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Airweave

Airweave lets agents search any app by connecting to apps, productivity tools, databases and document stores and turning their contents into searchable knowledge bases.

Introduction

Airweave enables agents to search and retrieve content from apps, productivity tools, databases and document stores. It handles extraction, embedding and serving, exposing a unified search interface via REST API or MCP.

Key Features

  • Syncs and extracts data from 25+ sources with minimal configuration.
  • Entity extraction and transformation pipeline with incremental updates and versioning.
  • Exposes search via REST API or MCP; supports multi-tenant OAuth2 flows.
  • SDKs for Python and TypeScript for easy integration.

Use Cases

  • Build searchable knowledge bases for RAG systems and intelligent Q&A.
  • Allow agents to access app data (documents, email, calendar) for automation tasks.
  • Provide semantic search for internal help desks, recommendations and knowledge workflows.

Technical Highlights

  • Backend: FastAPI; vector stores like Qdrant for embeddings.
  • Frontend: React + TypeScript with a connector-based UI for managing sources.
  • Deployment: Docker Compose for local dev; Kubernetes for production; also offers Airweave Cloud managed service.

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Airweave
Resource Info
🌱 Open Source 📚 RAG 💾 Data