Airweave is a infrastructure provider that solves the 'knowledge bottleneck' for AI agents. In the agent stack, they occupy the retrieval and memory layer, providing the mechanism for agents to access real-time information from external tools without developers needing to build custom integrations for every data source. Their focus on 'agentic search'—search that understands intent and complex relationships—is specifically designed for autonomous systems that must make decisions based on high-dimensional data.
For builders, Airweave is relevant because it integrates with popular frameworks like LangChain and Composio, functioning as a plug-and-play source of truth. By championing an open-source retrieval layer, they are pushing the ecosystem toward modularity, where data access is decoupled from model choice. This matters because it allows agents to remain grounded and accurate across diverse enterprise environments, which is the primary requirement for moving agents from experimental demos into production workflows.
Retrieval-Augmented Generation (RAG) is the current standard for grounding AI in specific data, but for many developers, it remains a fragmented process. Most teams build one-off pipelines that connect a single database to a single model. Airweave is an attempt to turn this manual effort into a standardized infrastructure layer. By sitting between enterprise data sources and AI agents, the platform provides a unified interface for context retrieval, allowing agents to query multiple apps and databases through a single request.
Airweave works by creating "collections"—searchable knowledge bases that sync data in real time from over 50 supported sources, including Slack, Notion, GitHub, and various SQL databases. This approach addresses the problem of stale data, as the platform continuously updates the context available to the agent. Instead of relying on static snapshots, developers can point their agents to an Airweave endpoint that handles the authentication, indexing, and retrieval logic.
Standard search tools often fail agents because they lack understanding of complex relationships or time-sensitive data. Airweave introduces what it calls "agentic search," which combines semantic, keyword, and hybrid search with an understanding of intent and context. This is particularly useful for agents that need to cross-reference information, such as checking a customer’s Stripe billing history against a recent Slack conversation to resolve a support ticket.
The search capabilities are time-aware, meaning the system understands when a piece of information was created or updated, ensuring that the most relevant and recent context is prioritized. This is a technical requirement for agents tasked with monitoring systems or managing real-world operations where the state of data changes rapidly. For example, the company uses its own product to power "Donke," an internal agent that monitors errors and retrieves relevant documentation to suggest fixes.
Founded in 2024 by Lennert Jansen, Rauf Akdemir, and Daan Manneke, Airweave is part of the Y Combinator X25 batch. The company maintains a dual presence in Amsterdam and San Francisco, reflecting a technical team that values both European engineering and Silicon Valley's agent ecosystem. In mid-2025, the company announced a $6 million seed round led by FCVC, with participation from Lux Capital and Elastic founder Shay Banon.
The company is committed to an open-source model, allowing developers to self-host the platform to maintain control over sensitive data. This transparency is a direct play for enterprise trust, where data privacy and security are the primary hurdles to AI adoption. For those who prefer a managed experience, Airweave offers a hosted cloud platform with SDKs for Python and JavaScript, enabling integration into existing agent frameworks like LangChain or Composio in a matter of minutes.
As the industry shifts from simple chatbots to autonomous agents, the bottleneck has moved from model reasoning to data access. Airweave is positioned to capture this demand by providing the "plumbing" for the agent economy. Its competitors include both specialized vector databases and all-in-one agent platforms, but Airweave's focus on being a dedicated, reusable retrieval layer is a specific bet on the modularity of the future AI stack. By providing a common language for agents to talk to data, they are building the infrastructure necessary for agents to perform work in high-stakes, data-rich environments.
A shared information retrieval layer between AI systems and data sources.
Airweave is hiring