Airweave – Connect Any App to Your AI Agents

TL;DR

TL;DR: Airweave is an open-source context retrieval layer that continuously syncs data from 50+ apps and databases into a unified, LLM-friendly search interface — letting AI agents query heterogeneous sources in a single request.

What Is Airweave?

Airweave sits between your data sources and AI systems as shared retrieval infrastructure. Rather than building fragile per-integration pipelines for every agent, you connect everything to Airweave once and let agents query it through a unified interface.

From the project README:

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.

Key capabilities:

• 50+ integrations — Airtable, Asana, Bitbucket, Box, Cal.com, ClickUp, Confluence, Dropbox, Intercom, and more
• Multiple query interfaces — SDKs (Python), REST API, MCP server, and native integrations with popular agent frameworks
• Continuous sync — data stays up-to-date without manual re-ingestion
• Self-hostable — single start.sh script with Docker; no cloud required
• MIT License — fully open source

Setup Workflow

Prerequisites

• Docker and docker-compose installed
• 4GB+ RAM recommended for local development

Step 1: Clone the Repository

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

Step 2: Start Airweave

./start.sh

The startup script automatically:

• Creates a .env file from .env.example
• Generates required secrets (ENCRYPTION_KEY, STATE_SECRET)
• Starts all services with health checks
• Optionally prompts for OpenAI or Mistral API keys

Step 3: Verify

Once startup completes (2–3 minutes on first run), the web UI is available at:

http://localhost:8080

Useful management commands:

| Command | Description | |---|---| | ./start.sh --restart | Restart all services | | ./start.sh --skip-frontend | Start backend only | | ./start.sh --destroy | Clean up all services and data |

How Agents Query Airweave

Once connected, agents retrieve context through four interfaces:

Python SDK:

from airweave import Airweave

client = Airweave(api_key="your-key")
results = client.search(
    query="Q3 sales figures from Salesforce",
    sources=["salesforce", "googlesheets"],
    top_k=10
)

REST API:

curl -X POST https://api.airweave.ai/v1/search \
  -H "Authorization: Bearer $AIRWEAVE_KEY" \
  -d '{"query": "customer feedback from last week", "sources": ["intercom", "zendesk"]}'

MCP Server: Airweave ships an MCP server so AI agents with MCP support (Claude, Cursor, etc.) can query it directly without custom code.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: airweave.ai (cloud hosted)
• Source repository: github.com/airweave-ai/airweave — README read in full
• License: MIT (verified via LICENSE file in repo)
• HN launch thread: news.ycombinator.com/item?id=45427482 (YC X25 Launch)
• Stars: 6,480 (verified via GitHub API)
• Source last checked: 2026-07-13

Practical Evaluation Checklist

• [ ] Clone and run ./start.sh on a clean machine
• [ ] Verify UI loads at http://localhost:8080
• [ ] Connect at least one integration (e.g., a GitHub repo or Google Sheets)
• [ ] Query via Python SDK and verify results are grounded in connected data
• [ ] Test the MCP server interface with an MCP-compatible AI client
• [ ] Confirm continuous sync by updating source data and re-querying

FAQ

Q: What is the difference between Airweave and a vector database like Pinecone or Weaviate?

A: Vector databases store and retrieve embeddings. Airweave sits one layer higher — it handles authentication, ingestion, and retrieval across heterogeneous external sources (SaaS tools, databases, file storage) and exposes the result through an LLM-friendly interface. You can think of it as “data connectivity for AI agents” rather than a standalone vector store.

Q: Does Airweave require all my data to be indexed in a vector store?

A: No. Airweave maintains its own index over your connected data sources. Agents query the index via SDK, REST, or MCP — they do not directly access your underlying systems after the initial connection.

Q: Can I self-host Airweave without paying for cloud storage?

A: Yes. The ./start.sh script launches all services (backend, frontend, PostgreSQL, Redis) locally via Docker. No cloud dependency is required for core functionality.

Q: Which LLM providers does Airweave support?

A: Airweave is model-agnostic on the query side. The backend can use OpenAI, Mistral, or any LLM that supports function calling. API keys are configured via environment variables.

Q: How does Airweave handle authentication with connected apps?

A: Each integration has its own OAuth or API key credential stored securely in the backend. Airweave handles token refresh and access scoping per integration.

Security Notes

• Secrets (ENCRYPTION_KEY, STATE_SECRET) are generated at startup and stored in the local .env file
• Credentials for connected apps are stored encrypted at rest in the local PostgreSQL instance
• No data is sent to Airweave Cloud unless you explicitly opt in
• MCP interface exposes only search/query operations, not write operations to connected apps

Conclusion

Airweave solves the “AI agent needs to know what’s in my tools” problem by acting as a universal retrieval bridge. Instead of building and maintaining separate integrations for every app an agent might need, you connect everything once to Airweave and let the retrieval layer handle the rest. For developers building multi-agent systems or AI assistants that need to pull data from heterogeneous sources, this reduces integration boilerplate significantly.

If you have used Airweave or are evaluating it, the comments are open.