Turn any codebase into semantically-aware, searchable knowledge for AI-powered workflows.

Key Features

AST-Powered Chunking - Extract functions, classes, and methods from 23+ programming languages
Parent-Child Relationships - Maintain hierarchical chunk-context for complete understanding
Semantic Search - Find relevant code using natural language queries
Multiple Search Modes - Semantic, symbol-based, pattern matching, and hybrid search
Smart Deduplication - Hash-based detection of duplicate code
TOON Format Export - Token-efficient output format for LLM prompts (40-60% token savings)
Full Pipeline Automation - One command to chunk, embed, and store
Docker-Ready - ChromaDB server included

Use Cases

Agentic AI Systems	RAG Applications	Code Intelligence
Dynamic code retrieval for autonomous coding agents	High-precision code retrieval for question answering	Cross-repository code search and discovery
Context provision for code generation	Context injection for code explanation and documentation	Duplicate and similar code detection
Multi-step reasoning over large codebases	Semantic code search across repositories	Legacy codebase analysis and understanding
Tool integration for agent frameworks	Parent-child relationship tracking for complete context	MCP-compliant async architecture

Getting Started

Prerequisites

Python 3.11 or higher
Docker (for ChromaDB)
OpenAI API key (for embeddings)

Installation

pip install contextinator

Verify the installation (requiers chromadb & openai api key setup):

contextinator --help

For detailed setup and configuration, see USAGE.md

Getting Started

Index a repository:

contextinator chunk-embed-store-embeddings \
  --repo-url https://github.com/user/repo \
  --save \
  --collection-name MyRepo

Search your codebase:

# Natural language semantic search
contextinator search "authentication logic" -c MyRepo

# Find specific functions
contextinator symbol authenticate_user -c MyRepo

# Export results in TOON format for LLM consumption
contextinator search "error handling" -c MyRepo --toon results.json

For comprehensive CLI and Python API documentation, see USAGE.md

Acknowledgements

Built with and inspired by amazing open-source projects:

Core Technologies

tree-sitter - Incremental parsing system for AST generation
ChromaDB - AI-native embedding database
OpenAI - Embedding generation API

Inspired By

Serena - Code intelligence and semantic search
Continue - AI-powered code assistant
Tabby - Self-hosted AI coding assistant
Semantic Code Search - Code search and retrieval
Aider - AI pair programming in the terminal
VS Code Copilot Chat - Conversational AI for code

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

TL;DR

Contextinator is a code intelligence tool that uses Abstract Syntax Tree (AST) parsing to extract semantic code chunks, generates embeddings, and stores them in a vector database. This enables AI systems to understand, navigate, and reason about codebases with precision.

Star History

Release files for contextinator 2.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for contextinator 2.0.2
File	Interpreter	ABI	Platform	Reset
contextinator-2.0.2-cp311-cp311-manylinux_2_34_x86_64.whl 1.1 MB Feb 15, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.34+ x86-64	Details

Release files / contextinator-2.0.2-cp311-cp311-manylinux_2_34_x86_64.whl

Download URL	contextinator-2.0.2-cp311-cp311-manylinux_2_34_x86_64.whl
Size	1.1 MB
Tags	CPython 3.11 Linux glibc 2.34+ x86-64
SHA-256 checksum How to use checksums	`745af31eb1fd1b64742a282facefdd990dfc753360426805df432e00ce4a13f3`
BLAKE2b-256 checksum How to use checksums	`0bcd045aa4ecca41ce3c44d67b6d96c0ff893a926282497457c866e9730250a3`
Upload date	Feb 15, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.14`