DeusData/codebase-memory-mcp
codebase-memory-mcp
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.
Usage guide
codebase-memory-mcp is an open-source project around aider, ast, claude-code with 4,635 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in C, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating codebase-memory-mcp for C AI workflows.
- Comparing a GitHub project with 4,635 stars and current repository activity.
Pros
- codebase-memory-mcp has visible GitHub traction with 4,635 stars. Topics: aider, ast, claude-code.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
codebase-memory-mcp should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
codebase-memory-mcp architecture preview
codebase-memory-mcp's main path starts at the entry surface, runs through Coding agent runtime, combines OpenAI / Claude / Gemini, Vector index / SQLite / Files / repository context, GitHub / MCP tools, and returns Code changes / developer feedback.
Entry
CLI / terminal entry
codebase-memory-mcp is primarily entered through a developer command or terminal workflow.
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
OpenAI / Claude / Gemini
Model calls are likely routed through OpenAI, Claude, Gemini based on README and topic signals.
OpenAI, Claude, Gemini
Context
Vector index / SQLite / Files / repository context
Context comes from Vector index, SQLite, Files / repository context, which constrains what the model or runtime can use.
Vector index, SQLite, Files / repository context
Tools
GitHub / MCP tools
Tool adapters let the runtime act outside the model through GitHub / MCP tools.
GitHub, MCP tools
Output
Code changes / developer feedback
The final result is code edits, explanations, repository actions, or developer-facing feedback.
coding output
Install tutorial
Before you install
- Local build tools for compiling the project
- A clean working directory for the first test run
Check the runtime environment
codebase-memory-mcp may require a local build toolchain. Check the compiler, package manager, and system dependencies first.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/DeusData/codebase-memory-mcp.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bashAdoption guidance and sources
Practical use cases
Knowledge-base assistant
Use it for document-grounded AI workflows where retrieval quality matters.
High-performance code intelligence MCP server. Indexes codebases into
This is one of the documented reasons to evaluate codebase-memory-mcp before choosing a stack.
Focus area: aider
This is one of the documented reasons to evaluate codebase-memory-mcp before choosing a stack.
AI Coding project comparison
Compare codebase-memory-mcp with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official codebase-memory-mcp setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented codebase-memory-mcp example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is codebase-memory-mcp?
codebase-memory-mcp is an open-source ai coding project. 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.
How do I install codebase-memory-mcp?
Start with the official README. The first detected setup step is: git clone https://github.com/DeusData/codebase-memory-mcp.git.
Is codebase-memory-mcp beginner-friendly?
If you already know the C ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can codebase-memory-mcp be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does codebase-memory-mcp need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt codebase-memory-mcp?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.