grepai

Full documentation available here — Installation guides, configuration options, AI agent integration, and more.

A privacy-first, CLI-native way to semantically search your codebase.

Search code by what it does, not just what it's called. grepai indexes the meaning of your code using vector embeddings, enabling natural language queries that find conceptually related code—even when naming conventions vary.

Why grepai?

grep was built in 1973 for exact text matching. Modern codebases need semantic understanding.

	grep / ripgrep	grepai
Search type	Exact text / regex	Semantic understanding
Query	"func.*Login"	"user authentication flow"
Finds	Exact pattern matches	Conceptually related code
AI Agent context	Requires many searches	Fewer, more relevant results

Built for AI Agents

grepai is designed to provide high-quality context to AI coding assistants. By returning semantically relevant code chunks, your agents spend less time searching and more time coding.

Getting Started

Installation

curl -sSL https://raw.githubusercontent.com/yoanbernabeu/grepai/main/install.sh | sh

Or download from Releases.

Quick Start

grepai init                        # Initialize in your project
grepai watch                       # Start background indexing daemon
grepai search "error handling"     # Search semantically
grepai trace callers "Login"       # Find who calls a function

Commands

Command	Description
grepai init	Initialize grepai in current directory
grepai watch	Start real-time file watcher daemon
grepai search <query>	Search codebase with natural language
grepai trace <cmd>	Analyze call graph (callers/callees)
grepai status	Browse index state interactively
grepai agent-setup	Configure AI agents integration
grepai update	Update grepai to the latest version

grepai search "authentication" -n 5       # Limit results (default: 10)
grepai search "authentication" --json     # JSON output for AI agents
grepai search "authentication" --json -c  # Compact JSON (~80% fewer tokens)

Background Daemon

Run the watcher as a background process:

grepai watch --background    # Start in background
grepai watch --status        # Check if running
grepai watch --stop          # Stop gracefully

Logs are stored in OS-specific directories:

Platform	Log Directory
Linux	~/.local/state/grepai/logs/
macOS	~/Library/Logs/grepai/
Windows	%LOCALAPPDATA%\grepai\logs\

Use --log-dir /custom/path to override (must be passed to all commands):

grepai watch --background --log-dir /custom/path    # Start in background
grepai watch --status --log-dir /custom/path        # Check if running
grepai watch --stop --log-dir /custom/path          # Stop gracefully

Self-Update

Keep grepai up to date:

grepai update --check    # Check for available updates
grepai update            # Download and install latest version
grepai update --force    # Force update even if already on latest

The update command:

• Fetches the latest release from GitHub
• Verifies checksum integrity
• Replaces the binary automatically
• Works on all supported platforms (Linux, macOS, Windows)

Call Graph Analysis

Find function relationships in your codebase:

grepai trace callers "Login"           # Who calls Login?
grepai trace callees "HandleRequest"   # What does HandleRequest call?
grepai trace graph "ProcessOrder" --depth 3  # Full call graph

Output as JSON for AI agents:

grepai trace callers "Login" --json

AI Agent Integration

grepai integrates natively with popular AI coding assistants. Run grepai agent-setup to auto-configure.

Agent	Configuration File
Cursor	.cursorrules
Windsurf	.windsurfrules
Claude Code	CLAUDE.md / .claude/settings.md
Gemini CLI	GEMINI.md
OpenAI Codex	AGENTS.md

MCP Server Mode

grepai can run as an MCP (Model Context Protocol) server, making it available as a native tool for AI agents:

grepai mcp-serve    # Start MCP server (stdio transport)

Configure in your AI tool's MCP settings:

{
  "mcpServers": {
    "grepai": {
      "command": "grepai",
      "args": ["mcp-serve"]
    }
  }
}

Available MCP tools:

• grepai_search — Semantic code search
• grepai_trace_callers — Find function callers
• grepai_trace_callees — Find function callees
• grepai_trace_graph — Build call graph
• grepai_index_status — Check index health

Claude Code Subagent

For enhanced exploration capabilities in Claude Code, create a specialized subagent:

grepai agent-setup --with-subagent

This creates .claude/agents/deep-explore.md with:

• Semantic search via grepai search
• Call graph tracing via grepai trace
• Workflow guidance for code exploration

Claude Code automatically uses this agent for deep codebase exploration tasks.

Configuration

Stored in .grepai/config.yaml:

embedder:
  provider: ollama          # ollama | lmstudio | openai
  model: nomic-embed-text
  endpoint: http://localhost:11434  # Custom endpoint (for Azure OpenAI, etc.)
  dimensions: 768           # Vector dimensions (depends on model)
store:
  backend: gob              # gob | postgres
chunking:
  size: 512
  overlap: 50
search:
  boost:
    enabled: true           # Structural boosting for better relevance
trace:
  mode: fast                # fast (regex) | precise (tree-sitter)
external_gitignore: ""      # Path to external gitignore (e.g., ~/.config/git/ignore)

Note

: Old configs without endpoint or dimensions are automatically updated with sensible defaults.

Search Boost (enabled by default)

grepai automatically adjusts search scores based on file paths. Patterns are language-agnostic:

Category	Patterns	Factor
Tests	/tests/, /test/, __tests__, _test., .test., .spec.	×0.5
Mocks	/mocks/, /mock/, .mock.	×0.4
Fixtures	/fixtures/, /testdata/	×0.4
Generated	/generated/, .generated., .gen.	×0.4
Docs	.md, /docs/	×0.6
Source	/src/, /lib/, /app/	×1.1

Customize or disable in .grepai/config.yaml. See documentation for details.

Hybrid Search (optional)

Enable hybrid search to combine vector similarity with text matching:

search:
  hybrid:
    enabled: true
    k: 60

Uses Reciprocal Rank Fusion to merge results. Useful when queries contain exact identifiers.

Embedding Providers

Ollama (Default) — Privacy-first, runs locally:

ollama pull nomic-embed-text

LM Studio — Local, OpenAI-compatible API:

# Start LM Studio and load an embedding model
# Default endpoint: http://127.0.0.1:1234

OpenAI — Cloud-based:

export OPENAI_API_KEY=sk-...

Storage Backends

• GOB (Default): File-based, zero config
• PostgreSQL + pgvector: For large monorepos
• Qdrant: Docker-based vector database

Requirements

• Ollama, LM Studio, or OpenAI API key (for embeddings)
• Go 1.22+ (only for building from source)

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT License - Yoan Bernabeu 2026