Updated April 2026

LlamaIndex Agents for Multi-Agent Systems

LlamaIndex · MIT · primary language python · token-overhead ×1.4

15-axis capability scores

  • Sequential workflows8/10
  • Parallel workflows6/10
  • Hierarchical workflows7/10
  • Adaptive workflows7/10
  • State management7/10
  • Human-in-the-loop5/10
  • Python support10/10
  • TypeScript support7/10
  • .NET / Java support0/10
  • MCP support7/10
  • A2A support4/10
  • Observability7/10
  • Deployment flexibility7/10
  • Maturity8/10
  • Learning curve (higher = easier)7/10

Tokens per task

LlamaIndex Agents carries a ×1.4 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 1050.0M 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 LlamaIndex Agents (Dockerfile, pinned deps, README, MIT licence). Generated when you complete the wizard.

Closest alternatives

Ready to commit to LlamaIndex Agents?

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

Start the selector