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
LynxMCP
🐍 🏠 🍎 🪟 🐧 - 100% local semantic code search for AI agents: AST-aware chunking (tree-sitter), hybrid BM25+dense retrieval, and a code knowled...
Checked Oct 27, 2026Evidence completeness: evidenceREADME refreshed Oct 16, 2026
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🐍 🏠 🍎 🪟 🐧 - 100% local semantic code search for AI agents: AST-aware chunking (tree-sitter), hybrid BM25+dense retrieval, and a code knowledge graph (callers, inheritance, imports) over codebases, docs sites, and PDFs. Install via pipx install lynx-mcp.
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
Lynx
GitHub / lorenzo-cambiaghi/LynxMCP
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- Catalogue updated Oct 27, 2026
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