Why this fit
Teams evaluating AI models, assistants and generative workflows, particularly search and retrieval, AI agents that connect over MCP — source available on GitHub.
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Catalogue record
github.com
rag-rat
🦀 🏠 🍎 🪟 🐧 - Local repo-intelligence index and MCP server for coding agents — "what a repository knows about itself." Hybrid BM25 + vector se...
Checked Oct 27, 2026Evidence completeness: evidenceREADME refreshed Oct 16, 2026
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🦀 🏠 🍎 🪟 🐧 - Local repo-intelligence index and MCP server for coding agents — "what a repository knows about itself." Hybrid BM25 + vector semantic search, a tree-sitter code graph (callers/callees/imports across Rust, TS/TSX, Kotlin, C/C++, Python) with an optional compiler-grade SCIP oracle, queryable git/GitHub history, and typed, source-anchored repo memories (Invariant, Decision, Risk, …) that persist across sessions. Every result carries provenance — confidence labels and coverage warnin...
Best for
Teams evaluating AI models, assistants and generative workflows, particularly search and retrieval, AI agents that connect over MCP — source available on GitHub.
Known limitations
Workflows that require fully deterministic, auditable outputs with no human review step.
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Evidence summary
Known limitations
Workflows that require fully deterministic, auditable outputs with no human review step.
Catalogue confidence
Catalogue data checked Oct 27, 2026.
GitHub-backed record with source-attributed context.
Repository context
rag-rat
GitHub / cq27-dev/rag-rat
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- Catalogue updated Oct 27, 2026
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