LangGraph is a framework for building stateful, multi-step LLM workflows as graphs. Instead of a linear chain, you model your application as:
- Nodes (steps: call LLM, call tool, route, validate, etc.)
- Edges (transitions: deterministic or conditional)
- State (shared memory passed and updated across steps)
This is useful when your application has branching, loops, retries, or multiple agents collaborating.
Use LangGraph when you need:
- Orchestration with control flow (branching, cycles, fallbacks)
- Multi-agent setups (planner/executor, reviewer, specialized agents)
- Long-running workflows with checkpoints/resume
- More predictable behavior than a purely free-form agent loop
- Explicit control flow: the graph makes execution paths visible and testable.
- Reliability: easier to add guards, retries, validation, and routing.
- State management: clear model of what is remembered and why.
- If your workflow is simple and linear (a basic chain or a few function calls).
- If you don’t need explicit state/control flow (keep it simpler).
- If you mainly need integrations and reusable building blocks, see LangChain.