README

Open-source context retrieval layer for AI agents and RAG systems.


  
  
  
  



  
  
  
  
  



  


 What is Airweave?
Airweave connects to your apps, tools, and databases, continuously syncs their data, and exposes it through a unified, LLM-friendly search interface. AI agents query Airweave to retrieve relevant, grounded, up-to-date context from multiple sources in a single request.

 Where it fits
Airweave sits between your data sources and AI systems as shared retrieval infrastructure. It handles authentication, ingestion, syncing, indexing, and retrieval so you don't have to rebuild fragile pipelines for every agent or integration.

 How it works
1. Connect your apps, databases, and documents (50+ integrations)
2. Airweave syncs, indexes, and exposes your data through a unified retrieval layer
3. Agents query Airweave via our SDKs, REST API, MCP, or native integrations with popular agent frameworks
4. Agents retrieve relevant, grounded context on demand

 Quickstart

 Cloud-hosted: app.airweave.ai



 Self-hosted

bash
git clone https://github.com/airweave-ai/airweave.git
cd airweave
./start.sh


→ http://localhost:8080

 Requires Docker and docker-compose

 Supported Integrations














































 SDKs

bash
pip install airweave-sdk         Python
npm install @airweave/sdk        TypeScript


python
from airweave import AirweaveSDK

client = AirweaveSDK(api_key="YOUR_API_KEY")
results = client.collections.search.instant(
    readable_id="my-collection",
    query="Find recent failed payments"
)





 CLI

Search collections, manage sources, and trigger syncs from your terminal:

bash
pip install airweave-cli


bash
airweave auth login
airweave search "quarterly revenue figures" --collection finance-data


The CLI outputs rich interactive results in your terminal and clean JSON when piped — making it work for both developers and AI agents.



 Tech Stack

- Frontend: React/TypeScript with ShadCN
- Backend: FastAPI (Python)
- Databases: PostgreSQL (metadata), Vespa (vectors)
- Workers: Temporal (orchestration), Redis (pub/sub)
- Deployment: Docker Compose (dev), Kubernetes (prod)

 Contributing

We welcome contributions! See our Contributing Guide.

 License

MIT License


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