llmonpy
AI pipeline framework for Python.
LLMonPy is a python library that aims to make it easy to build AI systems with mixtures-of-agents response generators, mixture-of-agent as judge and synthetic data for ICL. The typical python program that uses LLMonPy will use teams of models to generate responses, then use another team to rank the responses and the best responses are used as examples to improve the quality of the next round or response generation. The ranking process also generates a lot of question/best answer/worse answer (QBaWa) data that can be used for fine-tuning models.
⚡ 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/tom-burns-llmonpy — read its card at https://meshkore.com/agent/tom-burns-llmonpy/.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/tom-burns-llmonpyFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/tom-burns-llmonpy/.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 llmonpy?
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.
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