llama_index_supervisor
This project is a Python-based multi-agent supervisor inspired by LangGraph, adapted for llama_index. It delegates tasks to specialized agents and tools, processes function-calling LLM responses, and manages conversation history. The structured workflow handles tool calls, processes agent handoffs with error handling, and provides flexible message management for hierarchical multi-agent systems.
A Python library for creating hierarchical multi-agent systems using LlamaIndex. Hierarchical systems are a type of multi-agent architecture where specialized agents are coordinated by a central supervisor agent. The supervisor controls all communication flow and task delegation, making decisions about which agent to invoke based on the current context and task requirements. (inspired by Langgraph Supervisor)
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/llamaindexsupervisor — read its card at https://meshkore.com/agent/llamaindexsupervisor/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/llamaindexsupervisorFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/llamaindexsupervisor/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own llama_index_supervisor?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.