From df191731918482c5eaf2934a2df0c9cb3571db0c Mon Sep 17 00:00:00 2001 From: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com> Date: Wed, 10 Dec 2025 14:02:36 -0500 Subject: [PATCH] fix: update readme (#6570) remove usage of deprecated `create_react_agent` and generally redirecting to docs bc they're so good now :) --- README.md | 59 ++++++++++++++++++++++------------------ libs/langgraph/README.md | 59 ++++++++++++++++++++++------------------ 2 files changed, 66 insertions(+), 52 deletions(-) diff --git a/README.md b/README.md index 8e5df3ce0..f7eb80247 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/) [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues) -[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/) +[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview) Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. @@ -23,47 +23,55 @@ Install LangGraph: pip install -U langgraph ``` -Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): +Create a simple workflow: ```python -# pip install -qU "langchain[anthropic]" to call the model +from langgraph.graph import START, StateGraph +from typing_extensions import TypedDict -from langgraph.prebuilt import create_react_agent -def get_weather(city: str) -> str: - """Get weather for a given city.""" - return f"It's always sunny in {city}!" +class State(TypedDict): + text: str -agent = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=[get_weather], - prompt="You are a helpful assistant" -) -# Run the agent -agent.invoke( - {"messages": [{"role": "user", "content": "what is the weather in sf"}]} -) +def node_a(state: State) -> dict: + return {"text": state["text"] + "a"} + + +def node_b(state: State) -> dict: + return {"text": state["text"] + "b"} + + +graph = StateGraph(State) +graph.add_node("node_a", node_a) +graph.add_node("node_b", node_b) +graph.add_edge(START, "node_a") +graph.add_edge("node_a", "node_b") + +print(graph.compile().invoke({"text": ""})) +# {'text': 'ab'} ``` -For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/). +Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart). + +To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents). ## Core benefits LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits: -- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. -- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. -- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. +- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. +- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. +- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics. -- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows. +- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows. ## LangGraph’s ecosystem While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - [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. -- [LangSmith Deployment](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/). +- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — 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://docs.langchain.com/oss/python/langgraph/studio). - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development. > [!NOTE] @@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L ## Additional resources -- [Guides](https://langchain-ai.github.io/langgraph/guides/): 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/examples/): Guided examples on getting started with LangGraph. +- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.). +- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components. +- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph. - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback. - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course. -- [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. - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale. ## Acknowledgements diff --git a/libs/langgraph/README.md b/libs/langgraph/README.md index 8e5df3ce0..f7eb80247 100644 --- a/libs/langgraph/README.md +++ b/libs/langgraph/README.md @@ -11,7 +11,7 @@ [![Version](https://img.shields.io/pypi/v/langgraph.svg)](https://pypi.org/project/langgraph/) [![Downloads](https://static.pepy.tech/badge/langgraph/month)](https://pepy.tech/project/langgraph) [![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langgraph)](https://github.com/langchain-ai/langgraph/issues) -[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://langchain-ai.github.io/langgraph/) +[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.langchain.com/oss/python/langgraph/overview) Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. @@ -23,47 +23,55 @@ Install LangGraph: pip install -U langgraph ``` -Then, create an agent [using prebuilt components](https://langchain-ai.github.io/langgraph/agents/agents/): +Create a simple workflow: ```python -# pip install -qU "langchain[anthropic]" to call the model +from langgraph.graph import START, StateGraph +from typing_extensions import TypedDict -from langgraph.prebuilt import create_react_agent -def get_weather(city: str) -> str: - """Get weather for a given city.""" - return f"It's always sunny in {city}!" +class State(TypedDict): + text: str -agent = create_react_agent( - model="anthropic:claude-3-7-sonnet-latest", - tools=[get_weather], - prompt="You are a helpful assistant" -) -# Run the agent -agent.invoke( - {"messages": [{"role": "user", "content": "what is the weather in sf"}]} -) +def node_a(state: State) -> dict: + return {"text": state["text"] + "a"} + + +def node_b(state: State) -> dict: + return {"text": state["text"] + "b"} + + +graph = StateGraph(State) +graph.add_node("node_a", node_a) +graph.add_node("node_b", node_b) +graph.add_edge(START, "node_a") +graph.add_edge("node_a", "node_b") + +print(graph.compile().invoke({"text": ""})) +# {'text': 'ab'} ``` -For more information, see the [Quickstart](https://langchain-ai.github.io/langgraph/agents/agents/). Or, to learn how to build an [agent workflow](https://langchain-ai.github.io/langgraph/concepts/low_level/) with a customizable architecture, long-term memory, and other complex task handling, see the [LangGraph basics tutorials](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/). +Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart). + +To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents). ## Core benefits LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits: -- [Durable execution](https://langchain-ai.github.io/langgraph/concepts/durable_execution/): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. -- [Human-in-the-loop](https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. -- [Comprehensive memory](https://langchain-ai.github.io/langgraph/concepts/memory/): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. +- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off. +- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution. +- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. - [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics. -- [Production-ready deployment](https://langchain-ai.github.io/langgraph/concepts/deployment_options/): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows. +- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows. ## LangGraph’s ecosystem While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with: - [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. -- [LangSmith Deployment](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/). +- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — 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://docs.langchain.com/oss/python/langgraph/studio). - [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development. > [!NOTE] @@ -71,12 +79,11 @@ While LangGraph can be used standalone, it also integrates seamlessly with any L ## Additional resources -- [Guides](https://langchain-ai.github.io/langgraph/guides/): 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/examples/): Guided examples on getting started with LangGraph. +- [Guides](https://docs.langchain.com/oss/python/langgraph/guides): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.). +- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components. +- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph. - [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback. - [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course. -- [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. - [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale. ## Acknowledgements