What problem it solves

LangGraph provides infrastructure for long-running, stateful workflows and agents that need to persist through failures, incorporate human oversight, and maintain memory across sessions. It also supports debugging and deployment of agent systems with complex execution paths.

Key features

• Durable execution — Build agents that persist through failures and run for extended periods, automatically resuming from where they stopped.

  Source: README

• Human-in-the-loop workflows — Incorporate human oversight by inspecting and modifying agent state during execution.

  Source: README

• Comprehensive memory — Support short-term working memory for ongoing reasoning and long-term persistent memory across sessions.

  Source: README

• Debugging and observability — Use visualization tools to trace execution paths, capture state transitions, and inspect runtime metrics through LangSmith.

  Source: README

• Production-ready deployment — Deploy agent systems with infrastructure designed for stateful, long-running workflows.

  Source: README

Quick start

Copied verbatim from the project's README.

pip install -U langgraph

Who it may suit

This project may suit developers building long-running, stateful agents or workflows, especially those who need persistence, human-in-the-loop controls, memory, debugging, or deployment support.

Topics to discuss

• Which durable execution and human-in-the-loop patterns are most useful for your agent workflows?

• How do you structure state transitions and memory across long-running agent sessions?

• What deployment and observability requirements should the ecosystem address next?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/langchain-ai/langgraph#readme

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