Airweave
Let Agents Search Any App
179 followers
Let Agents Search Any App
179 followers
Airweave is an open-source dev tool that lets agents search any app. It connects to apps, databases, or document stores and turns their contents into searchable knowledge bases for agents.
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Launch Team / Built With
Render Sandboxes: a computer for every agentRun untrusted code safely. Idle sandboxes cost nothing
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A few months ago we were building agents that interacted with various apps. We were frustrated when they struggled with vague natural language requests like "resolve that Jira ticket about missing auth configs", "refund payments in Stripe for unsatisfied customers", or "what were Q1 returns from the financial sheet in gdrive?" The agents would then inefficiently chain multiple function calls, fail to retrieve data, or hallucinate answers.
We also noticed that despite the rise of MCP creating more desire for agents to interact with external resources, the majority of agent dev tooling focused on function calling and actions instead of search. We were annoyed by the lack of tooling that enabled agents to semantically search workspace or database contents, so we started building Airweave first as an internal solution. Then we decided to open-source it and pursue it full time after we saw the positive reactions from agent builders.
The best part for us about building Airweave is continually seeing the amazing things our users create with it.
Looking forward to the comments 👋
- Lennert & Rauf
Congrats on the launch. Can you tell me more about how the underlying data sync works? Does it run on a schedule? What if there are conflicting data? How “real-time” is the data getting indexed?
thanks @renebrandel! ofc, data is synced either manually or scheduled using cron jobs. event-based updates are a pro-feature that also depend on whether the source's API support webhooks. And we use content hashing for version tracking of inserts, updates, and deletions. Lmk if you have any other questions!
@renebrandel Maturity of the API is really important here. If the API allows easy changelogs/webhooks, it can be near real-time. Some more legacy APIs we can tune to 5 min.
You guys are building the most important layer for the next generation of mcp-native applications. 🦾🫡
appreciate that @justin_lee27!! 🙏
@justin_lee27 Love working with you team!!
Congrats on the launch! Your product looks really cool. Do you see this as a ranking opportunity for startups as well with LLM responses?
thanks @emirkarabeg ! Airweave returns a scored list of result that can be passed to rerankers, so teams can tune how results are ordered before they’re fed to LLMs down the line. or airweave can also apply reranking and recency bias itself. If you’re thinking of another ranking use case, let me know! curious to hear
Congrats! The looks awesome. Curious do you see this as a chance for startups to improve their rankings through LLM-generated responses too?
@bereket_engida we keep getting surprised by how the community uses Airweave. That definitely sounds plausible as well
Airweave sounds like a huge step forward in enabling AI agents to truly understand and interact with data across apps and databases. Can Airweave handle complex queries across multiple apps at once, or is it designed to focus on a single app/database at a time?
@evgenii_zaitsev1 it's specifically designed to be able to manage multiple sources at once! You can define a collection which contains multiple source_connections. That collection you can search :)
This looks awesome! On the demo it looks like it is faster vs Claude - do you have an estimate on that? Also, what when there are multiple endpoints to look through?
@antoine_bouchardy endlessly chained MCP often end up in hallucination, which gives you nothing, so I guess infinitely faster (lol). Airweave aggregates all the knowledge into one endpoint that the search logic behind then manages