Why this fit
Teams evaluating AI models, assistants and generative workflows, particularly AI agents that connect over MCP — source available on GitHub.
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Catalogue record
github.com
CodeCortex
📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency g...
Checked Oct 27, 2026Evidence completeness: evidenceREADME refreshed Oct 16, 2026
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📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter native parsing (28 languages) and serves via MCP. 14 tools, 85% token reduction. Works with Claude Code, Cursor, Codex, and any MCP client.
Best for
Teams evaluating AI models, assistants and generative workflows, particularly 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
CodeCortex
GitHub / rushikeshmore/CodeCortex
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- Catalogue updated Oct 27, 2026
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