eventMap, ok := event.(map[string]any)
+	if !ok {
+		t.Fatalf("unexpected event type: %T", event)
+	}
+	if eventMap["step"] != "start" {
+		t.Fatalf("unexpected stream event payload: %#v", eventMap)
+	}
+
+	eventRegular, err := handler.ReceiveStream("regular")
+	if err != nil {
+		t.Fatalf("receive regular stream failed: %v", err)
+	}
+	eventRegularMap, ok := eventRegular.(map[string]any)
+	if !ok {
+		t.Fatalf("unexpected regular event type: %T", eventRegular)
+	}
+	if eventRegularMap["value"] != float64(2) {
+		t.Fatalf("unexpected regular stream payload: %#v", eventRegularMap)
+	}
+
+	if err := handler.CloseAllStreams(); err != nil {
+		t.Fatalf("close all streams failed: %v", err)
+	}
+
+	closedEvent, err := handler.ReceiveStream("high")
+	if err != nil {
+		t.Fatalf("receive stream after close failed: %v", err)
+	}
+	if closedEvent != nil {
+		t.Fatalf("expected nil stream event after close, got %#v", closedEvent)
+	}
+
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	if !result.Done {
+		t.Fatalf("expected final state done=true, got %#v", result)
+	}
+}
+
+func TestOnlyCustomStreamModeSupported(t *testing.T) {
+	workflow := &streamWorkflow{}
+	graph := ag.NewAdvancedStateGraph[streamState]()
+	graph.AddCustomOutputStream("regular")
+	graph.AddEntryNode(workflow.startNode)
+	graph.AddFinishNode(workflow.finishNode)
+
+	handler, err := graph.Compile().Start(nil, streamState{Done: false}, "values")
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	_, runErr := handler.WaitForResult()
+	if runErr == nil {
+		t.Fatalf("expected run error for unsupported stream mode")
+	}
+	if !strings.Contains(runErr.Error(), "only `custom` is supported") {
+		t.Fatalf("unexpected error: %v", runErr)
+	}
+}

langgraph-go/tests/test_update_elision_test.go

@@ -0,0 +1,135 @@
+package tests
+
+import (
+	"reflect"
+	"testing"
+	"time"
+
+	ag "github.com/langchain-ai/langgraph/langgraph-go/advancedgraph"
+)
+
+type updateElisionState struct {
+	X  int             `json:"x"`
+	S  updateStruct    `json:"s"`
+	M  map[string]int  `json:"m"`
+	L  []int           `json:"l"`
+	PS *updateStruct   `json:"ps"`
+	PM *map[string]int `json:"pm"`
+	PL *[]int          `json:"pl"`
+}
+
+type updateStruct struct {
+	V int `json:"v"`
+}
+
+type updateElisionWorkflow struct{}
+
+func makeState(v int) updateElisionState {
+	m := map[string]int{"n": v}
+	l := []int{v}
+	return updateElisionState{
+		X:  v,
+		S:  updateStruct{V: v},
+		M:  map[string]int{"n": v},
+		L:  []int{v},
+		PS: &updateStruct{V: v},
+		PM: &m,
+		PL: &l,
+	}
+}
+
+func (w *updateElisionWorkflow) startNoopNode(ctx *ag.Context, _ any, _ updateElisionState) (ag.Command, error) {
+	return ag.Command{
+		Goto: []ag.Send{
+			{Node: w.fastNode},
+			{Node: w.slowNoopNode},
+		},
+	}, nil
+}
+
+func (w *updateElisionWorkflow) startChangedNode(ctx *ag.Context, _ any, _ updateElisionState) (ag.Command, error) {
+	return ag.Command{
+		Goto: []ag.Send{
+			{Node: w.fastNode},
+			{Node: w.slowChangedNode},
+		},
+	}, nil
+}
+
+func (w *updateElisionWorkflow) fastNode(ctx *ag.Context, _ any, state updateElisionState) (ag.Command, error) {
+	_ = state
+	return ag.Command{Update: makeState(1)}, nil
+}
+
+func (w *updateElisionWorkflow) slowNoopNode(ctx *ag.Context, _ any, state updateElisionState) (ag.Command, error) {
+	time.Sleep(100 * time.Millisecond)
+	// Returns same state as initial snapshot; without runtime elision this can overwrite newer updates.
+	return ag.Command{Update: state}, nil
+}
+
+func (w *updateElisionWorkflow) slowChangedNode(ctx *ag.Context, _ any, _ updateElisionState) (ag.Command, error) {
+	time.Sleep(100 * time.Millisecond)
+	// Real change should not be elided.
+	return ag.Command{Update: makeState(2)}, nil
+}
+
+func assertStateEquals(t *testing.T, got updateElisionState, expected updateElisionState) {
+	t.Helper()
+	if got.X != expected.X {
+		t.Fatalf("unexpected X: got=%d want=%d", got.X, expected.X)
+	}
+	if got.S != expected.S {
+		t.Fatalf("unexpected S: got=%#v want=%#v", got.S, expected.S)
+	}
+	if !reflect.DeepEqual(got.M, expected.M) {
+		t.Fatalf("unexpected M: got=%#v want=%#v", got.M, expected.M)
+	}
+	if !reflect.DeepEqual(got.L, expected.L) {
+		t.Fatalf("unexpected L: got=%#v want=%#v", got.L, expected.L)
+	}
+	if got.PS == nil || expected.PS == nil || *got.PS != *expected.PS {
+		t.Fatalf("unexpected PS: got=%#v want=%#v", got.PS, expected.PS)
+	}
+	if got.PM == nil || expected.PM == nil || !reflect.DeepEqual(*got.PM, *expected.PM) {
+		t.Fatalf("unexpected PM: got=%#v want=%#v", got.PM, expected.PM)
+	}
+	if got.PL == nil || expected.PL == nil || !reflect.DeepEqual(*got.PL, *expected.PL) {
+		t.Fatalf("unexpected PL: got=%#v want=%#v", got.PL, expected.PL)
+	}
+}
+
+func TestNoopSlowUpdateCanOverrideFastUpdate(t *testing.T) {
+	workflow := &updateElisionWorkflow{}
+	graph := ag.NewAdvancedStateGraph[updateElisionState]()
+	graph.AddEntryNode(workflow.startNoopNode)
+	graph.AddNode(workflow.fastNode)
+	graph.AddFinishNode(workflow.slowNoopNode)
+
+	handler, err := graph.Compile().Start(nil, makeState(0))
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	assertStateEquals(t, result, makeState(0))
+}
+
+func TestChangedSlowUpdateOverridesFastUpdate(t *testing.T) {
+	workflow := &updateElisionWorkflow{}
+	graph := ag.NewAdvancedStateGraph[updateElisionState]()
+	graph.AddEntryNode(workflow.startChangedNode)
+	graph.AddNode(workflow.fastNode)
+	graph.AddFinishNode(workflow.slowChangedNode)
+
+	handler, err := graph.Compile().Start(nil, makeState(0))
+	if err != nil {
+		t.Fatalf("start failed: %v", err)
+	}
+	result, err := handler.WaitForResult()
+	if err != nil {
+		t.Fatalf("result failed: %v", err)
+	}
+	assertStateEquals(t, result, makeState(2))
+}

libs/checkpoint-conformance/Makefile

@@ -0,0 +1,12 @@
+.PHONY: format lint test
+
+format:
+	uv run ruff format .
+	uv run ruff check --fix .
+
+lint:
+	uv run ruff check .
+	uv run ty check
+
+test:
+	uv run pytest $(TEST)

libs/checkpoint-conformance/README.md

@@ -0,0 +1,111 @@
+# langgraph-checkpoint-conformance
+
+Conformance test suite for [LangGraph](https://github.com/langchain-ai/langgraph) checkpointer implementations.
+
+Validates that a `BaseCheckpointSaver` subclass correctly implements the checkpoint storage contract — blob round-trips, metadata preservation, namespace isolation, incremental channel updates, and more.
+
+## Installation
+
+```bash
+pip install langgraph-checkpoint-conformance
+```
+
+## Quick start
+
+Register your checkpointer with `@checkpointer_test` and run `validate()`:
+
+```python
+import asyncio
+from langgraph.checkpoint.conformance import checkpointer_test, validate
+
+@checkpointer_test(name="MyCheckpointer")
+async def my_checkpointer():
+    saver = MyCheckpointer(...)
+    yield saver
+    # cleanup runs after yield
+
+async def main():
+    report = await validate(my_checkpointer)
+    report.print_report()
+    assert report.passed_all_base()
+
+asyncio.run(main())
+```
+
+Or in a pytest test:
+
+```python
+import pytest
+from langgraph.checkpoint.conformance import checkpointer_test, validate
+
+@checkpointer_test(name="MyCheckpointer")
+async def my_checkpointer():
+    yield MyCheckpointer(...)
+
+@pytest.mark.asyncio
+async def test_conformance():
+    report = await validate(my_checkpointer)
+    report.print_report()
+    assert report.passed_all_base()
+```
+
+## Capabilities
+
+The suite tests **base** capabilities (required) and **extended** capabilities (optional, auto-detected):
+
+| Capability | Required | Method |
+|---|---|---|
+| `put` | yes | `aput` |
+| `put_writes` | yes | `aput_writes` |
+| `get_tuple` | yes | `aget_tuple` |
+| `list` | yes | `alist` |
+| `delete_thread` | yes | `adelete_thread` |
+| `delete_for_runs` | no | `adelete_for_runs` |
+| `copy_thread` | no | `acopy_thread` |
+| `prune` | no | `aprune` |
+
+Extended capabilities are detected by checking whether the method is overridden from `BaseCheckpointSaver`. If not overridden, those tests are skipped.
+
+## Options
+
+### Progress output
+
+```python
+from langgraph.checkpoint.conformance.report import ProgressCallbacks
+
+# Dot-style progress (. per pass, F per fail)
+report = await validate(my_checkpointer, progress=ProgressCallbacks.default())
+
+# Verbose (per-test names + stacktraces on failure)
+report = await validate(my_checkpointer, progress=ProgressCallbacks.verbose())
+```
+
+### Skip capabilities
+
+```python
+@checkpointer_test(name="MyCheckpointer", skip_capabilities={"prune"})
+async def my_checkpointer():
+    yield MyCheckpointer(...)
+```
+
+### Run specific capabilities
+
+```python
+report = await validate(my_checkpointer, capabilities={"put", "list"})
+```
+
+### Lifespan (one-time setup/teardown)
+
+For expensive setup like database creation:
+
+```python
+async def db_lifespan():
+    await create_database()
+    yield
+    await drop_database()
+
+@checkpointer_test(name="PostgresSaver", lifespan=db_lifespan)
+async def pg_checkpointer():
+    async with PostgresSaver.from_conn_string(CONN_STRING) as saver:
+        yield saver
+```

libs/checkpoint-conformance/langgraph/checkpoint/conformance/__init__.py

@@ -0,0 +1,9 @@
+"""langgraph-checkpoint-conformance: conformance test suite for checkpointer implementations."""
+
+from langgraph.checkpoint.conformance.initializer import checkpointer_test
+from langgraph.checkpoint.conformance.validate import validate
+
+__all__ = [
+    "checkpointer_test",
+    "validate",
+]

libs/checkpoint-conformance/langgraph/checkpoint/conformance/capabilities.py

@@ -0,0 +1,93 @@
+"""Capability detection for checkpointer implementations."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import TYPE_CHECKING
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+if TYPE_CHECKING:
+    pass
+
+
+class Capability(str, Enum):
+    """Capabilities that a checkpointer may support."""
+
+    PUT = "put"
+    PUT_WRITES = "put_writes"
+    GET_TUPLE = "get_tuple"
+    LIST = "list"
+    DELETE_THREAD = "delete_thread"
+    DELETE_FOR_RUNS = "delete_for_runs"
+    COPY_THREAD = "copy_thread"
+    PRUNE = "prune"
+
+
+# Capabilities that every checkpointer must support.
+BASE_CAPABILITIES = frozenset(
+    {
+        Capability.PUT,
+        Capability.PUT_WRITES,
+        Capability.GET_TUPLE,
+        Capability.LIST,
+        Capability.DELETE_THREAD,
+    }
+)
+
+# Capabilities that are optional extensions.
+EXTENDED_CAPABILITIES = frozenset(
+    {
+        Capability.DELETE_FOR_RUNS,
+        Capability.COPY_THREAD,
+        Capability.PRUNE,
+    }
+)
+
+ALL_CAPABILITIES = BASE_CAPABILITIES | EXTENDED_CAPABILITIES
+
+# Maps capability to the async method name on BaseCheckpointSaver (or subclass).
+_CAPABILITY_METHOD_MAP: dict[Capability, str] = {
+    Capability.PUT: "aput",
+    Capability.PUT_WRITES: "aput_writes",
+    Capability.GET_TUPLE: "aget_tuple",
+    Capability.LIST: "alist",
+    Capability.DELETE_THREAD: "adelete_thread",
+    Capability.DELETE_FOR_RUNS: "adelete_for_runs",
+    Capability.COPY_THREAD: "acopy_thread",
+    Capability.PRUNE: "aprune",
+}
+
+
+@dataclass(frozen=True)
+class DetectedCapabilities:
+    """Result of capability detection for a checkpointer type."""
+
+    detected: frozenset[Capability]
+    missing: frozenset[Capability]
+
+    @classmethod
+    def from_instance(cls, saver: BaseCheckpointSaver) -> DetectedCapabilities:
+        """Detect capabilities from a checkpointer instance."""
+        inner_type = type(saver)
+        detected: set[Capability] = set()
+
+        for cap, method_name in _CAPABILITY_METHOD_MAP.items():
+            if _is_overridden(inner_type, method_name):
+                detected.add(cap)
+
+        detected_fs = frozenset(detected)
+        return cls(
+            detected=detected_fs,
+            missing=ALL_CAPABILITIES - detected_fs,
+        )
+
+
+def _is_overridden(inner_type: type, method: str) -> bool:
+    """Check if *method* on *inner_type* differs from the base class default."""
+    base = getattr(BaseCheckpointSaver, method, None)
+    impl = getattr(inner_type, method, None)
+    if base is None or impl is None:
+        return impl is not None
+    return impl is not base

libs/checkpoint-conformance/langgraph/checkpoint/conformance/initializer.py

@@ -0,0 +1,100 @@
+"""Checkpointer test registration and factory management."""
+
+from __future__ import annotations
+
+from collections.abc import AsyncGenerator, Callable
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import Any
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+# Type for the lifespan async context manager factory.
+LifespanFactory = Callable[[], AsyncGenerator[None, None]]
+
+# Module-level registry of decorated checkpointer factories.
+_REGISTRY: dict[str, RegisteredCheckpointer] = {}
+
+
+async def _noop_lifespan() -> AsyncGenerator[None, None]:
+    yield
+
+
+@dataclass
+class RegisteredCheckpointer:
+    """A registered checkpointer test factory."""
+
+    name: str
+    factory: Callable[[], AsyncGenerator[BaseCheckpointSaver, None]]
+    skip_capabilities: set[str] = field(default_factory=set)
+    lifespan: LifespanFactory = _noop_lifespan
+
+    @asynccontextmanager
+    async def create(self) -> AsyncGenerator[BaseCheckpointSaver, None]:
+        """Create a fresh checkpointer instance via the async generator."""
+        gen = self.factory()
+        try:
+            saver = await gen.__anext__()
+            yield saver
+        finally:
+            try:
+                await gen.__anext__()
+            except StopAsyncIteration:
+                pass
+
+    @asynccontextmanager
+    async def enter_lifespan(self) -> AsyncGenerator[None, None]:
+        """Enter the lifespan context (once per validation run)."""
+        gen = self.lifespan()
+        try:
+            await gen.__anext__()
+            yield
+        finally:
+            try:
+                await gen.__anext__()
+            except StopAsyncIteration:
+                pass
+
+
+def checkpointer_test(
+    name: str,
+    *,
+    skip_capabilities: set[str] | None = None,
+    lifespan: LifespanFactory | None = None,
+) -> Callable[[Any], RegisteredCheckpointer]:
+    """Register an async generator as a checkpointer test factory.
+
+    The factory is called once per capability suite to create a fresh
+    checkpointer.  The optional `lifespan` is an async generator that
+    runs once for the entire validation run (e.g. to create/destroy a
+    database).
+
+    Example::
+
+        @checkpointer_test(name="InMemorySaver")
+        async def memory_checkpointer():
+            yield InMemorySaver()
+
+    With lifespan::
+
+        async def pg_lifespan():
+            await create_database()
+            yield
+            await drop_database()
+
+        @checkpointer_test(name="PostgresSaver", lifespan=pg_lifespan)
+        async def pg_checkpointer():
+            yield PostgresSaver(conn_string="...")
+    """
+
+    def decorator(fn: Any) -> RegisteredCheckpointer:
+        registered = RegisteredCheckpointer(
+            name=name,
+            factory=fn,
+            skip_capabilities=skip_capabilities or set(),
+            lifespan=lifespan or _noop_lifespan,
+        )
+        _REGISTRY[name] = registered
+        return registered
+
+    return decorator

libs/checkpoint-conformance/langgraph/checkpoint/conformance/report.py

@@ -0,0 +1,198 @@
+"""Capability report: results, progress callbacks, and pretty-printing."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+from langgraph.checkpoint.conformance.capabilities import (
+    BASE_CAPABILITIES,
+    EXTENDED_CAPABILITIES,
+    Capability,
+)
+
+# Callback type for per-test progress reporting.
+# (capability_name, test_name, passed, error_msg_or_None) -> None
+OnTestResult = Callable[[str, str, bool, str | None], None]
+
+# Callback type for capability-level events.
+# (capability_name, detected) -> None
+OnCapabilityStart = Callable[[str, bool], None]
+
+
+class ProgressCallbacks:
+    """Grouped callbacks for progress reporting during validation."""
+
+    def __init__(
+        self,
+        *,
+        on_capability_start: Callable[[str, bool], None] | None = None,
+        on_test_result: OnTestResult | None = None,
+        on_capability_end: Callable[[str], None] | None = None,
+    ) -> None:
+        self.on_capability_start = on_capability_start
+        self.on_test_result = on_test_result
+        self.on_capability_end = on_capability_end
+
+    @classmethod
+    def default(cls) -> ProgressCallbacks:
+        """Dot-style progress: ``.`` per pass, ``F`` per fail."""
+
+        def _cap_start(capability: str, detected: bool) -> None:
+            if detected:
+                print(f"  {capability}: ", end="", flush=True)
+            else:
+                print(f"  ⊘ {capability} (not implemented)")
+
+        def _test_result(
+            capability: str, test_name: str, passed: bool, error: str | None
+        ) -> None:
+            print("." if passed else "F", end="", flush=True)
+
+        def _cap_end(capability: str) -> None:
+            print()  # newline after dots
+
+        return cls(
+            on_capability_start=_cap_start,
+            on_test_result=_test_result,
+            on_capability_end=_cap_end,
+        )
+
+    @classmethod
+    def verbose(cls) -> ProgressCallbacks:
+        """Per-test output with names and errors."""
+
+        def _cap_start(capability: str, detected: bool) -> None:
+            if detected:
+                print(f"  {capability}:")
+            else:
+                print(f"  ⊘ {capability} (not implemented)")
+
+        def _test_result(
+            capability: str, test_name: str, passed: bool, error: str | None
+        ) -> None:
+            icon = "✓" if passed else "✗"
+            print(f"    {icon} {test_name}")
+            if error:
+                for line in error.rstrip().splitlines():
+                    print(f"      {line}")
+
+        return cls(
+            on_capability_start=_cap_start,
+            on_test_result=_test_result,
+        )
+
+    @classmethod
+    def quiet(cls) -> ProgressCallbacks:
+        """No progress output."""
+        return cls()
+
+
+@dataclass
+class CapabilityResult:
+    """Result of running a single capability's test suite."""
+
+    detected: bool = False
+    passed: bool | None = None  # None = skipped
+    tests_passed: int = 0
+    tests_failed: int = 0
+    tests_skipped: int = 0
+    failures: list[str] = field(default_factory=list)
+
+
+@dataclass
+class CapabilityReport:
+    """Aggregate report across all capabilities."""
+
+    checkpointer_name: str
+    results: dict[str, CapabilityResult] = field(default_factory=dict)
+
+    def passed_all_base(self) -> bool:
+        """Whether all base capability tests passed."""
+        for cap in BASE_CAPABILITIES:
+            result = self.results.get(cap.value)
+            if result is None or result.passed is not True:
+                return False
+        return True
+
+    def passed_all(self) -> bool:
+        """Whether every detected capability's tests passed."""
+        for result in self.results.values():
+            if result.detected and result.passed is not True:
+                return False
+        return True
+
+    def conformance_level(self) -> str:
+        """Return a human-readable conformance level string."""
+        if self.passed_all():
+            return "FULL"
+        if self.passed_all_base():
+            return "BASE+PARTIAL"
+        return "BASE" if self._any_base_passed() else "NONE"
+
+    def _any_base_passed(self) -> bool:
+        for cap in BASE_CAPABILITIES:
+            result = self.results.get(cap.value)
+            if result and result.passed is True:
+                return True
+        return False
+
+    def print_report(self) -> None:
+        """Pretty-print the report to stdout."""
+        width = 52
+        border = "=" * width
+        print(f"\n{'':>2}{border}")
+        print(f"{'':>2}  Checkpointer Validation: {self.checkpointer_name}")
+        print(f"{'':>2}{border}")
+
+        def _section(title: str, caps: frozenset[Capability]) -> None:
+            print(f"{'':>2}  {title}")
+            for cap in sorted(caps, key=lambda c: c.value):
+                result = self.results.get(cap.value)
+                if result is None:
+                    icon = "  "
+                    suffix = "(no tests)"
+                elif not result.detected:
+                    icon = "⊘ "
+                    suffix = "(not implemented)"
+                elif result.passed is True:
+                    icon = "✅"
+                    suffix = ""
+                elif result.passed is False:
+                    icon = "❌"
+                    suffix = f"({result.tests_failed} failed)"
+                else:
+                    icon = "⏭ "
+                    suffix = "(skipped)"
+                print(f"{'':>2}    {icon} {cap.value:20s} {suffix}")
+            print()
+
+        _section("BASE CAPABILITIES", BASE_CAPABILITIES)
+        _section("EXTENDED CAPABILITIES", EXTENDED_CAPABILITIES)
+
+        total = sum(1 for r in self.results.values() if r.detected)
+        passed = sum(
+            1 for r in self.results.values() if r.detected and r.passed is True
+        )
+        level = self.conformance_level()
+        print(f"{'':>2}  Result: {level} ({passed}/{total})")
+        print(f"{'':>2}{border}\n")
+
+    def to_dict(self) -> dict[str, Any]:
+        """Return a JSON-serializable dict."""
+        return {
+            "checkpointer_name": self.checkpointer_name,
+            "conformance_level": self.conformance_level(),
+            "results": {
+                name: {
+                    "detected": r.detected,
+                    "passed": r.passed,
+                    "tests_passed": r.tests_passed,
+                    "tests_failed": r.tests_failed,
+                    "tests_skipped": r.tests_skipped,
+                    "failures": r.failures,
+                }
+                for name, r in self.results.items()
+            },
+        }

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/__init__.py

@@ -0,0 +1,27 @@
+"""Test spec modules for each checkpointer capability."""
+
+from langgraph.checkpoint.conformance.spec.test_copy_thread import (
+    run_copy_thread_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_delete_for_runs import (
+    run_delete_for_runs_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_delete_thread import (
+    run_delete_thread_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_get_tuple import run_get_tuple_tests
+from langgraph.checkpoint.conformance.spec.test_list import run_list_tests
+from langgraph.checkpoint.conformance.spec.test_prune import run_prune_tests
+from langgraph.checkpoint.conformance.spec.test_put import run_put_tests
+from langgraph.checkpoint.conformance.spec.test_put_writes import run_put_writes_tests
+
+__all__ = [
+    "run_put_tests",
+    "run_put_writes_tests",
+    "run_get_tuple_tests",
+    "run_list_tests",
+    "run_delete_thread_tests",
+    "run_delete_for_runs_tests",
+    "run_copy_thread_tests",
+    "run_prune_tests",
+]

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_copy_thread.py

@@ -0,0 +1,250 @@
+"""COPY_THREAD capability tests — acopy_thread."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _setup_source_thread(
+    saver: BaseCheckpointSaver,
+    tid: str,
+    *,
+    n: int = 3,
+    namespaces: list[str] | None = None,
+) -> list[dict]:
+    """Create n checkpoints on tid (optionally across namespaces). Returns stored configs."""
+    nss = namespaces or [""]
+    stored = []
+    for ns in nss:
+        parent_cfg = None
+        for i in range(n):
+            config = generate_config(tid, checkpoint_ns=ns)
+            if parent_cfg:
+                config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                    "checkpoint_id"
+                ]
+            cp = generate_checkpoint(channel_values={"step": i})
+            cp["channel_versions"] = {"step": 1}
+            parent_cfg = await saver.aput(
+                config, cp, generate_metadata(step=i), {"step": 1}
+            )
+            stored.append(parent_cfg)
+    return stored
+
+
+async def test_copy_thread_basic(saver: BaseCheckpointSaver) -> None:
+    """Checkpoints appear on target thread."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src)
+
+    await saver.acopy_thread(src, dst)
+    results = []
+    async for tup in saver.alist(generate_config(dst)):
+        results.append(tup)
+    assert len(results) == 3, f"Expected 3 copied checkpoints, got {len(results)}"
+
+
+async def test_copy_thread_all_checkpoints(saver: BaseCheckpointSaver) -> None:
+    """All checkpoints copied, not just latest."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=3)
+
+    await saver.acopy_thread(src, dst)
+    src_results = []
+    async for tup in saver.alist(generate_config(src)):
+        src_results.append(tup)
+
+    dst_results = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_results.append(tup)
+
+    assert len(dst_results) == len(src_results)
+    # Verify content matches
+    for s, d in zip(
+        sorted(src_results, key=lambda t: t.checkpoint["id"]),
+        sorted(dst_results, key=lambda t: t.checkpoint["id"]),
+        strict=True,
+    ):
+        assert s.checkpoint["channel_values"] == d.checkpoint["channel_values"], (
+            f"channel_values mismatch for checkpoint {s.checkpoint['id']}"
+        )
+
+
+async def test_copy_thread_preserves_metadata(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Metadata intact on copied checkpoints."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=2)
+
+    await saver.acopy_thread(src, dst)
+    src_tuples = []
+    async for tup in saver.alist(generate_config(src)):
+        src_tuples.append(tup)
+
+    dst_tuples = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_tuples.append(tup)
+
+    for s, d in zip(
+        sorted(src_tuples, key=lambda t: t.metadata.get("step", 0)),
+        sorted(dst_tuples, key=lambda t: t.metadata.get("step", 0)),
+        strict=True,
+    ):
+        for key in s.metadata:
+            assert s.metadata.get(key) == d.metadata.get(key), (
+                f"metadata[{key!r}] mismatch: {s.metadata.get(key)!r} != {d.metadata.get(key)!r}"
+            )
+
+
+async def test_copy_thread_preserves_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Root + child namespaces copied."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=1, namespaces=["", "child:1"])
+
+    await saver.acopy_thread(src, dst)
+    for ns in ["", "child:1"]:
+        results = []
+        async for tup in saver.alist(generate_config(dst, checkpoint_ns=ns)):
+            results.append(tup)
+        assert len(results) == 1, (
+            f"Expected 1 checkpoint in namespace '{ns}', got {len(results)}"
+        )
+
+
+async def test_copy_thread_preserves_writes(saver: BaseCheckpointSaver) -> None:
+    """Pending writes copied."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    configs = await _setup_source_thread(saver, src, n=1)
+
+    # Add a write to the source
+    await saver.aput_writes(configs[-1], [("ch", "write_val")], str(uuid4()))
+
+    await saver.acopy_thread(src, dst)
+    tup = await saver.aget_tuple(generate_config(dst))
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected 1 write, got {len(tup.pending_writes)}"
+    )
+    assert tup.pending_writes[0][1] == "ch", (
+        f"channel mismatch: {tup.pending_writes[0][1]!r}"
+    )
+    assert tup.pending_writes[0][2] == "write_val", (
+        f"value mismatch: {tup.pending_writes[0][2]!r}"
+    )
+
+
+async def test_copy_thread_preserves_ordering(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Checkpoint order maintained."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=4)
+
+    await saver.acopy_thread(src, dst)
+    src_ids = []
+    async for tup in saver.alist(generate_config(src)):
+        src_ids.append(tup.checkpoint["id"])
+
+    dst_ids = []
+    async for tup in saver.alist(generate_config(dst)):
+        dst_ids.append(tup.checkpoint["id"])
+
+    # Order should match (both newest-first)
+    assert src_ids == dst_ids
+
+
+async def test_copy_thread_source_unchanged(saver: BaseCheckpointSaver) -> None:
+    """Source thread still intact after copy."""
+    src = str(uuid4())
+    dst = str(uuid4())
+    await _setup_source_thread(saver, src, n=2)
+
+    # Snapshot source before copy
+    src_before = []
+    async for tup in saver.alist(generate_config(src)):
+        src_before.append(tup.checkpoint["id"])
+
+    await saver.acopy_thread(src, dst)
+    # Source should be unchanged
+    src_after = []
+    async for tup in saver.alist(generate_config(src)):
+        src_after.append(tup.checkpoint["id"])
+
+    assert src_before == src_after
+
+
+async def test_copy_thread_nonexistent_source(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Graceful handling of non-existent source thread."""
+    src = str(uuid4())
+    dst = str(uuid4())
+
+    # Should not raise (or raise a known error)
+    try:
+        await saver.acopy_thread(src, dst)
+    except Exception:
+        pass  # Some implementations may raise; that's acceptable
+
+    # Destination should be empty
+    results = []
+    async for tup in saver.alist(generate_config(dst)):
+        results.append(tup)
+    assert len(results) == 0
+
+
+ALL_COPY_THREAD_TESTS = [
+    test_copy_thread_basic,
+    test_copy_thread_all_checkpoints,
+    test_copy_thread_preserves_metadata,
+    test_copy_thread_preserves_namespaces,
+    test_copy_thread_preserves_writes,
+    test_copy_thread_preserves_ordering,
+    test_copy_thread_source_unchanged,
+    test_copy_thread_nonexistent_source,
+]
+
+
+async def run_copy_thread_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all copy_thread tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_COPY_THREAD_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("copy_thread", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "copy_thread", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_delete_for_runs.py

@@ -0,0 +1,218 @@
+"""DELETE_FOR_RUNS capability tests — adelete_for_runs."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _put_with_run_id(
+    saver: BaseCheckpointSaver,
+    tid: str,
+    run_id: str,
+    *,
+    checkpoint_ns: str = "",
+    parent_config: dict | None = None,
+) -> dict:
+    """Put a checkpoint with a run_id in metadata, return stored config."""
+    config = generate_config(tid, checkpoint_ns=checkpoint_ns)
+    if parent_config:
+        config["configurable"]["checkpoint_id"] = parent_config["configurable"][
+            "checkpoint_id"
+        ]
+    cp = generate_checkpoint()
+    md = generate_metadata(run_id=run_id)
+    return await saver.aput(config, cp, md, {})
+
+
+async def test_delete_for_runs_single(saver: BaseCheckpointSaver) -> None:
+    """One run_id removed."""
+    tid = str(uuid4())
+    run1, run2 = str(uuid4()), str(uuid4())
+
+    stored1 = await _put_with_run_id(saver, tid, run1)
+    await _put_with_run_id(saver, tid, run2, parent_config=stored1)
+
+    # Pre-delete: verify both runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run1 in pre_run_ids, "Pre-delete: run1 should exist"
+    assert run2 in pre_run_ids, "Pre-delete: run2 should exist"
+
+    await saver.adelete_for_runs([run1])
+    # run1's checkpoint should be gone; run2 should remain
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run1 not in run_ids
+    assert run2 in run_ids
+
+
+async def test_delete_for_runs_multiple(saver: BaseCheckpointSaver) -> None:
+    """List of run_ids removed."""
+    tid = str(uuid4())
+    run1, run2, run3 = str(uuid4()), str(uuid4()), str(uuid4())
+
+    s1 = await _put_with_run_id(saver, tid, run1)
+    s2 = await _put_with_run_id(saver, tid, run2, parent_config=s1)
+    await _put_with_run_id(saver, tid, run3, parent_config=s2)
+
+    # Pre-delete: verify all 3 runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run1 in pre_run_ids, "Pre-delete: run1 should exist"
+    assert run2 in pre_run_ids, "Pre-delete: run2 should exist"
+    assert run3 in pre_run_ids, "Pre-delete: run3 should exist"
+
+    await saver.adelete_for_runs([run1, run2])
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run1 not in run_ids
+    assert run2 not in run_ids
+    assert run3 in run_ids
+
+
+async def test_delete_for_runs_preserves_other_runs(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Unrelated runs untouched."""
+    tid = str(uuid4())
+    run_keep = str(uuid4())
+    run_delete = str(uuid4())
+
+    await _put_with_run_id(saver, tid, run_keep)
+    await _put_with_run_id(saver, tid, run_delete)
+
+    # Pre-delete: verify both runs exist
+    pre_results = []
+    async for tup in saver.alist(generate_config(tid)):
+        pre_results.append(tup)
+    pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+    assert run_keep in pre_run_ids, "Pre-delete: run_keep should exist"
+    assert run_delete in pre_run_ids, "Pre-delete: run_delete should exist"
+
+    await saver.adelete_for_runs([run_delete])
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    run_ids = {t.metadata.get("run_id") for t in results}
+    assert run_keep in run_ids
+
+
+async def test_delete_for_runs_removes_writes(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Associated writes cleaned up."""
+    tid = str(uuid4())
+    run1 = str(uuid4())
+
+    stored = await _put_with_run_id(saver, tid, run1)
+    await saver.aput_writes(stored, [("ch", "val")], str(uuid4()))
+
+    # Pre-delete: verify writes exist
+    pre_tup = await saver.aget_tuple(stored)
+    assert pre_tup is not None, "Pre-delete: checkpoint should exist"
+    assert pre_tup.pending_writes is not None and len(pre_tup.pending_writes) == 1, (
+        f"Pre-delete: expected 1 write, got {len(pre_tup.pending_writes) if pre_tup.pending_writes else 0}"
+    )
+
+    await saver.adelete_for_runs([run1])
+    # The checkpoint (and its writes) should be gone
+    tup = await saver.aget_tuple(stored)
+    assert tup is None
+
+
+async def test_delete_for_runs_empty_list_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Empty list no error."""
+    await saver.adelete_for_runs([])
+
+
+async def test_delete_for_runs_nonexistent_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Missing run_ids no error."""
+    await saver.adelete_for_runs([str(uuid4())])
+
+
+async def test_delete_for_runs_across_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """All namespaces cleaned."""
+    tid = str(uuid4())
+    run1 = str(uuid4())
+
+    await _put_with_run_id(saver, tid, run1, checkpoint_ns="")
+    await _put_with_run_id(saver, tid, run1, checkpoint_ns="child:1")
+
+    # Pre-delete: verify run1 present in both namespaces
+    for ns in ["", "child:1"]:
+        pre_results = []
+        async for tup in saver.alist(generate_config(tid, checkpoint_ns=ns)):
+            pre_results.append(tup)
+        pre_run_ids = {t.metadata.get("run_id") for t in pre_results}
+        assert run1 in pre_run_ids, f"Pre-delete: run1 should exist in ns='{ns}'"
+
+    await saver.adelete_for_runs([run1])
+    for ns in ["", "child:1"]:
+        results = []
+        async for tup in saver.alist(generate_config(tid, checkpoint_ns=ns)):
+            results.append(tup)
+        run_ids = {t.metadata.get("run_id") for t in results}
+        assert run1 not in run_ids
+
+
+ALL_DELETE_FOR_RUNS_TESTS = [
+    test_delete_for_runs_single,
+    test_delete_for_runs_multiple,
+    test_delete_for_runs_preserves_other_runs,
+    test_delete_for_runs_removes_writes,
+    test_delete_for_runs_empty_list_noop,
+    test_delete_for_runs_nonexistent_noop,
+    test_delete_for_runs_across_namespaces,
+]
+
+
+async def run_delete_for_runs_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all delete_for_runs tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_DELETE_FOR_RUNS_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("delete_for_runs", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "delete_for_runs", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_delete_thread.py

@@ -0,0 +1,149 @@
+"""DELETE_THREAD capability tests — adelete_thread."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_delete_thread_removes_checkpoints(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """All checkpoints gone after delete."""
+    tid = str(uuid4())
+    parent_cfg = None
+    for i in range(3):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent_cfg = await saver.aput(config, cp, generate_metadata(step=i), {})
+
+    # Pre-delete: verify data exists
+    assert await saver.aget_tuple(generate_config(tid)) is not None, (
+        "Pre-delete: checkpoint should exist"
+    )
+
+    await saver.adelete_thread(tid)
+
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is None
+
+    results = []
+    async for t in saver.alist(generate_config(tid)):
+        results.append(t)
+    assert len(results) == 0
+
+
+async def test_delete_thread_removes_writes(saver: BaseCheckpointSaver) -> None:
+    """Pending writes gone after delete."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+    await saver.aput_writes(stored, [("ch", "val")], str(uuid4()))
+
+    # Pre-delete: verify writes exist
+    pre_tup = await saver.aget_tuple(generate_config(tid))
+    assert pre_tup is not None, "Pre-delete: checkpoint should exist"
+    assert pre_tup.pending_writes is not None and len(pre_tup.pending_writes) == 1, (
+        f"Pre-delete: expected 1 write, got {len(pre_tup.pending_writes) if pre_tup.pending_writes else 0}"
+    )
+
+    await saver.adelete_thread(tid)
+
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is None
+
+
+async def test_delete_thread_removes_all_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Root + child namespaces both removed."""
+    tid = str(uuid4())
+
+    for ns in ["", "child:1"]:
+        cfg = generate_config(tid, checkpoint_ns=ns)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(), {})
+
+    # Pre-delete: verify each namespace has data
+    for ns in ["", "child:1"]:
+        pre = await saver.aget_tuple(generate_config(tid, checkpoint_ns=ns))
+        assert pre is not None, f"Pre-delete: namespace '{ns}' should have data"
+
+    await saver.adelete_thread(tid)
+
+    for ns in ["", "child:1"]:
+        tup = await saver.aget_tuple(generate_config(tid, checkpoint_ns=ns))
+        assert tup is None
+
+
+async def test_delete_thread_preserves_other_threads(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Other threads untouched."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+
+    for tid in (tid1, tid2):
+        cfg = generate_config(tid)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(), {})
+
+    await saver.adelete_thread(tid1)
+
+    assert await saver.aget_tuple(generate_config(tid1)) is None
+    assert await saver.aget_tuple(generate_config(tid2)) is not None
+
+
+async def test_delete_thread_nonexistent_noop(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """No error for missing thread."""
+    # Should not raise
+    await saver.adelete_thread(str(uuid4()))
+
+
+ALL_DELETE_THREAD_TESTS = [
+    test_delete_thread_removes_checkpoints,
+    test_delete_thread_removes_writes,
+    test_delete_thread_removes_all_namespaces,
+    test_delete_thread_preserves_other_threads,
+    test_delete_thread_nonexistent_noop,
+]
+
+
+async def run_delete_thread_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all delete_thread tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_DELETE_THREAD_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("delete_thread", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "delete_thread", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_get_tuple.py

@@ -0,0 +1,253 @@
+"""GET_TUPLE capability tests — aget_tuple retrieval."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_get_tuple_nonexistent_returns_none(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Missing thread returns None."""
+    config = generate_config(str(uuid4()))
+    tup = await saver.aget_tuple(config)
+    assert tup is None
+
+
+async def test_get_tuple_latest_when_no_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Returns newest checkpoint when no checkpoint_id in config."""
+    tid = str(uuid4())
+    ids = []
+    parent_cfg = None
+    for i in range(3):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent_cfg = await saver.aput(config, cp, generate_metadata(step=i), {})
+        ids.append(cp["id"])
+
+    # Get without checkpoint_id — should return the latest
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is not None
+    assert tup.checkpoint["id"] == ids[-1]
+    assert tup.metadata["step"] == 2, (
+        f"Expected latest step=2, got {tup.metadata['step']}"
+    )
+
+
+async def test_get_tuple_specific_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Returns exact match when checkpoint_id specified."""
+    tid = str(uuid4())
+
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {})
+
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    # Fetch the first one specifically
+    tup = await saver.aget_tuple(stored1)
+    assert tup is not None
+    assert tup.checkpoint["id"] == cp1["id"]
+
+
+async def test_get_tuple_config_structure(saver: BaseCheckpointSaver) -> None:
+    """tuple.config has thread_id, checkpoint_ns, checkpoint_id."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    conf = tup.config["configurable"]
+    assert conf["thread_id"] == tid
+    assert conf.get("checkpoint_ns", "") == "", (
+        f"Expected checkpoint_ns='', got {conf.get('checkpoint_ns')!r}"
+    )
+    assert conf["checkpoint_id"] == cp["id"]
+
+
+async def test_get_tuple_checkpoint_fields(saver: BaseCheckpointSaver) -> None:
+    """All Checkpoint fields present."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint(channel_values={"k": "v"})
+    cp["channel_versions"] = {"k": 1}
+    stored = await saver.aput(config, cp, generate_metadata(), {"k": 1})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    c = tup.checkpoint
+    assert c["id"] == cp["id"], f"id mismatch: {c['id']!r} != {cp['id']!r}"
+    assert c["v"] == 1, f"Expected v=1, got {c['v']!r}"
+    assert "ts" in c and c["ts"], "ts should be non-empty"
+    assert c["channel_values"] == {"k": "v"}, f"channel_values: {c['channel_values']!r}"
+    assert "channel_versions" in c
+    assert "versions_seen" in c
+
+
+async def test_get_tuple_metadata(saver: BaseCheckpointSaver) -> None:
+    """metadata populated correctly."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    md = generate_metadata(source="input", step=-1)
+    stored = await saver.aput(config, cp, md, {})
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.metadata["source"] == "input"
+    assert tup.metadata["step"] == -1
+
+
+async def test_get_tuple_parent_config(saver: BaseCheckpointSaver) -> None:
+    """parent_config when parent exists, None otherwise."""
+    tid = str(uuid4())
+
+    # First checkpoint — no parent
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {})
+
+    tup1 = await saver.aget_tuple(stored1)
+    assert tup1 is not None
+    assert tup1.parent_config is None
+
+    # Second checkpoint — has parent
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    tup2 = await saver.aget_tuple(stored2)
+    assert tup2 is not None
+    assert tup2.parent_config is not None
+    assert (
+        tup2.parent_config["configurable"]["checkpoint_id"]
+        == stored1["configurable"]["checkpoint_id"]
+    )
+
+
+async def test_get_tuple_pending_writes(saver: BaseCheckpointSaver) -> None:
+    """pending_writes from put_writes visible."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("ch", "val")], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected 1 write, got {len(tup.pending_writes)}"
+    )
+    assert tup.pending_writes[0][0] == task_id, (
+        f"task_id mismatch: {tup.pending_writes[0][0]!r}"
+    )
+    assert tup.pending_writes[0][1] == "ch", (
+        f"channel mismatch: {tup.pending_writes[0][1]!r}"
+    )
+    assert tup.pending_writes[0][2] == "val", (
+        f"value mismatch: {tup.pending_writes[0][2]!r}"
+    )
+
+
+async def test_get_tuple_respects_namespace(saver: BaseCheckpointSaver) -> None:
+    """checkpoint_ns filtering."""
+    tid = str(uuid4())
+
+    cfg_root = generate_config(tid, checkpoint_ns="")
+    cp_root = generate_checkpoint()
+    stored_root = await saver.aput(cfg_root, cp_root, generate_metadata(), {})
+
+    cfg_child = generate_config(tid, checkpoint_ns="child:1")
+    cp_child = generate_checkpoint()
+    stored_child = await saver.aput(cfg_child, cp_child, generate_metadata(), {})
+
+    tup_root = await saver.aget_tuple(stored_root)
+    assert tup_root is not None
+    assert tup_root.checkpoint["id"] == cp_root["id"]
+
+    tup_child = await saver.aget_tuple(stored_child)
+    assert tup_child is not None
+    assert tup_child.checkpoint["id"] == cp_child["id"]
+
+
+async def test_get_tuple_nonexistent_checkpoint_id(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Specific but missing checkpoint_id returns None."""
+    tid = str(uuid4())
+    nonexistent_id = str(uuid4())
+    # Put one checkpoint so the thread exists
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    await saver.aput(config, cp, generate_metadata(), {})
+
+    # Ask for a non-existent checkpoint_id
+    bad_cfg = generate_config(tid, checkpoint_id=nonexistent_id)
+    tup = await saver.aget_tuple(bad_cfg)
+    assert tup is None
+
+
+ALL_GET_TUPLE_TESTS = [
+    test_get_tuple_nonexistent_returns_none,
+    test_get_tuple_latest_when_no_checkpoint_id,
+    test_get_tuple_specific_checkpoint_id,
+    test_get_tuple_config_structure,
+    test_get_tuple_checkpoint_fields,
+    test_get_tuple_metadata,
+    test_get_tuple_parent_config,
+    test_get_tuple_pending_writes,
+    test_get_tuple_respects_namespace,
+    test_get_tuple_nonexistent_checkpoint_id,
+]
+
+
+async def run_get_tuple_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all get_tuple tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_GET_TUPLE_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("get_tuple", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "get_tuple", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_list.py

@@ -0,0 +1,414 @@
+"""LIST capability tests — alist with various filters."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _setup_list_data(saver: BaseCheckpointSaver) -> dict:
+    """Populate saver with test data for list tests. Returns lookup info."""
+    tid = str(uuid4())
+    ids = []
+    parent_cfg = None
+    for i in range(4):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        source = "input" if i % 2 == 0 else "loop"
+        md = generate_metadata(source=source, step=i)
+        parent_cfg = await saver.aput(config, cp, md, {})
+        ids.append(cp["id"])
+
+    return {
+        "thread_id": tid,
+        "checkpoint_ids": ids,
+        "latest_config": parent_cfg,
+    }
+
+
+async def test_list_all(saver: BaseCheckpointSaver) -> None:
+    """No filters returns all checkpoints for the thread."""
+    data = await _setup_list_data(saver)
+    tid = data["thread_id"]
+
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    assert len(results) == 4
+
+
+async def test_list_by_thread(saver: BaseCheckpointSaver) -> None:
+    """Filter by thread_id — other threads not returned."""
+    data = await _setup_list_data(saver)
+
+    # List for a non-existent thread
+    results = []
+    async for tup in saver.alist(generate_config(str(uuid4()))):
+        results.append(tup)
+    assert len(results) == 0
+
+    # List for actual thread
+    results = []
+    async for tup in saver.alist(generate_config(data["thread_id"])):
+        results.append(tup)
+    assert len(results) == 4
+
+
+async def test_list_by_namespace(saver: BaseCheckpointSaver) -> None:
+    """Filter by checkpoint_ns."""
+    tid = str(uuid4())
+
+    # Root namespace
+    cfg1 = generate_config(tid, checkpoint_ns="")
+    cp1 = generate_checkpoint()
+    await saver.aput(cfg1, cp1, generate_metadata(), {})
+
+    # Child namespace
+    cfg2 = generate_config(tid, checkpoint_ns="child:1")
+    cp2 = generate_checkpoint()
+    await saver.aput(cfg2, cp2, generate_metadata(), {})
+
+    root_results = []
+    async for tup in saver.alist(generate_config(tid, checkpoint_ns="")):
+        root_results.append(tup)
+    assert len(root_results) == 1
+
+    child_results = []
+    async for tup in saver.alist(generate_config(tid, checkpoint_ns="child:1")):
+        child_results.append(tup)
+    assert len(child_results) == 1
+
+
+async def test_list_ordering(saver: BaseCheckpointSaver) -> None:
+    """Newest first (descending checkpoint_id)."""
+    data = await _setup_list_data(saver)
+    ids = data["checkpoint_ids"]
+
+    results = []
+    async for tup in saver.alist(generate_config(data["thread_id"])):
+        results.append(tup.checkpoint["id"])
+
+    # Should be in reverse order (newest first)
+    assert results == list(reversed(ids))
+
+
+async def test_list_metadata_filter_single_key(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """filter={'source': 'input'} returns only input checkpoints."""
+    data = await _setup_list_data(saver)
+
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        filter={"source": "input"},
+    ):
+        results.append(tup)
+
+    assert len(results) == 2, (
+        f"Expected 2 'input' checkpoints (steps 0,2), got {len(results)}"
+    )
+    for tup in results:
+        assert tup.metadata["source"] == "input"
+
+
+async def test_list_metadata_filter_step(saver: BaseCheckpointSaver) -> None:
+    """filter={'step': 1} returns matching checkpoints."""
+    data = await _setup_list_data(saver)
+
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        filter={"step": 1},
+    ):
+        results.append(tup)
+
+    assert len(results) == 1
+    assert results[0].metadata["step"] == 1
+
+
+async def test_list_before(saver: BaseCheckpointSaver) -> None:
+    """Pagination cursor — only checkpoints before the given one."""
+    data = await _setup_list_data(saver)
+    ids = data["checkpoint_ids"]
+
+    # Use the 3rd checkpoint as the 'before' cursor (index 2)
+    before_cfg = generate_config(data["thread_id"], checkpoint_id=ids[2])
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        before=before_cfg,
+    ):
+        results.append(tup)
+
+    # Should only include checkpoints before ids[2]
+    result_ids = [t.checkpoint["id"] for t in results]
+    assert ids[2] not in result_ids
+    assert ids[3] not in result_ids
+    assert set(result_ids) == {ids[0], ids[1]}, (
+        f"Expected {{ids[0], ids[1]}}, got {set(result_ids)}"
+    )
+
+
+async def test_list_limit(saver: BaseCheckpointSaver) -> None:
+    """limit=1, limit=N."""
+    data = await _setup_list_data(saver)
+
+    results = []
+    async for tup in saver.alist(generate_config(data["thread_id"]), limit=1):
+        results.append(tup)
+    assert len(results) == 1
+
+    results = []
+    async for tup in saver.alist(generate_config(data["thread_id"]), limit=2):
+        results.append(tup)
+    assert len(results) == 2
+
+
+async def test_list_limit_plus_before(saver: BaseCheckpointSaver) -> None:
+    """Pagination with limit."""
+    data = await _setup_list_data(saver)
+    ids = data["checkpoint_ids"]
+
+    before_cfg = generate_config(data["thread_id"], checkpoint_id=ids[3])
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        before=before_cfg,
+        limit=1,
+    ):
+        results.append(tup)
+
+    assert len(results) == 1
+    assert results[0].checkpoint["id"] == ids[2]
+
+
+async def test_list_combined_thread_and_filter(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """thread_id + metadata filter combined."""
+    data = await _setup_list_data(saver)
+
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        filter={"source": "loop"},
+    ):
+        results.append(tup)
+
+    assert len(results) == 2, (
+        f"Expected 2 'loop' checkpoints (steps 1,3), got {len(results)}"
+    )
+    for tup in results:
+        assert tup.metadata["source"] == "loop"
+
+
+async def test_list_empty_result(saver: BaseCheckpointSaver) -> None:
+    """No matches returns empty."""
+    results = []
+    async for tup in saver.alist(
+        generate_config(str(uuid4())),
+        filter={"source": "nonexistent"},
+    ):
+        results.append(tup)
+    assert len(results) == 0
+
+
+async def test_list_includes_pending_writes(saver: BaseCheckpointSaver) -> None:
+    """pending_writes in listed tuples."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    await saver.aput_writes(stored, [("ch", "val")], str(uuid4()))
+
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    assert len(results) == 1
+    assert results[0].pending_writes is not None
+    assert len(results[0].pending_writes) == 1, (
+        f"Expected 1 write, got {len(results[0].pending_writes)}"
+    )
+    assert results[0].pending_writes[0][1] == "ch", (
+        f"channel mismatch: {results[0].pending_writes[0][1]!r}"
+    )
+    assert results[0].pending_writes[0][2] == "val", (
+        f"value mismatch: {results[0].pending_writes[0][2]!r}"
+    )
+
+
+async def test_list_multiple_namespaces(saver: BaseCheckpointSaver) -> None:
+    """Root namespace checkpoint listed correctly."""
+    tid = str(uuid4())
+
+    for ns in ["", "child:1", "child:2"]:
+        cfg = generate_config(tid, checkpoint_ns=ns)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(), {})
+
+    # List with root namespace filter — should return exactly the root checkpoint
+    results = []
+    async for tup in saver.alist(generate_config(tid, checkpoint_ns="")):
+        results.append(tup)
+    assert len(results) == 1, f"Expected 1 root checkpoint, got {len(results)}"
+
+
+async def test_list_metadata_filter_multiple_keys(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """filter with multiple keys — all must match."""
+    tid = str(uuid4())
+
+    # Create checkpoints with different metadata combos
+    for source, step in [("input", 1), ("loop", 1), ("input", 2)]:
+        cfg = generate_config(tid)
+        cp = generate_checkpoint()
+        await saver.aput(cfg, cp, generate_metadata(source=source, step=step), {})
+
+    results = []
+    async for tup in saver.alist(
+        generate_config(tid),
+        filter={"source": "input", "step": 2},
+    ):
+        results.append(tup)
+
+    assert len(results) == 1, (
+        f"Expected 1 match for source=input+step=2, got {len(results)}"
+    )
+    assert results[0].metadata["source"] == "input"
+    assert results[0].metadata["step"] == 2
+
+
+async def test_list_metadata_filter_no_match(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Multi-key filter that matches nothing returns empty."""
+    data = await _setup_list_data(saver)
+
+    results = []
+    async for tup in saver.alist(
+        generate_config(data["thread_id"]),
+        filter={"source": "update", "step": 99},
+    ):
+        results.append(tup)
+
+    assert len(results) == 0
+
+
+async def test_list_metadata_custom_keys(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Custom (non-standard) metadata keys are filterable."""
+    tid = str(uuid4())
+
+    cfg = generate_config(tid)
+    cp = generate_checkpoint()
+    await saver.aput(cfg, cp, generate_metadata(score=42, run_id="run-abc"), {})
+
+    cfg2 = generate_config(tid)
+    cp2 = generate_checkpoint()
+    await saver.aput(cfg2, cp2, generate_metadata(score=99, run_id="run-xyz"), {})
+
+    # Filter by custom key
+    results = []
+    async for tup in saver.alist(
+        generate_config(tid),
+        filter={"score": 42},
+    ):
+        results.append(tup)
+
+    assert len(results) == 1
+    assert results[0].metadata["score"] == 42
+    assert results[0].metadata["run_id"] == "run-abc"
+
+
+async def test_list_global_search(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """alist(None, filter=...) searches across all threads."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+
+    # Use a unique marker so we don't collide with other tests' data
+    marker = str(uuid4())
+
+    cfg1 = generate_config(tid1)
+    cp1 = generate_checkpoint()
+    await saver.aput(cfg1, cp1, generate_metadata(source="input", marker=marker), {})
+
+    cfg2 = generate_config(tid2)
+    cp2 = generate_checkpoint()
+    await saver.aput(cfg2, cp2, generate_metadata(source="loop", marker=marker), {})
+
+    # Search across all threads with filter
+    results = []
+    async for tup in saver.alist(None, filter={"source": "input", "marker": marker}):
+        results.append(tup)
+    assert len(results) == 1
+    assert results[0].config["configurable"]["thread_id"] == tid1
+
+    # Search with marker only — should find both
+    results = []
+    async for tup in saver.alist(None, filter={"marker": marker}):
+        results.append(tup)
+    assert len(results) == 2
+
+
+ALL_LIST_TESTS = [
+    test_list_all,
+    test_list_by_thread,
+    test_list_by_namespace,
+    test_list_ordering,
+    test_list_metadata_filter_single_key,
+    test_list_metadata_filter_step,
+    test_list_metadata_filter_multiple_keys,
+    test_list_metadata_filter_no_match,
+    test_list_metadata_custom_keys,
+    test_list_global_search,
+    test_list_before,
+    test_list_limit,
+    test_list_limit_plus_before,
+    test_list_combined_thread_and_filter,
+    test_list_empty_result,
+    test_list_includes_pending_writes,
+    test_list_multiple_namespaces,
+]
+
+
+async def run_list_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all list tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_LIST_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("list", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result("list", test_fn.__name__, False, traceback.format_exc())
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_prune.py

@@ -0,0 +1,217 @@
+"""PRUNE capability tests — aprune(strategy)."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def _setup_thread(saver: BaseCheckpointSaver, tid: str, n: int = 3) -> list[dict]:
+    """Create n checkpoints on tid. Returns stored configs."""
+    stored = []
+    parent_cfg = None
+    for i in range(n):
+        config = generate_config(tid)
+        if parent_cfg:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent_cfg = await saver.aput(config, cp, generate_metadata(step=i), {})
+        stored.append(parent_cfg)
+    return stored
+
+
+async def test_prune_keep_latest_single_thread(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Only latest checkpoint survives."""
+    tid = str(uuid4())
+    configs = await _setup_thread(saver, tid, n=4)
+
+    await saver.aprune([tid], strategy="keep_latest")
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+
+    assert len(results) == 1
+    assert (
+        results[0].config["configurable"]["checkpoint_id"]
+        == configs[-1]["configurable"]["checkpoint_id"]
+    )
+
+
+async def test_prune_keep_latest_multiple_threads(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Each thread keeps its latest."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+    c1 = await _setup_thread(saver, tid1, n=3)
+    c2 = await _setup_thread(saver, tid2, n=2)
+
+    await saver.aprune([tid1, tid2], strategy="keep_latest")
+    for tid, expected_last in [(tid1, c1[-1]), (tid2, c2[-1])]:
+        results = []
+        async for tup in saver.alist(generate_config(tid)):
+            results.append(tup)
+        assert len(results) == 1
+        assert (
+            results[0].config["configurable"]["checkpoint_id"]
+            == expected_last["configurable"]["checkpoint_id"]
+        )
+
+
+async def test_prune_keep_latest_across_namespaces(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Latest per namespace kept."""
+    tid = str(uuid4())
+
+    # Root namespace: 3 checkpoints
+    parent = None
+    for i in range(3):
+        cfg = generate_config(tid, checkpoint_ns="")
+        if parent:
+            cfg["configurable"]["checkpoint_id"] = parent["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent = await saver.aput(cfg, cp, generate_metadata(step=i), {})
+    root_latest = parent
+
+    # Child namespace: 2 checkpoints
+    parent = None
+    for i in range(2):
+        cfg = generate_config(tid, checkpoint_ns="child:1")
+        if parent:
+            cfg["configurable"]["checkpoint_id"] = parent["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        parent = await saver.aput(cfg, cp, generate_metadata(step=i), {})
+    child_latest = parent
+
+    assert root_latest is not None
+    assert child_latest is not None
+    await saver.aprune([tid], strategy="keep_latest")
+    for ns, expected in [("", root_latest), ("child:1", child_latest)]:
+        results = []
+        async for tup in saver.alist(generate_config(tid, checkpoint_ns=ns)):
+            results.append(tup)
+        assert len(results) == 1
+        assert (
+            results[0].config["configurable"]["checkpoint_id"]
+            == expected["configurable"]["checkpoint_id"]
+        )
+
+
+async def test_prune_keep_latest_preserves_writes(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Latest checkpoint's writes kept."""
+    tid = str(uuid4())
+    configs = await _setup_thread(saver, tid, n=3)
+
+    # Add writes to the latest
+    await saver.aput_writes(configs[-1], [("ch", "val")], str(uuid4()))
+
+    await saver.aprune([tid], strategy="keep_latest")
+    tup = await saver.aget_tuple(generate_config(tid))
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected 1 write, got {len(tup.pending_writes)}"
+    )
+    assert tup.pending_writes[0][1] == "ch", (
+        f"channel mismatch: {tup.pending_writes[0][1]!r}"
+    )
+    assert tup.pending_writes[0][2] == "val", (
+        f"value mismatch: {tup.pending_writes[0][2]!r}"
+    )
+
+
+async def test_prune_delete_all(saver: BaseCheckpointSaver) -> None:
+    """delete_all strategy removes everything."""
+    tid = str(uuid4())
+    await _setup_thread(saver, tid, n=3)
+
+    await saver.aprune([tid], strategy="delete")
+    results = []
+    async for tup in saver.alist(generate_config(tid)):
+        results.append(tup)
+    assert len(results) == 0
+
+
+async def test_prune_preserves_other_threads(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Unlisted threads untouched."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+    await _setup_thread(saver, tid1, n=3)
+    await _setup_thread(saver, tid2, n=2)
+
+    # Snapshot tid2 before prune
+    pre_ids = []
+    async for tup in saver.alist(generate_config(tid2)):
+        pre_ids.append(tup.checkpoint["id"])
+
+    await saver.aprune([tid1], strategy="keep_latest")
+    # tid2 should be fully intact — same checkpoint IDs
+    post_ids = []
+    async for tup in saver.alist(generate_config(tid2)):
+        post_ids.append(tup.checkpoint["id"])
+    assert post_ids == pre_ids, f"tid2 changed: {pre_ids} -> {post_ids}"
+
+
+async def test_prune_empty_list_noop(saver: BaseCheckpointSaver) -> None:
+    """Empty thread_ids no error."""
+    await saver.aprune([], strategy="keep_latest")
+
+
+async def test_prune_nonexistent_noop(saver: BaseCheckpointSaver) -> None:
+    """Missing threads no error."""
+    await saver.aprune([str(uuid4())], strategy="keep_latest")
+
+
+ALL_PRUNE_TESTS = [
+    test_prune_keep_latest_single_thread,
+    test_prune_keep_latest_multiple_threads,
+    test_prune_keep_latest_across_namespaces,
+    test_prune_keep_latest_preserves_writes,
+    test_prune_delete_all,
+    test_prune_preserves_other_threads,
+    test_prune_empty_list_noop,
+    test_prune_nonexistent_noop,
+]
+
+
+async def run_prune_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all prune tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_PRUNE_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("prune", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result("prune", test_fn.__name__, False, traceback.format_exc())
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_put.py

@@ -0,0 +1,411 @@
+"""PUT capability tests — aput + aget_tuple round-trip."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from typing import Any
+from uuid import uuid4
+
+from langgraph.checkpoint.base import (
+    BaseCheckpointSaver,
+    ChannelVersions,
+)
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_put_returns_config(saver: BaseCheckpointSaver) -> None:
+    """aput returns a RunnableConfig with thread_id, checkpoint_ns, checkpoint_id."""
+    config = generate_config()
+    cp = generate_checkpoint(channel_values={"k": "v"})
+    cp["channel_versions"] = {"k": 1}
+    md = generate_metadata()
+
+    result = await saver.aput(config, cp, md, {"k": 1})
+
+    assert "configurable" in result
+    conf = result["configurable"]
+    assert "thread_id" in conf
+    assert "checkpoint_ns" in conf
+    assert "checkpoint_id" in conf
+    assert conf["checkpoint_id"] == cp["id"]
+
+
+async def test_put_roundtrip(saver: BaseCheckpointSaver) -> None:
+    """put then get_tuple returns identical checkpoint."""
+    config = generate_config()
+    cp = generate_checkpoint(channel_values={"msg": "hello"})
+    cp["channel_versions"] = {"msg": 1}
+    md = generate_metadata(source="input", step=-1)
+
+    stored_config = await saver.aput(config, cp, md, {"msg": 1})
+
+    tup = await saver.aget_tuple(stored_config)
+    assert tup is not None
+    assert tup.checkpoint["id"] == cp["id"]
+    assert tup.checkpoint["channel_values"] == {"msg": "hello"}
+
+
+async def test_put_preserves_channel_values(saver: BaseCheckpointSaver) -> None:
+    """Various types (str, int, list, dict, bytes, None) round-trip correctly."""
+    values: dict[str, Any] = {
+        "str_val": "hello",
+        "int_val": 42,
+        "list_val": [1, 2, 3],
+        "dict_val": {"nested": True},
+    }
+    config = generate_config()
+    cp = generate_checkpoint(channel_values=values)
+    versions: ChannelVersions = {k: 1 for k in values}
+    cp["channel_versions"] = versions
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, versions)
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    for k, v in values.items():
+        assert tup.checkpoint["channel_values"].get(k) == v, (
+            f"channel_values[{k}]: expected {v!r}, got {tup.checkpoint['channel_values'].get(k)!r}"
+        )
+
+
+async def test_put_preserves_channel_versions(saver: BaseCheckpointSaver) -> None:
+    """ChannelVersions round-trip correctly."""
+    versions: ChannelVersions = {"a": 1, "b": 2}
+    config = generate_config()
+    cp = generate_checkpoint(
+        channel_values={"a": "x", "b": "y"}, channel_versions=versions
+    )
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, versions)
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    # Compare version values — checkpointers may convert int to str
+    for k, expected in versions.items():
+        actual = tup.checkpoint["channel_versions"].get(k)
+        assert actual is not None, f"channel_versions[{k}] missing"
+        assert str(actual).split(".")[0] == str(expected).split(".")[0], (
+            f"channel_versions[{k}]: expected {expected!r}, got {actual!r}"
+        )
+
+
+async def test_put_preserves_versions_seen(saver: BaseCheckpointSaver) -> None:
+    """versions_seen dict round-trips."""
+    vs: dict[str, ChannelVersions] = {"node1": {"ch": 1}, "node2": {"ch": 2}}
+    config = generate_config()
+    cp = generate_checkpoint(versions_seen=vs)
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    for node in vs:
+        assert node in tup.checkpoint["versions_seen"], f"versions_seen[{node}] missing"
+
+
+async def test_put_preserves_metadata(saver: BaseCheckpointSaver) -> None:
+    """Metadata source, step, parents, and custom keys round-trip."""
+    md = generate_metadata(source="loop", step=3, custom_key="custom_value")
+    config = generate_config()
+    cp = generate_checkpoint()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.metadata["source"] == "loop"
+    assert tup.metadata["step"] == 3
+    assert tup.metadata.get("custom_key") == "custom_value"
+
+
+async def test_put_root_namespace(saver: BaseCheckpointSaver) -> None:
+    """checkpoint_ns='' works."""
+    config = generate_config(checkpoint_ns="")
+    cp = generate_checkpoint()
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.config["configurable"].get("checkpoint_ns", "") == ""
+
+
+async def test_put_child_namespace(saver: BaseCheckpointSaver) -> None:
+    """checkpoint_ns='child:abc' works."""
+    config = generate_config(checkpoint_ns="child:abc")
+    cp = generate_checkpoint()
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.config["configurable"]["checkpoint_ns"] == "child:abc"
+
+
+async def test_put_default_namespace(saver: BaseCheckpointSaver) -> None:
+    """Config without checkpoint_ns defaults to ''."""
+    tid = str(uuid4())
+    config = {"configurable": {"thread_id": tid, "checkpoint_ns": ""}}
+    cp = generate_checkpoint()
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+
+
+async def test_put_multiple_checkpoints_same_thread(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Sequential puts on same thread, all retrievable."""
+    tid = str(uuid4())
+    ids = []
+    parent_cfg = None
+    for i in range(3):
+        config = generate_config(tid)
+        if parent_cfg is not None:
+            config["configurable"]["checkpoint_id"] = parent_cfg["configurable"][
+                "checkpoint_id"
+            ]
+        cp = generate_checkpoint()
+        md = generate_metadata(step=i)
+        parent_cfg = await saver.aput(config, cp, md, {})
+        ids.append(cp["id"])
+
+    # All three should be retrievable
+    for cid in ids:
+        cfg = generate_config(tid, checkpoint_id=cid)
+        tup = await saver.aget_tuple(cfg)
+        assert tup is not None, f"checkpoint {cid} not found"
+        assert tup.checkpoint["id"] == cid
+
+
+async def test_put_multiple_threads_isolated(saver: BaseCheckpointSaver) -> None:
+    """Different thread_ids don't interfere."""
+    tid1, tid2 = str(uuid4()), str(uuid4())
+
+    config1 = generate_config(tid1)
+    cp1 = generate_checkpoint(channel_values={"x": "thread1"})
+    cp1["channel_versions"] = {"x": 1}
+    await saver.aput(config1, cp1, generate_metadata(), {"x": 1})
+
+    config2 = generate_config(tid2)
+    cp2 = generate_checkpoint(channel_values={"x": "thread2"})
+    cp2["channel_versions"] = {"x": 1}
+    await saver.aput(config2, cp2, generate_metadata(), {"x": 1})
+
+    tup1 = await saver.aget_tuple(generate_config(tid1))
+    tup2 = await saver.aget_tuple(generate_config(tid2))
+    assert tup1 is not None and tup2 is not None
+    assert tup1.checkpoint["channel_values"]["x"] == "thread1"
+    assert tup2.checkpoint["channel_values"]["x"] == "thread2"
+
+
+async def test_put_parent_config(saver: BaseCheckpointSaver) -> None:
+    """parent checkpoint_id tracked correctly."""
+    tid = str(uuid4())
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {})
+
+    # Second checkpoint — its config carries the parent checkpoint_id
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    tup = await saver.aget_tuple(stored2)
+    assert tup is not None
+    assert tup.parent_config is not None
+    assert (
+        tup.parent_config["configurable"]["checkpoint_id"]
+        == stored1["configurable"]["checkpoint_id"]
+    )
+
+
+async def test_put_incremental_channel_update(saver: BaseCheckpointSaver) -> None:
+    """Only updated channels need new blobs; unchanged channels loaded from prior versions."""
+    tid = str(uuid4())
+
+    # Checkpoint 1: both channels are new
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint(
+        channel_values={"a": "v1", "b": "v2"},
+        channel_versions={"a": 1, "b": 1},
+    )
+    stored1 = await saver.aput(
+        config1, cp1, generate_metadata(step=0), {"a": 1, "b": 1}
+    )
+
+    # Checkpoint 2: only 'a' is updated
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint(
+        channel_values={"a": "v1_updated", "b": "v2"},
+        channel_versions={"a": 2, "b": 1},
+    )
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {"a": 2})
+
+    # cp2 should reconstruct full channel_values from blobs at mixed versions
+    tup2 = await saver.aget_tuple(stored2)
+    assert tup2 is not None
+    assert tup2.checkpoint["channel_values"].get("a") == "v1_updated", (
+        f"a: expected 'v1_updated', got {tup2.checkpoint['channel_values'].get('a')!r}"
+    )
+    assert tup2.checkpoint["channel_values"].get("b") == "v2", (
+        f"b: expected 'v2', got {tup2.checkpoint['channel_values'].get('b')!r}"
+    )
+
+    # cp1 should still return original values
+    tup1 = await saver.aget_tuple(stored1)
+    assert tup1 is not None
+    assert tup1.checkpoint["channel_values"].get("a") == "v1"
+    assert tup1.checkpoint["channel_values"].get("b") == "v2"
+
+
+async def test_put_new_channel_added(saver: BaseCheckpointSaver) -> None:
+    """A channel that appears for the first time in a later checkpoint."""
+    tid = str(uuid4())
+
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint(
+        channel_values={"a": "v1"},
+        channel_versions={"a": 1},
+    )
+    stored1 = await saver.aput(config1, cp1, generate_metadata(step=0), {"a": 1})
+
+    # Checkpoint 2: 'b' is brand new, 'a' is unchanged
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint(
+        channel_values={"a": "v1", "b": "new_channel"},
+        channel_versions={"a": 1, "b": 1},
+    )
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {"b": 1})
+
+    tup2 = await saver.aget_tuple(stored2)
+    assert tup2 is not None
+    assert tup2.checkpoint["channel_values"].get("a") == "v1", (
+        f"a: expected 'v1', got {tup2.checkpoint['channel_values'].get('a')!r}"
+    )
+    assert tup2.checkpoint["channel_values"].get("b") == "new_channel", (
+        f"b: expected 'new_channel', got {tup2.checkpoint['channel_values'].get('b')!r}"
+    )
+
+
+async def test_put_channel_removed(saver: BaseCheckpointSaver) -> None:
+    """Channel no longer in channel_versions should not appear in loaded values."""
+    tid = str(uuid4())
+
+    config1 = generate_config(tid)
+    cp1 = generate_checkpoint(
+        channel_values={"a": "v1", "b": "v2"},
+        channel_versions={"a": 1, "b": 1},
+    )
+    stored1 = await saver.aput(
+        config1, cp1, generate_metadata(step=0), {"a": 1, "b": 1}
+    )
+
+    # Checkpoint 2: 'b' dropped from channel_versions
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint(
+        channel_values={"a": "v1_updated"},
+        channel_versions={"a": 2},
+    )
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {"a": 2})
+
+    tup2 = await saver.aget_tuple(stored2)
+    assert tup2 is not None
+    assert tup2.checkpoint["channel_values"].get("a") == "v1_updated"
+    assert "b" not in tup2.checkpoint["channel_values"], (
+        f"'b' should not be present, got {tup2.checkpoint['channel_values']}"
+    )
+
+
+async def test_put_preserves_run_id(saver: BaseCheckpointSaver) -> None:
+    """run_id in metadata round-trips correctly."""
+    run_id = str(uuid4())
+    config = generate_config()
+    cp = generate_checkpoint()
+    md = generate_metadata(source="loop", step=0, run_id=run_id)
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.metadata.get("run_id") == run_id, (
+        f"run_id: expected {run_id!r}, got {tup.metadata.get('run_id')!r}"
+    )
+
+
+async def test_put_preserves_versions_seen_values(saver: BaseCheckpointSaver) -> None:
+    """versions_seen values (not just keys) round-trip correctly."""
+    vs: dict[str, ChannelVersions] = {
+        "node1": {"ch_a": 1, "ch_b": 2},
+        "node2": {"ch_a": 3},
+    }
+    config = generate_config()
+    cp = generate_checkpoint(versions_seen=vs)
+    md = generate_metadata()
+
+    stored = await saver.aput(config, cp, md, {})
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    for node, expected_versions in vs.items():
+        assert node in tup.checkpoint["versions_seen"], f"versions_seen[{node}] missing"
+        actual_versions = tup.checkpoint["versions_seen"][node]
+        for ch, expected_v in expected_versions.items():
+            actual_v = actual_versions.get(ch)
+            assert actual_v is not None, f"versions_seen[{node}][{ch}] missing"
+            assert str(actual_v).split(".")[0] == str(expected_v).split(".")[0], (
+                f"versions_seen[{node}][{ch}]: expected {expected_v!r}, got {actual_v!r}"
+            )
+
+
+ALL_PUT_TESTS = [
+    test_put_returns_config,
+    test_put_roundtrip,
+    test_put_preserves_channel_values,
+    test_put_preserves_channel_versions,
+    test_put_preserves_versions_seen,
+    test_put_preserves_metadata,
+    test_put_root_namespace,
+    test_put_child_namespace,
+    test_put_default_namespace,
+    test_put_multiple_checkpoints_same_thread,
+    test_put_multiple_threads_isolated,
+    test_put_parent_config,
+    test_put_incremental_channel_update,
+    test_put_new_channel_added,
+    test_put_channel_removed,
+    test_put_preserves_run_id,
+    test_put_preserves_versions_seen_values,
+]
+
+
+async def run_put_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all put tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_PUT_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("put", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result("put", test_fn.__name__, False, traceback.format_exc())
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/spec/test_put_writes.py

@@ -0,0 +1,302 @@
+"""PUT_WRITES capability tests — aput_writes + pending_writes retrieval."""
+
+from __future__ import annotations
+
+import traceback
+from collections.abc import Callable
+from uuid import uuid4
+
+from langgraph.checkpoint.base import BaseCheckpointSaver
+
+from langgraph.checkpoint.conformance.test_utils import (
+    generate_checkpoint,
+    generate_config,
+    generate_metadata,
+)
+
+
+async def test_put_writes_basic(saver: BaseCheckpointSaver) -> None:
+    """Write stored, visible in aget_tuple pending_writes."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("channel1", "value1")], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    # Verify exact write tuple: (task_id, channel, value)
+    matching = [w for w in tup.pending_writes if w[0] == task_id and w[1] == "channel1"]
+    assert len(matching) == 1, f"Expected 1 write, got {len(matching)}"
+    assert matching[0][2] == "value1", f"Value mismatch: {matching[0][2]!r}"
+
+
+async def test_put_writes_multiple_writes_same_task(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Multiple (channel, value) pairs in a single call."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    writes = [("ch1", "v1"), ("ch2", "v2"), ("ch3", "v3")]
+    await saver.aput_writes(stored, writes, task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 3, (
+        f"Expected 3 writes, got {len(tup.pending_writes)}"
+    )
+    channels = {w[1] for w in tup.pending_writes}
+    assert channels == {"ch1", "ch2", "ch3"}, f"Expected exact channels, got {channels}"
+    # Verify values per channel
+    for expected_ch, expected_val in writes:
+        match = [
+            w for w in tup.pending_writes if w[0] == task_id and w[1] == expected_ch
+        ]
+        assert len(match) == 1, f"Expected 1 write for {expected_ch}, got {len(match)}"
+        assert match[0][2] == expected_val, (
+            f"Value mismatch for {expected_ch}: {match[0][2]!r}"
+        )
+
+
+async def test_put_writes_multiple_tasks(saver: BaseCheckpointSaver) -> None:
+    """Different task_ids produce separate writes."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    t1, t2 = str(uuid4()), str(uuid4())
+    await saver.aput_writes(stored, [("ch", "from_t1")], t1)
+    await saver.aput_writes(stored, [("ch", "from_t2")], t2)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 2, (
+        f"Expected 2 writes, got {len(tup.pending_writes)}"
+    )
+    # Verify values per task
+    t1_writes = [w for w in tup.pending_writes if w[0] == t1 and w[1] == "ch"]
+    t2_writes = [w for w in tup.pending_writes if w[0] == t2 and w[1] == "ch"]
+    assert len(t1_writes) == 1, f"Expected 1 write from t1, got {len(t1_writes)}"
+    assert len(t2_writes) == 1, f"Expected 1 write from t2, got {len(t2_writes)}"
+    assert t1_writes[0][2] == "from_t1", f"t1 value: {t1_writes[0][2]!r}"
+    assert t2_writes[0][2] == "from_t2", f"t2 value: {t2_writes[0][2]!r}"
+
+
+async def test_put_writes_preserves_task_id(saver: BaseCheckpointSaver) -> None:
+    """task_id in pending_writes matches what was passed to aput_writes."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("ch", "val")], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert any(w[0] == task_id for w in tup.pending_writes)
+
+
+async def test_put_writes_preserves_channel_and_value(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """Channel name + value round-trip."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("my_channel", {"data": 123})], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    match = [w for w in tup.pending_writes if w[0] == task_id and w[1] == "my_channel"]
+    assert len(match) == 1, f"Expected 1 write, got {len(match)}"
+    assert match[0][2] == {"data": 123}, f"Value mismatch: {match[0][2]!r}"
+
+
+async def test_put_writes_task_path(saver: BaseCheckpointSaver) -> None:
+    """task_path parameter accepted without error."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    # Should not raise
+    await saver.aput_writes(stored, [("ch", "v")], task_id, task_path="a:b:c")
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1
+
+
+async def test_put_writes_idempotent(saver: BaseCheckpointSaver) -> None:
+    """Duplicate (task_id, idx) doesn't duplicate writes."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(stored, [("ch", "val")], task_id)
+    await saver.aput_writes(stored, [("ch", "val")], task_id)
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    assert len(tup.pending_writes) == 1, (
+        f"Expected exactly 1 write total, got {len(tup.pending_writes)}"
+    )
+    # Should not have duplicated
+    matching = [w for w in tup.pending_writes if w[0] == task_id and w[1] == "ch"]
+    assert len(matching) == 1
+
+
+async def test_put_writes_special_channels(saver: BaseCheckpointSaver) -> None:
+    """ERROR and INTERRUPT channels handled correctly."""
+    from langgraph.checkpoint.serde.types import ERROR, INTERRUPT
+
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp = generate_checkpoint()
+    stored = await saver.aput(config, cp, generate_metadata(), {})
+
+    task_id = str(uuid4())
+    await saver.aput_writes(
+        stored,
+        [(ERROR, "something went wrong"), (INTERRUPT, {"reason": "human_input"})],
+        task_id,
+    )
+
+    tup = await saver.aget_tuple(stored)
+    assert tup is not None
+    assert tup.pending_writes is not None
+    channels = {w[1] for w in tup.pending_writes}
+    assert ERROR in channels
+    assert INTERRUPT in channels
+    # Verify values
+    err_writes = [w for w in tup.pending_writes if w[0] == task_id and w[1] == ERROR]
+    assert len(err_writes) == 1, f"Expected 1 ERROR write, got {len(err_writes)}"
+    assert err_writes[0][2] == "something went wrong", (
+        f"ERROR value: {err_writes[0][2]!r}"
+    )
+    int_writes = [
+        w for w in tup.pending_writes if w[0] == task_id and w[1] == INTERRUPT
+    ]
+    assert len(int_writes) == 1, f"Expected 1 INTERRUPT write, got {len(int_writes)}"
+    assert int_writes[0][2] == {"reason": "human_input"}, (
+        f"INTERRUPT value: {int_writes[0][2]!r}"
+    )
+
+
+async def test_put_writes_across_namespaces(saver: BaseCheckpointSaver) -> None:
+    """Writes isolated by checkpoint_ns."""
+    tid = str(uuid4())
+
+    # Root namespace checkpoint + write
+    cfg_root = generate_config(tid, checkpoint_ns="")
+    cp_root = generate_checkpoint()
+    stored_root = await saver.aput(cfg_root, cp_root, generate_metadata(), {})
+    root_task = str(uuid4())
+    await saver.aput_writes(stored_root, [("ch", "root_val")], root_task)
+
+    # Child namespace checkpoint + write
+    cfg_child = generate_config(tid, checkpoint_ns="child:1")
+    cp_child = generate_checkpoint()
+    stored_child = await saver.aput(cfg_child, cp_child, generate_metadata(), {})
+    child_task = str(uuid4())
+    await saver.aput_writes(stored_child, [("ch", "child_val")], child_task)
+
+    # Verify isolation — root should have exactly 1 write with root_val
+    tup_root = await saver.aget_tuple(stored_root)
+    assert tup_root is not None
+    assert tup_root.pending_writes is not None
+    root_ch = [w for w in tup_root.pending_writes if w[1] == "ch"]
+    assert len(root_ch) == 1, f"Expected 1 root write, got {len(root_ch)}"
+    assert root_ch[0][2] == "root_val", f"Root value: {root_ch[0][2]!r}"
+
+    # Child should have exactly 1 write with child_val
+    tup_child = await saver.aget_tuple(stored_child)
+    assert tup_child is not None
+    assert tup_child.pending_writes is not None
+    child_ch = [w for w in tup_child.pending_writes if w[1] == "ch"]
+    assert len(child_ch) == 1, f"Expected 1 child write, got {len(child_ch)}"
+    assert child_ch[0][2] == "child_val", f"Child value: {child_ch[0][2]!r}"
+
+
+async def test_put_writes_cleared_on_next_checkpoint(
+    saver: BaseCheckpointSaver,
+) -> None:
+    """New checkpoint starts with fresh pending_writes."""
+    tid = str(uuid4())
+    config = generate_config(tid)
+    cp1 = generate_checkpoint()
+    stored1 = await saver.aput(config, cp1, generate_metadata(step=0), {})
+
+    await saver.aput_writes(stored1, [("ch", "old_write")], str(uuid4()))
+
+    # New checkpoint
+    config2 = generate_config(tid)
+    config2["configurable"]["checkpoint_id"] = stored1["configurable"]["checkpoint_id"]
+    cp2 = generate_checkpoint()
+    stored2 = await saver.aput(config2, cp2, generate_metadata(step=1), {})
+
+    tup = await saver.aget_tuple(stored2)
+    assert tup is not None
+    # New checkpoint should have no pending writes
+    writes = tup.pending_writes or []
+    assert len(writes) == 0
+
+
+ALL_PUT_WRITES_TESTS = [
+    test_put_writes_basic,
+    test_put_writes_multiple_writes_same_task,
+    test_put_writes_multiple_tasks,
+    test_put_writes_preserves_task_id,
+    test_put_writes_preserves_channel_and_value,
+    test_put_writes_task_path,
+    test_put_writes_idempotent,
+    test_put_writes_special_channels,
+    test_put_writes_across_namespaces,
+    test_put_writes_cleared_on_next_checkpoint,
+]
+
+
+async def run_put_writes_tests(
+    saver: BaseCheckpointSaver,
+    on_test_result: Callable[[str, str, bool, str | None], None] | None = None,
+) -> tuple[int, int, list[str]]:
+    """Run all put_writes tests. Returns (passed, failed, failure_names)."""
+    passed = 0
+    failed = 0
+    failures: list[str] = []
+    for test_fn in ALL_PUT_WRITES_TESTS:
+        try:
+            await test_fn(saver)
+            passed += 1
+            if on_test_result:
+                on_test_result("put_writes", test_fn.__name__, True, None)
+        except Exception as e:
+            failed += 1
+            msg = f"{test_fn.__name__}: {e}"
+            failures.append(msg)
+            if on_test_result:
+                on_test_result(
+                    "put_writes", test_fn.__name__, False, traceback.format_exc()
+                )
+    return passed, failed, failures

libs/checkpoint-conformance/langgraph/checkpoint/conformance/test_utils.py

@@ -0,0 +1,212 @@
+"""Test utilities: checkpoint generators, assertion helpers, bulk operations."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+from uuid import uuid4
+
+from langchain_core.runnables import RunnableConfig
+from langgraph.checkpoint.base import (
+    ChannelVersions,
+    Checkpoint,
+    CheckpointMetadata,
+    CheckpointTuple,
+)
+from langgraph.checkpoint.base.id import uuid6
+
+
+def generate_checkpoint(
+    *,
+    checkpoint_id: str | None = None,
+    channel_values: dict[str, Any] | None = None,
+    channel_versions: ChannelVersions | None = None,
+    versions_seen: dict[str, ChannelVersions] | None = None,
+) -> Checkpoint:
+    """Create a well-formed Checkpoint with sensible defaults."""
+    return Checkpoint(
+        v=1,
+        id=checkpoint_id or str(uuid6(clock_seq=-1)),
+        ts=datetime.now(timezone.utc).isoformat(),
+        channel_values=channel_values if channel_values is not None else {},
+        channel_versions=channel_versions if channel_versions is not None else {},
+        versions_seen=versions_seen if versions_seen is not None else {},
+        pending_sends=[],  # ty: ignore[invalid-key]
+        updated_channels=None,
+    )
+
+
+def generate_config(
+    thread_id: str | None = None,
+    *,
+    checkpoint_ns: str = "",
+    checkpoint_id: str | None = None,
+) -> RunnableConfig:
+    """Create a RunnableConfig targeting a specific thread / namespace / checkpoint."""
+    configurable: dict[str, Any] = {
+        "thread_id": thread_id or str(uuid4()),
+        "checkpoint_ns": checkpoint_ns,
+    }
+    if checkpoint_id is not None:
+        configurable["checkpoint_id"] = checkpoint_id
+    return {"configurable": configurable}
+
+
+def generate_metadata(
+    source: str = "loop",
+    step: int = 0,
+    **extra: Any,
+) -> CheckpointMetadata:
+    """Create CheckpointMetadata with defaults."""
+    md: dict[str, Any] = {"source": source, "step": step, "parents": {}}
+    md.update(extra)
+    return md
+
+
+async def put_test_checkpoint(
+    saver: Any,
+    *,
+    thread_id: str | None = None,
+    checkpoint_ns: str = "",
+    parent_config: RunnableConfig | None = None,
+    channel_values: dict[str, Any] | None = None,
+    channel_versions: ChannelVersions | None = None,
+    metadata: CheckpointMetadata | None = None,
+    new_versions: ChannelVersions | None = None,
+) -> RunnableConfig:
+    """Put a single test checkpoint and return the stored config.
+
+    Handles wiring up parent_config, channel_values -> new_versions, etc.
+    """
+    tid = thread_id or str(uuid4())
+    cp = generate_checkpoint(
+        channel_values=channel_values,
+        channel_versions=channel_versions,
+    )
+
+    # When channel_values are provided, ensure channel_versions + new_versions
+    # are consistent so the checkpointer stores the blobs correctly.
+    vals = channel_values or {}
+    cv = channel_versions
+    if cv is None and vals:
+        cv = {k: 1 for k in vals}
+        cp["channel_versions"] = cv
+    nv = new_versions
+    if nv is None:
+        nv = cv or {}
+
+    md = metadata or generate_metadata()
+
+    config = generate_config(tid, checkpoint_ns=checkpoint_ns)
+    if parent_config is not None:
+        config["configurable"]["checkpoint_id"] = parent_config["configurable"][
+            "checkpoint_id"
+        ]
+
+    return await saver.aput(config, cp, md, nv)
+
+
+async def put_test_checkpoints(
+    saver: Any,
+    *,
+    n_threads: int = 1,
+    n_checkpoints: int = 1,
+    namespaces: list[str] | None = None,
+    channel_values: dict[str, Any] | None = None,
+) -> list[RunnableConfig]:
+    """Convenience: put multiple checkpoints across threads/namespaces.
+
+    Returns the stored configs in insertion order.
+    """
+    nss = namespaces or [""]
+    stored: list[RunnableConfig] = []
+    for t in range(n_threads):
+        tid = f"thread-{t}"
+        for ns in nss:
+            parent: RunnableConfig | None = None
+            for _c in range(n_checkpoints):
+                cfg = await put_test_checkpoint(
+                    saver,
+                    thread_id=tid,
+                    checkpoint_ns=ns,
+                    parent_config=parent,
+                    channel_values=channel_values,
+                )
+                parent = cfg
+                stored.append(cfg)
+    return stored
+
+
+def assert_checkpoint_equal(
+    actual: Checkpoint,
+    expected: Checkpoint,
+    *,
+    check_channel_values: bool = True,
+) -> None:
+    """Assert two checkpoints are semantically equal."""
+    assert actual["v"] == expected["v"], f"v mismatch: {actual['v']} != {expected['v']}"
+    assert actual["id"] == expected["id"], (
+        f"id mismatch: {actual['id']} != {expected['id']}"
+    )
+    assert actual["channel_versions"] == expected["channel_versions"], (
+        "channel_versions mismatch"
+    )
+    assert actual["versions_seen"] == expected["versions_seen"], (
+        "versions_seen mismatch"
+    )
+    if check_channel_values:
+        assert actual["channel_values"] == expected["channel_values"], (
+            "channel_values mismatch"
+        )
+
+
+def assert_tuple_equal(
+    actual: CheckpointTuple,
+    expected: CheckpointTuple,
+    *,
+    check_writes: bool = True,
+    check_channel_values: bool = True,
+) -> None:
+    """Assert two CheckpointTuples are semantically equal."""
+    # Config
+    a_conf = actual.config["configurable"]
+    e_conf = expected.config["configurable"]
+    assert a_conf["thread_id"] == e_conf["thread_id"], (
+        f"thread_id mismatch: {a_conf['thread_id']} != {e_conf['thread_id']}"
+    )
+    assert a_conf.get("checkpoint_ns", "") == e_conf.get("checkpoint_ns", ""), (
+        "checkpoint_ns mismatch"
+    )
+    assert a_conf["checkpoint_id"] == e_conf["checkpoint_id"], "checkpoint_id mismatch"
+
+    # Checkpoint
+    assert_checkpoint_equal(
+        actual.checkpoint,
+        expected.checkpoint,
+        check_channel_values=check_channel_values,
+    )
+
+    # Metadata
+    for k, v in expected.metadata.items():
+        assert actual.metadata.get(k) == v, (
+            f"metadata[{k}] mismatch: {actual.metadata.get(k)} != {v}"
+        )
+
+    # Parent config
+    if expected.parent_config is not None:
+        assert actual.parent_config is not None, "expected parent_config, got None"
+        assert (
+            actual.parent_config["configurable"]["checkpoint_id"]
+            == expected.parent_config["configurable"]["checkpoint_id"]
+        ), "parent checkpoint_id mismatch"
+    else:
+        assert actual.parent_config is None, (
+            f"expected no parent_config, got {actual.parent_config}"
+        )
+
+    # Pending writes
+    if check_writes and expected.pending_writes is not None:
+        assert actual.pending_writes is not None
+        assert len(actual.pending_writes) == len(expected.pending_writes), (
+            f"pending_writes length mismatch: {len(actual.pending_writes)} != {len(expected.pending_writes)}"
+        )

libs/checkpoint-conformance/langgraph/checkpoint/conformance/validate.py

@@ -0,0 +1,124 @@
+"""Core conformance runner — detects capabilities, runs test suites, builds report."""
+
+from __future__ import annotations
+
+from langgraph.checkpoint.conformance.capabilities import (
+    Capability,
+    DetectedCapabilities,
+)
+from langgraph.checkpoint.conformance.initializer import RegisteredCheckpointer
+from langgraph.checkpoint.conformance.report import (
+    CapabilityReport,
+    CapabilityResult,
+    ProgressCallbacks,
+)
+from langgraph.checkpoint.conformance.spec.test_copy_thread import run_copy_thread_tests
+from langgraph.checkpoint.conformance.spec.test_delete_for_runs import (
+    run_delete_for_runs_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_delete_thread import (
+    run_delete_thread_tests,
+)
+from langgraph.checkpoint.conformance.spec.test_get_tuple import run_get_tuple_tests
+from langgraph.checkpoint.conformance.spec.test_list import run_list_tests
+from langgraph.checkpoint.conformance.spec.test_prune import run_prune_tests
+from langgraph.checkpoint.conformance.spec.test_put import run_put_tests
+from langgraph.checkpoint.conformance.spec.test_put_writes import run_put_writes_tests
+
+# Maps capability to its runner function.
+_RUNNERS = {
+    Capability.PUT: run_put_tests,
+    Capability.PUT_WRITES: run_put_writes_tests,
+    Capability.GET_TUPLE: run_get_tuple_tests,
+    Capability.LIST: run_list_tests,
+    Capability.DELETE_THREAD: run_delete_thread_tests,
+    Capability.DELETE_FOR_RUNS: run_delete_for_runs_tests,
+    Capability.COPY_THREAD: run_copy_thread_tests,
+    Capability.PRUNE: run_prune_tests,
+}
+
+
+async def validate(
+    registered: RegisteredCheckpointer,
+    *,
+    capabilities: set[str] | None = None,
+    progress: ProgressCallbacks | None = None,
+) -> CapabilityReport:
+    """Run the validation suite against a registered checkpointer.
+
+    Args:
+        registered: A RegisteredCheckpointer (from @checkpointer_test decorator).
+        capabilities: If given, only run tests for these capability names.
+            Otherwise, auto-detect and run all applicable tests.
+        progress: Optional progress callbacks for incremental output.
+            Use ``ProgressCallbacks.default()`` for dot-style,
+            ``ProgressCallbacks.verbose()`` for per-test output, or
+            ``None`` / ``ProgressCallbacks.quiet()`` for silent mode.
+
+    Returns:
+        A CapabilityReport with per-capability results.
+    """
+    report = CapabilityReport(checkpointer_name=registered.name)
+
+    # Determine which capabilities to test.
+    caps_to_test: set[Capability]
+    if capabilities is not None:
+        caps_to_test = {Capability(c) for c in capabilities}
+    else:
+        caps_to_test = set(Capability)
+
+    async with registered.enter_lifespan():
+        for cap in Capability:
+            if cap in caps_to_test and cap.value not in registered.skip_capabilities:
+                # Create a fresh checkpointer for each capability suite.
+                async with registered.create() as saver:
+                    detected = DetectedCapabilities.from_instance(saver)
+                    is_detected = cap in detected.detected
+
+                    if not is_detected:
+                        if progress and progress.on_capability_start:
+                            progress.on_capability_start(cap.value, False)
+                        report.results[cap.value] = CapabilityResult(
+                            detected=False,
+                            passed=None,
+                            tests_skipped=1,
+                        )
+                        continue
+
+                    runner = _RUNNERS.get(cap)
+                    if runner is None:
+                        report.results[cap.value] = CapabilityResult(
+                            detected=True,
+                            passed=None,
+                            tests_skipped=1,
+                        )
+                        continue
+
+                    if progress and progress.on_capability_start:
+                        progress.on_capability_start(cap.value, True)
+
+                    passed, failed, failures = await runner(
+                        saver,
+                        on_test_result=progress.on_test_result if progress else None,
+                    )
+
+                    if progress and progress.on_capability_end:
+                        progress.on_capability_end(cap.value)
+
+                    report.results[cap.value] = CapabilityResult(
+                        detected=True,
+                        passed=failed == 0,
+                        tests_passed=passed,
+                        tests_failed=failed,
+                        failures=failures,
+                    )
+            else:
+                if progress and progress.on_capability_start:
+                    progress.on_capability_start(cap.value, False)
+                report.results[cap.value] = CapabilityResult(
+                    detected=False,
+                    passed=None,
+                    tests_skipped=1,
+                )
+
+    return report

libs/checkpoint-conformance/pyproject.toml

@@ -0,0 +1,65 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "langgraph-checkpoint-conformance"
+version = "0.0.1"
+description = "Conformance test suite for LangGraph checkpointer implementations."
+authors = [{name = "William FH", email = "13333726+hinthornw@users.noreply.github.com"}]
+requires-python = ">=3.10"
+readme = "README.md"
+license = "MIT"
+dependencies = [
+    "langgraph-checkpoint>=2.0.0",
+]
+
+[project.urls]
+Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-conformance"
+
+[dependency-groups]
+test = [
+  "pytest",
+  "pytest-asyncio",
+]
+lint = [
+  "ruff",
+  "ty",
+]
+dev = [
+  {include-group = "test"},
+  {include-group = "lint"},
+]
+
+[tool.hatch.build.targets.wheel]
+include = ["langgraph"]
+
+[tool.pytest.ini_options]
+addopts = "--strict-markers --strict-config --durations=5 -vv"
+testpaths = ["tests"]
+asyncio_mode = "auto"
+
+[tool.ty.rules]
+# The extended methods (acopy_thread, adelete_for_runs, aprune) are checked
+# at runtime via capability detection and may not exist on the installed
+# base class. Dict literal inference is also overly strict for RunnableConfig.
+unresolved-attribute = "ignore"
+invalid-argument-type = "ignore"
+invalid-return-type = "ignore"
+
+[tool.ruff]
+lint.select = [
+  "E",  # pycodestyle
+  "F",  # Pyflakes
+  "UP", # pyupgrade
+  "B",  # flake8-bugbear
+  "I",  # isort
+]
+lint.ignore = ["E501", "B008"]
+target-version = "py310"
+
+[[tool.uv.index]]
+name = "testpypi"
+url = "https://test.pypi.org/simple/"
+publish-url = "https://test.pypi.org/legacy/"
+explicit = true

libs/checkpoint-conformance/tests/test_validate_memory.py

@@ -0,0 +1,21 @@
+"""Self-tests: run the conformance suite against InMemorySaver."""
+
+from __future__ import annotations
+
+import pytest
+from langgraph.checkpoint.memory import InMemorySaver
+
+from langgraph.checkpoint.conformance import checkpointer_test, validate
+
+
+@checkpointer_test(name="InMemorySaver")
+async def memory_checkpointer():
+    yield InMemorySaver()
+
+
+@pytest.mark.asyncio
+async def test_validate_memory_base():
+    """InMemorySaver passes all base capability tests."""
+    report = await validate(memory_checkpointer)
+    report.print_report()
+    assert report.passed_all_base(), f"Base tests failed: {report.to_dict()}"

libs/checkpoint-postgres/Makefile

@@ -1,4 +1,4 @@
-.PHONY: test test_watch lint format
+.PHONY: test test_watch lint type format
 
 ######################
 # TESTING AND COVERAGE
@@ -61,6 +61,9 @@ lint lint_diff lint_package lint_tests:
 	[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE)
 	[ "$(PYTHON_FILES)" = "" ] || uv run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
 
+type:
+	mkdir -p $(MYPY_CACHE) && uv run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
+
 format format_diff:
 	uv run ruff format $(PYTHON_FILES)
 	uv run ruff check --select I --fix $(PYTHON_FILES)

libs/checkpoint-postgres/langgraph/store/postgres/aio.py

@@ -550,6 +550,11 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                 Will be applied regardless of whether the PostgresStore instance was initialized with a pipeline.
                 If pipeline mode is not supported, will fall back to using transaction context manager.
         """
+        is_pooled_conn = isinstance(self.conn, AsyncConnectionPool)
+        # With AsyncConnectionPool, each _cursor() call checks out its own connection.
+        # The pool does not hand out the same connection concurrently, so a shared lock
+        # across calls is unnecessary here.
+        lock = asyncio.Lock() if is_pooled_conn else self.lock
         async with _ainternal.get_connection(self.conn) as conn:
             if self.pipe:
                 # a connection in pipeline mode can be used concurrently
@@ -566,21 +571,21 @@ class AsyncPostgresStore(AsyncBatchedBaseStore, BasePostgresStore[_ainternal.Con
                 # thread/coroutine at a time, so we acquire a lock
                 if self.supports_pipeline:
                     async with (
-                        self.lock,
+                        lock,
                         conn.pipeline(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
                 else:
                     async with (
-                        self.lock,
+                        lock,
                         conn.transaction(),
                         conn.cursor(binary=True, row_factory=dict_row) as cur,
                     ):
                         yield cur
             else:
                 async with (
-                    self.lock,
+                    lock,
                     conn.cursor(binary=True, row_factory=dict_row) as cur,
                 ):
                     yield cur

libs/checkpoint-postgres/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langgraph-checkpoint-postgres"
-version = "3.0.3"
+version = "3.0.4"
 description = "Library with a Postgres implementation of LangGraph checkpoint saver."
 authors = []
 requires-python = ">=3.10"
@@ -13,14 +13,14 @@ license = "MIT"
 license-files = ['LICENSE']
 dependencies = [
   "langgraph-checkpoint>=2.1.2,<5.0.0",
-  "orjson>=3.10.1",
+  "orjson>=3.11.5",
   "psycopg>=3.2.0",
   "psycopg-pool>=3.2.0",
 ]
 
 [project.urls]
 Source = "https://github.com/langchain-ai/langgraph/tree/main/libs/checkpoint-postgres"
-Twitter = "https://x.com/LangChainAI"
+Twitter = "https://x.com/LangChain"
 Slack = "https://www.langchain.com/join-community"
 Reddit = "https://www.reddit.com/r/LangChain/"