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Sydney Runkle
2026-03-02 07:53:49 -08:00
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<picture class="github-only">
<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg">
<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/static/wordmark_light.svg">
<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/static/wordmark_dark.svg" width="80%">
<source media="(prefers-color-scheme: light)" srcset="https://langchain-ai.github.io/langgraph/images/logo-light.svg">
<source media="(prefers-color-scheme: dark)" srcset="https://langchain-ai.github.io/langgraph/images/logo-dark.svg">
<img alt="LangGraph Logo" src="https://langchain-ai.github.io/langgraph/images/logo-dark.svg" width="80%">
</picture>
<div>
@@ -56,6 +56,9 @@ Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/pytho
To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
> [!TIP]
> For developing, debugging, and deploying AI agents and LLM applications, see [LangSmith](https://docs.langchain.com/langsmith/home).
## Core benefits
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits: