airweave

This project addresses the challenge of unifying data retrieval for AI agents from multiple, diverse sources such as apps, tools, and databases. It continuously syncs and indexes data, providing a single, LLM-friendly search interface that reduces the need to build custom, brittle pipelines for each integration. The architecture involves a backend with FastAPI and Vespa for vector search, alongside orchestration via Temporal, allowing real-time, grounded context retrieval. It suits ML engineers and AI teams who need to supply agents with up-to-date, relevant information from varied internal systems without reinventing ingestion and indexing. With 5,520 stars and recent commits, it shows active development but limited growth, suggesting it is still gaining traction. It is not ideal if you require a lightweight or single-source search solution, where simpler tools like Haystack or Weaviate might be more appropriate.

Choose this when you need a unified retrieval layer aggregating data from many apps and databases for AI agents, especially if you want to avoid building custom sync and indexing pipelines. If your focus is on a single data source or simpler vector search, consider Haystack or Weaviate as alternatives, which offer more mature ecosystems for those use cases.

Typically deployed in teams building internal retrieval-augmented generation systems that query multiple structured and unstructured data sources, such as combining CRM data, document stores, and analytics databases. Common use cases include internal RAG search over business apps and syncing diverse enterprise data for AI-driven insights.

Latest commit: 2026-01-09. Activity data is based on daily RepoPi snapshots of the GitHub repository.