grepai
grepai is a privacy-first semantic code search CLI tool that enables natural language search across codebases by understanding code meaning through AI embeddings, making it easier for both developers and AI agents to find relevant code instantly.
https://yoanbernabeu.github.io/grepai?ref=producthunt
Product Information
Updated:Jan 27, 2026
What is grepai
grepai represents the next evolution of code search tools, designed for the AI era as an alternative to traditional text-based search tools like grep. It's an open-source CLI tool written in Go that creates a semantic map of your codebase, allowing developers and AI coding assistants to search code based on meaning rather than just keywords. Unlike its predecessor grep from 1973, grepai introduces semantic understanding capabilities while maintaining complete privacy by running 100% locally on your machine.
Key Features of grepai
grepai is a privacy-first semantic code search CLI tool that uses vector embeddings to understand and index code meaning rather than just text patterns. It enables natural language searches, real-time code indexing, call graph analysis, and seamless integration with AI coding assistants. The tool runs locally, supports multiple programming languages, and offers both local and cloud-based embedding options while maintaining code privacy.
Semantic Code Search: Uses vector embeddings to understand code meaning, allowing developers to search using natural language queries instead of exact text matches
Real-time Index Updates: Maintains a live index of your codebase through a background daemon that automatically monitors and updates when files change
Call Graph Analysis: Traces function calls and dependencies across the codebase, helping developers understand relationships between different parts of the code
AI Integration Support: Offers native MCP integration and JSON output format for seamless integration with AI coding assistants like Cursor, Claude Code, and Windsurf
Use Cases of grepai
AI-Assisted Development: Provides AI coding assistants with semantic context about codebases, reducing token usage and improving response accuracy
Code Refactoring: Helps developers understand code dependencies and impact before making changes, enabling confident refactoring
Large Codebase Navigation: Enables efficient exploration and understanding of large codebases through natural language queries and semantic search
Pros
100% local operation with complete privacy
Supports multiple programming languages
Easy integration with AI coding tools
No manual re-indexing required
Cons
Requires initial setup and configuration
Local embedding might be slower than cloud options
Depends on external embedding providers (either Ollama locally or OpenAI in cloud)
How to Use grepai
Install grepai: Run the installation command: 'curl -sSL https://raw.githubusercontent.com/yoanbernabeu/grepai/main/install.sh | sh'
Initialize project: Navigate to your project directory and run 'grepai init' to create initial configuration in .grepai/config.yaml
Configure embedder: Edit .grepai/config.yaml to set up embedder - either local (Ollama) or cloud (OpenAI). Default is Ollama with nomic-embed-text model
Start indexing daemon: Run 'grepai watch' to start the background indexing process that maintains the codebase index. Can use --background flag for daemon mode
Search code semantically: Use 'grepai search "your natural language query"' to find relevant code. Be descriptive in queries like 'user authentication flow' rather than just 'auth'
Analyze call graphs: Use trace commands to analyze function relationships: 'grepai trace callers "FunctionName"' to find who calls a function, 'grepai trace callees' to see what it calls
Get AI-friendly output: Add --json flag for JSON output, --compact to reduce token usage. Example: 'grepai search "error handling" --json --compact'
Integrate with AI tools: For AI integration, use 'grepai mcp-serve' to run as MCP server or configure in tools like Cursor, Claude Code, or Windsurf
grepai FAQs
grepai is a privacy-first CLI tool for semantic code search that indexes the meaning of code, not just text patterns. It allows developers to find relevant code using natural language queries and trace function calls.
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