Shared retrieval layer
Lets AI systems query one retrieval layer instead of wiring separate search logic into each app or agent.
Airweave
ReclamarAirweave is an open-source context retrieval layer for AI agents and RAG systems. Connect apps and databases, sync data in real time, and search unified context.
Overview
Airweave is an open-source context retrieval layer for AI agents and RAG systems. It sits between data sources and AI systems so applications can retrieve relevant context from connected apps, tools, and databases instead of relying on isolated retrieval logic.
The product is designed to make grounded answers easier to generate on demand. The homepage shows a workflow where users create a searchable collection, connect sources, sync data, and then query that collection through an LLM-friendly interface. Airweave also publishes Academy articles about context engineering and information retrieval, suggesting the product is aimed at teams building retrieval infrastructure for AI systems.
Features
Multiple search modes
Supports semantic, keyword, hybrid, time-aware, and agentic search so agents can match both literal terms and user intent.
Real-time data sync
Keeps connected data current with real-time syncing rather than static snapshots or manual refreshes.
Unified search across sources
Connects apps, tools, docs, and databases, then exposes them through a unified search interface for AI systems.
Developer SDK workflow
Provides an SDK-based workflow for creating a collection, attaching a source connection, and querying retrieved context.
Use Cases
Grounded AI assistants
Build agents that answer questions using live context from connected apps and databases instead of static prompt data.
RAG backends
Create retrieval infrastructure for RAG pipelines that need a shared layer for searching across multiple sources.
Operational search and debugging
Search synced code, issue trackers, or workspace data to enrich alerts and troubleshooting workflows, as shown in Airweave's own internal usage example.
Cross-source context retrieval
Query several connected systems through one interface when the same agent needs context from different tools in a single request.
Pros and Cons
Pros
- Open-source context retrieval layer for AI agents and RAG systems.
- Real-time syncing helps keep retrieved context current.
- Unified search interface reduces the need to build separate retrieval pipelines for each source.
- Supports several search approaches, including semantic, keyword, hybrid, time-aware, and agentic search.
- Public code examples and Academy content provide a clear entry point for implementation and concepts.
Cons
- The website does not currently provide public pricing details, and the `/pricing` URL returns a 404 page.
- Source coverage on supported integrations is incomplete, so the full connector list and sync options are not clear from the public pages provided.
FAQ
What does Airweave do?
Airweave is a context retrieval layer for AI agents and RAG systems. It connects to apps, tools, and databases, syncs their data in real time, and exposes it through a unified search interface.
How do teams get started with it?
The homepage shows a Python SDK example and installation commands for `airweave-sdk`, and it links readers to the quickstart documentation at `docs.airweave.ai/quickstart`.
What kinds of sources can it search?
The product description and examples show Airweave being used to retrieve context from apps and databases, including a synced GitHub codebase and a Linear workspace.
Is pricing listed on the website?
The source material supports a shared retrieval workflow for AI agents and RAG pipelines, but it does not publish pricing details on the site; the `/pricing` page currently returns a 404.
Quick Facts
- Category
- Developer Tool
- Platform
- Web app with SDK
- Primary users
- AI teams, agent builders, and RAG developers
- Source domain
- airweave.ai
- Notable workflow
- Create a collection, connect sources, sync data, then search it from an agent
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