Learning path · Agents & Orchestration · 64
LangGraph
Graph-based agent orchestration modeling workflows as state machines with nodes, edges, and checkpointed state.
Why it matters
- Explicit control flow aids debugging and compliance audits.
- Natural fit for cyclic tool loops with stop conditions.
- Tradeoff: more boilerplate than role-playing frameworks.
Key ideas
- State machine graphs
- Checkpointing
- Deterministic routing
Top resources
LangGraph treats agent workflows as graphs: nodes are functions or model calls; edges encode transitions conditioned on state. Checkpointing enables human approval mid-flight and crash recovery. Choose LangGraph when you need predictable control flow, retries, and observability over improvisational dialogue. Compared to CrewAI's role-playing metaphors or AutoGen's conversational patterns, LangGraph optimizes operability and testability. Model failure transitions explicitly—retry, escalate, abort—instead of hoping the LLM improvises recovery prose users cannot action.
Updated 2026-08-09 · Full learning path