Airweave

Airweave is an open - source context retrieval layer for AI agents and RAG systems. It connects and synchronizes data from various applications, tools, and databases, and provides relevant, real - time, multi - source contextual information to AI agents through a unified search interface.

Knowledge management and memory Search tools #AI agent #Context retrieval #Data synchronization #Open - source tool .Python

rating : 5 points

downloads : 5.9K

update time : 2026-03-13

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What is Airweave?

Airweave is an open - source context retrieval layer designed specifically for AI agents and Retrieval - Augmented Generation (RAG) systems. It acts as a bridge between data sources and AI systems, connecting, continuously synchronizing, and indexing data from different applications, databases, and documents, and then making it available to AI agents through a unified search interface.

How to use Airweave?

Using Airweave is very simple: 1) Connect your data sources (supports over 50 integrations), 2) Airweave automatically synchronizes, indexes, and exposes the data, 3) AI agents query Airweave through the SDK, REST API, or MCP protocol, 4) Get relevant, up - to - date contextual information.

Use cases

Airweave is particularly suitable for AI agent systems that need to obtain context from multiple data sources, such as customer support assistants, internal knowledge Q&A systems, and automated workflow agents. It solves the pain point of rebuilding fragile data pipelines for each agent.

Main Features

Multi - data source integration

Supports over 50 application and database integrations, including Google Drive, Notion, Slack, GitHub, Salesforce, etc., to access all data sources uniformly.

Automatic data synchronization

Continuously monitors changes in data sources and automatically synchronizes updates to ensure that AI agents always get the latest information without manual refresh.

Unified retrieval interface

Provides standardized search APIs and SDKs. AI agents can access all connected data sources through a single query, simplifying the development process.

LLM - optimized format

The returned data is optimized and formatted for large - language models to understand, improving the response quality and accuracy of AI agents.

Flexible deployment options

Supports both cloud - hosted and self - hosted methods. You can choose the appropriate deployment scheme according to security and privacy requirements.

MCP protocol support

Natively supports the Model Context Protocol and can be seamlessly integrated with various AI development tools and frameworks.

Advantages

Simplify AI agent development: No need to rebuild data pipelines for each agent

Unified data access: Access all data sources through a single interface

Real - time data synchronization: Ensure that AI agents get the latest information

Open - source and transparent: Completely open - source, customizable and extensible

Flexible deployment: Supports cloud services and self - hosting

Limitations

Initial setup requires configuring multiple data source connections

Self - hosting requires certain technical operation and maintenance capabilities

Some enterprise - level applications may require additional authentication configuration

How to Use

Choose a deployment method

Choose cloud - hosted (quick start) or self - hosted (full control) according to your needs. For cloud - hosted, access app.airweave.ai. Self - hosting requires a Docker environment.

Connect data sources

Add the data sources to be connected in the Airweave interface, such as Google Drive, Notion, Slack, etc. Complete OAuth authentication or API key configuration as guided.

Configure synchronization settings

Set the data synchronization frequency and scope. You can choose full - scale synchronization or incremental synchronization, and configure specific folders or projects to be indexed.

Integrate into an AI agent

Install the Airweave SDK in the AI agent code, authenticate with the API key, and then query data through the search interface.

Test queries

Use example queries to test the Airweave retrieval function and ensure that the returned data is in the expected format and content.

Usage Examples

Customer support assistant

Build an AI customer support assistant that can access the company's knowledge base (Confluence), support ticket system (Zendesk), and customer database (HubSpot) to provide accurate solutions.

Project research assistant

Create a research assistant that can search internal documents (Google Drive), code repositories (GitHub), and meeting records (Zoom transcripts) to help new employees quickly understand the project.

Sales intelligence agent

Develop a sales intelligence AI agent that accesses CRM (Salesforce), emails (Gmail), and call records (Fireflies) to provide customer background information for the sales team.

Frequently Asked Questions

What is the difference between Airweave and traditional RAG systems?

How is data stored and protected in Airweave?

Which data sources support real - time synchronization?

How to extend Airweave to support new data sources?

How many server resources are required for self - hosting?

How to handle API limits and quotas of data sources?

Related Resources

Official documentation

Complete installation, configuration, and usage guides, including detailed descriptions of all connectors

GitHub repository

Open - source code repository, including front - end, back - end, and all connector implementations

Example notebooks

Jupyter Notebook examples showing how to use the Airweave SDK to build AI agents

Discord community

Communicate with other users and developers, get help, and share experiences

Demo video

Demonstration of Airweave features and use cases

Integration list

View all supported application and database integrations

🚀 Airweave

An open - source context retrieval layer for AI agents and RAG systems, connecting apps, tools, and databases, and providing a unified search interface.

🚀 Quick Start

Cloud - hosted

You can use the cloud - hosted version at app.airweave.ai.

Self - hosted

If you prefer to self - host Airweave, follow these steps:

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

Then access it at http://localhost:8080.

⚠️ Important Note

Docker and docker - compose are required for self - hosting.

✨ Features

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

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

📦 Installation

Self - hosted Installation

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

💻 Usage Examples

Install SDKs

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

Python SDK Example

from airweave import AirweaveSDK

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

You can find more details in the documentation and examples.