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docs: correct the published language count to 161 on every surface
Seven surfaces disagreed with reality and with each other: README.md, docs/index.html, docs/llms.txt and pkg/npm/README.md said 158, while server.json, the chocolatey nuspec and scripts/package-release.sh said 159. 161 grammars are vendored. 21 individual claims corrected. The count is now gated by tests/test_language_count_contract.sh, so the next language to land cannot ship a stale figure. Signed-off-by: Martin Vogel <martin.vogel.tech@gmail.com>
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@@ -16,7 +16,7 @@
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**The fastest and most efficient code intelligence engine for AI coding agents.** Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a native executable with a small verified runtime-asset set for macOS, Linux, and Windows — download, run `install`, done.
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High-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-sitter/) AST analysis across all 158 languages, enhanced with [**Hybrid LSP** semantic type resolution](#hybrid-lsp) for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, Rust, and Perl — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 15 MCP tools. No language runtime, hosted service, or API key. Plug and play across 44 supported automatic/conditional client surfaces.
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High-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-sitter/) AST analysis across all 161 languages, enhanced with [**Hybrid LSP** semantic type resolution](#hybrid-lsp) for Python, TypeScript / JavaScript / JSX / TSX, PHP, C#, Go, C, C++, Java, Kotlin, Rust, and Perl — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 15 MCP tools. No language runtime, hosted service, or API key. Plug and play across 44 supported automatic/conditional client surfaces.
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> **Research** — The design and benchmarks behind this project are described in the preprint [*Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP*](https://arxiv.org/abs/2603.27277) (arXiv:2603.27277). Evaluated across 31 real-world repositories: 83% answer quality, 10× fewer tokens, 2.1× fewer tool calls vs. file-by-file exploration.
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@@ -32,7 +32,7 @@ High-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-si
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- **Extreme indexing speed** — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline: LZ4 compression, in-memory SQLite, fused Aho-Corasick pattern matching. Memory released after indexing.
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- **Plug and play** — native executable plus authenticated release-owned assets for macOS (arm64/amd64), Linux (arm64/amd64), and Windows (amd64). The native install needs no Docker, language runtime, or API keys. Download → `install` → restart agent → done.
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- **158 languages** — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
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- **161 languages** — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
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- **120x fewer tokens** — 5 structural queries: ~3,400 tokens vs ~412,000 via file-by-file search. One graph query replaces dozens of grep/read cycles.
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- **44 supported automatic/conditional client surfaces** — `install` configures detected clients and safely activates conditional clients only when their documented platform, marker, or explicit existing config path is present. See [Multi-Agent Support](#multi-agent-support) for the complete matrix and manual/UI-only boundaries.
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- **Built-in graph visualization** — 3D interactive UI at `localhost:9749`, served from the binary itself.
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@@ -760,14 +760,14 @@ codebase-memory-mcp ships a **lightweight C implementation of language type-reso
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**Two-layer architecture:**
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1. **Tree-sitter pass** — fast, syntactic, runs for every one of the 158 languages. Extracts definitions, calls, imports.
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1. **Tree-sitter pass** — fast, syntactic, runs for every one of the 161 languages. Extracts definitions, calls, imports.
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2. **Hybrid LSP pass** — type-aware, runs above the tree-sitter pass per-language. Refines call edges using the import graph plus a per-file or pre-built cross-file definition registry. Languages without a Hybrid LSP pass yet fall back to textual resolution, so you always get *some* answer.
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The result is a knowledge graph accurate enough to drive `trace_path` across packages, inheritance hierarchies, and stdlib calls — without paying for a language server process per project.
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## Language Support
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158 languages, all parsed via vendored tree-sitter grammars compiled into the binary. Benchmarked against 64 real open-source repositories (78 to 49K nodes):
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161 languages, all parsed via vendored tree-sitter grammars compiled into the binary. Benchmarked against 64 real open-source repositories (78 to 49K nodes):
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| Tier | Score | Languages |
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|------|-------|-----------|
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@@ -793,7 +793,7 @@ src/
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traces/ Runtime trace ingestion
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ui/ Local HTTP server + verified external 3D-UI asset pack
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foundation/ Platform abstractions (threads, filesystem, logging, memory)
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internal/cbm/ Vendored tree-sitter grammars (158 languages) + AST extraction engine
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internal/cbm/ Vendored tree-sitter grammars (161 languages) + AST extraction engine
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```
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## Security
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@@ -4,7 +4,7 @@
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>codebase-memory-mcp — Code Intelligence Knowledge Graph for AI Coding Agents</title>
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<meta name="description" content="codebase-memory-mcp is an open-source MCP server that indexes any codebase into a persistent knowledge graph so AI coding agents answer structural questions with ~120x fewer tokens. 158 languages, Hybrid LSP type resolution, local semantic vector search, code-clone detection, sub-1ms queries, Linux kernel indexed in 3 minutes. Native executable with verified assets and no language runtime. One command configures 44 automatic/conditional client surfaces.">
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<meta name="description" content="codebase-memory-mcp is an open-source MCP server that indexes any codebase into a persistent knowledge graph so AI coding agents answer structural questions with ~120x fewer tokens. 161 languages, Hybrid LSP type resolution, local semantic vector search, code-clone detection, sub-1ms queries, Linux kernel indexed in 3 minutes. Native executable with verified assets and no language runtime. One command configures 44 automatic/conditional client surfaces.">
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<meta name="keywords" content="MCP server, code intelligence, knowledge graph, tree-sitter, Hybrid LSP, semantic code search, code embeddings, code clone detection, cross-repo analysis, data-flow analysis, Claude Code, Codex CLI, Gemini CLI, Cursor, Zed, code exploration, token reduction, call graph, dead code detection">
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<meta name="author" content="DeusData">
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<meta name="robots" content="index, follow, max-image-preview:large, max-snippet:-1">
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@@ -12,7 +12,7 @@
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<!-- Open Graph -->
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<meta property="og:title" content="codebase-memory-mcp — Code Intelligence Knowledge Graph for AI Coding Agents">
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<meta property="og:description" content="Index any codebase into a persistent knowledge graph. AI agents answer structural questions with ~120x fewer tokens. 158 languages, Hybrid LSP, sub-1ms queries, native executable with verified assets.">
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<meta property="og:description" content="Index any codebase into a persistent knowledge graph. AI agents answer structural questions with ~120x fewer tokens. 161 languages, Hybrid LSP, sub-1ms queries, native executable with verified assets.">
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<meta property="og:type" content="website">
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<meta property="og:url" content="https://deusdata.github.io/codebase-memory-mcp/">
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<meta property="og:image" content="https://deusdata.github.io/codebase-memory-mcp/graph-ui-screenshot.png">
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@@ -24,7 +24,7 @@
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<!-- Twitter Card -->
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<meta name="twitter:card" content="summary_large_image">
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<meta name="twitter:title" content="codebase-memory-mcp — Code Intelligence for AI Coding Agents">
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<meta name="twitter:description" content="Index any codebase into a knowledge graph. ~120x fewer tokens for AI code exploration. 158 languages, Hybrid LSP, sub-1ms queries, native executable with verified assets.">
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<meta name="twitter:description" content="Index any codebase into a knowledge graph. ~120x fewer tokens for AI code exploration. 161 languages, Hybrid LSP, sub-1ms queries, native executable with verified assets.">
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<meta name="twitter:image" content="https://deusdata.github.io/codebase-memory-mcp/graph-ui-screenshot.png">
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<link rel="canonical" href="https://deusdata.github.io/codebase-memory-mcp/">
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@@ -41,7 +41,7 @@
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"applicationSubCategory": "Model Context Protocol (MCP) server",
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"operatingSystem": "macOS, Linux, Windows",
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"softwareVersion": "0.8.1",
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"description": "An open-source MCP server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links, so AI coding agents answer structural questions with roughly 120x fewer tokens than file-by-file search. Parses 158 languages via tree-sitter with Hybrid LSP semantic type resolution for 9 language families. Ships as a native executable with authenticated release-owned assets; the native install needs no language runtime.",
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"description": "An open-source MCP server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links, so AI coding agents answer structural questions with roughly 120x fewer tokens than file-by-file search. Parses 161 languages via tree-sitter with Hybrid LSP semantic type resolution for 9 language families. Ships as a native executable with authenticated release-owned assets; the native install needs no language runtime.",
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"url": "https://deusdata.github.io/codebase-memory-mcp/",
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"downloadUrl": "https://github.com/DeusData/codebase-memory-mcp/releases/latest",
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"codeRepository": "https://github.com/DeusData/codebase-memory-mcp",
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@@ -125,7 +125,7 @@
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"name": "What is codebase-memory-mcp?",
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"acceptedAnswer": {
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"@type": "Answer",
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"text": "codebase-memory-mcp is an open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query that graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens. It parses 158 languages and ships as a native executable with authenticated release-owned assets; the native install needs no language runtime."
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"text": "codebase-memory-mcp is an open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query that graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens. It parses 161 languages and ships as a native executable with authenticated release-owned assets; the native install needs no language runtime."
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}
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},
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{
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@@ -141,7 +141,7 @@
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"name": "Which programming languages does codebase-memory-mcp support?",
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"acceptedAnswer": {
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"@type": "Answer",
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"text": "It supports 158 languages through vendored tree-sitter grammars compiled into the binary, including Python, Go, JavaScript, TypeScript, Rust, Java, C, C++, C#, PHP, Ruby, Kotlin, Swift, and many more. Nine language families — Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust — additionally get Hybrid LSP semantic type resolution."
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"text": "It supports 161 languages through vendored tree-sitter grammars compiled into the binary, including Python, Go, JavaScript, TypeScript, Rust, Java, C, C++, C#, PHP, Ruby, Kotlin, Swift, and many more. Nine language families — Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust — additionally get Hybrid LSP semantic type resolution."
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}
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},
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{
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@@ -444,7 +444,7 @@
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The fastest, most efficient code intelligence engine for AI coding agents. It indexes any
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repository into a persistent knowledge graph — full-indexing an average repo in seconds and the
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Linux kernel in 3 minutes — so your agent answers structural questions with ~120x fewer tokens.
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Tree-sitter parsing across 158 languages, Hybrid LSP type resolution, native executable with verified assets.
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Tree-sitter parsing across 161 languages, Hybrid LSP type resolution, native executable with verified assets.
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</p>
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<div>
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<div class="stat"><div class="number">~120x</div><div class="label">fewer tokens</div></div>
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@@ -483,7 +483,7 @@
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Protocol (MCP)</a> server that indexes a codebase into a persistent knowledge graph of functions,
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classes, call chains, HTTP routes, and cross-service links. Instead of reading files one at a time,
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an AI coding agent queries the graph — answering structural questions with roughly 120x fewer
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tokens. It parses 158 languages and ships as a native executable with authenticated release-owned assets;
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tokens. It parses 161 languages and ships as a native executable with authenticated release-owned assets;
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the native install needs no language runtime.
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</p>
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<p class="muted">
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@@ -606,7 +606,7 @@
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</tbody>
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</table>
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<p class="muted">
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The two-layer pipeline runs a fast syntactic tree-sitter pass for every one of the 158 languages,
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The two-layer pipeline runs a fast syntactic tree-sitter pass for every one of the 161 languages,
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then a type-aware Hybrid LSP pass on top for the families above. Languages without a Hybrid LSP pass
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yet fall back to textual resolution, so you always get an answer.
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</p>
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@@ -699,7 +699,7 @@
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<h2>Features</h2>
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<div class="features">
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<div class="feature">
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<h3>158 languages</h3>
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<h3>161 languages</h3>
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<p>Python, Go, JS, TS, TSX, Rust, Java, C++, C#, C, PHP, Ruby, Kotlin, Scala, Zig, Elixir, Haskell, OCaml, Swift, Dart, Lean 4, and many more via vendored tree-sitter grammars compiled into the binary.</p>
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</div>
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<div class="feature">
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+1
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# codebase-memory-mcp
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> An open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query the graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens (~3,400 vs ~412,000 across five structural queries). Parses 158 languages via vendored tree-sitter grammars, with Hybrid LSP semantic type resolution for Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust. It also generates semantic graph edges (SEMANTICALLY_RELATED for vocabulary-mismatch matches, SIMILAR_TO for near-clone detection) and supports semantic vector search via bundled nomic-embed-code embeddings compiled into the native executable — no API key, no Ollama, no Docker. Ships a native executable with an authenticated integration asset; the UI variant adds one content-addressed frontend pack. All processing is local — the embeddings run on-device; there is no embedded LLM and no API key.
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> An open-source Model Context Protocol (MCP) server that indexes a codebase into a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. AI coding agents query the graph instead of reading files one by one, answering structural questions with roughly 120x fewer tokens (~3,400 vs ~412,000 across five structural queries). Parses 161 languages via vendored tree-sitter grammars, with Hybrid LSP semantic type resolution for Python, TypeScript/JavaScript, PHP, C#, Go, C/C++, Java, Kotlin, and Rust. It also generates semantic graph edges (SEMANTICALLY_RELATED for vocabulary-mismatch matches, SIMILAR_TO for near-clone detection) and supports semantic vector search via bundled nomic-embed-code embeddings compiled into the native executable — no API key, no Ollama, no Docker. Ships a native executable with an authenticated integration asset; the UI variant adds one content-addressed frontend pack. All processing is local — the embeddings run on-device; there is no embedded LLM and no API key.
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## Key facts
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@@ -18,7 +18,7 @@ release-owned runtime assets. Chocolatey retains the complete extracted runtime
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set and exposes its executable through a PATH shim.
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Full indexes an average repository in milliseconds, the Linux kernel (28M LOC) in
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3 minutes. Answers structural queries in under 1ms. Supports 159 languages via
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3 minutes. Answers structural queries in under 1ms. Supports 161 languages via
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vendored tree-sitter grammars. No hosted service or API key. Plug and play across
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11 coding agents including Claude Code, Codex CLI, Gemini CLI, and Zed.
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+2
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@@ -7,7 +7,7 @@
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**The fastest and most efficient code intelligence engine for AI coding agents.** Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. This npm wrapper downloads, verifies, and caches the selected native runtime set: the executable, its authenticated integration asset, and—when requested—the content-addressed UI pack.
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High-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-sitter/) AST analysis across 158 languages — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 15 MCP tools. No hosted service or API key; this package requires Node.js to manage and launch the cached native runtime set. Plug and play across 44 automatic/conditional client surfaces.
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High-quality parsing through [tree-sitter](https://tree-sitter.github.io/tree-sitter/) AST analysis across 161 languages — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 15 MCP tools. No hosted service or API key; this package requires Node.js to manage and launch the cached native runtime set. Plug and play across 44 automatic/conditional client surfaces.
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## Installation
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@@ -29,7 +29,7 @@ Restart your agent. Say **"Index this project"** — done.
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- **Extreme indexing speed** — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline with LZ4 compression and in-memory SQLite.
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- **Plug and play** — verified native runtime sets for macOS (arm64/amd64), Linux (arm64/amd64), and Windows (arm64/amd64). No Docker or API keys; Node.js owns package download, cache repair, and launch.
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- **158 languages** — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
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- **161 languages** — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
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- **120x fewer tokens** — 5 structural queries: ~3,400 tokens vs ~412,000 via file-by-file search.
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- **44 supported automatic/conditional client surfaces** — `install` configures the appropriate MCP, durable-context, and documented hook surfaces without widening client permissions.
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- **Detected automatically (38)** — Claude Code, Codex CLI, Gemini CLI, Zed, OpenCode, Antigravity, Aider, KiloCode, VS Code, Cursor, Windsurf, Augment / Auggie, OpenClaw, Kiro, Junie, Hermes, OpenHands, Cline, Warp, Qwen Code, GitHub Copilot CLI, Factory Droid, Crush, Goose, Mistral Vibe, Grok Build, Qoder CLI, Kimi Code CLI, GitLab Duo CLI, Rovo Dev CLI, Amp, Devin CLI / Local, Tabnine, Amazon Q Developer IDE, CodeBuddy Code CLI, IBM Bob Shell, Pochi, and Pi.
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@@ -293,7 +293,7 @@ build_mcpb_bundle() {
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"name": "codebase-memory-mcp",
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"display_name": "Codebase Memory",
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"version": "$mcpb_version",
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"description": "Codebase knowledge graph for AI agents — 159 languages, sub-ms queries, 99% fewer tokens.",
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"description": "Codebase knowledge graph for AI agents — 161 languages, sub-ms queries, 99% fewer tokens.",
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"author": { "name": "DeusData", "url": "https://github.com/DeusData" },
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"repository": { "type": "git", "url": "https://github.com/DeusData/codebase-memory-mcp" },
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"homepage": "https://deusdata.github.io/codebase-memory-mcp/",
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"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
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"name": "io.github.DeusData/codebase-memory-mcp",
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"title": "Codebase Memory",
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"description": "Codebase knowledge graph for AI agents \u2014 159 languages, sub-ms queries, 99% fewer tokens.",
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"description": "Codebase knowledge graph for AI agents \u2014 161 languages, sub-ms queries, 99% fewer tokens.",
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"repository": {
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"url": "https://github.com/DeusData/codebase-memory-mcp",
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"source": "github"
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@@ -73,7 +73,7 @@ EXEMPT=(
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)
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failures=0
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claims=0
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distinct=0
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for path in "${SURFACES[@]}"; do
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if [[ ! -f "$path" ]]; then
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@@ -90,7 +90,7 @@ for path in "${SURFACES[@]}"; do
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continue
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fi
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while IFS= read -r n; do
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claims=$((claims + 1))
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distinct=$((distinct + 1))
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if [[ "$n" != "$expected" ]]; then
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echo "FAIL: $path claims $n languages, $expected grammars are vendored" >&2
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failures=$((failures + 1))
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@@ -120,4 +120,4 @@ if ((failures > 0)); then
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exit 1
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fi
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echo "Language-count contract passed ($claims claims across ${#SURFACES[@]} surfaces, registry = $expected)"
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echo "Language-count contract passed (${#SURFACES[@]} surfaces, $distinct distinct value(s) checked, $expected grammars vendored)"
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