[CLI] Handle dependencies js up --watch

So you can return a dict with extra args

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -7,35 +7,29 @@ body:
       value: >
         Thank you for taking the time to file a bug report. 
         
-        Use this to report bugs in LangChain. 
-        
-        If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
-        to ask for help with your issue.
+        Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use [GitHub Discussions](https://github.com/langchain-ai/langgraph/discussions).
         
         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:
         
-        [LangGraph documentation](https://langchain-ai.github.io/langgraph/).
+        [LangGraph Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
+        [LangGraph Github Issues](https://github.com/langchain-ai/langgraph/issues),
+        [LangGraph how-to guides](https://langchain-ai.github.io/langgraph/how-tos/).
         [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
         [GitHub search](https://github.com/langchain-ai/langgraph),
-        [LangChain Github Discussions](https://github.com/langchain-ai/langgraph/discussions),
-        [LangChain Github Issues](https://github.com/langchain-ai/langgraph/issues),
-        [LangChain ChatBot](https://chat.langchain.com/)
   - type: checkboxes
     id: checks
     attributes:
       label: Checked other resources
-      description: Please confirm and check all the following options.
+      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.
       options:
-        - label: I added a very descriptive title to this issue.
+        - label: This is a bug, not a usage question. For questions, please use GitHub Discussions.
           required: true
-        - label: I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
+        - label: I added a clear and detailed title that summarizes the issue.
           required: true
-        - label: I used the GitHub search to find a similar question and didn't find it.
+        - label: I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
           required: true
-        - label: I am sure that this is a bug in LangGraph/LangChain rather than my code.
-          required: true
-        - label: I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
+        - 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.
           required: true
   - type: textarea
     id: reproduction
@@ -45,14 +39,6 @@ body:
       label: Example Code
       description: |
         Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
-        
-        If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
-        
-        **Important!** 
-        
-        * Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
-        * Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
       placeholder: |
         from langgraph.graph import StateGraph
 
@@ -92,25 +78,8 @@ body:
     attributes:
       label: System Info
       description: |
-        Please share your system info with us. 
-        
-        "pip freeze | grep langchain" 
-        platform (windows / linux / mac)
-        python version
-        
-        OR if you're on a recent version of langchain-core you can paste the output of:
-        
         python -m langchain_core.sys_info
       placeholder: |
-        "pip freeze | grep langgraph"
-        platform
-        python version
-        
-        Alternatively, if you're on a recent version of langchain-core you can paste the output of:
-        
         python -m langchain_core.sys_info
-        
-        These will only surface LangChain packages, don't forget to include any other relevant
-        packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
     validations:
       required: true

.github/scripts/run_langgraph_cli_test.py

@@ -0,0 +1,115 @@
+import asyncio
+import json
+import os
+import pathlib
+import sys
+import langgraph_cli
+import langgraph_cli.docker
+import langgraph_cli.config
+
+from langgraph_cli.exec import Runner, subp_exec
+from langgraph_cli.progress import Progress
+from langgraph_cli.constants import DEFAULT_PORT
+
+
+def test(
+    config: pathlib.Path,
+    port: int,
+    tag: str,
+    verbose: bool,
+):
+    with Runner() as runner, Progress(message="Pulling...") as set:
+        # check docker available
+        capabilities = langgraph_cli.docker.check_capabilities(runner)
+        # open config
+        config_json = langgraph_cli.config.validate_config_file(config)
+
+        set("Running...")
+        args = [
+            "run",
+            "--rm",
+            "-p",
+            f"{port}:8000",
+        ]
+        if isinstance(config_json["env"], str):
+            args.extend(
+                [
+                    "--env-file",
+                    str(config.parent / config_json["env"]),
+                ]
+            )
+        else:
+            for k, v in config_json["env"].items():
+                args.extend(
+                    [
+                        "-e",
+                        f"{k}={v}",
+                    ]
+                )
+        if capabilities.healthcheck_start_interval:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "1",
+                    "--health-start-period",
+                    "10s",
+                    "--health-start-interval",
+                    "1s",
+                ]
+            )
+        else:
+            args.extend(
+                [
+                    "--health-interval",
+                    "5s",
+                    "--health-retries",
+                    "2",
+                ]
+            )
+
+        _task = None
+
+        def on_stdout(line: str):
+            nonlocal _task
+            if "GET /ok" in line or "Uvicorn running on" in line:
+                set("")
+                sys.stdout.write(
+                    f"""Ready!
+- API: http://localhost:{port}
+"""
+                )
+                sys.stdout.flush()
+                _task.cancel()
+                return True
+            return False
+
+        async def subp_exec_task(*args, **kwargs):
+            nonlocal _task
+            _task = asyncio.create_task(subp_exec(*args, **kwargs))
+            await _task
+
+        try:
+            runner.run(
+                subp_exec_task(
+                    "docker",
+                    *args,
+                    tag,
+                    verbose=verbose,
+                    on_stdout=on_stdout,
+                )
+            )
+        except asyncio.CancelledError:
+            pass
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-t", "--tag", type=str)
+    parser.add_argument("-c", "--config", type=str, default="./langgraph.json")
+    parser.add_argument("-p", "--port", default=DEFAULT_PORT)
+    args = parser.parse_args()
+    test(pathlib.Path(args.config), args.port, args.tag, verbose=True)

.github/workflows/_integration_test.yml

@@ -39,22 +39,36 @@ jobs:
       - name: Install cli globally
         if: steps.changed-files.outputs.all
         run: pip install -e .
-      - name: Start service A
+      - name: Build and test service A
         if: steps.changed-files.outputs.all
+        working-directory: libs/cli/examples
         run: |
-          timeout 60 langgraph test -c examples/langgraph.json --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service B
+          # The build-arg isn't used; just testing that we accept other args
+          langgraph build -t langgraph-test-a  --base-image "langchain/langgraph-trial"
+          cp .env.example .envg
+          timeout 60 python ../../../.github/scripts/run_langgraph_cli_test.py -c langgraph.json -t langgraph-test-a
+      - name: Build and test service B
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service C
+          langgraph build -t langgraph-test-b  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-b
+      - name: Build and test service C
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_a
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
-      - name: Start service D
+          langgraph build -t langgraph-test-c  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-c
+      - name: Build and test service D
         if: steps.changed-files.outputs.all
         working-directory: libs/cli/examples/graphs_reqs_b
         run: |
-          timeout 60 langgraph test --verbose || (exit "$(($? == 124 ? 0 : $?))")
+          langgraph build -t langgraph-test-d  --base-image "langchain/langgraph-trial"
+          timeout 60 python ../../../../.github/scripts/run_langgraph_cli_test.py -t langgraph-test-d
+
+      - name: Build JS service
+        if: steps.changed-files.outputs.all
+        working-directory: libs/cli/js-examples
+        run: |
+          langgraph build -t langgraph-test-e
+  

.github/workflows/_lint.yml

@@ -42,7 +42,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: lint-${{ inputs.working-directory }}
 
       - name: Check Poetry File

.github/workflows/_test.yml

@@ -31,7 +31,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: test-${{ inputs.working-directory }}
       - name: Login to Docker Hub
         uses: docker/login-action@v3

.github/workflows/_test_langgraph.yml

@@ -19,14 +19,19 @@ jobs:
           - "3.13"
         core-version:
           - "latest"
+        ff-send-v2:
+          - "false"
         include:
           - python-version: "3.11"
-            core-version: ">=0.2.39,<0.3.0"
+            core-version: ">=0.2.42,<0.3.0"
+          - python-version: "3.11"
+            core-version: "latest"
+            ff-send-v2: "true"
 
     defaults:
       run:
         working-directory: libs/langgraph
-    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }})"
+    name: "test #${{ matrix.python-version }} (langchain-core: ${{ matrix.core-version }}, ff-send-v2: ${{ matrix.ff-send-v2 }})"
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
@@ -52,8 +57,10 @@ jobs:
 
       - name: Run tests
         shell: bash
+        env:
+          LANGGRAPH_FF_SEND_V2: ${{ matrix.ff-send-v2 }}
         run: |
-          make test
+          make test_parallel
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/_test_release.yml

@@ -29,7 +29,6 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
@@ -93,3 +92,5 @@ jobs:
           # This is *only for CI use* and is *extremely dangerous* otherwise!
           # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
           skip-existing: true
+          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
+          attestations: false

.github/workflows/deploy_docs.yml

@@ -44,6 +44,8 @@ jobs:
   deploy:
     # needs: run-changed-notebooks
     runs-on: ubuntu-latest
+    env:
+      GITHUB_TOKEN: ${{ secrets.MKDOCS_GITHUB_TOKEN }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -58,8 +60,14 @@ jobs:
 
       - name: Install dependencies
         run: |
-          poetry install --with test
-          poetry run pip install -U pytest pytest-check-links langsmith langchain GitPython
+          poetry install --with test --no-root
+          poetry run pip install -U \
+            pytest \
+            pytest-check-links \
+            langsmith \
+            langchain \
+            GitPython \
+            "git+https://${GITHUB_TOKEN}@github.com/langchain-ai/mkdocs-material-insiders.git"
 
       - name: Lint Docs
         # This step lints the docs using the existing linting set up.
@@ -80,9 +88,13 @@ jobs:
               --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
               --check-links-ignore "https://x.com/.*" \
               --check-links-ignore "https://github\.com/.*" \
+              --check-links-ignore "http://localhost:8123/.*" \
               --check-links-ignore "/.*\.(ipynb|html)$" \
               --check-links-ignore "https://python\.langchain\.com/.*" \
-              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html')
+              --check-links-ignore "https://openai\.com/.*" \
+              --check-links-ignore "https://pepy\.tech/.*" \
+              --check-links $(find docs/site -name "index.html" | grep -v 'storm/index.html') 
+              
           else
             echo "Fetching changes from origin/main..."
             git fetch origin main
@@ -93,6 +105,7 @@ jobs:
               echo "Running link check on HTML files matching changed notebook files..."
               poetry run pytest -v \
                 --check-links-ignore "https://(api|web|docs)\.smith\.langchain\.com/.*" \
+                --check-links-ignore "http://localhost:8123/.*" \
                 --check-links-ignore "https://x.com/.*" \
                 --check-links-ignore "https://github\.com/.*" \
                 --check-links-ignore "/.*\.(ipynb|html)$" \

.github/workflows/release.yml

@@ -31,7 +31,6 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       # We want to keep this build stage *separate* from the release stage,
@@ -169,7 +168,6 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
 
       - name: Import published package
         shell: bash
@@ -256,7 +254,6 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       - uses: actions/download-artifact@v4
@@ -270,6 +267,8 @@ jobs:
           packages-dir: ${{ inputs.working-directory }}/dist/
           verbose: true
           print-hash: true
+          # Temp workaround since attestations are on by default as of gh-action-pypi-publish v1.11.0
+          attestations: false
 
   mark-release:
     needs:
@@ -296,7 +295,6 @@ jobs:
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
       - uses: actions/download-artifact@v4

CONTRIBUTING.md

@@ -49,7 +49,7 @@ gain understanding of concepts and how they interact by showing one way to achie
 
 They should **avoid** giving
 multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
-be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction.
+be completely production-ready, it should be useful and practically satisfy the goal that you clearly stated in the tutorial's introduction.
 
 To quote the Diataxis website:
 

Makefile

@@ -13,7 +13,7 @@ serve-clean-docs: clean-docs
 	poetry run python -m mkdocs serve -c -f docs/mkdocs.yml --strict -w ./libs/langgraph
 
 serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph --dirty
+	poetry run python -m mkdocs serve -f docs/mkdocs.yml -w ./libs/langgraph  -w ./libs/checkpoint --dirty
 
 clean-docs:
 	find ./docs/docs -name "*.ipynb" -type f -delete

README.md

@@ -16,6 +16,8 @@
 
 LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
 
+[LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform) is infrastructure for deploying LangGraph agents. It is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework. The LangGraph Platform consists of several components that work together to support the development, deployment, debugging, and monitoring of LangGraph applications: [LangGraph Server](https://langchain-ai.github.io/langgraph/concepts/langgraph_server) (APIs), [LangGraph SDKs](https://langchain-ai.github.io/langgraph/concepts/sdk) (clients for the APIs), [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli) (command line tool for building the server), [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio) (UI/debugger),
+
 To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
 
 ### Key Features
@@ -26,6 +28,16 @@ To learn more about LangGraph, check out our first LangChain Academy course, *In
 - **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
 - **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
 
+### LangGraph Platform
+
+LangGraph Platform is a commercial solution for deploying agentic applications to production, built on the open-source LangGraph framework.
+Here are some common issues that arise in complex deployments, which LangGraph Platform addresses:
+
+- **Streaming support**: LangGraph Server provides [multiple streaming modes](https://langchain-ai.github.io/langgraph/concepts/streaming) optimized for various application needs
+- **Background runs**: Runs agents asynchronously in the background
+- **Support for long running agents**: Infrastructure that can handle long running processes
+- **[Double texting](https://langchain-ai.github.io/langgraph/concepts/double_texting)**: Handle the case where you get two messages from the user before the agent can respond
+- **Handle burstiness**: Task queue for ensuring requests are handled consistently without loss, even under heavy loads
 
 ## Installation
 
@@ -226,7 +238,7 @@ final_state["messages"][-1].content
 * [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
 * [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/high_level/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
 * [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
-* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
+* [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform): LangGraph Platform is a commercial solution for deploying agentic applications in production, built on the open-source LangGraph framework.
 
 ## Contributing
 

docs/_scripts/prepare_notebooks_for_ci.py

@@ -36,10 +36,11 @@ NOTEBOOKS_NO_EXECUTION = [
     "docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb",
     # this loads a massive dataset from gcp
     "docs/docs/tutorials/usaco/usaco.ipynb",
+    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
+    "docs/docs/how-tos/autogen-integration.ipynb",
     # TODO: need to update these notebooks to make sure they are runnable in CI
     "docs/docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
     "docs/docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb",  # taking a very long time to run
     "docs/docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
     "docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # Cannot create a consistent method resolution error from VCR
     "docs/docs/how-tos/map-reduce.ipynb"  # flakiness from structured output, only when running with VCR

docs/cassettes/multi-agent-network_26a0d4df-ff99-40f0-92a8-0b3f2c591040.msgpack.zlib

@@ -0,0 +1 @@
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docs/cassettes/review-tool-calls_f9a0d5d4-52ff-49e0-a6f4-41f9a0e844d8.msgpack.zlib

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docs/cassettes/wait-user-input_a9f599b5-1a55-406b-a76b-f52b3ca06975.msgpack.zlib

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docs/cassettes/wait-user-input_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib

@@ -1 +1 @@
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docs/docs/cloud/concepts/api.md

@@ -1,216 +0,0 @@
-# API Concepts
-
-This page describes the high-level concepts of the LangGraph Cloud API. The conceptual guide of LangGraph (Python library) is [here](../../concepts/high_level.md).
-
-## Data Models
-
-The LangGraph Cloud API consists of a few core data models: [Assistants](#assistants), [Threads](#threads), [Runs](#runs), and [Cron Jobs](#cron-jobs).
-
-### Assistants
-
-When building agents, it is fairly common to make rapid changes that *do not* alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration. This can have at least two use-cases:
-
-* Assistants give developers a quick and easy way to modify and version graph version for experimentation.
-* Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents  (e.g., for business users). 
-
-#### Configuring Assistants
-
-In practice, an assistant is just an *instance* of a graph with a specific configuration. Because of this, multiple assistants can reference the same graph but can contain different configurations, such as prompts, models, and other graph configuration options. The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../reference/api/api_ref.html#tag/assistantscreate) and [this how-to](../how-tos/configuration_cloud.md) for more details on how to create assistants.
-
-#### Versioning Assistants
-
-![assistant versions](./assistant_version.png)
-
-Once you've created an assistant, you can save and version it to track changes to the configuration over time. You can think about this at three levels:
-
-1) The graph lays out the general agent application logic 
-2) The agent configuration options represent parameters that can be changed 
-3) Assistant versions save and track specific settings of the agent configuration options 
-
-For example, if you have an agent that helps for planning trips, you can create a new assistant *for each user* that passes specific user preferences (e.g., desired airline and car service). As each user interacts with their own assistant, assistant versions can be saved that track the specific desires of the user. Read [this how-to](../how-tos/assistant_versioning.md) to learn how you can use assistant versioning through both the [Studio](../how-tos/index.md/#langgraph-studio) and the SDK.
-
-### Threads
-
-A thread contains the accumulated state of a group of runs. If a run is executed on a thread, then the [state][state] of the underlying graph of the assistant will be persisted to the thread. A thread's current and historical state can be retrieved. To persist state, a thread must be created prior to executing a run.
-
-The state of a thread at a particular point in time is called a checkpoint.
-
-For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/low_level.md#persistence).
-
-The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../reference/api/api_ref.html#tag/threadscreate) for more details.
-
-### Runs
-
-A run is an invocation of an assistant. Each run may have its own input, configuration, and metadata, which may affect execution and output of the underlying graph. A run can optionally be executed on a thread.
-
-The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../reference/api/api_ref.html#tag/runscreate) for more details.
-
-### Cron Jobs
-
-It's often useful to run graphs on some schedule. LangGraph Cloud supports cron jobs, which run on a user defined schedule. The user specifies a schedule, an assistant, and some input. After than, on the specified schedule LangGraph cloud will:
-
-- Create a new thread with the specified assistant
-- Send the specified input to that thread
-
-Note that this sends the same input to the thread every time. See the [how-to guide](../how-tos/cron_jobs.md) for creating cron jobs.
-
-The LangGraph Cloud API provides several endpoints for creating and managing cron jobs. See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.
-
-## Features
-
-The LangGraph Cloud API offers several features to support complex agent architectures.
-
-### Streaming
-
-Streaming is critical for making LLM applications feel responsive to end users. When creating a streaming run, the streaming mode determines what data is streamed back to the API client. The LangGraph Cloud API supports five streaming modes.
-
-- `values`: Stream the full state of the graph after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs) is executed. See the [how-to guide](../how-tos/stream_values.md) for streaming values.
-- `messages`: Stream complete messages (at the end of node execution) as well as tokens for any messages generated inside a node. This mode is primarily meant for powering chat applications. This is only an option if your graph contains a `messages` key. See the [how-to guide](../how-tos/stream_messages.md) for streaming messages.
-- `updates`: Streams updates to the state of the graph after each node is executed. See the [how-to guide](../how-tos/stream_updates.md) for streaming updates.
-- `events`: Stream all events (including the state of the graph) that occur during graph execution. See the [how-to guide](../how-tos/stream_events.md) for streaming events. This can be used to do token-by-token streaming for LLMs.
-- `debug`: Stream debug events throughout graph execution. See the [how-to guide](../how-tos/stream_debug.md) for streaming debug events.
-
-You can also specify multiple streaming modes at the same time. See the [how-to guide](../how-tos/stream_multiple.md) for configuring multiple streaming modes at the same time.
-
-See the [API reference](../reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/stream) for how to create streaming runs.
-
-Streaming modes `values`, `updates`, and `debug` are very similar to modes available in the LangGraph library - for a deeper conceptual explanation of those, you can see the LangGraph library documentation [here](../../concepts/low_level.md#streaming).
-
-Streaming mode `events` is the same as using `.astream_events` in the LangGraph library - for a deeper conceptual explanation of this, you can see the LangGraph library documentation [here](../../concepts/low_level.md#streaming).
-
-#### `mode="messages"`
-Streaming mode `messages` is a new streaming mode, currently only available in the API. What does this mode enable?
-
-This mode is focused on streaming back messages. It currently assumes that you have a `messages` key in your graph that is a list of messages. Assuming we have a simple react agent deployed, what does this stream look like?
-
-All events emitted have two attributes:
-
-- `event`: This is the name of the event
-- `data`: This is data associated with the event
-
-Let's run it on a question that should trigger a tool call:
-
-```python
-thread = await client.threads.create()
-input = {"messages": [{"role": "user", "content": "what's the weather in sf?"}]}
-
-events = []
-async for event in client.runs.stream(
-    thread["thread_id"],
-    assistant_id="agent",  # This may need to change depending on the graph you deployed
-    input=input,
-    stream_mode="messages",
-):
-    print(event.event)
-```
-```shell
-metadata
-messages/complete
-messages/metadata
-messages/partial
-...
-messages/partial
-messages/complete
-messages/complete
-messages/metadata
-messages/partial
-...
-messages/partial
-messages/complete
-end
-```
-
-We first get some `metadata` - this is metadata about the run. 
-
-```python
-StreamPart(event='metadata', data={'run_id': '1ef657cf-ae55-6f65-97d4-f4ed1dbdabc6'})
-```
-
-We then get a `messages/complete` event - this a fully formed message getting emitted. In this case,
-this was the just the input message we sent in. 
-
-```python
-StreamPart(event='messages/complete', data=[{'content': 'hi!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '833c09a3-bb19-46c9-81d9-1e5954ec5f92', 'example': False}])
-```
-
-We then get a `messages/metadata` - this is just letting us know that a new message is starting.
-
-```python
-StreamPart(event='messages/metadata', data={'run-985c0f14-9f43-40d4-a505-4637fc58e333': {'metadata': {'created_by': 'system', 'run_id': '1ef657de-7594-66df-8eb2-31518e4a1ee2', 'graph_id': 'agent', 'thread_id': 'c178eab5-e293-423c-8e7d-1d113ffe7cd9', 'model_name': 'openai', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca', 'langgraph_step': 1, 'langgraph_node': 'agent', 'langgraph_triggers': ['start:agent'], 'langgraph_task_idx': 0, 'ls_provider': 'openai', 'ls_model_name': 'gpt-4o', 'ls_model_type': 'chat', 'ls_temperature': 0.0}}})
-```
-
-We then get a BUNCH of `messages/partial` events - these are the individual tokens from the LLM! In the case below, we can see the START of a tool call.
-
-```python
-StreamPart(event='messages/partial', data=[{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs', 'function': {'arguments': '', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-985c0f14-9f43-40d4-a505-4637fc58e333', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'tavily_search_results_json', 'args': '', 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs', 'error': None}], 'usage_metadata': None}])
-```
-
-After that, we get a `messages/complete` event - this is the AIMessage finishing. It's now a complete tool call:
-
-```python
-StreamPart(event='messages/complete', data=[{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs', 'function': {'arguments': '{"query":"current weather in San Francisco"}', 'name': 'tavily_search_results_json'}, 'type': 'function'}]}, 'response_metadata': {'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5'}, 'type': 'ai', 'name': None, 'id': 'run-985c0f14-9f43-40d4-a505-4637fc58e333', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'current weather in San Francisco'}, 'id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs'}], 'invalid_tool_calls': [], 'usage_metadata': None}])
-```
-
-After that, we get ANOTHER `messages/complete` event. This is a tool message - our agent has called a tool, gotten a response, and now inserting it into the state in the form of a tool message.
-
-```python
-StreamPart(event='messages/complete', data=[{'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1724877689, \'localtime\': \'2024-08-28 13:41\'}, \'current\': {\'last_updated_epoch\': 1724877000, \'last_updated\': \'2024-08-28 13:30\', \'temp_c\': 23.3, \'temp_f\': 73.9, \'is_day\': 1, \'condition\': {\'text\': \'Partly cloudy\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/116.png\', \'code\': 1003}, \'wind_mph\': 15.0, \'wind_kph\': 24.1, \'wind_degree\': 310, \'wind_dir\': \'NW\', \'pressure_mb\': 1014.0, \'pressure_in\': 29.93, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 57, \'cloud\': 25, \'feelslike_c\': 25.0, \'feelslike_f\': 77.1, \'windchill_c\': 20.9, \'windchill_f\': 69.6, \'heatindex_c\': 23.3, \'heatindex_f\': 74.0, \'dewpoint_c\': 12.9, \'dewpoint_f\': 55.2, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 6.0, \'gust_mph\': 19.5, \'gust_kph\': 31.3}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0112eba5-7660-4375-9f24-c7a1d6777b97', 'tool_call_id': 'call_w8Hr8dHGuZCPgRfd5FqRBArs'}])
-```
-
-After that, we see the agent doing another LLM call and streaming back a response. We then get an `end` event:
-
-```python
-StreamPart(event='end', data=None)
-```
-
-And that's it! This is more focused streaming mode specifically focused on streaming back messages. See this [how-to guide](../how-tos/stream_messages.md) for more information.
-
-
-### Human-in-the-Loop
-
-There are many occasions where the graph cannot run completely autonomously. For instance, the user might need to input some additional arguments to a function call, or select the next edge for the graph to continue on. In these instances, we need to insert some human in the loop interaction, which you can learn about in the [human in the loop how-tos](../how-tos/index.md#human-in-the-loop).
-
-### Double Texting
-
-Many times users might interact with your graph in unintended ways. For instance, a user may send one message and before the graph has finished running send a second message. To solve this issue of "double-texting" (i.e. prompting the graph a second time before the first run has finished), LangGraph has provided four different solutions, all of which are covered in the [Double Texting how-tos](../how-tos/index.md#double-texting). These options are:
-
-- `reject`: This is the simplest option, this just rejects any follow up runs and does not allow double texting. See the [how-to guide](../how-tos/reject_concurrent.md) for configuring the reject double text option.
-- `enqueue`: This is a relatively simple option which continues the first run until it completes the whole run, then sends the new input as a separate run. See the [how-to guide](../how-tos/enqueue_concurrent.md) for configuring the enqueue double text option.
-- `interrupt`: This option interrupts the current execution but saves all the work done up until that point. It then inserts the user input and continues from there. If you enable this option, your graph should be able to handle weird edge cases that may arise. See the [how-to guide](../how-tos/interrupt_concurrent.md) for configuring the interrupt double text option.
-- `rollback`: This option rolls back all work done up until that point. It then sends the user input in, basically as if it just followed the original run input. See the [how-to guide](../how-tos/rollback_concurrent.md) for configuring the rollback double text option.
-
-### Stateless Runs
-
-All runs use the built-in checkpointer to store checkpoints for runs. However, it can often be useful to just kick off a run without worrying about explicitly creating a thread and without wanting to keep those checkpointers around. Stateless runs allow you to do this by exposing an endpoint that:
-
-- Takes in user input
-- Under the hood, creates a thread
-- Runs the agent but skips all checkpointing steps
-- Cleans up the thread afterwards
-
-Stateless runs are still retried as regular retries are per node, while everything still in memory, so doesn't use checkpoints.
-
-The only difference is in stateless background runs, if the task worker dies halfway (not because the run itself failed, for some external reason) then the whole run will be retried like any background run, but
-
-- whereas a stateful background run would retry from the last successful checkpoint
-- a stateless background run would retry from the beginning
-
-See the [how-to guide](../how-tos/stateless_runs.md) for creating stateless runs.
-
-### Webhooks
-
-For all types of runs, langgraph cloud supports completion webhooks. When you create the run you can pass a webhook URL to be called when the completes (successfully or not). This is especially useful for background runs and cron jobs, as the webhook can give you an indication the run has completed and you can perform further actions for your appilcation.
-
-See this [how-to guide](../how-tos/webhooks.md) to learn about how to use webhooks with LangGraph Cloud.
-
-## Deployment
-
-The LangGraph Cloud offers several features to support secure and robost deployments.
-
-### Authentication
-
-LangGraph applications deployed to LangGraph Cloud are automatically configured with LangSmith authentication. In order to call the API, a valid <a href="https://docs.smith.langchain.com/how_to_guides/setup/create_account_api_key#api-keys" target="_blank">LangSmith API key</a> is required.
-
-### Local Testing
-
-Before deploying your app in production to LangGraph Cloud, you may wish to test out your graph locally in order to ensure that everything is running as expected. Luckily, LangGraph makes this easy for you through use of the LangGraph CLI. Read more in this [how-to guide](../deployment/test_locally.md) or look at the [CLI reference](../reference/cli.md) to learn more.

docs/docs/cloud/concepts/cloud.md

@@ -1,28 +0,0 @@
-# Cloud Concepts
-
-This page describes the high-level concepts of the LangGraph Cloud deployment.
-
-## Deployment
-
-A deployment is an instance of a LangGraph API. A single deployment can have many [revisions](#revision). When a deployment is created, all of the necessary infrastructure (e.g. database, containers, secrets store) are automatically provisioned. See the [architecture diagram](#architecture) below for more details.
-
-See the [how-to guide](../deployment/cloud.md#create-new-deployment) for creating a new deployment.
-
-## Revision
-
-A revision is an iteration of a [deployment](#deployment). When a new deployment is created, an initial revision is automatically created. To deploy new code changes or update environment variable configurations for a deployment, a new revision must be created. When a revision is created, a new container image is built automatically.
-
-See the [how-to guide](../deployment/cloud.md#create-new-revision) for creating a new revision.
-
-## Asynchronous Deployment
-
-Infrastructure for [deployments](#deployment) and [revisions](#revision) are provisioned and deployed asynchronously. They are not deployed immediately after submission. Currently, deployment can take up to several minutes.
-
-## Architecture
-
-!!! warning "Subject to Change"
-    The LangGraph Cloud deployment architecture may change in the future.
-
-A high-level diagram of a LangGraph Cloud deployment.
-
-![diagram](langgraph_cloud_architecture.png)

docs/docs/cloud/deployment/cloud.md

@@ -11,7 +11,7 @@ LangGraph Cloud is available within <a href="https://www.langchain.com/langsmith
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
 1. In the top-right corner, select `+ New Deployment` to create a new deployment.
 1. In the `Create New Deployment` panel, fill out the required fields.
     1. `Deployment details`
@@ -38,7 +38,7 @@ When [creating a new deployment](#create-new-deployment), a new revision is crea
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
 1. Select an existing deployment to create a new revision for.
 1. In the `Deployment` view, in the top-right corner, select `+ New Revision`.
 1. In the `New Revision` modal, fill out the required fields.
@@ -56,7 +56,7 @@ Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmi
 
 Build and deployment logs are available for each revision.
 
-Starting from the `Deployment` view...
+Starting from the `LangGraph Cloud` view...
 
 1. Select the desired revision from the `Revisions` table. A panel slides open from the right-hand side and the `Build` tab is selected by default, which displays build logs for the revision.
 1. In the panel, select the `Deploy` tab to view deployment logs for the revision.
@@ -69,7 +69,7 @@ Interrupting a revision will stop deployment of the revision.
 !!! warning "Undefined Behavior"
     Interrupted revisions have undefined behavior. This is only useful if you need to deploy a new revision and you already have a revision "stuck" in progress. In the future, this feature may be removed.
 
-Starting from the `Deployment` view...
+Starting from the `LangGraph Cloud` view...
 
 1. Select the menu icon (three dots) on the right-hand side of the row for the desired revision from the `Revisions` table.
 1. Select `Interrupt` from the menu.
@@ -79,13 +79,13 @@ Starting from the `Deployment` view...
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
 1. Select the menu icon (three dots) on the right-hand side of the row for the desired deployment and select `Delete`.
 1. A `Confirmation` modal will appear. Select `Delete`.
 
 ## Deployment Settings
 
-Starting from the `Deployment` view...
+Starting from the `LangGraph Cloud` view...
 
 1. In the top-right corner, select the gear icon (`Deployment Settings`).
 1. Update the `Git Branch` to the desired branch.

docs/docs/cloud/deployment/semantic_search.md

@@ -0,0 +1,123 @@
+# How to add semantic search to your LangGraph deployment
+
+This guide explains how to add semantic search to your LangGraph deployment's cross-thread [store](../../concepts/persistence.md#memory-store), so that your agent can search for memories and other documents by semantic similarity.
+
+## Prerequisites
+
+- A LangGraph deployment (see [how to deploy](setup_pyproject.md))
+- API keys for your embedding provider (in this case, OpenAI)
+- `langchain >= 0.3.8` (if you specify using the string format below)
+
+## Steps
+
+1. Update your `langgraph.json` configuration file to include the store configuration:
+
+```json
+{
+    ...
+    "store": {
+        "index": {
+            "embed": "openai:text-embeddings-3-small",
+            "dims": 1536,
+            "fields": ["$"]
+        }
+    }
+}
+```
+
+This configuration:
+
+- Uses OpenAI's text-embeddings-3-small model for generating embeddings
+- Sets the embedding dimension to 1536 (matching the model's output)
+- Indexes all fields in your stored data (`["$"]` means index everything, or specify specific fields like `["text", "metadata.title"]`)
+
+2. To use the string embedding format above, make sure your dependencies include `langchain >= 0.3.8`:
+
+```toml
+# In pyproject.toml
+[project]
+dependencies = [
+    "langchain>=0.3.8"
+]
+```
+
+Or if using requirements.txt:
+
+```
+langchain>=0.3.8
+```
+
+## Usage
+
+Once configured, you can use semantic search in your LangGraph nodes. The store requires a namespace tuple to organize memories:
+
+```python
+def search_memory(state: State, *, store: BaseStore):
+    # Search the store using semantic similarity
+    # The namespace tuple helps organize different types of memories
+    # e.g., ("user_facts", "preferences") or ("conversation", "summaries")
+    results = store.search(
+        namespace=("memory", "facts"),  # Organize memories by type
+        query="your search query",
+        limit=3  # number of results to return
+    )
+    return results
+```
+
+## Custom Embeddings
+
+If you want to use custom embeddings, you can pass a path to a custom embedding function:
+
+```json
+{
+    ...
+    "store": {
+        "index": {
+            "embed": "path/to/embedding_function.py:embed",
+            "dims": 1536,
+            "fields": ["$"]
+        }
+    }
+}
+```
+
+The deployment will look for the function in the specified path. The function must be async and accept a list of strings:
+
+```python
+# path/to/embedding_function.py
+from openai import AsyncOpenAI
+
+client = AsyncOpenAI()
+
+async def aembed_texts(texts: list[str]) -> list[list[float]]:
+    """Custom embedding function that must:
+    1. Be async
+    2. Accept a list of strings
+    3. Return a list of float arrays (embeddings)
+    """
+    response = await client.embeddings.create(
+        model="text-embedding-3-small",
+        input=texts
+    )
+    return [e.embedding for e in response.data]
+```
+
+## Querying via the API
+
+You can also query the store using the LangGraph SDK. Since the SDK uses async operations:
+
+```python
+from langgraph_sdk import get_client
+
+async def search_store():
+    client = get_client()
+    results = await client.store.search_items(
+        ("memory", "facts"),
+        query="your search query",
+        limit=3  # number of results to return
+    )
+    return results
+
+# Use in an async context
+results = await search_store()
+```

docs/docs/cloud/deployment/setup.md

@@ -36,8 +36,8 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
 ```
-langgraph>=0.2.30,<0.3.0
-langgraph-checkpoint>=1.0.14
+langgraph>=0.2.56,<0.3.0
+langgraph-checkpoint>=2.0.5,<3.0
 langchain-core>=0.2.38,<0.4.0
 langsmith>=0.1.63
 orjson>=3.9.7

docs/docs/cloud/deployment/setup_pyproject.md

@@ -36,8 +36,8 @@ Dependencies can optionally be specified in one of the following files: `pyproje
 The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
 
 ```
-langgraph>=0.2.30,<0.3.0
-langgraph-checkpoint>=1.0.14
+langgraph>=0.2.56,<0.3.0
+langgraph-checkpoint>=2.0.5,<3.0
 langchain-core>=0.2.38,<0.4.0
 langsmith>=0.1.63
 orjson>=3.9.7

docs/docs/cloud/deployment/test_locally.md

@@ -8,19 +8,25 @@ Testing locally ensures that there are no errors or conflicts with Python depend
 
 Install the proper packages:
 
-```shell
-pip install langgraph-cli
-```
 
-Ensure you have an API key, which you can create from the LangSmith UI (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
+=== "pip" 
+    ```bash
+    pip install -U langgraph-cli
+    ```
+=== "Homebrew (macOS only)"
+    ```bash
+    brew install langgraph-cli
+    ```
+
+Ensure you have an API key, which you can create from the [LangSmith UI](https://smith.langchain.com) (Settings > API Keys). This is required to authenticate that you have LangGraph Cloud access. After you have saved the key to a safe place, place the following line in your `.env` file:
 
 ```python
-LANGCHAIN_API_KEY = *********
+LANGSMITH_API_KEY = *********
 ```
 
 ## Start the API server
 
-Once you have downloaded the CLI, you can run the following command to start the API server for local testing:
+Once you have installed the CLI, you can run the following command to start the API server for local testing:
 
 ```shell
 langgraph up
@@ -48,7 +54,7 @@ You can either initialize by passing authentication or by setting an environment
     from langgraph_sdk import get_client
 
     # only pass the url argument to get_client() if you changed the default port when calling langgraph up
-    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGCHAIN_API_KEY>)
+    client = get_client(url=<DEPLOYMENT_URL>,api_key=<LANGSMITH_API_KEY>)
     # Using the graph deployed with the name "agent"
     assistant_id = "agent"
     thread = await client.threads.create()
@@ -60,7 +66,7 @@ You can either initialize by passing authentication or by setting an environment
     import { Client } from "@langchain/langgraph-sdk";
 
     // only set the apiUrl if you changed the default port when calling langgraph up
-    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGCHAIN_API_KEY> });
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <LANGSMITH_API_KEY> });
     // Using the graph deployed with the name "agent"
     const assistantId = "agent";
     const thread = await client.threads.create();
@@ -72,13 +78,13 @@ You can either initialize by passing authentication or by setting an environment
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads \
       --header 'Content-Type: application/json'
-      --header 'x-api-key: <LANGCHAIN_API_KEY>'
+      --header 'x-api-key: <LANGSMITH_API_KEY>'
     ```
   
 
 #### Initialize with environment variables
 
-If you have a `LANGCHAIN_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
+If you have a `LANGSMITH_API_KEY` set in your environment, you do not need to explicitly pass authentication to the client
 
 === "Python"
 
@@ -148,7 +154,7 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
     }
     ```
 
-  === "CURL"
+=== "CURL"
 
     ```bash
     curl --request POST \
@@ -183,4 +189,4 @@ Now we can invoke our graph to ensure it is working. Make sure to change the inp
      ' 
     ```
 
-If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.
+If your graph works correctly, you should see your graph output displayed in the console. Of course, there are many more ways you might need to test your graph, for a full list of commands you can send with the SDK, see the [Python](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) and [JS/TS](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/js_ts_sdk_ref/) references.

docs/docs/cloud/how-tos/assistant_versioning.md

@@ -1,6 +1,6 @@
 # How to version assistants
 
-In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read [this](../concepts/api.md/#versioning-assistants) conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:
+In this how-to guide we will walk through how you can create and manage different assistant versions. If you haven't already, you can read [this](../../concepts/assistants.md#versioning-assistants) conceptual guide to gain a better understanding of what assistant versioning is. This how-to assumes you have a graph that is configurable, which means you have defined a config schema and passed it to your graph as follows:
 
 === "Python"
 
@@ -86,19 +86,19 @@ To create an assistant using the studio do the following steps:
 
 1. Click on the "Create New Assistant" button:
 
-![click create](./img/click_create_assistant.png)
+    ![click create](./img/click_create_assistant.png)
 
-2. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
+1. Use the create assistant pane to enter info for the assistant you wish to create, and then click create:
 
-![create](./img/create_assistant.png)
+    ![create](./img/create_assistant.png)
 
-3. See that your assistant was created and is displayed in the Studio
+1. See that your assistant was created and is displayed in the Studio
 
-![view create](./img/create_assistant_view.png)
+    ![view create](./img/create_assistant_view.png)
 
-4. Click on the edit button next to the selected assistant to manage your created assistant:
+1. Click on the edit button next to the selected assistant to manage your created assistant:
 
-![create edit](./img/edit_created_assistant.png)
+    ![create edit](./img/edit_created_assistant.png)
 
 ## Create a new version for your assistant
 
@@ -131,15 +131,15 @@ Let's now say we wanted to add a system prompt to our assistant. We can do this
 
 1. First, click on the edit button next to the `openai_assistant`. Then, add a system prompt and click "Save New Version":
 
-![create new version](./img/create_new_version.png)
+    ![create new version](./img/create_new_version.png)
 
-2. Then you can see it is selected in the assistant dropdown:
+1. Then you can see it is selected in the assistant dropdown:
 
-![see version dropdown](./img/see_new_version.png)
+    ![see version dropdown](./img/see_new_version.png)
 
-3. And you can see all the version history in the edit pane for the assistant:
+1. And you can see all the version history in the edit pane for the assistant:
 
-![see versions](./img/see_version_history.png)
+    ![see versions](./img/see_version_history.png)
 
 ## Point your assistant to a different version
 

docs/docs/cloud/how-tos/configuration_cloud.md

@@ -83,7 +83,7 @@ We can now call `.get_schemas` to get schemas associated with this graph:
         assistant_id=assistant["assistant_id"]
     )
     # There are multiple types of schemas
-    # We can get the `config_schema` to look at the the configurable parameters
+    # We can get the `config_schema` to look at the configurable parameters
     print(schemas["config_schema"])
     ```
 
@@ -94,7 +94,7 @@ We can now call `.get_schemas` to get schemas associated with this graph:
       assistant["assistant_id"]
     );
     // There are multiple types of schemas
-    // We can get the `config_schema` to look at the the configurable parameters
+    // We can get the `config_schema` to look at the configurable parameters
     console.log(schemas.config_schema);
     ```
 

docs/docs/cloud/how-tos/copy_threads.md

@@ -4,7 +4,7 @@ You may wish to copy (i.e. "fork") an existing thread in order to keep the exist
 
 ## Setup
 
-This code assumes you already have a thread to copy. You can read about what a thread is [here](https://langchain-ai.github.io/langgraph/cloud/concepts/api/#threads) and learn how to stream a run on a thread in [these how-to guides](https://langchain-ai.github.io/langgraph/cloud/how-tos/#streaming).
+This code assumes you already have a thread to copy. You can read about what a thread is [here](../../concepts/langgraph_server.md#threads) and learn how to stream a run on a thread in [these how-to guides](../../how-tos/index.md#streaming_1).
 
 ### SDK initialization
 

docs/docs/cloud/how-tos/datasets_studio.md

@@ -0,0 +1,17 @@
+# Adding nodes as dataset examples in Studio
+
+In LangGraph Studio you can create dataset examples from the thread history in the right-hand pane. This can be especially useful when you want to evaluate intermediate steps of the agent.
+
+1. Click on the `Add to Dataset` button to enter the dataset mode.
+1. Select nodes which you want to add to dataset.
+1. Select the target dataset to create the example in.
+
+You can edit the example payload before sending it to the dataset, which is useful if you need to make changes to conform the example to the dataset schema.
+
+Finally, you can customise the target dataset by clicking on the `Settings` button.
+
+See [Evaluating intermediate steps](https://docs.smith.langchain.com/evaluation/how_to_guides/langgraph#evaluating-intermediate-steps) for more details on how to evaluate intermediate steps.
+
+<video controls allowfullscreen="true" poster="../img/studio_datasets.jpg">
+    <source src="https://langgraph-docs-assets.pages.dev/studio_datasets.mp4" type="video/mp4">
+</video>

docs/docs/cloud/how-tos/enqueue_concurrent.md

@@ -1,6 +1,6 @@
 # Enqueue
 
-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/api.md#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 
 The guide covers the `enqueue` option for double texting, which adds the interruptions to a queue and executes them in the order they are received by the client. Below is a quick example of using the `enqueue` option.
 

docs/docs/cloud/how-tos/index.md

@@ -1,84 +0,0 @@
----
-hide:
-  - toc
----
-
-# How-to Guides
-
-Welcome to the LangGraph Cloud how-to guides! These guides provide practical, step-by-step instructions for accomplishing key tasks in LangGraph Cloud.
-
-## Setup
-
-LangGraph Cloud gives you best in class observability, testing, and hosting services. Learn how to setup your app for deployment to LangGraph Cloud in these how-to guides 
-
-- [How to set up app for deployment (requirements.txt)](../deployment/setup.md)
-- [How to set up app for deployment (pyproject.toml)](../deployment/setup_pyproject.md)
-- [How to set up app for deployment (JavaScript)](../deployment/setup_javascript.md)
-- [How to customize Dockerfile](../deployment/custom_docker.md)
-- [How to test locally](../deployment/test_locally.md)
-
-## Deploy
-
-Learn how to deploy your app to LangGraph Cloud in these how to guides:
-
-- [How to deploy to LangGraph cloud](../deployment/cloud.md)
-
-
-## Streaming
-
-Streaming the results of your LLM application is vital for ensuring a good user experience, especially when your graph may call multiple models and take a long time to fully complete a run. Read about how to stream values from your graph in these how to guides:
-
-- [How to stream values](./stream_values.md)
-- [How to stream updates](./stream_updates.md)
-- [How to stream messages](./stream_messages.md)
-- [How to stream events](./stream_events.md)
-- [How to stream in debug mode](./stream_debug.md)
-- [How to stream multiple modes](./stream_multiple.md)
-
-## Double-texting
-
-Graph execution can take a while, and sometimes users may change their mind about the input they wanted to send before their original input has finished running. For example, a user might notice a typo in their original request and will edit the prompt and resend it. Deciding what to do in these cases is important for ensuring a smooth user experience and preventing your graphs from behaving in unexpected ways. The following how-to guides provide information on the various options LangGraph Cloud gives you for dealing with double-texting:
-
-- [How to use the interrupt option](./interrupt_concurrent.md)
-- [How to use the rollback option](./rollback_concurrent.md)
-- [How to use the reject option](./reject_concurrent.md)
-- [How to use the enqueue option](./enqueue_concurrent.md)
-
-## Human-in-the-loop
-
-When creating complex graphs, leaving every decision up to the LLM can be dangerous, especially when the decisions involve invoking certain tools or accessing specific documents. To remedy this, LangGraph allows you to insert human-in-the-loop behavior to ensure your graph does not have undesired outcomes. Read more about the different ways you can add human-in-the-loop capabilities to your LangGraph Cloud projects in these how-to guides:
-
-- [How to add a breakpoint](./human_in_the_loop_breakpoint.md)
-- [How to wait for user input](./human_in_the_loop_user_input.md)
-- [How to edit graph state](./human_in_the_loop_edit_state.md)
-- [How to replay and branch from prior states](./human_in_the_loop_time_travel.md)
-- [How to review tool calls](./human_in_the_loop_review_tool_calls.md)
-
-## LangGraph Studio
-
-LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
-
-- [How to enter LangGraph Studio](./test_deployment.md)
-- [How to enter LangGraph Studio for local deployment](./test_local_deployment.md)
-- [How to test your graph in LangGraph Studio](./invoke_studio.md)
-- [Interact with threads in LangGraph Studio](./threads_studio.md)
-
-## Different Types of Runs:
-
-LangGraph Cloud supports multiple types of runs besides streaming runs.
-
-- [How to run an agent in the background](./background_run.md)
-- [How to run multiple agents in the same thread](./same-thread.md)
-- [How to create cron jobs](./cron_jobs.md)
-- [How to create stateless runs](./stateless_runs.md)
-
-## Other 
-
-Other guides that may prove helpful!
-
-- [How to configure agents](./configuration_cloud.md)
-- [How to version assistants](./assistant_versioning.md)
-- [How to convert LangGraph calls to LangGraph cloud calls](./langgraph_to_langgraph_cloud.ipynb)
-- [How to integrate webhooks](./webhooks.md)
-- [How to copy threads](./copy_threads.md)
-- [How to check status of your threads](./check_thread_status.md)

docs/docs/cloud/how-tos/interrupt_concurrent.md

@@ -1,6 +1,6 @@
 # Interrupt
 
-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../concepts/api.md#double-texting).
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 
 The guide covers the `interrupt` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option does not delete the first run, but rather keeps it in the database but sets its status to `interrupted`. Below is a quick example of using the `interrupt` option.
 
@@ -94,6 +94,7 @@ Now we can start our two runs and join the second on euntil it has completed:
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
+    # sleep a bit to get partial outputs from the first run
     await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
@@ -114,6 +115,7 @@ Now we can start our two runs and join the second on euntil it has completed:
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
+    // sleep a bit to get partial outputs from the first run
     await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(

docs/docs/cloud/how-tos/reject_concurrent.md

@@ -1,6 +1,6 @@
 # Reject
 
-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 
 The guide covers the `reject` option for double texting, which rejects the new run of the graph by throwing an error and continues with the original run until completion. Below is a quick example of using the `reject` option.
 

docs/docs/cloud/how-tos/rollback_concurrent.md

@@ -1,6 +1,6 @@
 # Rollback
 
-This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide][double-texting].
+This guide assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](../../concepts/double_texting.md).
 
 The guide covers the `rollback` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the `interrupt` option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the `rollback` option.
 
@@ -95,7 +95,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
         assistant_id,
         input={"messages": [{"role": "user", "content": "what's the weather in sf?"}]},
     )
-    await asyncio.sleep(2)
     run = await client.runs.create(
         thread["thread_id"],
         assistant_id,
@@ -115,7 +114,6 @@ Now let's run a thread with the multitask parameter set to "rollback":
       assistantId,
       { input: { messages: [{ role: "human", content: "what's the weather in sf?" }] } }
     );
-    await new Promise(resolve => setTimeout(resolve, 2000)); 
 
     let run = await client.runs.create(
       thread["thread_id"],
@@ -139,7 +137,7 @@ Now let's run a thread with the multitask parameter set to "rollback":
     --data "{
       \"assistant_id\": \"agent\",
       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what\'s the weather in sf?\"}]},
-    }" && sleep 2 && curl --request POST \
+    }" && curl --request POST \
     --url <DEPLOY<ENT_URL>>/threads/<THREAD_ID>/runs \
     --header 'Content-Type: application/json' \
     --data "{

docs/docs/cloud/how-tos/stream_debug.md

@@ -1,5 +1,8 @@
 # How to stream debug events
 
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
 This guide covers how to stream debug events from your graph (`stream_mode="debug"`). Streaming debug events produces responses containing `type` and `timestamp` keys. Debug events correspond to different steps in the graph's execution, and there are three different types of steps that will get streamed back to you:
 
 - `checkpoint`: These events will get streamed anytime the graph saves its state, which occurs after every super-step. Read more about checkpoints [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#checkpointer)

docs/docs/cloud/how-tos/stream_events.md

@@ -1,6 +1,9 @@
 # How to stream events
 
-This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently. Read more about events in this [conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#astream_events-for-streaming-tokens-of-llm-calls).
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md#streaming-llm-tokens-and-events-astream_events)
+
+This guide covers how to stream events from your graph (`stream_mode="events"`). Depending on the use case and user experience of your LangGraph application, your application may process event types differently.
 
 ## Setup
 
@@ -289,131 +292,4 @@ Output:
     
     
     Receiving new event of type: end...
-    None
-
-
-## Token-by-Token Streaming
-
-Token-by-token streaming can be implemented with the `events` streaming mode. The `on_chat_model_stream` event type should be processed to stream LLM responses token-by-token.
-
-=== "Python"
-
-    ```python
-    llm_response = ""
-
-    # stream token-by-token
-    async for chunk in client.runs.stream(
-        thread_id=thread["thread_id"],
-        assistant_id=assistant_id,
-        input=input,
-        stream_mode="events",
-    ):
-        if (
-            chunk.event == "events" and
-            chunk.data["event"] == "on_chat_model_stream" and
-            len(chunk.data["data"]["chunk"]["content"]) > 0 and
-            'text' in chunk.data["data"]["chunk"]["content"][0]
-        ):
-            llm_response += chunk.data["data"]["chunk"]["content"][0]['text']
-            print(llm_response)
-    ```
-
-=== "Javascript"
-
-    ```js
-    const llmResponse = "";
-    // stream events
-    const streamResponse = client.runs.stream(
-      thread["thread_id"],
-      assistantID,
-      {
-        input,
-        streamMode: "events"
-      }
-    );
-    for await (const chunk of streamResponse) {
-      if (chunk.event === "events" && chunk.data.event === "on_chat_model_stream" && chunk.data.chunk.content.length > 0 && 'text' in chunk.data.chunk.content[0]) {
-        llmResponse += chunk.data.data.chunk.content[0].text;
-        console.log(llmResponse);
-      }
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    curl --request POST \
-     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-     --header 'Content-Type: application/json' \
-     --data "{
-       \"assistant_id\": \"agent\",
-       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
-       \"stream_mode\": [
-         \"events\"
-       ]
-     }" | sed 's/\r$//' | awk '
-    /^event:/ { event = $2 }
-    /^data:/ {
-        json_data = substr($0, index($0, $2))
-
-        if (event == "events") {
-        print json_data
-        }
-    }' | jq -r '
-        select(.event == "on_chat_model_stream") |
-        .data.chunk.content[] | .text // empty
-    ' | awk '
-    BEGIN { llm_response="" }
-    $0 != "" && $0 != "null" {
-        llm_response = llm_response $0
-        print llm_response
-    }'
-    ```
-
-Output:
-
-    The
-    The search
-    The search results provide
-    The search results provide the current weather conditions
-    The search results provide the current weather conditions in San Francisco.
-    The search results provide the current weather conditions in San Francisco. According
-    The search results provide the current weather conditions in San Francisco. According to the data,
-    The search results provide the current weather conditions in San Francisco. According to the data, as
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12,
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024,
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C).
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The win
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is bl
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 k
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph).
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70%
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km).
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San
-    The search results provide the current weather conditions in San Francisco. According to the data, as of 3:19 PM on August 12, 2024, the weather in San Francisco is sunny with a temperature of 60.8°F (16°C). The wind is blowing from the west-southwest at 13.4 mph (21.6 kph). The humidity is 70% and visibility is 6 miles (10 km). Overall, it appears to be a nice sunny day in San Francisco.
-
-
+    None

docs/docs/cloud/how-tos/stream_messages.md

@@ -1,43 +1,9 @@
 # How to stream messages from your graph
 
-This guide covers how to stream messages from your graph. In order to use this mode, the state of the graph you are interacting with MUST have a `messages` key that is a list of messages.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
 
-E.g., the state should look something like:
-
-=== "Python"
-
-    ```python
-    from typing import Annotated
-    from typing_extensions import TypedDict
-    from langgraph.graph import add_messages
-    from langchain_core.messages import AnyMessage
-
-    class State(TypedDict):
-        messages: Annotated[list[AnyMessage], add_messages]
-    ```
-
-=== "Javascript"
-
-    ```js
-    import { type BaseMessage } from "@langchain/core/messages";
-    import { Annotation, messagesStateReducer } from "@langchain/langgraph";
-
-    export const StateAnnotation = Annotation.Root({
-      messages: Annotation<BaseMessage[]>({
-        reducer: messagesStateReducer,
-        default: () => [],
-      }),
-    });
-    ```
-
-Alternatively, you can use an instance or subclass of `from langgraph.graph import MessagesState` (`MessagesState` is equivalent to the implementation above). Or in Javascript: `import { MessagesAnnotation } from "@langchain/langgraph";`.
-
-With `stream_mode="messages"` two things will be streamed back:
-
-- It outputs messages produced by any chat model called inside (unless tagged in a special way)
-- It outputs messages returned from nodes (to allow for nodes to return `ToolMessages` and the like)
-
-Read more about how the `messages` streaming mode works [here](https://langchain-ai.github.io/langgraph/cloud/concepts/api/#modemessages)
+This guide covers how to stream messages from your graph. With `stream_mode="messages-tuple"`, messages (i.e. individual LLM tokens) from any chat model invocations inside your graph nodes will be streamed back.
 
 ## Setup
 
@@ -90,101 +56,9 @@ Output:
         'values': None
     }
 
-Let's also define a helper function for better formatting of the tool calls in messages (for CURL we will define a helper script called `process_stream.sh`)
-
-=== "Python"
-
-    ```python
-    def format_tool_calls(tool_calls):
-        if tool_calls:
-            formatted_calls = []
-            for call in tool_calls:
-                formatted_calls.append(
-                    f"Tool Call ID: {call['id']}, Function: {call['name']}, Arguments: {call['args']}"
-                )
-            return "\n".join(formatted_calls)
-        return "No tool calls"
-    ```
-
-=== "Javascript"
-
-    ```js
-    function formatToolCalls(toolCalls) {
-      if (toolCalls && toolCalls.length > 0) {
-        const formattedCalls = toolCalls.map(call => {
-          return `Tool Call ID: ${call.id}, Function: ${call.name}, Arguments: ${call.args}`;
-        });
-        return formattedCalls.join("\n");
-      }
-      return "No tool calls";
-    }
-    ```
-
-=== "CURL"
-
-    ```bash
-    # process_stream.sh
-
-    format_tool_calls() {
-        echo "$1" | jq -r 'map("Tool Call ID: \(.id), Function: \(.name), Arguments: \(.args)") | join("\n")'
-    }
-
-    process_data_item() {
-        local data_item="$1"
-
-        if echo "$data_item" | jq -e '.role == "user"' > /dev/null; then
-            echo "Human: $(echo "$data_item" | jq -r '.content')"
-        else
-            local tool_calls=$(echo "$data_item" | jq -r '.tool_calls // []')
-            local invalid_tool_calls=$(echo "$data_item" | jq -r '.invalid_tool_calls // []')
-            local content=$(echo "$data_item" | jq -r '.content // ""')
-            local response_metadata=$(echo "$data_item" | jq -r '.response_metadata // {}')
-
-            if [ -n "$content" ] && [ "$content" != "null" ]; then
-                echo "AI: $content"
-            fi
-
-            if [ "$tool_calls" != "[]" ]; then
-                echo "Tool Calls:"
-                format_tool_calls "$tool_calls"
-            fi
-
-            if [ "$invalid_tool_calls" != "[]" ]; then
-                echo "Invalid Tool Calls:"
-                format_tool_calls "$invalid_tool_calls"
-            fi
-
-            if [ "$response_metadata" != "{}" ]; then
-                local finish_reason=$(echo "$response_metadata" | jq -r '.finish_reason // "N/A"')
-                echo "Response Metadata: Finish Reason - $finish_reason"
-            fi
-        fi
-    }
-
-    while IFS=': ' read -r key value; do
-        case "$key" in
-            event)
-                event="$value"
-                ;;
-            data)
-                if [ "$event" = "metadata" ]; then
-                    run_id=$(echo "$value" | jq -r '.run_id')
-                    echo "Metadata: Run ID - $run_id"
-                    echo "------------------------------------------------"
-                elif [ "$event" = "messages/partial" ]; then
-                    echo "$value" | jq -c '.[]' | while read -r data_item; do
-                        process_data_item "$data_item"
-                    done
-                    echo "------------------------------------------------"
-                fi
-                ;;
-        esac
-    done
-    ```
-
 ## Stream graph in messages mode
 
-Now we can stream by messages, which will return complete messages (at the end of node execution) as well as tokens for any messages generated inside a node:
+Now we can stream LLM tokens for any messages generated inside a node in the form of tuples `(message, metadata)`. Metadata contains additional information that can be useful for filtering the streamed outputs to a specific node or LLM.
 
 === "Python"
 
@@ -192,41 +66,16 @@ Now we can stream by messages, which will return complete messages (at the end o
     input = {"messages": [{"role": "user", "content": "what's the weather in sf"}]}
     config = {"configurable": {"model_name": "openai"}}
 
-    async for event in client.runs.stream(
+    async for chunk in client.runs.stream(
         thread["thread_id"],
         assistant_id=assistant_id,
         input=input,
         config=config,
-        stream_mode="messages",
+        stream_mode="messages-tuple",
     ):
-        if event.event == "metadata":
-            print(f"Metadata: Run ID - {event.data['run_id']}")
-            print("-" * 50)
-        elif event.event == "messages/partial":
-            for data_item in event.data:
-                if "role" in data_item and data_item["role"] == "user":
-                    print(f"Human: {data_item['content']}")
-                else:
-                    tool_calls = data_item.get("tool_calls", [])
-                    invalid_tool_calls = data_item.get("invalid_tool_calls", [])
-                    content = data_item.get("content", "")
-                    response_metadata = data_item.get("response_metadata", {})
-
-                    if content:
-                        print(f"AI: {content}")
-
-                    if tool_calls:
-                        print("Tool Calls:")
-                        print(format_tool_calls(tool_calls))
-
-                    if invalid_tool_calls:
-                        print("Invalid Tool Calls:")
-                        print(format_tool_calls(invalid_tool_calls))
-
-                    if response_metadata:
-                        finish_reason = response_metadata.get("finish_reason", "N/A")
-                        print(f"Response Metadata: Finish Reason - {finish_reason}")
-            print("-" * 50)
+        print(f"Receiving new event of type: {chunk.event}...")
+        print(chunk.data)
+        print("\n\n")
     ```
 
 === "Javascript"
@@ -248,46 +97,13 @@ Now we can stream by messages, which will return complete messages (at the end o
       {
         input,
         config,
-        streamMode: "messages"
+        streamMode: "messages-tuple"
       }
     );
-
-    for await (const event of streamResponse) {
-      if (event.event === "metadata") {
-        console.log(`Metadata: Run ID - ${event.data.run_id}`);
-        console.log("-".repeat(50));
-      } else if (event.event === "messages/partial") {
-        event.data.forEach(dataItem => {
-          if (dataItem.role && dataItem.role === "user") {
-            console.log(`Human: ${dataItem.content}`);
-          } else {
-            const toolCalls = dataItem.tool_calls || [];
-            const invalidToolCalls = dataItem.invalid_tool_calls || [];
-            const content = dataItem.content || "";
-            const responseMetadata = dataItem.response_metadata || {};
-
-            if (content) {
-              console.log(`AI: ${content}`);
-            }
-
-            if (toolCalls.length > 0) {
-              console.log("Tool Calls:");
-              console.log(formatToolCalls(toolCalls));
-            }
-
-            if (invalidToolCalls.length > 0) {
-              console.log("Invalid Tool Calls:");
-              console.log(formatToolCalls(invalidToolCalls));
-            }
-
-            if (responseMetadata) {
-              const finishReason = responseMetadata.finish_reason || "N/A";
-              console.log(`Response Metadata: Finish Reason - ${finishReason}`);
-            }
-          }
-        });
-        console.log("-".repeat(50));
-      }
+    for await (const chunk of streamResponse) {
+      console.log(`Receiving new event of type: ${chunk.event}...`);
+      console.log(chunk.data);
+      console.log("\n\n");
     }
     ```
 
@@ -295,203 +111,221 @@ Now we can stream by messages, which will return complete messages (at the end o
 
     ```bash
     curl --request POST \
-    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
-    --header 'Content-Type: application/json' \
-    --data "{
-    \"assistant_id\": \"agent\",
-    \"config\":{\"configurable\":{\"model_name\":\"openai\"}},
-    \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"What's the weather in sf\"}]},
-    \"stream_mode\": [
-    \"messages\"
-    ]
-    }" | sed 's/\r$//' | ./process_stream.sh
+     --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
+     --header 'Content-Type: application/json' \
+     --data "{
+       \"assistant_id\": \"agent\",
+       \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in la\"}]},
+       \"stream_mode\": [
+         \"messages-tuple\"
+       ]
+     }" | \
+     sed 's/\r$//' | \
+     awk '
+     /^event:/ {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+         sub(/^event: /, "Receiving event of type: ", $0)
+         printf "%s...\n", $0
+         data_content = ""
+     }
+     /^data:/ {
+         sub(/^data: /, "", $0)
+         data_content = $0
+     }
+     END {
+         if (data_content != "") {
+             print data_content "\n"
+         }
+     }
+     '
     ```
 
 
 Output:
 
-    Metadata: Run ID - 1ef2fe5c-6a1d-6575-bc09-d7832711c17e
-    --------------------------------------------------
-    Invalid Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: 
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': ''}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    --------------------------------------------------
-    Tool Calls:
-    Tool Call ID: call_cg14F20jMBqWYrNgEkdWHwB3, Function: tavily_search_results_json, Arguments: {'query': 'current weather in San Francisco'}
-    Response Metadata: Finish Reason - tool_calls
-    --------------------------------------------------
-    --------------------------------------------------
-    AI: The
-    --------------------------------------------------
-    AI: The current
-    --------------------------------------------------
-    AI: The current weather
-    --------------------------------------------------
-    AI: The current weather in
-    --------------------------------------------------
-    AI: The current weather in San
-    --------------------------------------------------
-    AI: The current weather in San Francisco
-    --------------------------------------------------
-    AI: The current weather in San Francisco is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is over
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-s
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-south
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 k
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%,
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles).
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3.
-    --------------------------------------------------
-    AI: The current weather in San Francisco is overcast with a temperature of 13.9°C (57.0°F). The wind is blowing from the south-southwest at 6.9 mph (11.2 kph). The humidity is at 81%, and the visibility is 16 km (9 miles). The UV index is 3.
-    Response Metadata: Finish Reason - stop
-    --------------------------------------------------
+    Receiving new event of type: metadata...
+    {"run_id": "1ef971e0-9a84-6154-9047-247b4ce89c4d", "attempt": 1}
 
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "weat"
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "her in san "
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "type": "AIMessageChunk",
+        "tool_calls": [
+          {
+            "name": "tavily_search_results_json",
+            "args": {
+              "query": "francisco"
+            },
+            "id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+            "type": "tool_call"
+          }
+        ],
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1730475777, 'localtime': '2024-11-01 08:42'}, 'current': {'last_updated_epoch': 1730475000, 'last_updated': '2024-11-01 08:30', 'temp_c': 11.1, 'temp_f': 52.0, 'is_day': 1, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 192, 'wind_dir': 'SSW', 'pressure_mb': 1018.0, 'pressure_in': 30.07, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 89, 'cloud': 75, 'feelslike_c': 11.5, 'feelslike_f': 52.6, 'windchill_c': 10.0, 'windchill_f': 50.1, 'heatindex_c': 10.4, 'heatindex_f': 50.7, 'dewpoint_c': 9.1, 'dewpoint_f': 48.5, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 3.0, 'gust_mph': 6.7, 'gust_kph': 10.8}}\"}]",
+        "type": "tool",
+        "tool_call_id": "toolu_0114XKXdNtHQEa3ozmY1uDdM",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "action",
+        ...
+      }
+    ]
+
+    ...
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": "\n\nThe search",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " results provide",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " the current weather conditions",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+
+
+    Receiving new event of type: messages...
+    [
+      {
+        "content": [
+          {
+            "text": " in San Francisco.",
+            "type": "text",
+            "index": 0
+          }
+        ],
+        "type": "AIMessageChunk",
+        ...
+      },
+      {
+        "graph_id": "agent",
+        "langgraph_node": "agent",
+        ...
+      }
+    ]
+
+    ...

docs/docs/cloud/how-tos/stream_multiple.md

@@ -1,5 +1,8 @@
 # How to configure multiple streaming modes at the same time
 
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
 This guide covers how to configure multiple streaming modes at the same time.
 
 ## Setup
@@ -175,11 +178,6 @@ Output:
     
     
     
-    Receiving new event of type: messages/complete...
-    [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]
-    
-    
-    
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.117924+00:00', 'step': 0, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc81-68c8-8000-4e18ae7d67a5', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}]}, 'metadata': {'source': 'loop', 'step': 0, 'writes': None}}}
     
@@ -305,11 +303,6 @@ Output:
     
     
     
-    Receiving new event of type: messages/complete...
-    [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]
-    
-    
-    
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.124510+00:00', 'step': 1, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bc91-6a34-8001-26353c117c25', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 1, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}
     
@@ -469,12 +462,7 @@ Output:
     {'event': 'on_chain_stream', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'name': 'LangGraph', 'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'data': {'chunk': ['values', {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}]}, 'parent_ids': []}
     
     
-    
-    Receiving new event of type: messages/complete...
-    [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]
-    
-    
-    
+
     Receiving new event of type: debug...
     {'type': 'checkpoint', 'timestamp': '2024-06-24T21:34:06.134190+00:00', 'step': 3, 'payload': {'config': {'tags': [], 'metadata': {'created_by': 'system', 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'callbacks': [None], 'recursion_limit': 25, 'configurable': {'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25', 'user_id': '', 'graph_id': 'agent', 'thread_id': 'bfc68029-1f7b-400f-beab-6f9032a52da4', 'thread_ts': '1ef32717-bca9-6418-8003-8d0d0b06845c', 'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'}, 'run_id': '1ef32717-bc30-6cf2-8a26-33f63567bc25'}, 'values': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': "What's the weather in SF?", 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '7da1bafa-f53c-4df8-ba63-8dd517140b9f', 'example': False}, {'content': 'begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-2424dd6d-5cf5-4244-8d98-357640ce6e12', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}, {'content': 'tool_call__begin', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': None, 'id': '639ca779-403d-4915-a066-327e1f634c8b', 'tool_call_id': 'tool_call_id'}, {'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}, 'metadata': {'source': 'loop', 'step': 3, 'writes': {'agent': {'some_bytes': 'c29tZV9ieXRlcw==', 'some_byte_array': 'c29tZV9ieXRlX2FycmF5', 'dict_with_bytes': {'more_bytes': 'bW9yZV9ieXRlcw=='}, 'messages': [{'content': 'end', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-0f2ef0a1-0fc7-445c-9df4-55e8bb284575', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}}}}
     

docs/docs/cloud/how-tos/stream_updates.md

@@ -1,6 +1,9 @@
 # How to stream state updates of your graph
 
-This guide covers how to use `stream_mode="updates"` for your graph, which will stream the updates to the graph state that are made after each node is executed. This differs from using `stream_mode="values"`: instead of streaming the entire value of the state at each superstep, it only streams the updates from each of the nodes that made an update to the state at that superstep. Read [this conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#stream-and-astream) to learn more.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
+This guide covers how to use `stream_mode="updates"` for your graph, which will stream the updates to the graph state that are made after each node is executed. This differs from using `stream_mode="values"`: instead of streaming the entire value of the state at each superstep, it only streams the updates from each of the nodes that made an update to the state at that superstep.
 
 ## Setup
 
@@ -146,24 +149,69 @@ Now we can stream by updates, which outputs updates made to the state by each no
 Output:
 
     Receiving new event of type: metadata...
-    {'run_id': 'cfc96c16-ed9a-44bd-b5bb-c30e3c0725f0'}
-    
-    
-    
-    Receiving new event of type: data...
-    {'agent': {'messages': [{'content': [{'id': 'toolu_0148tMmDK51iLQfG1yaNwRHM', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-1a9d32b0-7007-4a36-abde-8df812a0ed94', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_0148tMmDK51iLQfG1yaNwRHM'}], 'invalid_tool_calls': []}]}}
-    
-    
-    
-    Receiving new event of type: data...
-    {'action': {'messages': [{'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716062239, \'localtime\': \'2024-05-18 12:57\'}, \'current\': {\'last_updated_epoch\': 1716061500, \'last_updated\': \'2024-05-18 12:45\', \'temp_c\': 18.9, \'temp_f\': 66.0, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 2.2, \'wind_kph\': 3.6, \'wind_degree\': 10, \'wind_dir\': \'N\', \'pressure_mb\': 1017.0, \'pressure_in\': 30.02, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 18.9, \'feelslike_f\': 66.0, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 6.0, \'gust_mph\': 7.5, \'gust_kph\': 12.0}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': 'a36e8cd1-0e96-4417-9c15-f10a945d2b42', 'tool_call_id': 'toolu_0148tMmDK51iLQfG1yaNwRHM'}]}}
-    
-    
-    
-    Receiving new event of type: data...
-    {'agent': {'messages': [{'content': 'The weather in Los Angeles is currently overcast with a temperature of around 66°F (18.9°C). There are light winds from the north at around 2-3 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-d5c1c2f0-b12d-41ce-990b-f36570e7483d', 'example': False, 'tool_calls': [], 'invalid_tool_calls': []}]}}
-    
-    
-    
+    {"run_id": "cfc96c16-ed9a-44bd-b5bb-c30e3c0725f0"}
+
+
+
+    Receiving new event of type: updates...
+    {
+      "agent": {
+        "messages": [
+          {
+            "type": "ai",
+            "tool_calls": [
+              {
+                "name": "tavily_search_results_json",
+                "args": {
+                  "query": "weather in los angeles"
+                },
+                "id": "toolu_0148tMmDK51iLQfG1yaNwRHM"
+              }
+            ],
+            ...
+          }
+        ]
+      }
+    }
+
+
+
+    Receiving new event of type: updates...
+    {
+      "action": {
+        "messages": [
+          {
+            "content": [
+              {
+                "url": "https://www.weatherapi.com/",
+                "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716062239, \"localtime\": \"2024-05-18 12:57\"}, \"current\": {\"last_updated_epoch\": 1716061500, \"last_updated\": \"2024-05-18 12:45\", \"temp_c\": 18.9, \"temp_f\": 66.0, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 2.2, \"wind_kph\": 3.6, \"wind_degree\": 10, \"wind_dir\": \"N\", \"pressure_mb\": 1017.0, \"pressure_in\": 30.02, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 18.9, \"feelslike_f\": 66.0, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 6.0, \"gust_mph\": 7.5, \"gust_kph\": 12.0}}"
+              }
+            ],
+            "type": "tool",
+            "name": "tavily_search_results_json",
+            "tool_call_id": "toolu_0148tMmDK51iLQfG1yaNwRHM",
+            ...
+          }
+        ]
+      }
+    }
+
+
+
+    Receiving new event of type: updates...
+    {
+      "agent": {
+        "messages": [
+          {
+            "content": "The weather in Los Angeles is currently overcast with a temperature of around 66°F (18.9°C). There are light winds from the north at around 2-3 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+            "type": "ai",
+            ...
+          }
+        ]
+      }
+    }
+
+
+
     Receiving new event of type: end...
     None

docs/docs/cloud/how-tos/stream_values.md

@@ -1,6 +1,9 @@
 # How to stream full state of your graph
 
-This guide covers how to use `stream_mode="values"`, which streams the value of the state at each superstep. This differs from using `stream_mode="updates"`: instead of streaming just the updates to the state from each node, it streams the entire graph state at that superstep. Read [this conceptual guide](https://langchain-ai.github.io/langgraph/concepts/low_level/#stream-and-astream) to learn more.
+!!! info "Prerequisites"
+    * [Streaming](../../concepts/streaming.md)
+
+This guide covers how to use `stream_mode="values"`, which streams the value of the state at each superstep. This differs from using `stream_mode="updates"`: instead of streaming just the updates to the state from each node, it streams the entire graph state at that superstep.
 
 ## Setup
 
@@ -133,30 +136,93 @@ Now we can stream by values, which streams the full state of the graph after eac
 Output:
 
     Receiving new event of type: metadata...
-    {'run_id': 'f08791ce-0a3d-44e0-836c-ff62cd2e2786'}
-    
-    
-    
+    {"run_id": "f08791ce-0a3d-44e0-836c-ff62cd2e2786"}
+
+
+
     Receiving new event of type: values...
-    {'messages': [{'role': 'human', 'content': 'what's the weather in la'}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "role": "human",
+          "content": "what's the weather in la"
+        }
+      ]
+    }
+
+
+
     Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "content": "",
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+      ]
+    }
+
+    ...
+
     Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}, {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0d5dab31-5ff8-4ae2-a560-bc4bcba7c9d7', 'tool_call_id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}]}
-    
-    
-    
-    Receiving new event of type: values...
-    {'messages': [{'content': 'what's the weather in la', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': 'faa15565-8823-4aa1-87af-e21b40526fae', 'example': False}, {'content': [{'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g', 'input': {'query': 'weather in los angeles'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-3fe1db7a-6b8d-4d83-ba07-8657190ad811', 'example': False, 'tool_calls': [{'name': 'tavily_search_results_json', 'args': {'query': 'weather in los angeles'}, 'id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}], 'invalid_tool_calls': []}, {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'tavily_search_results_json', 'id': '0d5dab31-5ff8-4ae2-a560-bc4bcba7c9d7', 'tool_call_id': 'toolu_01E5mSaZWm5rWJnCqmt63v4g'}, {'content': 'Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'ai', 'name': None, 'id': 'run-4d6d4c23-5aad-4042-b0d9-19407a9e08e3', 'example': False, 'tool_calls': [], 'invalid_tool_calls': []}]}
-    
-    
-    
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "content": "",
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+        {
+          "content": [
+            {
+              "url": "https://www.weatherapi.com/",
+              "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
+            }
+          ],
+          "type": "tool",
+          "name": "tavily_search_results_json",
+          "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+          ...
+        },
+        {
+          "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+          "type": "ai",
+          ...
+        }
+      ]
+    }
+
+
+
     Receiving new event of type: end...
     None
     
@@ -228,40 +294,42 @@ If we want to just get the final result, we can use this endpoint and just keep
 
 Output:
 
-    {'messages': [{'content': 'what's the weather in la',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'human',
-       'name': None,
-       'id': 'e78c2f94-d810-42fc-a399-11f6bb1b1092',
-       'example': False},
-      {'content': [{'id': 'toolu_01SBMoAGr4U9x3ibztm2UUom',
-         'input': {'query': 'weather in los angeles'},
-         'name': 'tavily_search_results_json',
-         'type': 'tool_use'}],
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'ai',
-       'name': None,
-       'id': 'run-80767ab8-09fc-40ec-9e45-657ddef5e0b1',
-       'example': False,
-       'tool_calls': [{'name': 'tavily_search_results_json',
-         'args': {'query': 'weather in los angeles'},
-         'id': 'toolu_01SBMoAGr4U9x3ibztm2UUom'}],
-       'invalid_tool_calls': []},
-      {'content': '[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'Los Angeles\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 34.05, \'lon\': -118.24, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1716310320, \'localtime\': \'2024-05-21 9:52\'}, \'current\': {\'last_updated_epoch\': 1716309900, \'last_updated\': \'2024-05-21 09:45\', \'temp_c\': 16.7, \'temp_f\': 62.1, \'is_day\': 1, \'condition\': {\'text\': \'Overcast\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/day/122.png\', \'code\': 1009}, \'wind_mph\': 8.1, \'wind_kph\': 13.0, \'wind_degree\': 250, \'wind_dir\': \'WSW\', \'pressure_mb\': 1015.0, \'pressure_in\': 29.97, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 65, \'cloud\': 100, \'feelslike_c\': 16.7, \'feelslike_f\': 62.1, \'vis_km\': 16.0, \'vis_miles\': 9.0, \'uv\': 5.0, \'gust_mph\': 12.5, \'gust_kph\': 20.2}}"}]',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'tool',
-       'name': 'tavily_search_results_json',
-       'id': 'af25e94a-c119-48c3-bbd3-096e42f472ac',
-       'tool_call_id': 'toolu_01SBMoAGr4U9x3ibztm2UUom'},
-      {'content': 'Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.',
-       'additional_kwargs': {},
-       'response_metadata': {},
-       'type': 'ai',
-       'name': None,
-       'id': 'run-b90f0037-e56a-4f3b-ad92-00d10d079a9e',
-       'example': False,
-       'tool_calls': [],
-       'invalid_tool_calls': []}]}
+    {
+      "messages": [
+        {
+          "content": "what's the weather in la",
+          "type": "human",
+          ...
+        },
+        {
+          "type": "ai",
+          "tool_calls": [
+            {
+              "name": "tavily_search_results_json",
+              "args": {
+                "query": "weather in los angeles"
+              },
+              "id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+            }
+          ],
+          ...
+        }
+        {
+          "content": [
+            {
+              "url": "https://www.weatherapi.com/",
+              "content": "{\"location\": {\"name\": \"Los Angeles\", \"region\": \"California\", \"country\": \"United States of America\", \"lat\": 34.05, \"lon\": -118.24, \"tz_id\": \"America/Los_Angeles\", \"localtime_epoch\": 1716310320, \"localtime\": \"2024-05-21 9:52\"}, \"current\": {\"last_updated_epoch\": 1716309900, \"last_updated\": \"2024-05-21 09:45\", \"temp_c\": 16.7, \"temp_f\": 62.1, \"is_day\": 1, \"condition\": {\"text\": \"Overcast\", \"icon\": \"//cdn.weatherapi.com/weather/64x64/day/122.png\", \"code\": 1009}, \"wind_mph\": 8.1, \"wind_kph\": 13.0, \"wind_degree\": 250, \"wind_dir\": \"WSW\", \"pressure_mb\": 1015.0, \"pressure_in\": 29.97, \"precip_mm\": 0.0, \"precip_in\": 0.0, \"humidity\": 65, \"cloud\": 100, \"feelslike_c\": 16.7, \"feelslike_f\": 62.1, \"vis_km\": 16.0, \"vis_miles\": 9.0, \"uv\": 5.0, \"gust_mph\": 12.5, \"gust_kph\": 20.2}}"
+            }
+          ],
+          "type": "tool",
+          "name": "tavily_search_results_json",
+          "tool_call_id": "toolu_01E5mSaZWm5rWJnCqmt63v4g"
+          ...
+        },
+        {
+          "content": "Based on the weather API results, the current weather in Los Angeles is overcast with a temperature of around 62°F (17°C). There are light winds from the west-southwest around 8-13 mph. The humidity is 65% and visibility is good at 9 miles. Overall, mild spring weather conditions in LA.",
+          "type": "ai",
+          ...
+        }
+      ]
+    }

docs/docs/cloud/how-tos/test_deployment.md

@@ -4,7 +4,7 @@ The LangGraph Studio UI connects directly to LangGraph Cloud deployments.
 
 Starting from the <a href="https://smith.langchain.com/" target="_blank">LangSmith UI</a>...
 
-1. In the left-hand navigation panel, select `Deployments`. The `Deployments` view contains a list of existing LangGraph Cloud deployments.
+1. In the left-hand navigation panel, select `LangGraph Cloud`. The `LangGraph Cloud` view contains a list of existing LangGraph Cloud deployments.
 1. Select an existing deployment to test with LangGraph Studio.
 1. In the top-right corner, select `Open LangGraph Studio`.
 1. [Invoke an assistant](./invoke_studio.md) or [view an existing thread](./threads_studio.md).

docs/docs/cloud/how-tos/webhooks.md

@@ -76,7 +76,9 @@ Output:
 
 ## Use graph with a webhook
 
-Now we can invoke a run with a webhook:
+To invoke a run with a webhook, we specify the `webhook` parameter with the desired endpoint when creating a run. Webhook requests are triggered by the end of a run.
+
+For example, if we can receive requests at `https://my-server.app/my-webhook-endpoint`, we can pass this to `stream`:
 
 === "Python"
 
@@ -89,7 +91,7 @@ Now we can invoke a run with a webhook:
         assistant_id=assistant_id,
         input=input,
         stream_mode="events",
-        webhook="your-webhook"
+        webhook="https://my-server.app/my-webhook-endpoint"
     ):
         # Do something with the stream output
         pass
@@ -107,7 +109,7 @@ Now we can invoke a run with a webhook:
       assistantID,
       {
         input: input,
-        webhook: "your-webhook"
+        webhook: "https://my-server.app/my-webhook-endpoint"
       }
     );
     for await (const chunk of streamResponse) {
@@ -124,8 +126,17 @@ Now we can invoke a run with a webhook:
         --data '{
             "assistant_id": <ASSISTANT_ID>,
             "input" : {"messages":[{"role": "user", "content": "Hello!"}]},
-            "webhook": <YOUR_WEBHOOK_URL>
+            "webhook": "https://my-server.app/my-webhook-endpoint"
         }'
     ```
 
-And that's it! Now you can trigger your custom webhooks whenever you want in your LangGraph applications!
+The schema for the payload sent to `my-webhook-endpoint` is that of a [run](../../concepts/langgraph_server.md/#runs). See [API Reference](https://langchain-ai.github.io/langgraph/cloud/reference/api/api_ref.html#model/run) for more detail. Note that the run input, configuration, etc. are included in the `kwargs` field.
+
+### Signing webhook requests
+
+To sign the webhook requests, we can specify a token parameter in the webhook URL, e.g.,
+```
+https://my-server.app/my-webhook-endpoint?token=...
+```
+
+The server should then extract the token from the request's parameters and validate it before processing the payload.

docs/docs/cloud/index.md

@@ -1,44 +0,0 @@
-# LangGraph Cloud (beta)
-
-!!! tip
-    - LangGraph is an MIT-licensed open-source library, which we are committed to maintaining and growing for the community.
-    - LangGraph Cloud is an optional managed hosting service for LangGraph, which provides additional features geared towards production deployments.
-    - We are actively contributing improvements back to LangGraph informed by our work on LangGraph Cloud.
-    - You can always deploy LangGraph applications on your own infrastructure using the open-source LangGraph project.
-
-!!! warning "Under Construction"
-    LangGraph Cloud documentation is under construction. Contents may change until general availability.
-
-
-<video controls preload="auto" allowfullscreen="true" poster="how-tos/img/studio_forks_poster.png">
-    <source src="how-tos/img/studio_forks.mp4" type="video/mp4">
-</video>
-
-
-## Overview
-
-LangGraph Cloud is a managed service for deploying and hosting LangGraph applications. Deploying applications with LangGraph Cloud shortens the time-to-market for developers. With one click, deploy a production-ready API with built-in persistence for your LangGraph application. LangGraph Cloud APIs are horizontally scalable and deployed with durable storage.
-
-The LangGraph Cloud API exposes functionality of your LangGraph application through [Assistants](./concepts/api.md#assistants). An assistant abstracts the cognitive architecture of your graph. Invoke an assistant by calling the pre-built [API endpoints](./reference/api/api_ref.md).
-
-LangGraph Cloud is seamlessly integrated with [LangSmith](https://www.langchain.com/langsmith) and is accessible from within the LangSmith UI.
-
-LangGraph Cloud applications can be tested and debugged using the [LangGraph Studio Desktop](https://github.com/langchain-ai/langgraph-studio). 
-
-## Key Features
-
-The LangGraph Cloud API supports key LangGraph features in addition to new functionality for enabling complex, agentic workflows.
-
-- **Assistants and Threads**: Assistants abstract the cognitive architecture of graphs and threads track the state/history of graphs.
-- **Streaming**: API support for [LangGraph streaming modes](../concepts/low_level.md#streaming) including setting multiple streaming modes at the same time.
-- **Human-in-the-Loop**: API support for [LangGraph human-in-the-loop features](../concepts/agentic_concepts.md#human-in-the-loop).
-- **Double Texting**: Configure how assistants respond when new input is received while processing a previous input. Interrupt, rollback, reject, or enqueue.
-- **Background Runs/Cron Jobs**: A built-in task queue enables background runs and scheduled cron jobs.
-- **Stateless Runs**: For simpler use cases, invoke an assistant without needing to create a thread.
-
-## Documentation
-
-- [Tutorials](./quick_start.md): Learn to build and deploy applications for LangGraph Cloud.
-- [How-to Guides](./how-tos/index.md): Learn how to set up a LangGraph application for deployment and implement features of the LangGraph Cloud API such as streaming tokens, configuring double texting, and creating cron jobs. Go here if you want to copy and run a specific code snippet.
-- [Conceptual Guides](./concepts/api.md): In-depth explanations of the core data models (e.g. assistants), key features of the LangGraph Cloud API (e.g. double texting), and the architecture of a LangGraph Cloud deployment.
-- [Reference](./reference/api/api_ref.md): References for the LangGraph Cloud API, the corresponding Python and JS/TS SDKs, the LangGraph CLI, and deployment environment variables.

docs/docs/cloud/quick_start.md

@@ -1,155 +1,194 @@
-# Quick Start
+# LangGraph Cloud Quick Start
 
-This quick start guide will cover how to build a simple agent that can look up things on the internet. We will then deploy it to LangGraph Cloud, use the LangGraph Studio to visualize and test it out, and use the LangGraph SDK to interact with it.
+In this tutorial you will build and deploy a simple chatbot agent that can look things up on the internet. You will be using [LangGraph Cloud](../concepts/langgraph_cloud.md), [LangGraph Studio](../concepts/langgraph_studio.md) to visualize and test it out, and [LangGraph SDK](./reference/sdk/python_sdk_ref.md) to interact with the deployed agent.
+
+If you want to learn how to build an agent like this from scratch, take a look at the [LangGraph Quick Start tutorial](../tutorials/introduction.ipynb).
 
 ## Set up requirements
 
 This tutorial will use:
 
-- Anthropic for the LLM - sign up and get an API key [here](https://console.anthropic.com/)
-- Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/)
-- LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/)
+- Anthropic for the LLM - sign up and get an API key [here](https://console.anthropic.com/).
+- Tavily for the search engine - sign up and get an API key [here](https://app.tavily.com/).
+- LangSmith for hosting - sign up and get an API key [here](https://smith.langchain.com/).
 
-## Set up local files
+## Create and configure your app
 
-1.  Create a new application with the following directory and files:
+First, let's set create all of the necessary files for our LangGraph application.
 
-=== "Python"
+1. __Create application directory and files__
 
-        <my-app>/
-        |-- agent.py            # code for your LangGraph agent
-        |-- requirements.txt    # Python packages required for your graph
-        |-- langgraph.json      # configuration file for LangGraph
-        |-- .env                # environment files with API keys
+    Create a new application `my-app` with the following file structure:
 
-=== "Javascript"
-
-        <my-app>/
-        |-- agent.ts            # code for your LangGraph agent
-        |-- package.json        # Javascript packages required for your graph
-        |-- langgraph.json      # configuration file for LangGraph
-        |-- .env                # environment files with API keys
-
-2.  The `agent.py`/`agent.ts` file should contain code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent. You can read more about it [here](../concepts/agentic_concepts.md#react-implementation).
-
-=== "Python"
-
-    ```python
-    from langchain_anthropic import ChatAnthropic
-    from langchain_community.tools.tavily_search import TavilySearchResults
-    from langgraph.prebuilt import create_react_agent
-
-    model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
-
-    tools = [TavilySearchResults(max_results=2)]
-
-    graph = create_react_agent(model, tools)
+    ```shell
+    mkdir my-app
     ```
 
-=== "Javascript"
+    === "Python"
 
-    ```ts
-    import { ChatAnthropic } from "@langchain/anthropic";
-    import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
-    import { createReactAgent } from "@langchain/langgraph/prebuilt";
+            my-app/
+            |-- agent.py            # code for your LangGraph agent
+            |-- requirements.txt    # Python packages required for your graph
+            |-- langgraph.json      # configuration file for LangGraph
+            |-- .env                # environment files with API keys
 
-    const model = new ChatAnthropic({
-      model: "claude-3-5-sonnet-20240620",
-    });
+    === "Javascript"
 
-    const tools = [
-      new TavilySearchResults({ maxResults: 3, }),
-    ];
+            my-app/
+            |-- agent.ts            # code for your LangGraph agent
+            |-- package.json        # Javascript packages required for your graph
+            |-- langgraph.json      # configuration file for LangGraph
+            |-- .env                # environment files with API keys
 
-    export const graph = createReactAgent({ llm: model, tools });
-    ```
 
-3.  The `requirements.txt`/`package.json` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
+1. __Define your graph__
 
-=== "Python"
+    === "Python"
+        The `agent.py` file should contain code with your graph.
 
-      ```python
-      langgraph
-      langchain_anthropic
-      tavily-python
-      langchain_community
-      ```
+    === "Javascript"
+        The `agent.ts` file should contain code with your graph.
 
-=== "Javascript"
+    The following code example is a simple chatbot agent (similar to the one in the [previous tutorial](../tutorials/introduction.ipynb)). Specifically, it uses [create_react_agent][langgraph.prebuilt.chat_agent_executor.create_react_agent], a prebuilt [ReAct](../concepts/agentic_concepts.md#react-implementation)-style agent.
 
-      ```js
-      {
-        "name": "my-app",
-        "packageManager": "yarn@1.22.22",
-        "dependencies": {
-          "@langchain/community": "^0.2.31",
-          "@langchain/core": "^0.2.31",
-          "@langchain/langgraph": "0.2.0",
-          "@langchain/openai": "^0.2.8"
+    The `agent` file needs to have a variable with a [CompiledGraph][langgraph.graph.graph.CompiledGraph] (in this case the `graph` variable).
+
+    === "Python"
+
+        ```python
+        # agent.py
+        from langchain_anthropic import ChatAnthropic
+        from langchain_community.tools.tavily_search import TavilySearchResults
+        from langgraph.prebuilt import create_react_agent
+
+        model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
+
+        tools = [TavilySearchResults(max_results=2)]
+
+        # compiled graph
+        graph = create_react_agent(model, tools)
+        ```
+
+    === "Javascript"
+
+        ```ts
+        // agent.ts
+        import { ChatAnthropic } from "@langchain/anthropic";
+        import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
+        import { createReactAgent } from "@langchain/langgraph/prebuilt";
+
+        const model = new ChatAnthropic({
+          model: "claude-3-5-sonnet-20240620",
+        });
+
+        const tools = [
+          new TavilySearchResults({ maxResults: 3, }),
+        ];
+
+        // compiled graph
+        export const graph = createReactAgent({ llm: model, tools });
+        ```
+
+1. __Specify dependencies__
+
+    === "Python"
+        You should add dependencies for your graph(s) to `requirements.txt`.
+
+    === "Javascript"
+        You should add dependencies for your graph(s) to `package.json`.
+
+    In this case we only require four packages for our graph to run:
+
+    === "Python"
+
+          ```python
+          langgraph
+          langchain_anthropic
+          tavily-python
+          langchain_community
+          ```
+
+    === "Javascript"
+
+          ```js
+          {
+            "name": "my-app",
+            "packageManager": "yarn@1.22.22",
+            "dependencies": {
+              "@langchain/community": "^0.3.11",
+              "@langchain/core": "^0.3.16",
+              "@langchain/langgraph": "0.2.18",
+              "@langchain/anthropic": "^0.3.7"
+            }
+          }
+          ```
+
+1. __Create LangGraph configuration file__
+
+    The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to deploy. In this case we only have one graph: the compiled `graph` object from `agent.py` / `agent.ts`.
+
+    === "Python"
+
+        ```json
+        {
+          "dependencies": ["."],
+          "graphs": {
+            "agent": "./agent.py:graph"
+          },
+          "env": ".env"
         }
-      }
-      ```
+        ```
 
-4.  The [`langgraph.json`][langgraph.json] file is a configuration file that describes what graph(s) you are going to host. In this case we only have one graph to host: the compiled `graph` object from `agent.py`/`agent.ts`.
+    === "Javascript"
 
-=== "Python"
+        ```json
+        {
+          "node_version": "20",
+          "dockerfile_lines": [],
+          "dependencies": ["."],
+          "graphs": {
+            "agent": "./src/agent.ts:graph"
+          },
+          "env": ".env"
+        }
+        ```
 
-    ```json
-    {
-      "dependencies": ["."],
-      "graphs": {
-        "agent": "./agent.py:graph"
-      },
-      "env": ".env"
-    }
-    ```
+    Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
 
-=== "Javascript"
+1. __Specify environment variables__
 
-    ```json
-    {
-      "node_version": "20",
-      "dockerfile_lines": [],
-      "dependencies": ["."],
-      "graphs": {
-        "agent": "./src/agent.ts:graph"
-      },
-      "env": ".env"
-    }
-    ```
+    The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. 
 
-Learn more about the LangGraph CLI configuration file [here](./reference/cli.md#configuration-file).
+    !!! warning
+        The `.env` file should NOT be included with the rest of source code in your Github repository. When creating a deployment using LangGraph Cloud, you will be able to specify the environment variables manually.
 
-5.  The `.env` file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step. NOTE: if you do add this, you should NOT check this into git. For this graph, we need two environment variables:
+    For this graph, we need two environment variables:
 
     ```shell
     ANTHROPIC_API_KEY=...
     TAVILY_API_KEY=...
     ```
 
-Now that we have set everything up on our local file system, we are ready to host our graph.
+!!! tip
+    Learn more about different application structure options [here](../how-tos/index.md#application-structure).
 
-## Test the graph build locally
+Now that we have set everything up on our local file system, we are ready to test our graph locally.
 
-### Using LangGraph Studio Desktop (recommended)
+## Test the app locally
 
-![LangGraph Studio Desktop](./img/graph_video_poster.png)
+To test the LangGraph app before deploying it using LangGraph Cloud, you can start the [LangGraph server](../concepts/langgraph_server.md) locally or use [LangGraph Studio](../concepts/langgraph_studio.md).
 
-Testing your graph locally is easy with LangGraph Studio Desktop. LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications
+## Using local server
 
-With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with [LangSmith](https://smith.langchain.com) so you can collaborate with teammates to debug failure modes.
+You can test your app by running [LangGraph server](../concepts/langgraph_server.md) locally. This is useful to make sure you have configured our [CLI configuration file][langgraph.json] correctly and can interact with your graph.
 
-### Using the LangGraph CLI
-
-Before deploying to the cloud, we probably want to test the building of our graph locally. This is useful to make sure we have configured our [CLI configuration file][langgraph.json] correctly and our graph runs.
-
-In order to do this we can first install the LangGraph CLI
+To run the server locally, you need to first install the LangGraph CLI:
 
 ```shell
 pip install langgraph-cli
 ```
 
-We can then test our API server locally. This requires access to LangGraph closed beta. In order to run the server locally, you will need to add your `LANGSMITH_API_KEY` to the .env file so we can validate you have access to LangGraph closed beta.
+You can then test our API server locally. In order to run the server locally, you will need to add your `LANGSMITH_API_KEY` to the `.env` file.
 
 ```shell
 langgraph up
@@ -160,10 +199,21 @@ This will start up the LangGraph API server locally. If this runs successfully,
 ```shell
 Ready!
 - API: http://localhost:8123
-2024-06-26 19:20:41,056:INFO:uvicorn.access 127.0.0.1:44138 - "GET /ok HTTP/1.1" 200
 ```
 
-You can now test this out! **Note: this local server is intended SOLELY for local testing purposes and is not performant enough for production applications, so please do not use it as such.** To test it out, you can go to another terminal window and run:
+First, let's verify that the server is running correctly by calling `/ok` endpoint:
+
+```shell
+curl --request GET --url http://localhost:8123/ok
+```
+
+Output:
+
+```
+{"ok": "true"}
+```
+
+Now we're ready to test the app with the real inputs!
 
 ```shell
 curl --request POST \
@@ -175,36 +225,57 @@ curl --request POST \
         "messages": [
             {
                 "role": "user",
-                "content": "How are you?"
+                "content": "What is the weather in NYC?"
             }
         ]
     },
-    "metadata": {},
-    "config": {
-        "configurable": {}
-    },
-    "multitask_strategy": "reject",
-    "stream_mode": [
-        "values"
-    ]
+    "stream_mode": "updates"
 }'
 ```
 
-If you get back a valid response, then all is functioning properly!
+Output:
 
-## Deploy to Cloud
+```
+...
 
-### Push your code to GitHub
+data: {
+  "agent": {
+    "messages": [
+      {
+        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
+        "type": "ai",
+        ...
+      }
+    ]
+  }
+}
+```
 
-Turn the `<my-app>` directory into a GitHub repo. You can use the GitHub CLI if you like, or just create a repo manually (if unfamiliar, instructions [here](https://docs.github.com/en/migrations/importing-source-code/using-the-command-line-to-import-source-code/adding-locally-hosted-code-to-github)).
+You can see that our agent responds with the up-to-date search results!
 
-### Deploy from GitHub with LangGraph Cloud
+###  Using LangGraph Studio Desktop
 
-Once you have created your github repository with a Python file containing your compiled graph as well as a `langgraph.json` file containing the configuration for hosting your graph, you can head over to LangSmith and click on the 🚀 icon on the left navbar to create a new deployment. Then click the `+ New Deployment` button.
+You can also test your app locally with [LangGraph Studio](../concepts/langgraph_studio.md). LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.
 
-![Langsmith Workflow](./img/cloud_deployment.png)
+With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.
 
-**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
+LangGraph Studio is available as a [desktop app](https://studio.langchain.com/) for MacOS users. Once you have installed the app, you can select `my-app` directory, which will automatically start the server locally and load the graph in the UI.
+
+To interact with your chatbot agent in LangGraph Studio, you can add a new message in the `Input` section and press `Submit`.
+
+![LangGraph Studio Desktop](./deployment/img/quick_start_studio.png)
+
+## Deploy to LangGraph Cloud
+
+Once you've tested your graph locally and verified that it works as expected, you can deploy it to the LangGraph Cloud.
+
+First, you'll need to turn the `my-app` directory into a GitHub repo and [push it to GitHub](https://docs.github.com/en/migrations/importing-source-code/using-the-command-line-to-import-source-code/adding-locally-hosted-code-to-github).
+
+Once you have created your GitHub repository with a Python file containing your compiled graph as well as a `langgraph.json` with the configuration, you can head over to [LangSmith](https://smith.langchain.com/) and click on the graph icon (`LangGraph Cloud`) on the bottom of the left navbar. This will open the LangGraph deployments page. On this page, click the `+ New Deployment` button in the top right corner.
+
+![Langsmith Workflow](./deployment/img/cloud_deployment.png)
+
+**_If you have not deployed to LangGraph Cloud before:_** there will be a button that shows up saying `Import from GitHub`. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.
 
 **_Once you have set up your GitHub connection:_** the new deployment page will look as follows:
 
@@ -213,53 +284,43 @@ Once you have created your github repository with a Python file containing your
 To deploy your application, you should do the following:
 
 1. Select your GitHub username or organization from the selector
-2. Search for your repo to deploy in the search bar and select it
-3. Choose any name
-4. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
-5. For Git Reference, you can select either the git branch for the code you want to deploy, or the exact commit SHA.
-6. If your chain relies on environment variables, add those in. They will be propagated to the underlying server so your code can access them. In this case, we need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.
-
-Putting this all together, you should have something as follows for your deployment details:
-
-![Deployment filled out](./deployment/img/deploy_filled_out.png)
+1. Search for your repo to deploy in the search bar and select it
+1. Choose a name for your deployment
+1. In the `Git Branch` field, you can specify either the branch for the code you want to deploy, or the exact commit SHA.
+1. In the `LangGraph API config file` field, enter the path to your `langgraph.json` file (which in this case is just `langgraph.json`)
+1. If your application needs environment variables, add those in the `Environment Variables` section. They will be propagated to the underlying server so your code can access them. In this case, we will need `ANTHROPIC_API_KEY` and `TAVILY_API_KEY`.
 
 Hit `Submit` and your application will start deploying!
 
-## Inspect Traces + Monitor Service
-
-### Deployments View
-
 After your deployment is complete, your deployments page should look as follows:
 
 ![Deployed page](./deployment/img/deployed_page.png)
 
-You can see that by default, you get access to the `Trace Count` monitoring chart and `Recent Traces` run view. These are powered by LangSmith.
+## Interact with your deployment
 
-You can click on `All Charts` to view all monitoring info for your server, or click on `See tracing project` to get more information on an individual trace.
+### Using LangGraph Studio (Cloud)
 
-### Access the Docs
-
-You can access the docs by clicking on the API docs link, which should send you to a page that looks like this:
-
-![API Docs page](./deployment/img/api_page.png)
-
-You won’t actually be able to test any of the API endpoints without authorizing first. To do so, grab your Langsmith API key and add it at the top where it says `API KEY (X-API-KEY)`. You should now be able to select any of the API endpoints, click `Test Request`, enter the parameters you would like to pass, and then click `Send` to view the results of the API call.
-
-## Interact with your deployment via LangGraph Studio
-
-If you click on your deployment you should see a blue button in the top right that says `LangGraph Studio`. Clicking on this button will take you to a page that looks like this:
-
-![Studio UI before being run](./deployment/img/graph_visualization.png)
-
-On this page you can test out your graph by passing in starting states and clicking `Start Run` (this should behave identically to calling `.invoke`). You will then be able to look into the execution thread for each run and explore the steps your graph is taking to produce its output.
+On the deployment page for your application,, you should see a button in the top right corner that says `LangGraph Studio`. Clicking on this button will take you to the web version of LangGraph Studio. This is the same UI that you interacted with when [testing the app locally](#using-langgraph-studio-recommended), but instead of using a local LangGraph server, it uses the one from your LangGraph Cloud deployment.
 
 ![Studio UI once being run](./deployment/img/graph_run.png)
 
-## Use with the SDK
+### Using LangGraph SDK
 
-Once you have tested that your hosted graph works as expected using LangGraph Studio, you can start using your hosted graph all over your organization by using the LangGraph SDK. Let's see how we can access our hosted graph and execute our run from a python file.
+You can also interact with your deployed LangGraph application programmatically, using [LangGraph SDK](./reference/sdk/python_sdk_ref.md).
 
-First, make sure you have the SDK installed by calling `pip install langgraph_sdk`.
+First, make sure you have the SDK installed:
+
+=== "Python"
+
+    ```shell
+    pip install langgraph_sdk
+    ```
+
+=== "Javascript"
+
+    ```shell
+    yarn add @langchain/langgraph-sdk
+    ```
 
 Before using, you need to get the URL of your LangGraph deployment. You can find this in the `Deployment` view. Click the URL to copy it to the clipboard.
 
@@ -278,8 +339,8 @@ The first thing to do when using the SDK is to setup our client, access our assi
 
      client = get_client(url=<DEPLOYMENT_URL>)
      # get default assistant
-     assistants = await client.assistants.search()
-     assistant = [a for a in assistants if not a["config"]][0]
+     assistants = await client.assistants.search(metadata={"created_by": "system"})
+     assistant = assistants[0]
      # create thread
      thread = await client.threads.create()
      print(thread)
@@ -292,8 +353,8 @@ The first thing to do when using the SDK is to setup our client, access our assi
 
      const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
      // get default assistant
-     const assistants = await client.assistants.search();
-     const assistant = assistants.find(a => !a.config);
+     const assistants = await client.assistants.search({ metadata: {"created_by": "system"} })
+     const assistant = assistants[0];
      // create thread
      const thread = await client.threads.create();
      console.log(thread)
@@ -307,8 +368,9 @@ The first thing to do when using the SDK is to setup our client, access our assi
          --header 'Content-Type: application/json' \
          --data '{
              "limit": 10,
-             "offset": 0
-         }' | jq -c 'map(select(.config == null or .config == {})) | .[0]' && \
+             "offset": 0,
+             "metadata": {"created_by": "system"}
+         }' &&
      curl --request POST \
          --url <DEPLOYMENT_URL>/threads \
          --header 'Content-Type: application/json' \
@@ -320,32 +382,35 @@ We can then execute a run on the thread:
 === "Python"
 
     ```python
-    input = {"messages":[{"role": "user", "content": "Hello! My name is Bagatur and I am 26 years old."}]}
+    input = {
+        "messages": [{"role": "user", "content": "What is the weather in NYC?"}]
+    }
 
     async for chunk in client.runs.stream(
-            thread['thread_id'],
-            assistant["assistant_id"],
-            input=input,
-            stream_mode="updates",
-        ):
-        if chunk.data and chunk.event != "metadata":
+        thread["thread_id"],
+        assistant["assistant_id"],
+        input=input,
+        stream_mode="updates",
+    ):
+        if chunk.data:
             print(chunk.data)
     ```
 
 === "Javascript"
 
     ```js
-    const input = { "messages":[{ "role": "user", "content": "Hello! My name is Bagatur and I am 26 years old." }] };
+    const input = { "messages": [{ "role": "user", "content": "What is the weather in NYC?" }] };
 
     const streamResponse = client.runs.stream(
       thread["thread_id"],
       assistant["assistant_id"],
       {
         input,
+        streamMode: "updates"
       }
     );
     for await (const chunk of streamResponse) {
-      if (chunk.data && chunk.event !== "metadata" ) {
+      if (chunk.data) {
         console.log(chunk.data);
       }
     }
@@ -357,43 +422,41 @@ We can then execute a run on the thread:
     curl --request POST \
       --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
       --header 'Content-Type: application/json' \
-      --data "{
-        \"assistant_id\": <ASSISTANT_ID>,
-        \"input\": {\"messages\": [{\"role\": \"human\", \"content\": \"Hello! My name is Bagatur and I am 26 years old.\"}]},
-      }" | sed 's/\r$//' | awk '
-      /^event:/ { event = $2 }
-      /^data:/ {
-          json_data = substr($0, index($0, $2))
-
-          if (event != "metadata") {
-          print json_data
-          }
+      --data '{
+        "assistant_id": <ASSISTANT_ID>,
+        "input": {
+        "messages": [
+            {
+                "role": "user",
+                "content": "What is the weather in NYC?"
+            }
+          ]
+        },
+        "stream_mode": "updates"
       }'
     ```
 
-
 Output:
 
-    {'agent': {'messages': [{'content': "Hi Bagatur! It's nice to meet you. How can I assist you today?", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_9cb5d38cf7'}, 'type': 'ai', 'name': None, 'id': 'run-c89118b7-1b1e-42b9-a85d-c43fe99881cd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
+```
+...
 
-## What's Next
+data: {
+  "agent": {
+    "messages": [
+      {
+        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
+        "type": "ai",
+        ...
+      }
+    ]
+  }
+}
+```
+
+## Next steps
 
 Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:
 
-### LangGraph Cloud How-tos
-
-If you want to learn more about streaming from hosted graphs, check out the Streaming [how-to guides](how-tos/index.md#streaming).
-
-To learn more about double-texting and all the ways you can handle it in your application, read up on these [how-to guides](how-tos/index.md#double-texting).
-
-To learn about how to include different human-in-the-loop behavior in your graph, take a look at [these how-tos](how-tos/index.md#human-in-the-loop).
-
-### LangGraph Tutorials
-
-Before hosting, you have to write a graph to host. Here are some tutorials to get you more comfortable with writing LangGraph graphs and give you inspiration for the types of graphs you want to host.
-
-[This tutorial](../tutorials/customer-support/customer-support.ipynb) walks you through how to write a customer support bot using LangGraph.
-
-If you are interested in writing a SQL agent, check out [this tutorial](../tutorials/sql-agent.ipynb).
-
-Check out the [LangGraph tutorials](../tutorials/index.md) page to read about more exciting use cases.
+* [LangGraph How-to guides](../how-tos/index.md)
+* [LangGraph Tutorials](../tutorials/index.md)

docs/docs/cloud/reference/api/open_agent_api_ref.html

@@ -1,19 +0,0 @@
-<!doctype html>
-<html>
-  <head>
-    <title>Open Assistants API Specification</title>
-    <meta charset="utf-8" />
-    <meta
-      name="viewport"
-      content="width=device-width, initial-scale=1" />
-  </head>
-  <body>
-    <script id="api-reference" data-url="./open_agent_api.json"></script>
-    <script>
-      var configuration = {}
-      document.getElementById('api-reference').dataset.configuration =
-        JSON.stringify(configuration)
-    </script>
-    <script src="https://cdn.jsdelivr.net/npm/@scalar/api-reference"></script>
-  </body>
-</html>

docs/docs/cloud/reference/cli.md

@@ -1,23 +1,39 @@
 # LangGraph CLI
-The LangGraph CLI includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, use the CLI to deploy a local API server.
+
+The LangGraph command line interface includes commands to build and run a LangGraph Cloud API server locally in [Docker](https://www.docker.com/). For development and testing, you can use the CLI to deploy a local API server as an alternative to the [Studio desktop app](../../concepts/langgraph_studio.md).
 
 ## Installation
+
 1. Ensure that Docker is installed (e.g. `docker --version`).
-2. Install the `langgraph-cli` Python package (e.g. `pip install langgraph-cli`).
+2. Install the `langgraph-cli` package:
+ 
+    === "pip"
+        ```bash    
+        pip install langgraph-cli
+        ```
+
+    === "Homebrew (MacOS only)"
+        ```bash
+        brew install langgraph-cli
+        ```
+ 
 3. Run the command `langgraph --help` to confirm that the CLI is installed.
 
 [](){#langgraph.json}
+
 ## Configuration File
+
 The LangGraph CLI requires a JSON configuration file with the following keys:
 
-| Key | Description |
-| --- | ----------- |
-| `dependencies` | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name. |
-| `graphs` | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
-| `env` | Path to `.env` file or a mapping from environment variable to its value. |
-| `python_version` | `3.11` or `3.12`. Defaults to `3.11`. |
-| `pip_config_file`| Path to `pip` config file. |
-| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image. |
+| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+| ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `dependencies`     | **Required**. Array of dependencies for LangGraph Cloud API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                  |
+| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
+| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `store`            | Configuration for adding semantic search to the BaseStore. Contains the following fields: <ul><li>`index`: Configuration for semantic search indexing with fields:<ul><li>`embed`: Embedding provider (e.g., "openai:text-embedding-3-small") or path to custom embedding function</li><li>`dims`: Dimension size of the embedding model. Used to initialize the vector table.</li><li>`fields` (optional): List of fields to index. Defaults to `["$"]`, meaningto index entire documents. Can be specific fields like `["text", "summary", "some.value"]`</li></ul></li></ul>                                                                  |
+| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 
 <div class="admonition tip">
     <p class="admonition-title">Note</p>
@@ -26,102 +42,220 @@ The LangGraph CLI requires a JSON configuration file with the following keys:
     </p>
 </div>
 
-Example:
+### Examples
+
+#### Basic Configuration
+
 ```json
 {
-    "dependencies": [
-        "langchain_openai",
-        "./your_package"
-    ],
-    "graphs": {
-        "my_graph_id": "./your_package/your_file.py:variable"
-    },
-    "env": "./.env"
+  "dependencies": ["."],
+  "graphs": {
+    "chat": "./chat/graph.py:graph"
+  }
 }
 ```
 
-Example:
+#### Adding semantic search to the store
+
+All deployments come with a DB-backed BaseStore. Adding an "index" configuration to your `langgraph.json` will enable [semantic search](../deployment/semantic_search.md) within the BaseStore of your deployment.
+
+The `fields` configuration determines which parts of your documents to embed:
+- If omitted or set to `["$"]`, the entire document will be embedded
+- To embed specific fields, use JSON path notation: `["metadata.title", "content.text"]`
+- Documents missing specified fields will still be stored but won't have embeddings for those fields
+- You can still override which fields to embed on a specific item at `put` time using the `index` parameter
+
 ```json
 {
-    "python_version": "3.11",
-    "dependencies": [
-        "langchain_openai",
-        "."
-    ],
-    "graphs": {
-        "my_graph_id": "./your_package/your_file.py:make_graph"
-    },
-    "env": {
-        "OPENAI_API_KEY": "secret-key"
+  "dependencies": ["."],
+  "graphs": {
+    "memory_agent": "./agent/graph.py:graph"
+  },
+  "store": {
+    "index": {
+      "embed": "openai:text-embedding-3-small",
+      "dims": 1536,
+      "fields": ["$"]
     }
+  }
 }
 ```
 
+!!! note "Common model dimensions"
+        - openai:text-embedding-3-large: 3072
+        - openai:text-embedding-3-small: 1536
+        - openai:text-embedding-ada-002: 1536
+        - cohere:embed-english-v3.0: 1024
+        - cohere:embed-english-light-v3.0: 384
+        - cohere:embed-multilingual-v3.0: 1024
+        - cohere:embed-multilingual-light-v3.0: 384
+
+#### Semantic search with a custom embedding function
+
+If you want to use semantic search with a custom embedding function, you can pass a path to a custom embedding function:
+
+```json
+{
+  "dependencies": ["."],
+  "graphs": {
+    "memory_agent": "./agent/graph.py:graph"
+  },
+  "store": {
+    "index": {
+      "embed": "./embeddings.py:embed_texts",
+      "dims": 768,
+      "fields": ["text", "summary"]
+    }
+  }
+}
+```
+
+The `embed` field in store configuration can reference a custom function that takes a list of strings and returns a list of embeddings. Example implementation:
+
+```python
+# embeddings.py
+def embed_texts(texts: list[str]) -> list[list[float]]:
+    """Custom embedding function for semantic search."""
+    # Implementation using your preferred embedding model
+    return [[0.1, 0.2, ...] for _ in texts]  # dims-dimensional vectors
+```
+
 ## Commands
+
 The base command for the LangGraph CLI is `langgraph`.
 
 **Usage**
+
 ```
 langgraph [OPTIONS] COMMAND [ARGS]
 ```
 
+### `dev`
+
+Run LangGraph API server in development mode with hot reloading and debugging capabilities. This lightweight server requires no Docker installation and is suitable for development and testing. State is persisted to a local directory.
+
+!!! note "Python only"
+
+    Currently, the CLI only supports Python >= 3.11.
+    JS support is coming soon.
+
+**Installation**
+
+This command requires the "inmem" extra to be installed:
+
+```bash
+pip install -U "langgraph-cli[inmem]"
+```
+
+**Usage**
+
+```
+langgraph dev [OPTIONS]
+```
+
+**Options**
+
+| Option                        | Default          | Description                                                                         |
+| ----------------------------- | ---------------- | ----------------------------------------------------------------------------------- |
+| `-c, --config FILE`           | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables |
+| `--host TEXT`                 | `127.0.0.1`      | Host to bind the server to                                                          |
+| `--port INTEGER`              | `2024`           | Port to bind the server to                                                          |
+| `--no-reload`                 |                  | Disable auto-reload                                                                 |
+| `--n-jobs-per-worker INTEGER` |                  | Number of jobs per worker. Default is 10                                            |
+| `--no-browser`                |                  | Disable automatic browser opening                                                   |
+| `--debug-port INTEGER`        |                  | Port for debugger to listen on                                                      |
+| `--help`                      |                  | Display command documentation                                                       |
+
 ### `build`
+
 Build LangGraph Cloud API server Docker image.
 
 **Usage**
+
 ```
 langgraph build [OPTIONS]
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--platform TEXT` | | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64` |
-| `-t, --tag TEXT` | | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image` |
-| `--pull / --no-pull` | `--pull` | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `--help` | | Display command documentation. |
+| Option               | Default          | Description                                                                                                                  |
+| -------------------- | ---------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+| `--platform TEXT`    |                  | Target platform(s) to build the Docker image for. Example: `langgraph build --platform linux/amd64,linux/arm64`              |
+| `-t, --tag TEXT`     |                  | **Required**. Tag for the Docker image. Example: `langgraph build -t my-image`                                               |
+| `--pull / --no-pull` | `--pull`         | Build with latest remote Docker image. Use `--no-pull` for running the LangGraph Cloud API server with locally built images. |
+| `-c, --config FILE`  | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables.                                         |
+| `--help`             |                  | Display command documentation.                                                                                               |
 
 ### `up`
-Start langgraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
+
+Start LangGraph API server. For local testing, requires a LangSmith API key with access to LangGraph Cloud closed beta. Requires a license key for production use.
 
 **Usage**
+
 ```
 langgraph up [OPTIONS]
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--wait` | | Wait for services to start before returning. Implies --detach |
-| `--postgres-uri TEXT` | Local database | Postgres URI to use for the database. |
-| `--watch` | | Restart on file changes |
-| `--debugger-base-url TEXT` | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API. |
-| `--debugger-port INTEGER` | | Pull the debugger image locally and serve the UI on specified port |
-| `--verbose` | | Show more output from the server logs. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `-d, --docker-compose FILE` | | Path to docker-compose.yml file with additional services to launch. |
-| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph test --port 8000` |
-| `--pull / --no-pull` | `pull` | Pull latest images. Use --no-pull for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--recreate / --no-recreate` | `no-recreate` | Recreate containers even if their configuration and image haven't changed |
-| `--help` | | Display command documentation. | 
+| Option                       | Default                   | Description                                                                                                             |
+| ---------------------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+| `--wait`                     |                           | Wait for services to start before returning. Implies --detach                                                           |
+| `--postgres-uri TEXT`        | Local database            | Postgres URI to use for the database.                                                                                   |
+| `--watch`                    |                           | Restart on file changes                                                                                                 |
+| `--debugger-base-url TEXT`   | `http://127.0.0.1:[PORT]` | URL used by the debugger to access LangGraph API.                                                                       |
+| `--debugger-port INTEGER`    |                           | Pull the debugger image locally and serve the UI on specified port                                                      |
+| `--verbose`                  |                           | Show more output from the server logs.                                                                                  |
+| `-c, --config FILE`          | `langgraph.json`          | Path to configuration file declaring dependencies, graphs and environment variables.                                    |
+| `-d, --docker-compose FILE`  |                           | Path to docker-compose.yml file with additional services to launch.                                                     |
+| `-p, --port INTEGER`         | `8123`                    | Port to expose. Example: `langgraph up --port 8000`                                                                     |
+| `--pull / --no-pull`         | `pull`                    | Pull latest images. Use `--no-pull` for running the server with locally-built images. Example: `langgraph up --no-pull` |
+| `--recreate / --no-recreate` | `no-recreate`             | Recreate containers even if their configuration and image haven't changed                                               |
+| `--help`                     |                           | Display command documentation.                                                                                          |
 
-### `test`
-Test your LangGraph in the cloud. The only function you can call from the SDK after testing your graph is `client.runs.stream(thread_id=None, ...)`
+### `dockerfile`
+
+Generate a Dockerfile for building a LangGraph Cloud API server Docker image.
 
 **Usage**
+
 ```
-langgraph test [OPTIONS]
+langgraph dockerfile [OPTIONS] SAVE_PATH
 ```
 
 **Options**
 
-| Option | Default | Description |
-| ------ | ------- | ----------- |
-| `--verbose` | | Show more output from the server logs. |
-| `-c, --config FILE` | `langgraph.json` | Path to configuration file declaring dependencies, graphs and environment variables. |
-| `-p, --port INTEGER` | `8123` | Port to expose. Example: `langgraph test --port 8000` |
-| `--pull / --no-pull` | `pull` | Pull latest images. Use --no-pull for running the server with locally-built images. Example: `langgraph up --no-pull` |
-| `--help` | | Display command documentation. |
+| Option              | Default          | Description                                                                                                     |
+| ------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------- |
+| `-c, --config FILE` | `langgraph.json` | Path to the [configuration file](#configuration-file) declaring dependencies, graphs and environment variables. |
+| `--help`            |                  | Show this message and exit.                                                                                     |
+
+Example:
+
+```bash
+langgraph dockerfile -c langgraph.json Dockerfile
+```
+
+This generates a Dockerfile that looks similar to:
+
+```dockerfile
+FROM langchain/langgraph-api:3.11
+
+ADD ./pipconf.txt /pipconfig.txt
+
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt langchain_community langchain_anthropic langchain_openai wikipedia scikit-learn
+
+ADD ./graphs /deps/__outer_graphs/src
+RUN set -ex && \
+    for line in '[project]' \
+                'name = "graphs"' \
+                'version = "0.1"' \
+                '[tool.setuptools.package-data]' \
+                '"*" = ["**/*"]'; do \
+        echo "$line" >> /deps/__outer_graphs/pyproject.toml; \
+    done
+
+RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
+
+ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
+```

docs/docs/concepts/application_structure.md

@@ -0,0 +1,167 @@
+# Application Structure
+
+!!! info "Prerequisites"
+
+    - [LangGraph Server](./langgraph_server.md)
+    - [LangGraph Glossary](./low_level.md)
+
+## Overview
+
+A LangGraph application consists of one or more graphs, a LangGraph API Configuration file (`langgraph.json`), a file that specifies dependencies, and an optional .env file that specifies environment variables.
+
+This guide shows a typical structure for a LangGraph application and shows how the required information to deploy a LangGraph application using the LangGraph Platform is specified.
+
+## Key Concepts
+
+To deploy using the LangGraph Platform, the following information should be provided:
+
+1. A [LangGraph API Configuration file](#configuration-file) (`langgraph.json`) that specifies the dependencies, graphs, environment variables to use for the application.
+2. The [graphs](#graphs) that implement the logic of the application.
+3. A file that specifies [dependencies](#dependencies) required to run the application.
+4. [Environment variable](#environment-variables) that are required for the application to run.
+
+## File Structure
+
+Below are examples of directory structures for Python and JavaScript applications:
+
+=== "Python (requirements.txt)"
+
+    ```plaintext
+    my-app/
+    ├── my_agent # all project code lies within here
+    │   ├── utils # utilities for your graph
+    │   │   ├── __init__.py
+    │   │   ├── tools.py # tools for your graph
+    │   │   ├── nodes.py # node functions for you graph
+    │   │   └── state.py # state definition of your graph
+    │   ├── requirements.txt # package dependencies
+    │   ├── __init__.py
+    │   └── agent.py # code for constructing your graph
+    ├── .env # environment variables
+    └── langgraph.json # configuration file for LangGraph
+    ```
+=== "Python (pyproject.toml)"
+
+    ```plaintext
+    my-app/
+    ├── my_agent # all project code lies within here
+    │   ├── utils # utilities for your graph
+    │   │   ├── __init__.py
+    │   │   ├── tools.py # tools for your graph
+    │   │   ├── nodes.py # node functions for you graph
+    │   │   └── state.py # state definition of your graph
+    │   ├── __init__.py
+    │   └── agent.py # code for constructing your graph
+    ├── .env # environment variables
+    ├── langgraph.json  # configuration file for LangGraph
+    └── pyproject.toml # dependencies for your project
+    ```
+
+=== "JS (package.json)"
+
+    ```plaintext
+    my-app/
+    ├── src # all project code lies within here
+    │   ├── utils # optional utilities for your graph
+    │   │   ├── tools.ts # tools for your graph
+    │   │   ├── nodes.ts # node functions for you graph
+    │   │   └── state.ts # state definition of your graph
+    │   └── agent.ts # code for constructing your graph
+    ├── package.json # package dependencies
+    ├── .env # environment variables
+    └── langgraph.json # configuration file for LangGraph
+    ```
+
+!!! note
+
+    The directory structure of a LangGraph application can vary depending on the programming language and the package manager used.
+
+
+## Configuration File
+
+The `langgraph.json` file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy a LangGraph application.
+
+The file supports specification of the following information:
+
+
+| Key                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| `dependencies`     | **Required**. Array of dependencies for LangGraph API server. Dependencies can be one of the following: (1) `"."`, which will look for local Python packages, (2) `pyproject.toml`, `setup.py` or `requirements.txt` in the app directory `"./local_package"`, or (3) a package name.                                                                                                                                                                                                                                                        |
+| `graphs`           | **Required**. Mapping from graph ID to path where the compiled graph or a function that makes a graph is defined. Example: <ul><li>`./your_package/your_file.py:variable`, where `variable` is an instance of `langgraph.graph.state.CompiledStateGraph`</li><li>`./your_package/your_file.py:make_graph`, where `make_graph` is a function that takes a config dictionary (`langchain_core.runnables.RunnableConfig`) and creates an instance of `langgraph.graph.state.StateGraph` / `langgraph.graph.state.CompiledStateGraph`.</li></ul> |
+| `env`              | Path to `.env` file or a mapping from environment variable to its value.                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+| `python_version`   | `3.11` or `3.12`. Defaults to `3.11`.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| `pip_config_file`  | Path to `pip` config file.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+| `dockerfile_lines` | Array of additional lines to add to Dockerfile following the import from parent image.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+!!! tip
+
+    The LangGraph CLI defaults to using the configuration file **langgraph.json** in the current directory.
+
+
+### Examples
+
+=== "Python"
+
+    * The dependencies involve a custom local package and the `langchain_openai` package.
+    * A single graph will be loaded from the file `./your_package/your_file.py` with the variable `variable`.
+    * The environment variables are loaded from the `.env` file.
+
+    ```json
+    {
+        "dependencies": [
+            "langchain_openai",
+            "./your_package"
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.py:agent"
+        },
+        "env": "./.env"
+    }
+    ```
+
+=== "JavaScript"
+
+    * The dependencies will be loaded from a dependency file in the local directory (e.g., `package.json`).
+    * A single graph will be loaded from the file `./your_package/your_file.js` with the function `agent`.
+    * The environment variable `OPENAI_API_KEY` is set inline.
+
+    ```json
+    {
+        "dependencies": [
+            "."
+        ],
+        "graphs": {
+            "my_agent": "./your_package/your_file.js:agent"
+        },
+        "env": {
+            "OPENAI_API_KEY": "secret-key"
+        }
+    }
+    ```
+
+## Dependencies
+
+A LangGraph application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written).
+
+You will generally need to specify the following information for dependencies to be set up correctly:
+
+1. A file in the directory that specifies the dependencies (e.g., `requirements.txt`, `pyproject.toml`, or `package.json`).
+2. A `dependencies` key in the [LangGraph configuration file](#configuration-file) that specifies the dependencies required to run the LangGraph application.
+3. Any additional binaries or system libraries can be specified using `dockerfile_lines` key in the [LangGraph configuration file](#configuration-file).
+
+## Graphs
+
+Use the `graphs` key in the [LangGraph configuration file](#configuration-file) to specify which graphs will be available in the deployed LangGraph application.
+
+You can specify one or more graphs in the configuration file. Each graph is identified by a name (which should be unique) and a path for either: (1) the compiled graph or (2) a function that makes a graph is defined.
+
+## Environment Variables
+
+If you're working with a deployed LangGraph application locally, you can configure environment variables in the `env` key of the [LangGraph configuration file](#configuration-file).
+
+For a production deployment, you will typically want to configure the environment variables in the deployment environment.
+
+## Related
+
+Please see the following resources for more information:
+
+- How-to guides for [Application Structure](../how-tos/index.md#application-structure).

docs/docs/concepts/assistants.md

@@ -0,0 +1,37 @@
+# Assistants
+
+!!! info "Prerequisites"
+
+    - [LangGraph Server](./langgraph_server.md)
+
+When building agents, it is fairly common to make rapid changes that *do not* alter the graph logic. For example, simply changing prompts or the LLM selection can have significant impacts on the behavior of the agents. Assistants offer an easy way to make and save these types of changes to agent configuration. This can have at least two use-cases:
+
+* Assistants give developers a quick and easy way to modify and version agents for experimentation.
+* Assistants can be modified via LangGraph Studio, offering a no-code way to configure agents  (e.g., for business users). 
+
+Assistants build off the concept of ["configuration"](low_level.md#configuration). 
+While ["configuration"](low_level.md#configuration) is available in the open source LangGraph library as well,  assistants are only present in [LangGraph Platform](langgraph_platform.md).
+This is because Assistants are tightly coupled to your deployed graph, and so we can only make them available when we are also deploying the graphs.
+
+## Configuring Assistants
+
+In practice, an assistant is just an *instance* of a graph with a specific configuration. Because of this, multiple assistants can reference the same graph but can contain different configurations, such as prompts, models, and other graph configuration options. The LangGraph Cloud API provides several endpoints for creating and managing assistants. See the [API reference](../cloud/reference/api/api_ref.html) and [this how-to](../cloud/how-tos/configuration_cloud.md) for more details on how to create assistants.
+
+## Versioning Assistants
+
+Once you've created an assistant, you can save and version it to track changes to the configuration over time. You can think about this at three levels:
+
+1) The graph lays out the general agent application logic 
+2) The agent configuration options represent parameters that can be changed 
+3) Assistant versions save and track specific settings of the agent configuration options 
+
+For example, let's imagine you have a general writing agent. You have created a general graph architecture that works well for writing. However, there are different types of writing, e.g. blogs vs tweets. In order to get the best performance on each use case, you need to make some minor changes to the models and prompts used. In this setup, you could create an assistant for each use case - one for blog writing and one for tweeting. These would share the same graph structure, but they may use different models and different prompts. Read [this how-to](../cloud/how-tos/assistant_versioning.md) to learn how you can use assistant versioning through both the [Studio](../concepts/langgraph_studio.md) and the SDK.
+
+![assistant versions](img/assistants.png)
+
+
+## Resources
+
+For more information on assistants, see the following resources:
+
+- [Assistants how-to guides](../how-tos/index.md#assistants)

docs/docs/concepts/breakpoints.md

@@ -0,0 +1,132 @@
+# Breakpoints
+
+Breakpoints pause graph execution at specific points and enable stepping through execution step by step. Breakpoints are powered by LangGraph's [**persistence layer**](./persistence.md), which saves the state after each graph step. Breakpoints can also be used to enable [**human-in-the-loop**](./human_in_the_loop.md) workflows, though we recommend using the [`interrupt` function](./human_in_the_loop.md#interrupt) for this purpose.
+
+## Requirements
+
+To use breakpoints, you will need to:
+
+1. [**Specify a checkpointer**](persistence.md#checkpoints) to save the graph state after each step.
+2. [**Set breakpoints**](#setting-breakpoints) to specify where execution should pause.
+3. **Run the graph** with a [**thread ID**](./persistence.md#threads) to pause execution at the breakpoint.
+4. **Resume execution** using `invoke`/`ainvoke`/`stream`/`astream` (see [**The `Command` primitive**](./human_in_the_loop.md#the-command-primitive)).
+
+## Setting breakpoints
+
+There are two places where you can set breakpoints:
+
+1. **Before** or **after** a node executes by setting breakpoints at **compile time** or **run time**. We call these [**static breakpoints**](#static-breakpoints).
+2. **Inside** a node using the [`NodeInterrupt` exception](#nodeinterrupt-exception).
+ 
+### Static breakpoints
+
+Static breakpoints are triggered either **before** or **after** a node executes. You can set static breakpoints by specifying `interrupt_before` and `interrupt_after` at **"compile" time** or **run time**.
+
+=== "Compile time"
+
+    ```python
+    graph = graph_builder.compile(
+        interrupt_before=["node_a"], 
+        interrupt_after=["node_b", "node_c"],
+        checkpointer=..., # Specify a checkpointer
+    )
+
+    thread_config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config)
+
+    # Optionally update the graph state based on user input
+    graph.update_state(update, config=thread_config)
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config)
+    ```
+
+=== "Run time"
+
+    ```python
+    graph.invoke(
+        inputs, 
+        config={"configurable": {"thread_id": "some_thread"}}, 
+        interrupt_before=["node_a"], 
+        interrupt_after=["node_b", "node_c"]
+    )
+
+    thread_config = {
+        "configurable": {
+            "thread_id": "some_thread"
+        }
+    }
+
+    # Run the graph until the breakpoint
+    graph.invoke(inputs, config=thread_config)
+
+    # Optionally update the graph state based on user input
+    graph.update_state(update, config=thread_config)
+
+    # Resume the graph
+    graph.invoke(None, config=thread_config)
+    ```
+
+    !!! note
+
+        You cannot set static breakpoints at runtime for **sub-graphs**.
+        If you have a sub-graph, you must set the breakpoints at compilation time.
+
+Static breakpoints can be especially useful for debugging if you want to step through the graph execution one
+node at a time or if you want to pause the graph execution at specific nodes.
+
+### `NodeInterrupt` exception
+
+We recommend that you [**use the `interrupt` function instead**](#the-interrupt-function) of the `NodeInterrupt` exception if you're trying to implement
+[human-in-the-loop](./human_in_the_loop.md) workflows. The `interrupt` function is easier to use and more flexible.
+
+??? node "`NodeInterrupt` exception"
+
+    The developer can define some *condition* that must be met for a breakpoint to be triggered. This concept of [dynamic breakpoints](./low_level.md#dynamic-breakpoints) is useful when the developer wants to halt the graph under *a particular condition*. This uses a `NodeInterrupt`, which is a special type of exception that can be raised from within a node based upon some condition. As an example, we can define a dynamic breakpoint that triggers when the `input` is longer than 5 characters.
+
+    ```python
+    def my_node(state: State) -> State:
+        if len(state['input']) > 5:
+            raise NodeInterrupt(f"Received input that is longer than 5 characters: {state['input']}")
+
+        return state
+    ```
+
+
+    Let's assume we run the graph with an input that triggers the dynamic breakpoint and then attempt to resume the graph execution simply by passing in `None` for the input.
+
+    ```python
+    # Attempt to continue the graph execution with no change to state after we hit the dynamic breakpoint 
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+    The graph will *interrupt* again because this node will be *re-run* with the same graph state. We need to change the graph state such that the condition that triggers the dynamic breakpoint is no longer met. So, we can simply edit the graph state to an input that meets the condition of our dynamic breakpoint (< 5 characters) and re-run the node.
+
+    ```python 
+    # Update the state to pass the dynamic breakpoint
+    graph.update_state(config=thread_config, values={"input": "foo"})
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+    Alternatively, what if we want to keep our current input and skip the node (`my_node`) that performs the check? To do this, we can simply perform the graph update with `as_node="my_node"` and pass in `None` for the values. This will make no update the graph state, but run the update as `my_node`, effectively skipping the node and bypassing the dynamic breakpoint.
+
+    ```python
+    # This update will skip the node `my_node` altogether
+    graph.update_state(config=thread_config, values=None, as_node="my_node")
+    for event in graph.stream(None, thread_config, stream_mode="values"):
+        print(event)
+    ```
+
+## Additional Resources 📚
+
+- [**Conceptual Guide: Persistence**](persistence.md): Read the persistence guide for more context about persistence.
+- [**Conceptual Guide: Human-in-the-loop**](human_in_the_loop.md): Read the human-in-the-loop guide for more context on integrating human feedback into LangGraph applications using breakpoints.
+- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.

docs/docs/concepts/bring_your_own_cloud.md

@@ -0,0 +1,54 @@
+# Bring Your Own Cloud (BYOC)
+
+!!! note Prerequisites
+
+    - [LangGraph Platform](./langgraph_platform.md)
+    - [Deployment Options](./deployment_options.md)
+
+## Architecture
+
+Split control plane (hosted by us) and data plane (hosted by you, managed by us).
+
+|                             | Control Plane                   | Data Plane                                    |
+|-----------------------------|---------------------------------|-----------------------------------------------|
+| What it does                | Manages deployments, revisions. | Runs your LangGraph graphs, stores your data. |
+| Where it is hosted          | LangChain Cloud account         | Your cloud account                            |
+| Who provisions and monitors | LangChain                       | LangChain                                     |
+
+LangChain has no direct access to the resources created in your cloud account, and can only interact with them via AWS APIs. Your data never leaves your cloud account / VPC at rest or in transit.
+
+![Architecture](img/byoc_architecture.png)
+
+## Requirements
+
+- You’re using AWS already.
+- You use `langgraph-cli` and/or [LangGraph Studio](./langgraph_studio.md) app to test graph locally.
+- You use `langgraph build` command to build image and then push it to your AWS ECR repository (`docker push`).
+
+## How it works
+
+- We provide you a [Terraform module](https://github.com/langchain-ai/terraform/tree/main/modules/langgraph_cloud_setup) which you run to set up our requirements
+    1. Creates an AWS role (which our control plane will later assume to provision and monitor resources)
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonVPCReadOnlyAccess.html
+            - Read VPCS to find subnets
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonECS_FullAccess.html
+            - Used to create/delete ECS resources for your LangGraph Cloud instances
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/SecretsManagerReadWrite.html
+            - Create secrets for your ECS resources
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/CloudWatchReadOnlyAccess.html
+            - Read CloudWatch metrics/logs to monitor your instances/push deployment logs
+        - https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonRDSFullAccess.html
+            - Provision `RDS` instances for your LangGraph Cloud instances
+    2. Either
+        - Tags an existing vpc / subnets as `langgraph-cloud-enabled`
+        - Creates a new vpc and subnets and tags them as `langgraph-cloud-enabled`
+- You create a LangGraph Cloud Project in `smith.langchain.com` providing
+    - the ID of the AWS role created in the step above
+    - the AWS ECR repo to pull the service image from
+- We provision the resources in your cloud account using the role above
+- We monitor those resources to ensure uptime and recovery from errors
+
+Notes for customers using [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting):
+
+- Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
+- You can however set up the project to trace to your self-hosted LangSmith instance if desired