📖 AI-Native Infrastructure: Architecture evolution guide from cloud-native to AI-native

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.

Airweave · Since 2024-12-24
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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.
Airweave
Score Breakdown
📚 RAG 💾 Data