About
Airweave builds an open-source context retrieval layer that connects AI agents and RAG systems to apps, databases, and other enterprise data sources. It sells to developers and teams building AI-powered applications, differentiating through real-time synchronization, prebuilt connectors, unified search, and reusable retrieval infrastructure rather than separate brittle integrations for each application.
Market
Airweave competes in the AI application-infrastructure market for context retrieval, RAG, and data connectivity for AI agents. It positions itself as an open-source shared layer between private applications, databases, and AI systems, continuously syncing data and exposing current, source-grounded context through unified search rather than requiring bespoke integrations for every agent. Its differentiation is the combination of broad enterprise connectors, hosted and self-hosted deployment, and access through APIs, SDKs, MCP, and agent frameworks; this overlaps with Ragie and Trieve's managed retrieval, Unstructured's ingestion and transformation platform, LlamaIndex's data-agent framework, and Onyx's open-source enterprise search.
Airweave is aimed at developers and AI/product/platform teams building long-running agents, retrieval-augmented generation systems, and context-heavy LLM applications. Its best-fit customers range from early-stage teams to enterprise organizations that need AI assistants or support agents to securely search across SaaS tools, documents, databases, and internal knowledge bases.
At a Glance
Problem
Airweave addresses a core bottleneck in AI agents and retrieval-augmented-generation systems: enterprise information is fragmented across apps, databases, and document stores, while the data is continually changing and difficult to access reliably. Without shared retrieval infrastructure, teams must repeatedly build and maintain authentication, ingestion, synchronization, indexing, and search pipelines for each agent or application, creating recurring engineering work and brittle user experiences.
The main use case is enabling an AI agent to answer questions or perform work using current, grounded information drawn from multiple enterprise systems in one request. Airweave began from the problem that agents could not meaningfully search or understand connected apps, which limited their ability to do useful work.
Product / Service
Airweave is an open-source context-retrieval layer positioned between data sources and AI systems. It connects to apps, tools, databases, and documents; authenticates and ingests their content; continuously syncs and indexes it; and exposes the resulting information through a unified, LLM-friendly search interface. The platform supports more than 50 integrations and can be queried through SDKs, a REST API, MCP, and integrations with agent frameworks.
The benefit is a reusable retrieval foundation rather than a separate RAG pipeline for every application. Agents can retrieve relevant, up-to-date context across multiple sources, while enterprise customers can deploy and integrate Airweave in on-premises, hybrid-cloud, and air-gapped environments, including custom data-source workflows.
Market
Airweave competes in the emerging market for AI-agent and RAG infrastructure, specifically shared context-retrieval and enterprise data-access layers. Its positioning is distinct from a traditional, application-specific RAG stack because it continuously synchronizes source data and is intended to serve multiple agents and applications. The competitive set is still forming; G2 lists UiPath Agentic Automation, Automation Anywhere Agentic Process Automation, and Zapier as alternatives, although these are broader automation products rather than direct like-for-like retrieval-layer vendors.
Airweave is not pre-revenue by implication, but its revenue is not disclosed in the available evidence. The company announced a $6 million seed round in July 2025 led by FCVC, with participation from LUX Capital, Y Combinator, Orange Collective, Pioneer Fund, Shay Banon, and other investors. It also cites work with one of the world's leading AI labs and enterprise deployment requirements, suggesting early enterprise traction, while the funding and customer evidence do not establish scale or revenue magnitude.
Founders & Leadership
Funding History
FCVC
Recent News
Airweave introduced an MCP server that exposes collections as searchable tools for AI assistants. The server supports integrations with MCP-compatible clients and allows agents to query Airweave collections during their reasoning process.
Airweave documented its connector framework, which syncs data from external sources and makes it available through a unified search layer for AI agents.
A third-party technical article highlighted Airweave as an open-source context-retrieval framework for AI agents, covering data access across more than 40 apps and databases and its MCP server.
Airweave described Donke, an LLM-powered error-monitoring agent that uses Airweave to search synced GitHub and Linear data, enrich alerts, and reduce duplicate issues. The company said Donke handles approximately 40,000 Airweave queries per month.
Airweave explained how its retrieval system combines semantic similarity with time-aware scoring, using decay to reduce the influence of older information and prioritize more recent context.
Active Roles
5Business Model
Airweave uses a freemium and subscription SaaS model: a free tier is available, while paid Pro and Team plans charge monthly fees based on capabilities and usage. It also offers custom enterprise plans and paid add-ons such as custom integrations, on-premises deployment, SSO, RBAC, and SLAs.