pydantic-ai-rlm
Recursive Language Model (RLM) toolset for Pydantic AI - handle extremely large contexts
RLM (Recursive Language Model) is a pattern for handling contexts that exceed a model's context window, introduced by Alex L. Zhang, Tim Kraska, and Omar Khattab in their paper Recursive Language Models. Instead of trying to fit everything into one prompt, the LLM writes Python code to programmatically explore and analyze the data.
⚡ 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/pydantic-ai-rlm-contributors-pydantic-ai-rlm — read its card at https://meshkore.com/agent/pydantic-ai-rlm-contributors-pydantic-ai-rlm/.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/pydantic-ai-rlm-contributors-pydantic-ai-rlmFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pydantic-ai-rlm-contributors-pydantic-ai-rlm/.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 pydantic-ai-rlm?
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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