LangGraph
LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. It solves the problem of building agents that need durable execution, human-in-the-loop control, and comprehensive memory. It is for developers who need fine-grained control over agent workflows and state management.
✨ Key features
- Durable execution: persists through failures and resumes from where it left off.
- Human-in-the-loop: inspect and modify agent state at any point.
- Comprehensive memory: short-term working memory and long-term persistent memory.
- Debugging with LangSmith: visualization, tracing, and runtime metrics.
- Production-ready deployment: scalable infrastructure for stateful workflows.
- Integrates with LangChain and LangSmith ecosystem.
🎯 Use cases
- Building stateful agents that run for extended periods.
- Creating agents with human oversight and approval steps.
- Implementing agents with memory across sessions.
- Deploying long-running workflows in production.
- Developing complex agent systems with subagents and planning.
📦 Installation
🧰 Requirements: Python runtime; no API keys required for basic use, but LangSmith integration may require an account.
pip install -U langgraph
🚀 Usage
No complete example is provided in the README; refer to the Quickstart guide at https://docs.langchain.com/oss/python/langgraph/quickstart.
⚠️ Good to know
LangGraph is a low-level framework, so it requires more manual orchestration compared to higher-level packages like Deep Agents.
❓ FAQ
Can LangGraph be used without LangChain?
Yes, LangGraph can be used standalone, but it also integrates seamlessly with LangChain products.
Is there a JavaScript version of LangGraph?
Yes, there is LangGraph.js, an equivalent JS/TS library.
What is Deep Agents?
Deep Agents is a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
How does LangGraph handle failures?
LangGraph provides durable execution, so agents persist through failures and automatically resume from exactly where they left off.
📊 Repository
🤖 Overview, features, install steps and FAQ were generated from the project's README on Sep 4, 2026. Always check the original source before running commands.