01 What is it?
LangGraph is the open framework for building stateful, multi-actor agent workflows as graphs. Each node is an LLM call or tool invocation, edges encode control flow, and the runtime persists state across turns. LangGraph is the natural choice when an agent needs to reason over multiple steps, branch on outputs, and resume after human-in-the-loop checkpoints.
02 Why implement it?
- Native support for stateful, long-running agent workflows
- Built-in human-in-the-loop checkpoints and approval gates
- Time-travel debugging and graph replay for audit
- First-class observability through LangSmith and Langfuse
- Production friendly: streaming, retries, persistence, sub-graphs
03 How I help
I design LangGraph workflows with security baked in from the first node: scoped tool authorization, sub-graph isolation, deterministic checkpoints for audit, and policy guardrails enforced at edge transitions. I also harden the persistence layer and the observability pipeline.
04 Expected deliverables
- LangGraph reference workflow with security guardrails
- Tool authorization model and policy engine integration
- Persistence and replay design with audit trail
- Observability integration (Langfuse, LangSmith)
- Red-team report on tool-chain abuse and prompt injection