Launch HN: Airweave (YC X25) – Let agents search any app

Hey HN, we’re Lennert and Rauf. We’re building Airweave (https://airweave.ai/), an open-source tool that lets AI agents search and retrieve context from your existing apps and databases through a single LLM-friendly API (or an MCP server, if that’s your thing). Our Github is at https://github.com/airweave-ai/airweave. We previously did a Show HN https://news.ycombinator.com/item?id=43964201 and since then we’ve recently launched the managed service and new search functionality.

Here’s an example of Cursor using Airweave https://www.youtube.com/watch?v=IvxidK9Ciy4. And here’s a general example of our new search functionality: https://www.youtube.com/watch?v=iqEqc_iGUO8

We came to this problem while building agentic applications for webshop owners and customer service, and noticing most failure modes weren’t about tool execution, but finding the right internal context to enable the right actions.

We started solving, what seemed at the time, a problem for our own use case, and quickly fell into a rabbithole of issues. Company and user data lives across SaaS and databases; it’s sparse, messy, and constantly changing. Agents need a data orchestration and retrieval layer that accepts free-form natural language queries and returns actionable results quickly.

Simply pointing an agent at an MCP server does not equate to fine-grained search functionality or deep understanding of the underlying resource. Most MCP servers are thin wrappers that expose an existing API in a more LLM-friendly way, but this doesn’t actually give the agent any new capabilities beyond what the resource or app already offered. Specifically, it doesn’t give the agent a way to thoroughly search and understand the contents of the resource.

Airweave connects to sources via their APIs, crawls and normalizes content, chunks it, extracts entity relationships, and indexes the chunks in a vector store alongside keyword fields and lightweight graph metadata in Postgres. Data sync is orchestrated with Temporal (handles pagination/rate limits, schedules, and change detection via timestamps and content hashes) so collections stay close to real-time with their sources.

On retrieval, Airweave can run semantic and BM25 keyword search in parallel, fuse results (RRF), apply recency bias, and re-rank. Agents can fetch ranked chunks with citations or ask for a synthesized answer. The same interface is exposed via REST, Python/TS SDKs, and MCP so agents can discover it like any other tool.

It’s been fun to see what users have built with Airweave; from legal AI assistants to research discovery agents and context augmentation for coding agents. We’re currently experimenting with agentic search patterns, layering different types of enrichment and indexing, RBAC on indexed data, and streaming architectures.

If this is interesting to you, feel free to take it for a spin. Curious to hear your thoughts and feedback on the problem and our solution!

Launch HN: Airweave (YC X25) – Let agents search any app
github.com

it's currently guided by community feedback, github issues, and user talks. and we rely on private e2e test suites for maintaining quality as we scale coverage

Looks good. Curious, how is auth handled? Lot of docs have permissions etc. Can you clarify how this is handled in both indexing side and searching side of things?

Seems like Google Agentspace but without the UI. Do you folks keep a persistent copy of the data being ingested? How are you planning on solving RBAC? IMO, all of these "search anything" apps are going to be leaky by design unless you're indexing/gathering on the fly using passthrough credentials...

Great question. We usually sync per user in cases where this matters. That seems inefficient until you realize the following: for most teams, workspace data is pretty small - at least compared to other data workloads (CRMs We plan to implement unified ACL syncs to dedupe the data or even have 1 sync per org, but that’s mostly a cost optimization; Airweave will just scale horizontally until then.