Аудио
cheetah
On-device streaming speech-to-text engine for real-time apps.
★ 670 звёздApache-2.0
Смотреть руководство →codealive-mcp — проект с открытым исходным кодом из категории «RAG и базы знаний» от CodeAlive-AI (CodeAlive-AI/codealive-mcp на GitHub). У него 90 звёзд на GitHub и 9 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 24 сентября 2026.
Codealive-mcp, in Python — built for context engine for large codebases, exposed throug...
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around claude code for a focused workflow.
Built around cursor for a focused workflow.
Built around graphrag for a focused workflow.
Built around large codebase for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/CodeAlive-AI/codealive-mcp.gitSet up Python dependencies.
cd codealive-mcp && pip install -r requirements.txtLaunch the application.
cd codealive-mcp && python main.pyIt context engine for large codebases, exposed through MCP. Gives AI coding agents precise repository context; benchmarked at frontier-agent quality with ~25x lower model cost and 45% fewer tokens with semantic search, giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
On-device streaming speech-to-text engine for real-time apps.
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