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The open-source context retrieval layer for agents
Open-source context retrieval for AI agents across workspaces. It connects to productivity tools, email, document stores, or any private data source and transforms their contents into searchable knowledge bases for agents.
Airweave is an open-source context retrieval layer that connects AI agents and RAG systems to apps, tools, and databases, providing real-time data synchronization and a unified search interface for grounded, up-to-date responses.
Airweave sits between data sources and AI systems, acting as shared retrieval infrastructure. It connects to apps, tools, and databases, syncs data in real time, and exposes it through a unified search interface. Users create a searchable collection, connect sources with authentication, and then query the collection to retrieve relevant context in natural language, enabling grounded answers from real data sources on demand via an LLM-friendly interface.
Who it’s for: Engineering teams building AI agents and RAG pipelines in need of reliable, up-to-date data retrieval from multiple data sources across apps and databases.
Website mentions signing up, demo booking, SDK usage, and enterprise-focused messaging indicate active product development and growth; explicit hiring/funding info not provided.
Building Airweave, the dev tool that turns any app into agent knowledge.
Open-source tool enabling AI agents to search any app
Airweave provides an open-source tool that makes data from productivity apps, databases, and document stores searchable by AI agents via a simple search endpoint. It targets agents interacting with tools like Google Drive, Slack, Jira, and Notion, enabling semantically searchable MCP-ready data to improve agent responses.
Context layer for production-grade AI agents
AI to understand engineering work
