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High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
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 single static binary for macOS, Linux, and Windows — download, run , done.
Selected from shared topics, language and repository description—not editorial ratings.
dajneem23 /
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
65/100 health
installHigh-quality parsing through tree-sitter AST analysis across all 158 languages, enhanced with Hybrid LSP semantic type resolution 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. Zero dependencies. Plug and play across 43 supported automatic/conditional client surfaces.
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 (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.
Security & Trust — This tool reads your codebase and writes to your agent configuration files. That is what it is designed to do. If you prefer to audit before running, the full source is here — every release binary is signed, checksummed, and scanned by 70+ antivirus engines. All processing happens 100% locally; your code never leaves your machine. Found a security issue? We want to know — see SECURITY.md. Security is Priority #1 for us.
install → restart agent → done.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 for the complete matrix and manual/UI-only boundaries.localhost:9749 (optional UI binary variant).Resource nodes for K8s kinds, Module nodes for Kustomize overlays with IMPORTS edges to referenced resources.One-line install (macOS / Linux):
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash
With graph visualization UI:
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash -s -- --ui
Windows (PowerShell):
# 1. Download the installer
Invoke-WebRequest -Uri https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.ps1 -OutFile install.ps1
# 2. (Optional but recommended) Inspect the script
notepad install.ps1
# 3. Unblock the downloaded file (removes Mark-of-the-Web restriction added by browsers/Invoke-WebRequest)
Unblock-File .\install.ps1
# 4. Run it
.\install.ps1
Note: If you see a script execution policy error, run
Set-ExecutionPolicy -Scope Process Bypassfirst, or invoke withPowerShell -ExecutionPolicy Bypass -File .\install.ps1.
Options: --ui (graph visualization), --skip-config (binary only, no agent setup), --dir=<path> (custom location).
Restart your coding agent. Say "Index this project" — done.
Download the archive for your platform from the latest release:
codebase-memory-mcp-<os>-<arch>.tar.gz (macOS/Linux) or .zip (Windows) — standardcodebase-memory-mcp-ui-<os>-<arch>.tar.gz / .zip — with graph visualizationExtract and install (each archive includes install.sh or install.ps1):
macOS / Linux:
tar xzf codebase-memory-mcp-*.tar.gz
./install.sh
Windows (PowerShell):
Expand-Archive codebase-memory-mcp-windows-amd64.zip -DestinationPath .
Unblock-File .\install.ps1
.\install.ps1
Restart your coding agent.
The install command automatically strips macOS quarantine attributes and ad-hoc signs the binary — no manual xattr/codesign needed.
The install command auto-detects installed coding agents and configures their documented MCP entries plus durable instructions, skills, and lifecycle hooks where supported.
The UI ships as a separate ui build (it embeds the frontend). The default install on every channel is the lean, headless server; opt into the UI build with:
--ui (see Quick Start)CBM_VARIANT=ui npm install -g codebase-memory-mcpCBM_VARIANT=ui pip install codebase-memory-mcpcodebase-memory-mcp-ui-<os>-<arch> archiveThen run it:
codebase-memory-mcp --ui=true --port=9749
Open http://localhost:9749 in your browser. The UI runs as a background thread alongside the MCP server — it's available whenever your agent is connected.
Enable automatic indexing on MCP session start:
codebase-memory-mcp config set auto_index true
When enabled, new projects are indexed automatically on first connection. Previously-indexed projects are registered with the background watcher for ongoing git-based change detection. Configurable file limit: config set auto_index_limit 50000.
Watcher registration is controlled separately by auto_watch (default true). Set config set auto_watch false to keep a session from registering its project with the background watcher — useful when working across many projects and you want each session contained to explicit indexing.
codebase-memory-mcp update
The MCP server also checks for updates on startup and notifies on the first tool call if a newer release is available.
codebase-memory-mcp uninstall
Removes owned agent config entries, skills, hooks, instructions, and the installed binary. Existing graph indexes are listed and deleted only after confirmation.
get_architecture returns languages, packages, entry points, routes, hotspots, boundaries, layers, and clusters in a single callmanage_adr persists architectural decisions across sessionsdetect_changes maps uncommitted changes to affected symbols with risk classificationMATCH (f:Function)-[:CALLS]->(g) WHERE f.name = 'main' RETURN g.namesemantic_query): vector search across the entire graph, powered by bundled Nomic nomic-embed-code embeddings (40K tokens, 768d int8) compiled into the binary — no API key, no Ollama, no Docker. 11-signal combined scoring (TF-IDF, RRI, API/Type/Decorator signatures, AST profiles, data flow, Halstead-lite, MinHash, module proximity, graph diffusion).cbm_camel_split tokenizer (camelCase / snake_case aware)search_graph): regex name patterns, label filters, min/max degree, file scopingsearch_code): graph-augmented grep over indexed files onlyEMITS / LISTENS_ON) for Socket.IO, EventEmitter, and generic pub-sub patterns across 8 languages with constant resolutionCROSS_* edges link nodes across multiple repos indexed under the same storeCALLS, IMPORTS, DEFINES, IMPLEMENTS, INHERITSHTTP_CALLS, ASYNC_CALLS (cross-service)EMITS, LISTENS_ON (channels)DATA_FLOWS with arg-to-param mapping + field access chainsSIMILAR_TO (MinHash + LSH near-clone detection, Jaccard scored)SEMANTICALLY_RELATED (vocabulary-mismatch, same-language, score ≥ 0.80)@myorg/pkg, github.com/foo/bar, use my_crate::foo resolved via manifest scanning (package.json, go.mod, Cargo.toml, pyproject.toml, composer.json,