Airweave is an open-source context retrieval layer designed to serve as shared infrastructure between data sources and AI agents. Written in Python, it functions as a middleware that connects to applications, tools, and databases, continuously syncs their data, and exposes it through a unified search interface optimized for language models. The project positions itself between data sources and AI systems, handling authentication, ingestion, syncing, indexing, and retrieval so that developers do not need to rebuild fragile data pipelines for each agent or integration.
The core workflow involves four steps: connecting to apps, databases, and documents through over 50 supported integrations; having Airweave sync, index, and expose data through a unified retrieval layer; allowing agents to query Airweave via SDKs, REST API, Model Context Protocol, or native integrations with popular agent frameworks; and enabling agents to retrieve relevant, grounded context on demand. The supported integrations span a wide range of enterprise and productivity tools including Airtable, Asana, Jira, Notion, Slack, Salesforce, HubSpot, GitHub, GitLab, Google Workspace applications, Confluence, Linear, Zendesk, Stripe, and many others.
Airweave offers both cloud-hosted and self-hosted deployment options. The cloud version is available at app.airweave.ai, while self-hosted deployment runs locally at http://localhost:8080 and requires Docker and docker-compose. The self-hosted setup uses a start.sh script that automates environment configuration, secret generation, service startup with health checks, and optional API key configuration for OpenAI or Mistral.
The repository shows active development and community engagement.
The codebase includes automated quality assurance through code quality checks, ESLint validation, and system tests for the public API.
Airweave provides multiple interfaces for interaction including SDKs with documentation, example notebooks, and a command-line interface that enables users to search collections, manage sources, and trigger syncs from the terminal. The CLI outputs rich interactive results for developers and clean JSON output when piped, making it functional for both human developers and AI agents. The project is classified across multiple categories including AI Agents, Framework, Orchestration, Workflows, LLM Applications, Tool Integration, State Management, Deployment, Observability, and Multi-agent Systems, reflecting its broad applicability across the AI infrastructure landscape.
