Updated April 2026

LangGraph for Multi-Agent Systems

LangChain · MIT · primary language python · token-overhead ×1.0

15-axis capability scores

  • Sequential workflows9/10
  • Parallel workflows9/10
  • Hierarchical workflows9/10
  • Adaptive workflows10/10
  • State management10/10
  • Human-in-the-loop10/10
  • Python support10/10
  • TypeScript support8/10
  • .NET / Java support0/10
  • MCP support7/10
  • A2A support5/10
  • Observability10/10
  • Deployment flexibility9/10
  • Maturity9/10
  • Learning curve (higher = easier)5/10

Tokens per task

LangGraph carries a ×1.0 token overhead multiplier against a 1.0 baseline (LangGraph). For a workload of 50,000 tasks per month at 15,000 base tokens, that is roughly 750.0M tokens per month before HITL or multi-agent fan-out.

Run the wizard for a calibrated estimate against your workload and chosen model.

Run the selector with your workload

Starter scaffold

Buzzi ships a 2-agent hello-world ZIP for LangGraph (Dockerfile, pinned deps, README, MIT licence). Generated when you complete the wizard.

Closest alternatives

Ready to commit to LangGraph?

Run the wizard, download the scaffold, and book a 30-minute scoping call with Buzzi.ai.

Start the selector