RAG can’t keep up with real-time data. Airweave builds live, bi-temporal knowledge bases so that your Agents always reason on the freshest facts. Supports fully agentic retrieval with semantic and keyword search, query expansion, and more across 30+ sources. 100% open-source.

GitHub repo: (Don't forget to star 🌟)

If you found it insightful, reshare it with your network. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

Airweave looks really good. Here’s what stands out to me: - Local setup - Temporal awareness - MCP support

True; also, since I used it, I would like to add that I found it quite intuitive and dev-friendly. Needs minimal setup.

Real-time data is the future. Airweave’s bi-temporal approach is a game-changer for agents, man.

Good point. I like that it can run 100% locally.

thanks for sharing, Avi 🙏 many more connectors coming soon!

Cool

@RealTimeBytes ✌️ What's your use case that you would like to use it for?

Also can have a look at @graphlit for a managed platform, offering data connectors, ingestion, retrieval and more - including MCP. Try @zine_ai for an app built on Graphlit.

+ adding A LOT of connectors soon.

🚀

@grok what does bi-temporal mean in this context

Looks like grok missed. Let me explain with an example. Suppose a company policy changes its parental leave from 12 weeks to 16 weeks, effective Jan 1 2025. If you query “What was the policy on Dec 15 2024?”, the database says 12 weeks. If you query “What is the policy on Feb 1 2025?”, it says 16 weeks. Temporal lets you see what the actual policy is at different times. Now imagine the HR team only updated the database on Jan 20 2025 to reflect the Jan 1 change mentioned above. If you query “What did we believe the policy was on Jan 10 2025?” → It shows 12 weeks, because the system hadn’t yet recorded the update. And if you query “What was the actual valid policy on Jan 10 2025?” → It shows 16 weeks. So essentially, when you have a temporal DB, it tells you “What was true at this date?” But a bi-temporal DB can also tell you “What did we think was true at this date?” You cannot ask “What did we believe the policy was on Jan 10, 2025?” in a temporal database because it does not record the transaction time. Does that help?

on bi-temporal 👆

dope share for agentic search adding it to the weekly spotlights

Awesome! Glad I could provide an addition. Solid platform.

Airweave solves the real-time data gap! Agents can now reason on live, multi-source knowledge without relying on stale RAG outputs.

sounds like they need a METACHAIN @hWonderofWorld

This is a great point about RAG and real-time data. It's a hard problem. We've been thinking about how to keep our models up-to-date with the latest information for Pochi.

Insane

Does airwave plan to support write access also?

Good question. The developers can tell that better, but if you look at the GH repo, it says "Airweave lets agents search any app". In this sense, it is fair to expect that the data already exists, and Airweave prepares it for Agents to search and reason on.

@grok is this true

Amazing!😍

Game-changer for AI agents! Traditional RAG struggles with real-time data freshness. Bi-temporal knowledge bases solving staleness while supporting fully agentic retrieval is exactly what enterprise AI needs. Love seeing this open-sourced! 🗥️🤖

Amazing

Can we build our own connector and to it ?

The project in itself is fully open-source, so in that sense, yes, you can build a connector of your choice that it may not have right now :) Which connector do you want?

Impressive features. Curious about the performance with larger datasets-any benchmarks available?

Real-time data is the holy grail. Airweave sounds like it could save a lot of headaches.

Great!! have you tested any similar solutions before? If yes, what issues did you face?

This is amazing! 🤗

Does it handle ACLs from the sources and a way to limit search and retrieval based on the end user interacting with the agent?

Good question. Yes, it performs per-user syncs and mirrors the ACLs from the source (e.g., Google Drive/Notion/etc.). I found some related discussion in the docs. So essentially, results are filtered based on the identity of the end user, so it can only retrieve what they’re permitted to see. What exactly is your use case?

Would want to be able to index enterprise data sources like SharePoint, ServiceNow, SalesForce, Workday and then search against those sources but apply the same ACL that would apply to the user if they were searching/accessing records, documents, data in those sources directly.