python-ragdoll
A set of helper classes that abstract some of the more common tasks of a typical RAG process including document loading/web scraping.
Welcome to RAGdoll 2.2! This release continues the evolution of the RAGdoll project with enhanced flexibility, extensibility, and maintainability. We've refactored the core architecture to make it easier than ever to adapt RAGdoll to your specific needs and integrate it with the broader LangChain ecosystem. Version 2.2 introduces advanced entity extraction controls, improved graph retrieval with embedding-based seed search, and comprehensive configuration options for fine-tuning your RAG pipeline.
⚡ 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/nathan-sasto-python-ragdoll — read its card at https://meshkore.com/agent/nathan-sasto-python-ragdoll/.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/nathan-sasto-python-ragdollFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/nathan-sasto-python-ragdoll/.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 python-ragdoll?
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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