LangGraph

LangGraph models an agent as a graph. Each node is a step, such as calling a model, running a tool, or checking a condition, and edges define the flow between steps, including loops and branches. A shared state object passes information along. This explicit structure gives developers control over what the agent does and when, which makes complex agents easier to reason about than a free-running loop.

Features aimed at production use set it apart. Checkpointing saves state so a run can pause and resume, or recover from failure. Human-in-the-loop steps let an agent wait for approval before taking an action, and time-travel debugging lets developers replay and edit earlier steps. It supports streaming of intermediate results, long-term memory, and multi-agent designs, in Python and JavaScript.

The library is free and open source. Hosting and monitoring are available through LangSmith, which has a free tier and paid plans. LangGraph has a steeper learning curve than high-level agent libraries, and its power is most valuable for complex workflows. CrewAI and AutoGen offer higher-level multi-agent abstractions, the OpenAI Agents SDK is simpler for OpenAI-centered work, and visual builders like n8n and Dify suit non-coders. Many production agents are built with LangGraph.