airweave open source analysis
Context retrieval for AI agents across apps and databases
Project overview
⭐ 5524 · Python · Last activity on GitHub: 2026-01-06
Why it matters for engineering teams
Airweave addresses the challenge of integrating context retrieval across multiple applications and databases, which is essential for creating responsive AI agents. It enables machine learning and AI engineering teams to efficiently connect large language models with relevant data sources, improving the accuracy and relevance of AI-driven responses. The project is mature enough for production use, offering a reliable, open source tool for engineering teams that require a self hosted option for managing knowledge graphs and vector databases. However, it may not be the best fit for teams seeking a fully managed cloud service or those with minimal AI infrastructure experience, as it requires some setup and maintenance effort.
When to use this project
Airweave is a strong choice when teams need a production ready solution for context retrieval that integrates with various databases and applications. Teams should consider alternatives if they prefer a fully managed service or lack the resources to manage a self hosted AI agent framework.
Team fit and typical use cases
Machine learning engineers and AI engineers benefit most from Airweave, using it to build intelligent agents that leverage knowledge graphs and vector databases for enhanced search and retrieval. It typically appears in products requiring sophisticated AI-driven search capabilities, such as recommendation systems, chatbots, and data discovery platforms.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.