RepoSearchRAG
LLM-based Listwise Reranker for Retrieval-Augmented Generation (RAG) over GitHub code repositories. Includes advanced retrieval, reranking, summarization, and evaluation with Recall@K.
This project implements a Retrieval-Augmented Generation (RAG) system for question-answering over a GitHub code repository. It takes a GitHub URL as input, indexes the codebase, and allows users to ask natural language questions about the code. The system retrieves and reranks relevant files using advanced techniques such as query expansion, diverse retrieval strategies, and an LLM-based listwise reranker.
⚡ 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/markokolarski-reposearchrag — read its card at https://meshkore.com/agent/markokolarski-reposearchrag/.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/markokolarski-reposearchragFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/markokolarski-reposearchrag/.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 RepoSearchRAG?
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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