CocoIndex
Open-source framework that keeps codebases, docs, and knowledge continuously indexed for AI agents. Delivers fresh context with minimal processing overhead.
CocoIndex is an open-source alternative to:
CocoIndex — Incremental Data Framework for AI Agents
CocoIndex is an open-source incremental data framework designed to provide AI agents with continuously fresh context. It eliminates the need for re-processing entire datasets by tracking changes and updating only the affected portions, ensuring sub-second freshness at any scale.
Key Features
- Incremental Processing: Only re-indexes deltas, ensuring minimal processing overhead and sub-second freshness regardless of repository size.
- Real-time Codebase Indexing: Maintains a live index of your codebase synchronized with every commit, providing structured context for coding and code-review agents.
- Semantic Search: Enables agents to find code by meaning, patterns, and intent rather than string matches.
- Call Graphs & Blast Radius Analysis: Visualizes dependencies and impact of changes before deployment, helping teams understand what a modification affects.
- Global Repository View: Identifies duplicates and provides architectural insights across the entire codebase, not just individual files.
- Persistent Data Pipeline: Features version tracking, caching, and lineage for reliable, explainable data transformations.
- Multi-source Integration: Supports codebases, meeting notes, web APIs, file systems, databases, and more as input sources.
- Python Native: Built with a Python API for easy integration and customization.
Tech / Self-Hosting
CocoIndex is open-source and designed for self-hosting. It provides:
- Control Plane: Manages persistent data pipelines, caching, and scheduling.
- Version Tracking: Tracks changes in code, schema, and data end-to-end.
- Observability: Monitors throughput, freshness, and cost metrics.
- Failure Management: Implements retries, back-off strategies, and dead letter queues to ensure data integrity.
The framework is optimized for low-latency, parallel processing and can scale to handle large repositories and complex data workflows.