LangGraph
langchain-ai/langgraph
Stateful, resilient agent graphs — cycles, persistence, and control flow for agents. GitHub: langchain-ai/langgraph (~39k stars).
GitHub repository
Overview
LangGraph (langchain-ai/langgraph) is listed in LimeDock Directories as an AI agent resource. Stateful, resilient agent graphs — cycles, persistence, and control flow for agents. Group: multi-agent. Community size: ~39k GitHub stars. Repo (copy/paste): https://github.com/langchain-ai/langgraph This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/langchain-ai/langgraph GitHub path: langchain-ai/langgraph
How to open it: 1. Copy: https://github.com/langchain-ai/langgraph 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** Simple chains aren’t enough; you need stateful agent graphs with retries.
1. Install LangGraph. 2. Build a small graph with a loop + human breakpoint. 3. Add persistence for crash recovery.
Example prompts
- “Build a LangGraph support agent with a human-approval node.”
- “LangGraph vs CrewAI?”
- “How do checkpoints work?”
Use cases and examples
- Evaluate langchain-ai/langgraph as an AI agent building block
- Pilot LangGraph on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- Python
- LangChain familiarity helps
Tips
- Explicit state beats hidden prompts.
- Add evals on graph paths.