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Enterprise-grade codebase intelligence for AI agents — hybrid semantic search over 40M+ LOC, locally.
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View guide →grepai: semantic code search and call graphs for AI agents, running 100% locally on your machine.
grepai is an open-source dev tools project by yoanbernabeu (yoanbernabeu/grepai on GitHub). It has 1,902 GitHub stars and 161 forks, is released under the MIT license, and was last updated on GitHub on 21 September 2026.
Decide which embedding provider to use; a local Ollama model keeps everything on your machine.
Install grepai with Homebrew or the install script, and pull the embedding model if you use Ollama.
Initialise grepai in your project and start the watch daemon to build and maintain the index.
Search semantically in plain language and trace callers before changing a function; optionally connect it to your agent as an MCP tool.
Natural-language queries find conceptually related code even when names differ, for example finding session handling when you search for authentication logic.
Trace who calls a function before changing it, reducing breakage from blind edits.
With local embedding providers, your code never leaves your machine, suiting private and regulated codebases.
A file watcher keeps the index up to date automatically as you edit.
Designed to feed coding agents relevant context instead of raw grep output, and exposes itself as an MCP tool agents can call directly.
Generated completions for zsh, bash, fish, and PowerShell cover commands, flags, and dynamic values like workspace and provider names.
Most users install the released binary instead of building from source.
git clone https://github.com/yoanbernabeu/grepai.git
cd grepaiInstall via Homebrew (or the README's install script) and pull the default embedding model.
brew install yoanbernabeu/tap/grepai
ollama pull nomic-embed-textRun these inside your project.
grepai init
grepai watch
grepai search "error handling"It is created and maintained by Yoan Bernabeu.
Yes, grepai is MIT-licensed and free. With Ollama or LM Studio embeddings there is no per-query cost at all.
No paid key is required: Ollama is the recommended default and runs locally. OpenAI embeddings are an optional alternative that would use your key.
ripgrep matches exact text and regex; grepai matches meaning through embeddings and adds call-graph tracing, at the cost of maintaining a local index.
Install it, run ollama pull nomic-embed-text, then grepai init and grepai watch in your project, and search in plain language.
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