LangGraph
An open-source framework from LangChain for building stateful, multi-step agent applications as graphs — nodes are agent steps, edges define control flow, and state persists across steps.
LangGraph is the production-grade successor to LangChain. Where LangChain encouraged chain-everything composition, LangGraph treats agents as explicit state machines. You define nodes (each a unit of work, often an LLM call), edges (conditional transitions between nodes), and a typed shared state that flows through the graph.
The framework wins on observability and debuggability. Every state transition is logged; the graph structure is visualizable; you can pause execution, modify state, and resume. For complex agents with branching control flow, multi-step planning, or human-in-the-loop checkpoints, this matters more than any LLM API feature.
In 2026 LangGraph is the de-facto choice for serious agent work in Python. The alternatives — bare vendor SDKs (Anthropic Agent SDK, OpenAI Agents SDK), CrewAI for role-based crews, AutoGen for research — each fit specific niches. LangGraph fits "I need a real production agent and I want to debug it like real software."
Frequently asked
Should I use LangGraph or LangChain?+
LangGraph for any new project. LangChain was the predecessor; LangGraph is what the LangChain team built once they understood the production problems. The remaining reason to use LangChain in 2026 is legacy code.
LangGraph vs CrewAI vs AutoGen?+
LangGraph for stateful general-purpose agents. CrewAI for role-based multi-agent crews. AutoGen for research-style conversational multi-agent. Pick LangGraph if you do not have a strong reason to pick the others.