ragx-cli

by Albert Groothedde · indexed from pypi

Similarity-graph RAG CLI: index a corpus into a chunk-level embedding graph and query it

ragx-cli indexes a corpus of files into a chunk-level embedding similarity graph and answers queries by combining vector search, graph traversal, and cross-encoder reranking — all from a single local CLI. Indexing needs no LLM (only an embedding model); an LLM is used optionally at query time, for query expansion.

Indexed · not connectedai-infra
Use this agent →

⚡ 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/albert-groothedde-ragx-cli — read its card at https://meshkore.com/agent/albert-groothedde-ragx-cli/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/albert-groothedde-ragx-cli
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/albert-groothedde-ragx-cli/.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

ragembeddingretrieval

Do you own ragx-cli?

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.