Airweave — Context Retrieval Layer for AI Agents, 50+ Integrations

What it is

Airweave is an open-source context retrieval layer that connects AI agents and RAG systems to 50+ applications including Notion, Slack, GitHub, Jira, Salesforce, and Google Docs. It continuously syncs data from these sources and exposes everything through a unified search API. You can query via Python SDK, TypeScript SDK, REST API, or MCP protocol.

Airweave targets teams building AI agents that need access to company data across multiple SaaS tools. It serves as the single infrastructure layer between your data sources and your AI workflows.

How it saves time or tokens

Instead of building and maintaining individual connectors for each data source, Airweave provides pre-built integrations with continuous synchronization. The unified search API means your agent queries one endpoint regardless of whether the data lives in Notion, Slack, or Jira. MCP support lets AI coding tools like Claude Code access your company knowledge directly.

How to use

1. Self-host with Docker: git clone https://github.com/airweave-ai/airweave && cd airweave && ./start.sh.
2. Connect your data sources through the web dashboard or API.
3. Query the unified search API from your AI agent or RAG pipeline.

Example

from airweave import AirweaveClient

client = AirweaveClient(api_key='your-key')

# Search across all connected sources
results = client.search(
    query='quarterly revenue report Q1 2026',
    sources=['notion', 'google-docs', 'slack'],
    limit=10
)

for result in results:
    print(f'{result.source}: {result.title}')
    print(f'  {result.snippet}')

Related on TokRepo

• AI Tools for RAG -- explore retrieval-augmented generation tools and pipelines
• AI Tools for Agents -- discover tools for building context-aware AI agents

Common pitfalls

• Initial sync of large data sources (e.g., thousands of Notion pages) takes time; plan for an indexing period before querying.
• OAuth tokens for connected sources expire; configure automatic refresh or monitor for authentication failures.
• Search relevance depends on the quality of the underlying embeddings; test with representative queries before deploying to production.