Airweave: Agent-Ready Knowledge from Any App, Database, or API

Introduction

Airweave (github.com/airweave-ai/airweave) is an open-source platform designed to let AI agents semantically search and retrieve information from a wide array of applications, databases, and APIs. Its core mission is to simplify the process of making diverse data sources "agent-ready" by transforming their content into a unified, searchable knowledge base. This empowers developers to build more capable AI agents that can leverage existing information without complex, bespoke data integration for each source.

Developed by the airweave-ai team (a Y Combinator S25 company), Airweave provides a data synchronization and transformation pipeline, exposing the processed knowledge through REST and potentially MCP (Message Control Protocol, or a similar specific Airweave term) endpoints for agent queries. It emphasizes ease of configuration, multi-tenancy, and efficient updates.

Key Features

Airweave offers a suite of features focused on creating accessible knowledge for AI agents:

Data Synchronization from Multiple Sources:
- Supports connecting to and synchronizing data from over 25 sources with minimal configuration. This can include apps, databases, and APIs.
Entity Extraction and Transformation Pipeline:
- Processes structured or unstructured data from connected sources.
- Breaks down content into processable "entities," which are the smallest unit of data in Airweave.
- Likely involves chunking, embedding generation, and metadata extraction to prepare data for semantic search.
Semantic Search for Agent Queries:
- Enables AI agents to query the processed knowledge base using semantic search, allowing them to find relevant information based on meaning rather than just keywords.
Persistent Storage with Versioning:
- Databases Used: Employs PostgreSQL for storing metadata and Qdrant (a vector database) for storing vector embeddings of the processed entities.
- Incremental Updates: Uses content hashing to efficiently detect changes and perform incremental updates to the knowledge base, ensuring data stays current.
- Versioning: Tracks changes to data, allowing for potential rollback or auditing.
Multi-Tenant Architecture:
- Designed to support multiple users or applications securely, with OAuth2 for authentication.
API Endpoints:
- REST API: Provides standard RESTful endpoints for agents and applications to interact with the knowledge base (e.g., search, retrieve data).
- MCP Endpoints: The documentation mentions "MCP compatible" and that "Airweave essentially builds a semantically searchable MCP server on top of the resource." MCP likely refers to a specific Message Control Protocol or a similar standardized interface relevant to AI agent communication or action execution.
Developer Tools & SDKs:
- Python SDK: Available via pip (pip install airweave-sdk) for programmatic interaction with Airweave.
- TypeScript/JavaScript SDK: Also available for frontend or Node.js applications.
Deployment:
- Docker Compose: For development and easy setup.
- Kubernetes (Prod): Intended for production deployments.
White-Labeling Support: Allows SaaS builders to integrate Airweave into their own applications and potentially offer it under their own brand.
Open Source: Licensed under the MIT License.

Specific Use Cases

Airweave is designed to enable AI agents to effectively utilize existing information from various sources:

Enhancing AI Agent Knowledge: Providing AI agents (e.g., customer support bots, internal assistants, research agents) with access to a broad and up-to-date knowledge base derived from company apps, databases, and APIs.
Building Context-Aware Agents: Allowing agents to retrieve relevant information based on user queries or ongoing tasks, leading to more accurate and helpful responses or actions.
Simplifying RAG (Retrieval Augmented Generation) Pipelines: Airweave handles the complex "retrieval" part by ingesting, processing, and exposing data for semantic search, which can then be fed into LLMs by an agent.
Enterprise Search for AI Agents: Enabling agents to perform semantic searches across disparate internal systems.
Automating Workflows Requiring Information Retrieval: Agents can use Airweave to find the necessary data (e.g., customer history from a CRM, product details from a database, project status from a project management tool) to complete tasks.
Third-Party App Integration for AI Agents: Making it easier for agents to "read" from and understand data held in various third-party applications without needing custom integrations for each one.

Usage Guide

Setting up and using Airweave typically involves deploying the server components and then interacting via its API or SDKs:

Prerequisites:
- Docker and Docker Compose (for local development/testing).
- Kubernetes (for production deployment, with Helm charts planned).
- Python for using the Python SDK.
Installation & Setup (Local Development with Docker Compose):
- Clone the Repository:
```
git clone [https://github.com/airweave-ai/airweave.git](https://github.com/airweave-ai/airweave.git)
cd airweave
```
- Build and Run: A start.sh script is often provided for convenience.
```
chmod +x start.sh
./start.sh
```
  This will typically use docker-compose up to build and start the necessary services (FastAPI backend, PostgreSQL, Qdrant).
- Access Dashboard: The Airweave dashboard is usually accessible at http://localhost:8080.
Connecting Data Sources:
- Use the Airweave dashboard or API to configure connections to your various applications, databases, or APIs (e.g., providing API keys, database credentials through a secure process).
- Airweave will then synchronize and process data from these sources.

Interacting with Airweave (for AI Agents):

REST API: Your AI agent or application will make calls to Airweave's REST (or MCP) endpoints to perform semantic searches or retrieve entities.
- The API reference (linked from docs.airweave.ai) details endpoints for managing sources, destinations, connections, syncs, performing searches, etc.

Python SDK:

# Conceptual example - refer to actual SDK documentation
# pip install airweave-sdk
from airweave import AirweaveClient

client = AirweaveClient(api_key="YOUR_AIRWEAVE_API_KEY", base_url="http://localhost:8080") # Or your deployed instance

# Example: Searching the knowledge base
query_text = "Find information about database migration tasks for project X."
results = client.search.query(query_text, sync_id="your_sync_id") # sync_id might scope the search

for entity in results.entities:
    print(entity.content) # Or access other entity attributes

TypeScript/JavaScript SDK: Similar usage patterns for Node.js or frontend applications.

Data Synchronization & Processing:
- Airweave handles the pipeline of connecting to sources, extracting data, transforming it (e.g., chunking, entity extraction), generating embeddings, and storing it in Qdrant for semantic search.
- It uses content hashing for efficient incremental updates.

Hardware Requirements (for Self-Hosting)

While the GitHub page doesn't specify exact hardware requirements, running Airweave (which includes PostgreSQL, Qdrant, and a FastAPI backend) locally or self-hosted will require:

CPU: A modern multi-core processor.
RAM: Sufficient RAM to run Docker containers for PostgreSQL, Qdrant (which can be memory-intensive depending on the size of the vector index), and the Airweave FastAPI application. 16GB might be a starting point for development, with 32GB+ being safer for larger datasets or production.
Storage: SSD storage is highly recommended for database performance (both PostgreSQL and Qdrant). The amount of storage will depend on the volume of data being ingested and indexed.
Network: Standard network connectivity.

For production deployments on Kubernetes, resource allocation will be managed via Kubernetes configurations.

Pricing & Plans

Airweave is an open-source project licensed under the MIT License.

The core software is free to use.
Costs associated with using Airweave typically relate to:
- The infrastructure for self-hosting it (servers, databases, Kubernetes cluster if used).
- Any costs related to accessing the data sources you connect (e.g., API fees from third-party apps).
- Computational costs for embedding generation if using external embedding APIs (though Airweave might also support local embedding models).
There might be a managed cloud offering or enterprise support from Airweave AI (the company) which would have its own pricing. The GitHub page mentions "White-labeling support for SaaS builders" and "Multi-tenant architecture," suggesting commercial applications are envisioned. The Y Combinator page for Airweave indicates it's a new startup (Spring 2025 batch).

License

Airweave is released under the MIT License. This is a permissive open-source license that allows for broad use, including modification, distribution, and commercial applications, with minimal restrictions (primarily requiring the inclusion of the original copyright and license notice).

Frequently Asked Questions (FAQ)

Q1: What is Airweave? A1: Airweave is an open-source platform that lets AI agents semantically search and retrieve information from various connected applications, databases, and APIs. It transforms content from these sources into an "agent-ready" knowledge base, simplifying data integration for AI agent development.

Q2: How does Airweave make data "agent-ready"? A2: Airweave provides a data pipeline that includes: * Data Synchronization: Connecting to 25+ sources. * Entity Extraction & Transformation: Breaking down structured or unstructured data into processable entities. * Embedding & Indexing: Generating vector embeddings for semantic search and storing them in a vector database (Qdrant). * API Access: Providing REST and MCP endpoints for agents to query this knowledge.

Q3: Is Airweave a vector database itself? A3: No, Airweave is not a vector database. It uses a vector database (specifically Qdrant) as one of its storage components to enable semantic search. It also uses PostgreSQL for metadata.

Q4: What kind of data sources can Airweave connect to? A4: The GitHub README mentions "25+ sources with minimal config," implying it can connect to various common business applications, databases, and APIs. The specific list of connectors would be detailed in its documentation or discoverable via its API/dashboard.

Q5: Is Airweave free? A5: Yes, the Airweave software available on GitHub is free and open-source under the MIT license. Costs would be associated with self-hosting the platform and any underlying services or external APIs it might connect to for its operations (e.g., embedding model APIs if not run locally).

Q6: How does Airweave help with building AI agents? A6: It provides a unified and semantically searchable knowledge layer for AI agents. Instead of building custom integrations for every data source an agent needs to access, developers can connect these sources to Airweave, and the agent can then query Airweave to get relevant context and information to perform its tasks.

Q7: What does "MCP compatible" or "MCP endpoints" mean? A7: While not universally defined, "MCP" in some agentic contexts can refer to "Machine Control Protocol" or similar, implying a standardized way for agents to interact with tools or data sources to perform actions or retrieve structured information. Airweave providing MCP endpoints suggests it adheres to or offers such an interface for agent interaction beyond standard REST.

As a relatively new project (Y Combinator S25), dedicated third-party blog posts and deep-dive tutorials specifically about Airweave AI might still be emerging. The best current resources are likely the official documentation and community channels. However, articles on related concepts would be helpful:

Official Airweave Documentation (Primary Source):
- Docs Home: https://docs.airweave.ai/
- Core Concepts: https://docs.airweave.ai/concepts
GitHub Repository: The README is comprehensive and serves as initial documentation.
- https://github.com/airweave-ai/airweave
Hacker News "Show HN" Thread: Often, new open-source projects are discussed here, providing insights and early user feedback. A search found a relevant thread:
- Show HN: Airweave – Let agents search any app: https://news.ycombinator.com/item?id=43964201 (This provides excellent context from the founders and community).
Articles on Building RAG and Knowledge Bases for AI Agents: While not Airweave-specific, articles on these topics explain the problems Airweave aims to solve.
- Example (Conceptual - search for current links on building RAG with FastAPI, Qdrant): "Building a Scalable RAG Pipeline for Your AI Agent" or "Best Practices for Creating Knowledge Bases for LLMs."
Y Combinator Company Profile: Provides a brief overview from the accelerator's perspective.
- https://www.ycombinator.com/companies/airweave

(Keep an eye on the Airweave GitHub, their official website, and AI/developer communities for new articles and tutorials as the project matures.)

Community & Support

Discord: The primary channel for getting help, discussing features, and interacting with the Airweave community and developers. (Link usually available on their GitHub or official website: https://discord.gg/airweave is often the pattern).
GitHub Issues: For reporting bugs, requesting features, and technical discussions related to the codebase.
- https://github.com/airweave-ai/airweave/issues
GitHub Discussions: For broader questions and community interaction.
- https://github.com/airweave-ai/airweave/discussions
Twitter/X: Follow @airweaveai (if this is their official handle) for updates.

Ethical Considerations & Limitations

Data Privacy & Security: As Airweave connects to various data sources, users are responsible for ensuring they have the rights to access and process that data. When self-hosted, the data remains within the user's infrastructure, offering a high degree of privacy. Security of the self-hosted Airweave instance, PostgreSQL, and Qdrant databases is the user's responsibility.
Accuracy of Extracted Knowledge: The quality of the "agent-ready knowledge" depends on the effectiveness of Airweave's entity extraction, transformation, and embedding processes, as well as the quality of the source data.
Permissions & Access Control: The multi-tenant architecture with OAuth2 aims to manage access, but proper configuration is key.
Dependency on Underlying Technologies: The performance and reliability of Airweave depend on the stability and performance of its components (PostgreSQL, Qdrant, FastAPI, and the connected data sources).
Early Stage Project: As a newer open-source project, features and APIs may evolve. Users should keep an eye on the changelog and release notes.

Airweave GitHub Repository (Main Source): https://github.com/airweave-ai/airweave
Airweave Official Documentation: https://docs.airweave.ai/
Airweave Official Website: https://airweave.ai/ (redirects to GitHub currently, but may become more distinct)
Python SDK on PyPI: https://pypi.org/project/airweave-sdk/
Qdrant (Vector Database): https://qdrant.tech/
FastAPI (Backend Framework): https://fastapi.tiangolo.com/
React (Frontend Framework): https://react.dev/