Codebase Memory Mcp
Codebase Memory Mcp is a high-performance code intelligence server that implements the Model Context Protocol (MCP), designed to provide AI assistants with a structured, queryable knowledge graph of an entire codebase. It accepts an entire repository, indexes it into a persistent semantic graph of code entities—such as functions, classes, variables, imports, and their relationships—across 158 programming languages. The entire indexing process completes in mere milliseconds, even for large projects, making it suitable for real-time development workflows. The tool operates as a standalone binary, requiring no external dependencies or complex setup. Once deployed, it watches a repository directory (or accepts a path) and builds a persistent knowledge graph that survives server restarts. This graph enables sub-millisecond queries that can retrieve precise code context, including definitions, references, call hierarchies, and cross-file dependencies. Because it directly understands code structure, it can return exactly the relevant snippets, reducing the token footprint for LLM prompts by up to 99% compared to traditional file-based or search-based approaches. Key features include broad language support (158 languages), persistent on-disk storage for instantaneous startup, sub-millisecond query latency, and a simple MCP interface that works with any MCP-compliant client (e.g., Claude Desktop, Continue, Cursor). The tool is optimized for speed and resource efficiency, making it ideal for embedding into AI coding assistants, automated documentation tools, and continuous integration pipelines that require fast, accurate code understanding. Under the hood, it likely leverages incremental parsing and a graph database to minimize reindexing time and memory usage. The knowledge graph can answer complex queries such as “find all functions that call X” or “what are the dependencies of module Y” orders of magnitude faster than grep or conventional LSP servers. By offloading code i
- Category
- Dev Tools
- Pricing
- Open Source
Problem solved
Developers and AI engineers often grapple with feeding relevant code context to large language models without overwhelming the context window with unnecessary tokens. Traditional methods like whole-file reading or keyword search produce tons of irrelevant code, leading to higher API costs, slower response times, and poorer model performance. Codebase Memory Mcp solves this by precomputing a compact, semantic representation of the codebase, allowing queries to fetch only the most relevant definitions and relationships in sub-milliseconds. This reduces token usage by up to 99% and enables faster, more accurate AI responses for code-related tasks.
Use cases
- AI code assistant integration: Connect to Claude Desktop or any MCP client to provide real-time, token-efficient code context when answering questions about the codebase.
- Onboarding and code exploration: New team members can query the knowledge graph to quickly understand module dependencies, locate key functions, and grasp project structure.
- Automated documentation: Extract endpoint definitions, class hierarchies, and data models to generate up-to-date API docs or architecture diagrams.
- Dependency analysis and refactoring: Identify all callers of a function, dead code, or tightly coupled modules before refactoring.
- CI/CD code quality checks: Integrate into pipelines to verify that new commits adhere to architectural rules by querying the graph for unexpected dependencies.
- Token optimization for LLM-powered IDEs: Use in editors like Cursor or Continue to drastically cut prompt sizes and latency when requesting code completions or explanations.
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Blazing fast: indexes repos in milliseconds, sub-ms queries.
- Massive language support: 158 programming languages.
- Token efficient: reduces context tokens by up to 99%.
- Easy to deploy: single static binary with no dependencies.
- Persistent: knowledge graph survives restarts, no reindexing on startup.
Cons
- Limited to MCP protocol; requires an MCP client.
- May not handle dynamic or uncommon languages perfectly; accuracy might vary.
- No built-in automatic reindexing on file changes; may require manual trigger or polling.
Official website: https://github.com/DeusData/codebase-memory-mcp
Canonical LambdaBase page: https://www.lambdabase.com/tools/codebase-memory-mcp