feat: CLI: Add __main__.py (#4637)

Main changes made:
- Add top level horizontal tabs
- Reorganize the sidenav
- Build out README/index page
- Consolidate how-tos under each section
- Remove duplicate content

---------

Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Tat Dat Duong <david@duong.cz>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Andrew Nguonly <andrewnguonly@users.noreply.github.com>
Co-authored-by: David Asamu <david.asamu@langchain.dev>
Co-authored-by: infra <mukil@langchain.dev>
Co-authored-by: Arjun Natarajan <arjun@langchain.dev>

.github/workflows/deploy_docs.yml

@@ -38,12 +38,13 @@ jobs:
         with:
           filter: "docs/docs/**"
 
-  run-changed-notebooks:
-    needs: get-changed-files
-    uses: ./.github/workflows/run_notebooks.yml
-    secrets: inherit
-    with:
-      changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
+  # TODO: Uncomment this to run on PRs
+  # run-changed-notebooks:
+  #   needs: get-changed-files
+  #   uses: ./.github/workflows/run_notebooks.yml
+  #   secrets: inherit
+  #   with:
+  #     changed-files: ${{ needs.get-changed-files.outputs.changed-files }}
 
   deploy:
     # needs: run-changed-notebooks
@@ -98,6 +99,7 @@ jobs:
       - name: Check links in notebooks
         env:
           LANGCHAIN_API_KEY: test
+        if: github.event_name == 'schedule'
         run: |
           if [ "${{ github.event_name }}" == "schedule" ]; then
             echo "Running link check on all HTML files matching notebooks in docs directory..."

.gitignore

@@ -180,3 +180,4 @@ Chinook.db
 
 .vercel
 .turbo
+.editorconfig

README.md

@@ -16,48 +16,41 @@
 > [!NOTE]
 > Looking for the JS version? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://langchain-ai.github.io/langgraphjs/).
 
-LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
+LangGraph is a low-level orchestration framework for building controllable agents. While [LangChain](https://python.langchain.com/docs/introduction/) provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
 
-```bash
+## Get started
+
+First, install LangGraph:
+
+```
 pip install -U langgraph
 ```
 
-To learn more about how to use LangGraph, check out [the docs](https://langchain-ai.github.io/langgraph/). We show a simple example below of how to create a ReAct agent.
+There are two ways to get started with LangGraph:
 
-```python
-# This code depends on pip install langchain[anthropic]
-from langgraph.prebuilt import create_react_agent
+- [Use prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): Construct agentic systems quickly and reliably without the need to implement orchestration, memory, or human feedback handling from scratch.
+- [Use LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/): Customize your architectures, use long-term memory, and implement human-in-the-loop to reliably handle complex tasks.
 
-def search(query: str):
-    """Call to surf the web."""
-    if "sf" in query.lower() or "san francisco" in query.lower():
-        return "It's 60 degrees and foggy."
-    return "It's 90 degrees and sunny."
+Once you have a LangGraph application and are ready to move into production, use [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/quick_start/) to test, debug, and deploy your application.
 
-agent = create_react_agent("anthropic:claude-3-7-sonnet-latest", tools=[search])
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
-```
+## What LangGraph provides
 
-> [!TIP]
-> Check out [this guide](https://langchain-ai.github.io/langgraph/tutorials/workflows/) that walks through implementing common patterns (workflows and agents) in LangGraph. 
+LangGraph provides low-level supporting infrastructure that sits underneath *any* workflow or agent. It does not abstract prompts or architecture, and provides three central benefits:
 
-## Why use LangGraph?
+### Persistence
 
-LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:
+LangGraph has a [persistence layer](https://langchain-ai.github.io/langgraph/concepts/persistence/), which offers a number of benefits:
 
-- **Reliability and controllability.** Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
-- **Low-level and extensible.** Build custom agents with fully descriptive, low-level primitives – free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
-- **First-class streaming support.** With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
+- [Memory](https://langchain-ai.github.io/langgraph/concepts/memory/): LangGraph persists arbitrary aspects of your application's state, supporting memory of conversations and other updates within and across user interactions;
+- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Because state is checkpointed, execution can be interrupted and resumed, allowing for decisions, validation, and corrections via human input.
 
-LangGraph is trusted in production and powering agents for companies like:
+### Streaming
 
-- [Klarna](https://blog.langchain.dev/customers-klarna/): Customer support bot for 85 million active users
-- [Elastic](https://www.elastic.co/blog/elastic-security-generative-ai-features): Security AI assistant for threat detection
-- [Uber](https://dpe.org/sessions/ty-smith-adam-huda/this-year-in-ubers-ai-driven-developer-productivity-revolution/): Automated unit test generation
-- [Replit](https://www.langchain.com/breakoutagents/replit): Code generation
-- And many more ([see list here](https://www.langchain.com/built-with-langgraph))
+LangGraph provides support for [streaming](https://langchain-ai.github.io/langgraph/concepts/streaming/) workflow / agent state to the user (or developer) over the course of execution. LangGraph supports streaming of both events ([such as feedback from a tool call](https://langchain-ai.github.io/langgraph/how-tos/streaming.ipynb#updates)) and [tokens from LLM calls](https://langchain-ai.github.io/langgraph/how-tos/streaming-tokens.ipynb) embedded in an application.
+
+### Debugging and deployment
+
+LangGraph provides an easy onramp for testing, debugging, and deploying applications via [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/). This includes [Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/), an IDE that enables visualization, interaction, and debugging of workflows or agents. This also includes numerous [options](https://langchain-ai.github.io/langgraph/tutorials/deployment/) for deployment. 
 
 ## LangGraph’s ecosystem
 
@@ -66,24 +59,14 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L
 - [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
 - [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
 
-## Pairing with LangGraph Platform
-
-While LangGraph is our open-source agent orchestration framework, enterprises that need scalable agent deployment can benefit from [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/langgraph_platform/).
-
-LangGraph Platform can help engineering teams:
-
-- **Accelerate agent development**: Quickly create agent UXs with configurable templates and [LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/) for visualizing and debugging agent interactions.
-- **Deploy seamlessly**: We handle the complexity of deploying your agent. LangGraph Platform includes robust APIs for memory, threads, and cron jobs plus auto-scaling task queues & servers.
-- **Centralize agent management & reusability**: Discover, reuse, and manage agents across the organization. Business users can also modify agents without coding.
-
 ## Additional resources
 
+- [Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
+- [Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
+- [Examples](https://langchain-ai.github.io/langgraph/tutorials/): Guided examples on getting started with LangGraph.
 - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
-- [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Simple walkthroughs with guided examples on getting started with LangGraph.
 - [Templates](https://langchain-ai.github.io/langgraph/concepts/template_applications/): Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
-- [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
-- [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
-- [Built with LangGraph](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
+- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship powerful, production-ready AI applications.
 
 ## Acknowledgements
 

docs/_scripts/generate_api_reference_links.py

@@ -277,7 +277,7 @@ def update_markdown_with_imports(markdown: str, path: str) -> str:
             f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
         )
         # Return the code block with prepended API reference links
-        return f"{indent}API Reference: {api_links}\n\n{original_code_block}"
+        return f"{indent}<sup><i>API Reference: {api_links}</i></sup>\n\n{original_code_block}"
 
     # Apply the replace_code_block function to all matches in the markdown
     updated_markdown = code_block_pattern.sub(replace_code_block, markdown)

docs/_scripts/generate_llms_text.py

@@ -20,15 +20,8 @@ def _make_llms_text(output_file: str) -> str:
         output_file: Path to output the consolidated text file
     """
     # Collect all markdown and notebook files
-    relative_paths = [
-        # Files relative to docs/docs/
-        "tutorials/introduction.ipynb",
-    ]
-    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
+    all_files = glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
 
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
-    )
     all_files.extend(
         glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
     )

docs/_scripts/notebook_convert.py

@@ -220,6 +220,24 @@ def _convert_links_in_markdown(markdown: str) -> str:
     )
 
 
+class HideCellTagPreprocessor(Preprocessor):
+    """
+    Removes cells that have '# hide-cell' at the beginning of the cell content.
+    This allows authors to include cells in the notebook that should not
+    appear in the generated markdown output.
+    """
+
+    def preprocess(self, nb, resources):
+        # Filter out cells with the '# hide-cell' comment at the beginning
+        nb.cells = [
+            cell
+            for cell in nb.cells
+            if not (cell.source.strip().startswith("# hide-cell"))
+        ]
+
+        return nb, resources
+
+
 class EscapePreprocessor(Preprocessor):
     def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
         super().__init__(**kwargs)
@@ -341,6 +359,7 @@ class ExtractAttachmentsPreprocessor(Preprocessor):
 
 exporter = MarkdownExporter(
     preprocessors=[
+        HideCellTagPreprocessor,
         EscapePreprocessor,
         ExtractAttachmentsPreprocessor,
     ],

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -38,9 +38,13 @@
 {%- endblock data_html -%}
 
 {%- block data_jpg scoped -%}
-![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
+<p>
+<img src="data:image/jpg;base64,{{ output.data['image/jpeg'] }}" />
+</p>
 {%- endblock data_jpg -%}
 
 {%- block data_png scoped -%}
-![](data:image/png;base64,{{ output.data['image/png'] }})
+<p>
+<img src="data:image/png;base64,{{ output.data['image/png'] }}" />
+</p>
 {%- endblock data_png -%}

docs/_scripts/notebook_hooks.py

@@ -18,28 +18,84 @@ DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
 
 REDIRECT_MAP = {
     # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
+    "how-tos/stream-values.ipynb": "how-tos/streaming.md#stream-graph-state",
+    "how-tos/stream-updates.ipynb": "how-tos/streaming.md#stream-graph-state",
+    "how-tos/streaming-content.ipynb": "how-tos/streaming.md",
+    "how-tos/stream-multiple.ipynb": "how-tos/streaming.md#stream-multiple-nodes",
+    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming.md#use-with-any-llm",
     "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
     "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
+    # graph-api
+    "how-tos/state-reducers.ipynb": "how-tos/graph-api#define-and-update-state",
+    "how-tos/sequence.ipynb": "how-tos/graph-api#create-a-sequence-of-steps",
+    "how-tos/branching.ipynb": "how-tos/graph-api#create-branches",
+    "how-tos/recursion-limit.ipynb": "how-tos/graph-api#create-and-control-loops",
+    "how-tos/visualization.ipynb": "how-tos/graph-api#visualize-your-graph",
+    "how-tos/input_output_schema.ipynb": "how-tos/graph-api#define-input-and-output-schemas",
+    "how-tos/pass_private_state.ipynb": "how-tos/graph-api#pass-private-state-between-nodes",
+    "how-tos/state-model.ipynb": "how-tos/graph-api#use-pydantic-models-for-graph-state",
+    "how-tos/map-reduce.ipynb": "how-tos/graph-api/#map-reduce-and-the-send-api",
+    "how-tos/command.ipynb": "how-tos/graph-api/#combine-control-flow-and-state-updates-with-command",
+    "how-tos/configuration.ipynb": "how-tos/graph-api/#add-runtime-configuration",
+    "how-tos/node-retries.ipynb": "how-tos/graph-api/#add-retry-policies",
+    "how-tos/return-when-recursion-limit-hits.ipynb": "how-tos/graph-api/#impose-a-recursion-limit",
+    # memory how-tos
+    "how-tos/memory/manage-conversation-history.ipynb": "how-tos/memory.ipynb",
+    "how-tos/memory/delete-messages.ipynb": "how-tos/memory.ipynb#delete-messages",
+    "how-tos/memory/add-summary-conversation-history.ipynb": "how-tos/memory.ipynb#summarize-messages",
+    # subgraph how-tos
+    "how-tos/subgraph-transform-state.ipynb": "how-tos/subgraph.ipynb#different-state-schemas",
+    "how-tos/subgraphs-manage-state.ipynb": "how-tos/subgraph.ipynb#add-persistence",
+    # persistence how-tos
+    "how-tos/persistence_postgres.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_mongodb.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/persistence_redis.ipynb": "how-tos/persistence.ipynb#use-in-production",
+    "how-tos/subgraph-persistence.ipynb": "how-tos/persistence.ipynb#use-with-subgraphs",
+    "how-tos/cross-thread-persistence.ipynb": "how-tos/persistence.ipynb#add-long-term-memory",
+    # tool calling how-tos
+    "how-tos/tool-calling-errors.ipynb": "how-tos/tool-calling.ipynb#handle-errors",
+    "how-tos/pass-config-to-tools.ipynb": "how-tos/tool-calling.ipynb#access-config",
+    "how-tos/pass-run-time-values-to-tools.ipynb": "how-tos/tool-calling.ipynb#read-state",
+    "how-tos/update-state-from-tools.ipynb": "how-tos/tool-calling.ipynb#update-state",
+    # multi-agent how-tos
+    "how-tos/agent-handoffs.ipynb": "how-tos/multi_agent.ipynb#handoffs",
+    "how-tos/multi-agent-network.ipynb": "how-tos/multi_agent.ipynb#use-in-a-multi-agent-system",
+    "how-tos/multi-agent-multi-turn-convo.ipynb": "how-tos/multi_agent.ipynb#multi-turn-conversation",
     # cloud redirects
-    "cloud/index.md": "concepts/index.md#langgraph-platform",
-    "cloud/how-tos/index.md": "how-tos/index.md#langgraph-platform",
+    "cloud/index.md": "index.md",
+    "cloud/how-tos/index.md": "concepts/langgraph_platform",
     "cloud/concepts/api.md": "concepts/langgraph_server.md",
     "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
     "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
+    # cloud streaming redirects
+    "cloud/how-tos/stream_values.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_updates.md": "cloud/how-tos/streaming.md#stream-graph-state",
+    "cloud/how-tos/stream_messages.md": "cloud/how-tos/streaming.md#messages",
+    "cloud/how-tos/stream_events.md": "cloud/how-tos/streaming.md#stream-events",
+    "cloud/how-tos/stream_debug.md": "cloud/how-tos/streaming.md#debug",
+    "cloud/how-tos/stream_multiple.md": "cloud/how-tos/streaming.md#stream-multiple-modes",
     # prebuit redirects
     "how-tos/create-react-agent.ipynb": "agents/agents.md#basic-configuration",
     "how-tos/create-react-agent-memory.ipynb": "agents/memory.md",
     "how-tos/create-react-agent-system-prompt.ipynb": "agents/context.md#prompts",
     "how-tos/create-react-agent-hitl.ipynb": "agents/human-in-the-loop.md",
     "how-tos/create-react-agent-structured-output.ipynb": "agents/agents.md#structured-output",
+    # Time-travel
+    "how-tos/human_in_the_loop/edit-graph-state.ipynb": "how-tos/human_in_the_loop/time-travel.ipynb",
+    # breakpoints
+    "how-tos/human_in_the_loop/dynamic_breakpoints.ipynb": "how-tos/human_in_the_loop/breakpoints.ipynb",
     # misc
     "prebuilt.md": "agents/prebuilt.md",
-    "reference/prebuilt.md": "reference/agents.md"
+    "reference/prebuilt.md": "reference/agents.md",
+    "concepts/high_level.md": "index.md",
+    "concepts/index.md": "index.md",
+    "concepts/v0-human-in-the-loop.md": "concepts/human-in-the-loop.md",
+    "how-tos/index.md": "index.md",
+    "tutorials/introduction.ipynb": "concepts/why-langgraph.md",
+    # deployment redirects
+    "how-tos/deploy-self-hosted.md": "cloud/deployment/self_hosted_data_plane.md",
+    "concepts/self_hosted.md": "concepts/langgraph_self_hosted_data_plane.md"
+
 }
 
 

docs/cassettes/add-summary-conversation-history_048805a4-3d97-4e76-ac45-8d80d4364c46.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/add-summary-conversation-history_40e5db8e-9db9-4ac7-9d76-a99fd4034bf3.msgpack.zlib

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docs/cassettes/add-summary-conversation-history_57b27553-21be-43e5-ac48-d1d0a3aa0dca.msgpack.zlib

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docs/cassettes/agent_supervisor_f803943c-6dd2-4b42-93ba-0210ec276132.msgpack.zlib

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docs/cassettes/delete-messages_57b27553-21be-43e5-ac48-d1d0a3aa0dca.msgpack.zlib

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

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docs/cassettes/multi-agent-network_68a547d4-0a15-43bd-aeed-c9ba1dfe388f.msgpack.zlib

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docs/cassettes/pass-config-to-tools_14.msgpack.zlib

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docs/cassettes/pass-config-to-tools_15.msgpack.zlib

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docs/cassettes/pass-config-to-tools_16.msgpack.zlib

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docs/cassettes/pass-run-time-values-to-tools_2e3fd1e2-cc19-4023-8ffa-b0fc13da9e09.msgpack.zlib

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docs/cassettes/persistence_postgres_180d6daf-8fa7-4608-bd2e-bfbf44ed5836.msgpack.zlib

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docs/cassettes/persistence_postgres_386b78bc-2f73-49ba-a2a4-47bce6fc49b7.msgpack.zlib

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docs/cassettes/persistence_postgres_5fe54e79-9eaf-44e2-b2d9-1e0284b984d0.msgpack.zlib

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docs/cassettes/persistence_redis_5fe54e79-9eaf-44e2-b2d9-1e0284b984d0.msgpack.zlib

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docs/cassettes/persistence_redis_6a39d1ff-ca37-4457-8b52-07d33b59c36e.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_12.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_14.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_16.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_31.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_34.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_41.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_43.msgpack.zlib

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docs/cassettes/subgraphs-manage-state_49.msgpack.zlib

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docs/cassettes/tool-calling-errors_11.msgpack.zlib

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docs/cassettes/tool-calling-errors_19.msgpack.zlib

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docs/cassettes/tool-calling_17.msgpack.zlib

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docs/cassettes/tool-calling_19.msgpack.zlib

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docs/cassettes/tool-calling_25.msgpack.zlib

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docs/cassettes/tool-calling_26.msgpack.zlib

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docs/cassettes/update-state-from-tools_9d71af94-572a-4961-88a7-665e792cf96a.msgpack.zlib

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docs/cassettes/update-state-from-tools_de34a58b-1765-4b63-a232-d46790aff884.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/docs/adopters.md

@@ -1,4 +1,4 @@
-# 🦜🕸️ Companies using LangGraph
+# 🦜🕸️ Case studies
 
 This list of companies using LangGraph and their success stories is compiled from public sources. If your company uses LangGraph, we'd love for you to share your story and add it to the list. You’re also welcome to contribute updates based on publicly available information from other companies, such as blog posts or press releases.
 

docs/docs/agents/agents.md

@@ -7,22 +7,31 @@ hide:
   - tags
 ---
 
-# Agents
+# LangGraph quickstart
 
-## What is an agent?
+This guide shows you how to set up and use LangGraph's **prebuilt**, **reusable** components, which are designed to help you construct agentic systems quickly and reliably.
 
-An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
+## Prerequisites
 
-The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
+Before you start this tutorial, ensure you have the following:
 
-<figure markdown="1">
-![image](./assets/agent.png){: style="max-height:400px"}
-<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
-</figure>
+- An [Anthropic](https://console.anthropic.com/settings/admin-keys) API key 
 
-## Basic configuration
+## 1. Install dependencies
 
-Use [`create_react_agent`][langgraph.prebuilt.chat_agent_executor.create_react_agent] to instantiate an agent:
+If you haven't already, install LangGraph and LangChain:
+
+```
+pip install -U langgraph "langchain[anthropic]"
+```
+
+!!! info 
+
+    LangChain is installed so the agent can call the [model](https://python.langchain.com/docs/integrations/chat/).
+
+## 2. Create an agent
+
+To create an agent, use [`create_react_agent`](langgraph.prebuilt.chat_agent_executor.create_react_agent):
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -48,10 +57,9 @@ agent.invoke(
 3. Provide a list of tools for the model to use.
 4. Provide a system prompt (instructions) to the language model used by the agent.
 
-## LLM configuration
+## 3. Configure an LLM
 
-Use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) to configure an LLM with specific parameters,
-such as temperature:
+To configure an LLM with specific parameters, such as temperature, use [init_chat_model](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html):
 
 ```python
 from langchain.chat_models import init_chat_model
@@ -71,77 +79,77 @@ agent = create_react_agent(
 )
 ```
 
-See the [models](./models.md) page for more information on how to configure LLMs.
+For more information on how to configure LLMs, see [Models](./models.md).
 
-## Custom Prompts
+## 4. Add a custom prompt
 
-Prompts instruct the LLM how to behave. They can be:
+Prompts instruct the LLM how to behave. Add one of the following types of prompts:
 
-* **Static**: A string is interpreted as a **system message**
-* **Dynamic**: a list of messages generated at **runtime** based on input or configuration
+* **Static**: A string is interpreted as a **system message**.
+* **Dynamic**: A list of messages generated at **runtime**, based on input or configuration.
 
-### Static prompts
+=== "Static prompt"
 
-Define a fixed prompt string or list of messages.
+    Define a fixed prompt string or list of messages:
 
-```python
-from langgraph.prebuilt import create_react_agent
+    ```python
+    from langgraph.prebuilt import create_react_agent
+
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # A static prompt that never changes
+        # highlight-next-line
+        prompt="Never answer questions about the weather."
+    )
+
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
+    )
+    ```
+
+=== "Dynamic prompt"
+
+    Define a function that returns a message list based on the agent's state and configuration:
+
+    ```python
+    from langchain_core.messages import AnyMessage
+    from langchain_core.runnables import RunnableConfig
+    from langgraph.prebuilt.chat_agent_executor import AgentState
+    from langgraph.prebuilt import create_react_agent
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # A static prompt that never changes
     # highlight-next-line
-    prompt="Never answer questions about the weather."
-)
+    def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
+        user_name = config["configurable"].get("user_name")
+        system_msg = f"You are a helpful assistant. Address the user as {user_name}."
+        return [{"role": "system", "content": system_msg}] + state["messages"]
 
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
-)
-```
+    agent = create_react_agent(
+        model="anthropic:claude-3-7-sonnet-latest",
+        tools=[get_weather],
+        # highlight-next-line
+        prompt=prompt
+    )
 
-### Dynamic prompts
+    agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
+        # highlight-next-line
+        config={"configurable": {"user_name": "John Smith"}}
+    )
+    ```
 
-Define a function that returns a message list based on the agent's state and configuration:
+    1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
 
-```python
-from langchain_core.messages import AnyMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.prebuilt.chat_agent_executor import AgentState
-from langgraph.prebuilt import create_react_agent
+        - Information passed at runtime, like a `user_id` or API credentials (using `config`).
+        - Internal agent state updated during a multi-step reasoning process (using `state`).
 
-# highlight-next-line
-def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]:  # (1)!
-    user_name = config["configurable"].get("user_name")
-    system_msg = f"You are a helpful assistant. Address the user as {user_name}."
-    return [{"role": "system", "content": system_msg}] + state["messages"]
+        Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
 
-agent = create_react_agent(
-    model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_weather],
-    # highlight-next-line
-    prompt=prompt
-)
+For more information, see [Context](./context.md).
 
-agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-    # highlight-next-line
-    config={"configurable": {"user_name": "John Smith"}}
-)
-```
+## 5. Add memory
 
-1. Dynamic prompts allow including non-message [context](./context.md) when constructing an input to the LLM, such as:
-
-    - Information passed at runtime, like a `user_id` or API credentials (using `config`).
-    - Internal agent state updated during a multi-step reasoning process (using `state`).
-
-    Dynamic prompts can be defined as functions that take `state` and `config` and return a list of messages to send to the LLM.
-
-See the [context](./context.md) page for more information.
-
-## Memory
-
-To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
+To allow multi-turn conversations with an agent, you need to enable [persistence](../concepts/persistence.md) by providing a `checkpointer` when creating an agent. At runtime, you need to provide a config containing `thread_id` — a unique identifier for the conversation (session):
 
 ```python
 from langgraph.prebuilt import create_react_agent
@@ -179,10 +187,9 @@ When you enable the checkpointer, it stores agent state at every step in the pro
 
 Note that in the above example, when the agent is invoked the second time with the same `thread_id`, the original message history from the first conversation is automatically included, together with the new user input.
 
-Please see the [memory guide](./memory.md) for more details on how to work with memory.
+For more information, see [Memory](./memory.md).
 
-
-## Structured output
+## 6. Configure structured output
 
 To produce structured responses conforming to a schema, use the `response_format` parameter. The schema can be defined with a `Pydantic` model or `TypedDict`. The result will be accessible via the `structured_response` field.
 
@@ -216,3 +223,8 @@ response["structured_response"]
 
     Structured output requires an additional call to the LLM to format the response according to the schema.
 
+## Next steps
+
+- [Deploy your agent locally](../tutorials/langgraph-platform/local-server.md)
+- [Learn more about prebuilt agents](../agents/overview.md)
+- [LangGraph Platform quickstart](../cloud/quick_start.md)

docs/docs/agents/human-in-the-loop.md

@@ -126,7 +126,7 @@ def add_human_in_the_loop(
     *,
     interrupt_config: HumanInterruptConfig = None,
 ) -> BaseTool:
-   """Wrap a tool to support human-in-the-loop review.""" 
+    """Wrap a tool to support human-in-the-loop review.""" 
     if not isinstance(tool, BaseTool):
         tool = create_tool(tool)
 
@@ -235,4 +235,4 @@ for chunk in agent.stream(
 
 ## Additional resources
 
-* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)
+* [Human-in-the-loop in LangGraph](../concepts/human_in_the_loop.md)

docs/docs/agents/memory.md

@@ -282,7 +282,7 @@ def greet(
 
 agent = create_react_agent(
     model="anthropic:claude-3-7-sonnet-latest",
-    tools=[get_user_info, greet],
+    tools=[update_user_info, greet],
     # highlight-next-line
     state_schema=CustomState
 )
@@ -420,4 +420,4 @@ LangGraph also allows you to [search](https://langchain-ai.github.io/langgraph/h
 
 ## Additional resources
 
-* [Memory in LangGraph](../concepts/memory.md)
+* [Memory in LangGraph](../concepts/memory.md)

docs/docs/agents/overview.md

@@ -12,6 +12,17 @@ hide:
 
 **LangGraph** provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the **prebuilt**, **reusable** components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback handling from scratch.
 
+## What is an agent?
+
+An *agent* consists of three components: a **large language model (LLM)**, a set of **tools** it can use, and a **prompt** that provides instructions.
+
+The LLM operates in a loop. In each iteration, it selects a tool to invoke, provides input, receives the result (an observation), and uses that observation to inform the next action. The loop continues until a stopping condition is met — typically when the agent has gathered enough information to respond to the user.
+
+<figure markdown="1">
+![image](./assets/agent.png){: style="max-height:400px"}
+<figcaption>Agent loop: the LLM selects tools and uses their outputs to fulfill a user request.</figcaption>
+</figure>
+
 ## Key features
 
 LangGraph includes several capabilities essential for building robust, production-ready agentic systems:

docs/docs/agents/prebuilt.md

@@ -1,14 +1,44 @@
----
-tags:
-  - agent
-hide:
-  - tags
----
-
+[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
 # Community Agents
 
+If you’re looking for other prebuilt libraries, explore the community-built options 
+below. These libraries can extend LangGraph's functionality in various ways.
+
+## 📚 Available Libraries
+
+[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
+| Name | GitHub URL | Description | Weekly Downloads | Stars |
+| --- | --- | --- | --- | --- |
+| **trustcall** | [hinthornw/trustcall](https://github.com/hinthornw/trustcall) | Tenacious tool calling built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/hinthornw/trustcall?style=social)
+| **breeze-agent** | [andrestorres123/breeze-agent](https://github.com/andrestorres123/breeze-agent) | A streamlined research system built inspired on STORM and built on LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/breeze-agent?style=social)
+| **langgraph-supervisor** | [langchain-ai/langgraph-supervisor-py](https://github.com/langchain-ai/langgraph-supervisor-py) | Build supervisor multi-agent systems with LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-supervisor-py?style=social)
+| **langmem** | [langchain-ai/langmem](https://github.com/langchain-ai/langmem) | Build agents that learn and adapt from interactions over time. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langmem?style=social)
+| **langchain-mcp-adapters** | [langchain-ai/langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters) | Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langchain-mcp-adapters?style=social)
+| **open-deep-research** | [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research) | Open source assistant for iterative web research and report writing. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/open_deep_research?style=social)
+| **langgraph-swarm** | [langchain-ai/langgraph-swarm-py](https://github.com/langchain-ai/langgraph-swarm-py) | Build swarm-style multi-agent systems using LangGraph. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-swarm-py?style=social)
+| **delve-taxonomy-generator** | [andrestorres123/delve](https://github.com/andrestorres123/delve) | A taxonomy generator for unstructured data | -12345 | ![GitHub stars](https://img.shields.io/github/stars/andrestorres123/delve?style=social)
+| **nodeology** | [xyin-anl/Nodeology](https://github.com/xyin-anl/Nodeology) | Enable researcher to build scientific workflows easily with simplified interface. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/xyin-anl/Nodeology?style=social)
+| **langgraph-bigtool** | [langchain-ai/langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool) | Build LangGraph agents with large numbers of tools. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-bigtool?style=social)
+| **ai-data-science-team** | [business-science/ai-data-science-team](https://github.com/business-science/ai-data-science-team) | An AI-powered data science team of agents to help you perform common data science tasks 10X faster. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/business-science/ai-data-science-team?style=social)
+| **langgraph-reflection** | [langchain-ai/langgraph-reflection](https://github.com/langchain-ai/langgraph-reflection) | LangGraph agent that runs a reflection step. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-reflection?style=social)
+| **langgraph-codeact** | [langchain-ai/langgraph-codeact](https://github.com/langchain-ai/langgraph-codeact) | LangGraph implementation of CodeAct agent that generates and executes code instead of tool calling. | -12345 | ![GitHub stars](https://img.shields.io/github/stars/langchain-ai/langgraph-codeact?style=social)
+
+## ✨ Contributing Your Library
+
+Have you built an awesome open-source library using LangGraph? We'd love to feature 
+your project on the official LangGraph documentation pages! 🏆
+
 To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml](https://github.com/langchain-ai/langgraph/blob/main/docs/_scripts/third_party_page/packages.yml) file.
 
-[//]: # (This file is stub. Do not edit this file directly!)
-[//]: # (1. Update the `packages.yml` file in the `docs/_scripts/third_party_page` directory.)
-[//]: # (2. From the /docs directory, run `make build-prebuilt` to generate an updated version of this file for testing locally.)
+**Guidelines**
+
+- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
+  for JavaScript/TypeScript, etc.) 📦
+- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
+  the Functional API (exposing an `entrypoint`).
+- The package must include documentation (e.g., a `README.md` or docs site) 
+  explaining how to use it.
+  
+We'll review your contribution and merge it in!
+
+Thanks for contributing! 🚀

docs/docs/agents/run_agents.md

@@ -72,7 +72,7 @@ more about [LangChain messages](https://python.langchain.com/docs/concepts/messa
 Agent output is a dictionary containing:
 
 - `messages`: A list of all messages exchanged during execution (user input, assistant replies, tool invocations).
-- Optionally, `structured_response` if [structured output](./agents.md#structured-output) is configured.
+- Optionally, `structured_response` if [structured output](./agents.md#6-configure-structured-output) is configured.
 - If using a custom `state_schema`, additional keys corresponding to your defined fields may also be present in the output. These can hold updated state values from tool execution or prompt logic.
 
 See the [context guide](./context.md) for more details on working with custom state schemas and accessing context.

docs/docs/agents/tools.md

@@ -65,7 +65,7 @@ class MultiplyInputSchema(BaseModel):
 # highlight-next-line
 @tool("multiply_tool", args_schema=MultiplyInputSchema)
 def multiply(a: int, b: int) -> int:
-   return a * b
+    return a * b
 ```
 
 For additional customization, refer to the [custom tools guide](https://python.langchain.com/docs/how_to/custom_tools/).

docs/docs/cloud/concepts/cron_jobs.md

@@ -0,0 +1,15 @@
+## Cron jobs
+
+There are many situations in which it is useful to run an assistant on a schedule. 
+
+For example, say that you're building an assistant that runs daily and sends an email summary
+of the day's news. You could use a cron job to run the assistant every day at 8:00 PM.
+
+LangGraph Cloud supports cron jobs, which run on a user-defined schedule. The user specifies a schedule, an assistant, and some input. After that, on the specified schedule, the server 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](../../cloud/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](../../cloud/reference/api/api_ref.html#tag/runscreate/POST/threads/{thread_id}/runs/crons) for more details.

docs/docs/cloud/concepts/runs.md

@@ -0,0 +1,5 @@
+# Runs
+
+A run is an invocation of an [assistant](../../concepts/assistants.md). 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](./threads.md).
+
+The LangGraph Cloud API provides several endpoints for creating and managing runs. See the [API reference](../../cloud/reference/api/api_ref.html#tag/thread-runs/) for more details.

docs/docs/cloud/concepts/streaming.md

@@ -0,0 +1,138 @@
+# Streaming
+
+Streaming is critical for making LLM applications feel responsive to end users.  
+When creating a streaming run, the **streaming mode** determines what kinds of data are streamed back to the API client.
+
+## Supported streaming modes
+
+LangGraph Platform supports the following streaming modes:
+
+| Mode                 | Description                                                                                                                                                    | LangGraph Library Method                                                |
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------|
+| **`values`**         | Stream the full graph state after each [super-step](https://langchain-ai.github.io/langgraph/concepts/low_level/#graphs). [Guide](../how-tos/streaming.md#stream-graph-state) | `.stream()` / `.astream()` with `stream_mode="values"`                  |
+| **`updates`**        | Stream only the updates to the graph state after each node. [Guide](../how-tos/streaming.md#stream-graph-state)                                                              | `.stream()` / `.astream()` with `stream_mode="updates"`                 |
+| **`messages-tuple`** | Stream LLM tokens for any messages generated inside the graph (useful for chat apps). [Guide](../how-tos/streaming.md#messages)                                   | `.stream()` / `.astream()` with `stream_mode="messages"`                |
+| **`debug`**          | Stream debug information throughout graph execution. [Guide](../how-tos/streaming.md#debug)                                                                       | `.stream()` / `.astream()` with `stream_mode="debug"`                   |
+| **`custom`**         | Stream custom data. [Guide](../../how-tos/streaming.md#stream-custom-data)                                                                                                                                             | `.stream()` / `.astream()` with `stream_mode="custom"`                  |
+| **`events`**         | Stream all events (including the state of the graph); mainly useful when migrating large LCEL apps. [Guide](../how-tos/streaming.md#stream-events)                       | `.astream_events()`                                                     |
+
+✅ You can also **combine multiple modes** at the same time.  See the [how-to guide](../how-tos/streaming.md#stream-multiple-modes) for configuration details.
+
+## Stateless runs
+
+If you don't want to **persist the outputs** of a streaming run in the [checkpointer](../../concepts/persistence.md) DB, you can create a stateless run without creating a thread:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    async for chunk in client.runs.stream(
+        # highlight-next-line
+        None,  # (1)!
+        assistant_id,
+        input=inputs,
+        stream_mode="updates"
+    ):
+        print(chunk.data)
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // create a streaming run
+    // highlight-next-line
+    const streamResponse = client.runs.stream(
+      // highlight-next-line
+      null,  // (1)!
+      assistantID,
+      {
+        input,
+        streamMode: "updates"
+      }
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk.data);
+    }
+    ```
+
+    1. We are passing `None` instead of a `thread_id` UUID.
+
+=== "cURL"
+
+    ```bash
+    curl --request POST \
+    --url <DEPLOYMENT_URL>/runs/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    --data "{
+      \"assistant_id\": \"agent\",
+      \"input\": <inputs>,
+      \"stream_mode\": \"updates\"
+    }"
+    ```
+
+## Join and stream
+
+LangGraph Platform allows you to join an active [background run](../how-tos/background_run.md) and stream outputs from it. To do so, you can use [LangGraph SDK's](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/) `client.runs.join_stream` method:
+
+=== "Python"
+
+    ```python
+    from langgraph_sdk import get_client
+    client = get_client(url=<DEPLOYMENT_URL>, api_key=<API_KEY>)
+
+    # highlight-next-line
+    async for chunk in client.runs.join_stream(
+        thread_id,
+        # highlight-next-line
+        run_id,  # (1)!
+    ):
+        print(chunk)
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+
+=== "JavaScript"
+
+    ```js
+    import { Client } from "@langchain/langgraph-sdk";
+    const client = new Client({ apiUrl: <DEPLOYMENT_URL>, apiKey: <API_KEY> });
+
+    // highlight-next-line
+    const streamResponse = client.runs.joinStream(
+      threadID,
+      // highlight-next-line
+      runId  // (1)!
+    );
+    for await (const chunk of streamResponse) {
+      console.log(chunk);
+    }
+    ```
+
+    1. This is the `run_id` of an existing run you want to join.
+
+=== "cURL"
+
+    ```bash
+    curl --request GET \
+    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/<RUN_ID>/stream \
+    --header 'Content-Type: application/json' \
+    --header 'x-api-key: <API_KEY>'
+    ```
+
+!!! warning "Outputs not buffered"
+
+    When you use `.join_stream`, output is not buffered, so any output produced before joining will not be received.
+
+## API Reference
+
+For API usage and implementation, refer to the [API reference](../reference/api/api_ref.html#tag/thread-runs/POST/threads/{thread_id}/runs/stream). 
+

docs/docs/cloud/concepts/threads.md

@@ -0,0 +1,11 @@
+# Threads
+
+A thread contains the accumulated state of a sequence of [runs](./runs.md). If a run is executed on a thread, then the [state](../../concepts/low_level.md#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](../../concepts/persistence.md#checkpoints). Checkpoints can be used to restore the state of a thread at a later time.
+
+For more on threads and checkpoints, see this section of the [LangGraph conceptual guide](../../concepts/persistence.md).
+
+The LangGraph Cloud API provides several endpoints for creating and managing threads and thread state. See the [API reference](../../cloud/reference/api/api_ref.html#tag/threads) for more details.

docs/docs/cloud/concepts/webhooks.md

@@ -0,0 +1,7 @@
+# Webhooks
+
+Webhooks enable event-driven communication from your LangGraph Cloud application to external services. For example, you may want to issue an update to a separate service once an API call to LangGraph Cloud has finished running.
+
+Many LangGraph Cloud endpoints accept a `webhook` parameter. If this parameter is specified by a an endpoint that can accept POST requests, LangGraph Cloud will send a request at the completion of a run.
+
+See the corresponding [how-to guide](../../cloud/how-tos/webhooks.md) for more detail.

docs/docs/cloud/deployment/cloud.md

@@ -1,11 +1,11 @@
-# How to Deploy to Cloud SaaS (Beta)
+# How to Deploy to Cloud SaaS
 
 Before deploying, review the [conceptual guide for the Cloud SaaS](../../concepts/langgraph_cloud.md) deployment option.
 
 ## Prerequisites
 
 1. LangGraph Cloud applications are deployed from GitHub repositories. Configure and upload a LangGraph Cloud application to a GitHub repository in order to deploy it to LangGraph Cloud.
-1. [Verify that the LangGraph API runs locally](test_locally.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
+1. [Verify that the LangGraph API runs locally](../../tutorials/langgraph-platform/local-server.md). If the API does not run successfully (i.e. `langgraph dev`), deploying to LangGraph Cloud will fail as well.
 
 ## Create New Deployment
 

docs/docs/cloud/deployment/self_hosted_control_plane.md

@@ -1,4 +1,4 @@
-# How to Deploy Self-Hosted Control Plane (Beta)
+# How to Deploy Self-Hosted Control Plane
 
 Before deploying, review the [conceptual guide for the Self-Hosted Control Plane](../../concepts/langgraph_self_hosted_control_plane.md) deployment option.
 
@@ -6,25 +6,19 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
 
 1. You are using Kubernetes.
 1. You have self-hosted LangSmith deployed.
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster has access to.
 1. `KEDA` is installed on your cluster.
 
          helm repo add kedacore https://kedacore.github.io/charts 
          helm install keda kedacore/keda --namespace keda --create-namespace
-
-1. Ingress Configuration (recommended)
-    1. Install `Ingress Nginx` to serve as a reverse proxy for your deployment.
-
-            helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
-            helm repo update
-            helm install ingress-nginx ingress-nginx/ingress-nginx
-
-    1. Provision a root domain that will suffix all domains for your workloads (e.g. `us.langgraph.app`).
-    1. Provision wildcard certificates to terminate TLS for your deployments.
-    1. Note: If this step is skipped, you will need to provision domains/certs for each of your deployments.
-
+1. Ingress Configuration
+    1. You must set up an ingress for your LangSmith instance. All agents will be deployed as Kubernetes services behind this ingress.
+    1. You can use this guide to [set up an ingress](https://docs.smith.langchain.com/self_hosting/configuration/ingress) for your instance.
 1. You have slack space in your cluster for multiple deployments. `Cluster-Autoscaler` is recommended to automatically provision new nodes.
+1. A valid Dynamic PV provisioner or PVs available on your cluster. You can verify this by running:
+
+        kubectl get storageclass
 
 ## Setup
 
@@ -44,13 +38,12 @@ Before deploying, review the [conceptual guide for the Self-Hosted Control Plane
           pullPolicy: IfNotPresent
           tag: "aa9dff4"
 
-1. In your `values.yaml` file, enable the `langgraphPlatform` option.
-
+1. In your `values.yaml` file, enable the `langgraphPlatform` option. Note that you must also have a valid ingress setup:
         config:
           langgraphPlatform:
             enabled: true
             langgraphPlatformLicenseKey: "YOUR_LANGGRAPH_PLATFORM_LICENSE_KEY"
-            rootDomain: "YOUR_ROOT_DOMAIN"
+1. In your `values.yaml` file, configure the `hostBackendImage` and `operatorImage` options (if you need to mirror images)
 
 1. You can also configure base templates for your agents by overriding the base templates [here](https://github.com/langchain-ai/helm/blob/main/charts/langsmith/values.yaml#L898).
-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
+1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).

docs/docs/cloud/deployment/self_hosted_data_plane.md

@@ -1,10 +1,10 @@
-# How to Deploy Self-Hosted Data Plane (Beta)
+# How to Deploy Self-Hosted Data Plane
 
 Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](../../concepts/langgraph_self_hosted_data_plane.md) deployment option.
 
 ## Prerequisites
 
-1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](./test_locally.md).
+1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to [test your application locally](../../tutorials/langgraph-platform/local-server.md).
 1. Use the [LangGraph CLI](../../concepts/langgraph_cli.md) to build a Docker image (i.e. `langgraph build`) and push it to a registry your Kubernetes cluster or Amazon ECS cluster has access to.
 
 ## Kubernetes
@@ -46,7 +46,7 @@ Before deploying, review the [conceptual guide for the Self-Hosted Data Plane](.
         langgraph-dataplane-listener-7fccd788-wn2dx   0/1     Running             0          9s
         langgraph-dataplane-redis-0                   0/1     ContainerCreating   0          9s
 
-1. You create a deployment from the [Control Plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
+1. You create a deployment from the [control plane UI](../../concepts/langgraph_control_plane.md#control-plane-ui).
 
 ## Amazon ECS