ACI — Augmented Codebase Indexer

Language: English | 简体中文

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Ask your codebase a question. Get a precise answer — down to the line.

ACI indexes your code with embeddings and Tree-sitter AST parsing, then lets you search it with natural language. Results come back with exact file paths and line numbers, not just fuzzy matches.

$ aci search "function that validates JWT tokens"

src/auth/middleware.py:42  verify_token(token: str) -> Claims
src/auth/utils.py:118      decode_and_validate(raw: str) -> dict

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Why ACI?

Most code search tools give you grep or a fuzzy filename match. ACI gives you semantic understanding:

• You describe intent, it finds the implementation
• Hybrid search combines embeddings with keyword/grep for precision
• Multi-level indexing: raw chunks, function summaries, class summaries, file summaries
• Incremental updates — only re-indexes what changed
• Works with Python, JavaScript/TypeScript, Go, Java, C, C++

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Get Started

# Install
uv sync

# Configure (add your embedding API key)
cp .env.example .env

# Index your codebase
aci index /path/to/your/project

# Search
aci search "error handling in the HTTP layer"

That’s it. See Installation for full setup details.

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Interfaces

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Documentation

• Installation & Configuration
• CLI Usage & Search Syntax
• MCP Integration
• Security
• Chunking Algorithm

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MCP — Let Your LLM Search the Code

ACI ships a first-class MCP server so agents can index and search your codebase directly.

{
  "mcpServers": {
    "aci": {
      "command": "uv",
      "args": ["run", "aci-mcp"],
      "cwd": "/path/to/your/project"
    }
  }
}

For Docker-based deployment (recommended for agentic tools), see MCP Integration.

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Requirements

• Python 3.10+
• Qdrant (auto-started locally via Docker, or point to Qdrant Cloud)
• Any OpenAI-compatible embedding API (OpenAI, SiliconFlow, etc.)

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Development governance: AGENTS.md