Code Memory

code-memory

[Zero Telemetry] [No API Key] [Offline First]

A deterministic, high-precision code intelligence layer exposed as a Model Context Protocol (MCP) server.

• Zero telemetry — your code never leaves your machine
• No API key required — runs entirely locally with sentence-transformers
• 1 min setup — just uvx code-memory and you're ready
• Token saving by 50% — precise code retrieval instead of dumping entire files

Please help star code-memory if you like this project!

Why code-memory?

Finding the right context from a large codebase is expensive, inaccurate, and limited by context windows. Dumping files into prompts wastes tokens, and LLMs lose track of the actual task as context fills up.

Instead of manually hunting with grep/find or dumping raw file text, code-memory runs semantic searches against a locally indexed codebase. Inspired by claude-context, but designed from the ground up for large-scale local search.

Supported Languages

Full AST Support (structural parsing with symbol extraction): Python, JavaScript/TypeScript, Java, Go, Rust, C/C++, Ruby, Kotlin

Fallback Support (whole-file indexing): C#, Swift, Scala, Lua, Shell, Config (yaml/toml/json), Web (html/css), SQL, Markdown

Files matching .gitignore patterns are automatically skipped.

Architecture: Progressive Disclosure

Instead of a single monolithic search, code-memory routes queries through three purpose-built tools:

This forces the LLM to pick the right retrieval strategy before any data is fetched.

Installation

From PyPI (Recommended)

# Install with pippip install code-memory
# Or with uvx (for MCP hosts)uvx code-memory

From Source

# Clone the repogit clone https://github.com/kapillamba4/code-memory.gitcd code-memory
# Install dependenciesuv sync
# Run the MCP server (stdio transport)uv run mcp run code_memory/server.py

Pre-built Binaries (Standalone)

Download standalone executables from GitHub Releases — no Python installation required.

# Linux/macOS: Download and make executablechmod +x code-memory-*./code-memory-*
# Windows: Run directlycode-memory-windows-x86_64.exe

Note: The first run will download the embedding model (~600MB) to ~/.cache/huggingface/. Subsequent runs use the cached model.

Quickstart

Prerequisites

• Python ≥ 3.13
• uv package manager (recommended) or pip

Install uv if you don't have it:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install & Run

# Install from PyPIpip install code-memory
# Or run directly with uvxuvx code-memory

Development

# Run with the MCP Inspector for interactive debugginguv run mcp dev code_memory/server.py
# Run testsuv run pytest tests/ -v
# Lint and formatuv run ruff check .uv run ruff format .
# Build packageuv build
# Build standalone binary (requires pyinstaller)pip install pyinstallerpyinstaller --clean code-memory.spec# Binary output: dist/code-memory

Configure Your MCP Host

You can use either uvx (requires Python) or the standalone binary (no dependencies).

Using uvx (Python required)

Gemini CLI / Gemini Code Assist

Add to your MCP settings (e.g. ~/.gemini/settings.json):

{  "mcpServers": {    "code-memory": {      "command": "uvx",      "args": ["code-memory"]    }  }}

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{  "mcpServers": {    "code-memory": {      "command": "uvx",      "args": ["code-memory"]    }  }}

Claude Code (CLI)

Add to .mcp.json in your project root or ~/.mcp.json for global access:

{  "mcpServers": {    "code-memory": {      "command": "uvx",      "args": ["code-memory"]    }  }}

VS Code (Copilot / Continue)

Add to .vscode/mcp.json in your workspace:

{  "servers": {    "code-memory": {      "command": "uvx",      "args": ["code-memory"]    }  }}

Using Standalone Binary (No Python required)

Replace the path with the location of your downloaded binary:

{  "mcpServers": {    "code-memory": {      "command": "/path/to/code-memory-linux-x86_64"    }  }}

For Windows:

{  "mcpServers": {    "code-memory": {      "command": "C:\\path\\to\\code-memory-windows-x86_64.exe"    }  }}

Shared SSE Server (Reduce Memory Usage)

By default, each MCP host project launches its own code-memory process, which loads the embedding model (~1–2 GB) once per project. To avoid this, you can run a single shared instance over SSE (Server-Sent Events) and point all your MCP hosts at it.

Start the shared server

# Using uvx (recommended)uvx code-memory --transport sse
# Custom port and hostuvx code-memory --transport sse --port 8765 --host 127.0.0.1
# Using standalone binary./code-memory-linux-x86_64 --transport sse

The server listens on http://127.0.0.1:8765/sse by default.

Configure MCP hosts to use the shared server

Instead of launching a new process, point your MCP host at the running SSE endpoint.

Claude Desktop

{  "mcpServers": {    "code-memory": {      "url": "http://127.0.0.1:8765/sse"    }  }}

VS Code (Copilot / Continue)

{  "servers": {    "code-memory": {      "url": "http://127.0.0.1:8765/sse"    }  }}

Claude Code (CLI) — .mcp.json

{  "mcpServers": {    "code-memory": {      "url": "http://127.0.0.1:8765/sse"    }  }}

Tip: Configure uvx code-memory --transport sse to start via a single-instance service manager (e.g. systemd user service, launchd agent, or another one-time login/startup mechanism) so the shared server starts automatically.

Security: The SSE endpoint is unauthenticated. Keep the default --host 127.0.0.1 so only local processes can connect; do not bind to 0.0.0.0 or a public interface unless you've put authentication in front of it.

Configuration

CLI Options

Environment Variables

Example:

CODE_MEMORY_LOG_LEVEL=DEBUG uvx code-memory

Custom Embedding Model

You can use a different embedding model by setting the EMBEDDING_MODEL environment variable:

EMBEDDING_MODEL="BAAI/bge-small-en-v1.5" uvx code-memory

For MCP hosts, add the environment variable to your configuration:

{  "mcpServers": {    "code-memory": {      "command": "uvx",      "args": ["code-memory"],      "env": {        "EMBEDDING_MODEL": "BAAI/bge-small-en-v1.5"      }    }  }}

Note: Changing the embedding model will invalidate existing indexes. You'll need to re-run index_codebase after switching models.

Tools

index_codebase

Indexes or re-indexes source files and documentation in the given directory. Run this before using search_code or search_docs to ensure the database is up to date. Uses tree-sitter for language-agnostic structural extraction and generates dense vector embeddings using sentence-transformers (runs locally, in-process) for semantic search.

index_codebase(directory=".")

search_code

Perform semantic search and find structural code definitions, locate where functions/classes are defined, or map out dependency references (call graphs). Uses hybrid retrieval (BM25 + vector embeddings) to find exact matches and semantic similarities.

search_code(query="parse python files", search_type="definition")search_code(query="how do we establish the database connection", search_type="references")search_code(query="src/auth/", search_type="file_structure")

search_docs

Understand the codebase conceptually — how things work, architectural patterns, SOPs. Searches markdown documentation, READMEs, and docstrings extracted from code.

search_docs(query="how does the authentication flow work?")search_docs(query="installation instructions", top_k=5)

search_history

Debug regressions and understand developer intent through Git history.

search_history(query="fix login timeout", search_type="commits")search_history(query="src/auth/login.py", search_type="file_history", target_file="src/auth/login.py")search_history(query="server.py", search_type="blame", target_file="server.py", line_start=1, line_end=20)

Project Structure

code-memory/├── code_memory/           # Package source│   ├── server.py          # MCP server entry point (FastMCP)│   ├── db.py              # SQLite database layer with sqlite-vec│   ├── parser.py          # Tree-sitter-based code parser│   ├── doc_parser.py      # Markdown documentation parser│   ├── queries.py         # Hybrid retrieval query layer│   ├── git_search.py      # Git history search module│   ├── errors.py          # Custom exception hierarchy│   ├── validation.py      # Input validation functions│   ├── logging_config.py  # Structured logging configuration│   └── api_types.py       # MCP response TypedDicts├── tests/                 # Test suite├── pyproject.toml         # Project metadata & dependencies└── prompts/               # Milestone prompt engineering files

Troubleshooting

"Git repository not found" error

Make sure you're running search_history from within a git repository. The tool searches upward from the current directory to find .git.

Empty search results

Run index_codebase(directory=".") first to index your code and documentation. The index is stored locally in code_memory.db.

Slow indexing

Indexing generates embeddings using a local sentence-transformers model. The first run downloads the model (~600MB for jina-code-embeddings-0.5b). Subsequent runs are faster.

Embedding model errors

Ensure you have enough disk space and memory. The jina-code-embeddings-0.5b model requires ~1GB RAM when loaded.

Privacy & Security

Your code never leaves your machine. Unlike cloud-based code intelligence tools, code-memory runs entirely locally:

• Zero telemetry — no usage data, analytics, or tracking
• Zero external API calls — all processing happens in-process
• Zero cloud dependencies — works without internet (after initial setup)
• Your data stays local — indexes stored in local SQLite database

This makes code-memory ideal for:

• Proprietary and confidential codebases
• Security-conscious organizations
• Air-gapped development environments
• Privacy-focused developers

See COMPARISON.md for a detailed comparison with cloud-based alternatives.

Air-gapped & Offline Support

code-memory works in completely isolated environments:

Method 1: Pre-built Binary + Cached Model

1. On a connected machine, run code-memory once to cache the embedding model:

   bash

   uvx code-memory# Model downloads to ~/.cache/huggingface/

2. Transfer to air-gapped machine:

   • Standalone binary from GitHub Releases
   • Model cache directory (~/.cache/huggingface/hub/models--*)

3. Run on air-gapped machine — no network required.

Method 2: Offline pip Install

1. Download the wheel from PyPI on a connected machine
2. Transfer and install: pip install code-memory-*.whl
3. Pre-cache the model as above
4. Run offline

Roadmap

• Milestone 1 — Project scaffolding & MCP protocol wiring
• Milestone 2 — Implement search_code with AST parsing + SQLite + sqlite-vec
• Milestone 3 — Implement search_history with Git integration
• Milestone 4 — Implement search_docs with semantic search
• Milestone 5 — Production hardening & packaging

Contributing

See CONTRIBUTING.md for development setup and guidelines.

Changelog

See CHANGELOG.md for version history.

License

MIT