Airweave provides a unified, semantically searchable layer for AI agents to access data from diverse sources like applications, databases, and APIs. It targets developers building AI-powered applications, enabling them to easily integrate and retrieve information from various data silos with minimal configuration.

How It Works

Airweave acts as a semantic middleware, connecting to data sources and transforming them into a queryable format for AI agents. It breaks down data into processable entities, stores them, and exposes them via REST and Message Queue Telemetry Transport (MQTT) endpoints. The system supports automated synchronization with versioning and hashing to efficiently update data, leveraging a FastAPI backend and a React frontend.

Quick Start & Requirements

• Install/Run: Clone the repository (git clone https://github.com/airweave-ai/airweave.git), cd airweave, chmod +x start.sh, and run ./start.sh.
• Prerequisites: Docker Compose for local development.
• Resources: Local setup via Docker Compose.
• Docs: https://github.com/airweave-ai/airweave

Highlighted Details

• Supports over 25 integrations, with more being added.
• Features white-labeled, multi-tenant support with OAuth2.
• Includes entity generators for consistent data formatting from various sources.
• Automated data synchronization with versioning and hashing for efficient updates.
• Async-first architecture for large-scale data handling.

Maintenance & Community

• Active development with CI/CD pipelines for Ruff, ESLint, and backend tests.
• Community support available via Discord: https://discord.com/invite/484HY9Ehxt.
• Roadmap includes expanded integrations, Redis/worker queues, webhooks, Kubernetes support, and commercial offerings.

Licensing & Compatibility

• Community edition is MIT licensed.
• Additional modules for enterprise or advanced features may be licensed separately (open-core model).
• Compatible with commercial use under the MIT license for the community edition.

Limitations & Caveats

The project is primarily set up for local development via Docker Compose, with Kubernetes support for production scale planned for the future. While async-first, managed Redis workers for production scale are also noted as upcoming.