and ofc, feel free to reach out if your team needs help with setup

On permissioning: we default to per-user syncs that adopt the permissions of the syncing user and mirror source ACLs (e.g., Drive items a user owns or that are sharedWithMe). In practice, founders avoid leaking private docs by either (a) having each user sync their own corpus, or (b) using a centrally-scoped token limited to Shared Drives/team folders and excluding personal “My Drive.” You can also keep separate collections and only expose cross-user search behind your own checks. We’re exploring richer org-level RBAC mapping on a per-customer basis (e.g., mapping Drive/SharePoint groups to index ACLs), but the above works today.

@Weves: Thanks, appreciate it!

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!

Comments URL: https://news.ycombinator.com/item?id=45427482

Points: 164

# Comments: 30

A couple of months ago we were building agents that interacted with different apps and were frustrated when they struggled to handle vague natural language requests like "resolve that one Linear issue about missing auth configs", "if you get an email from an unsatisfied customer, reimburse their payment in Stripe", or "what were the returns for Q1 based on the financials sheet in gdrive?", only to have the agent inefficiently chain together loads of function calls to find the data or not find it at all and hallucinate.

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 got positive reactions from coworkers and other agent builders.

Airweave connects to productivity tools, databases, or document stores via their APIs and transforms their contents into searchable knowledge bases, accessible through a standardized interface for the agent. The search interface is exposed via REST or MCP. When using MCP, Airweave essentially builds a semantically searchable MCP server on top of the resource. The platform handles the entire data pipeline from connection and extraction to chunking, embedding, and serving. To ensure knowledge is current, it has automated sync capabilities, with configurable schedules and change detection through content hashing.

We built it with support for white-labeled multi-tenancy to provide OAuth2-based integration across multiple user accounts while maintaining privacy and security boundaries. We're also actively working on permission-awareness (i.e., RBAC on the data) for the platform.

So happy to share learnings and get insights from your experiences. looking forward to comments!

Comments URL: https://news.ycombinator.com/item?id=43964201

Points: 176

# Comments: 40

we originally built this for our previous agent startup as an internal solution to ensure agents could find the relevant data on apps they're using. We then pivoted to this after some early positive reactions and decided to open-source it.

here's a short demo: https://tinyurl.com/demo-airweave

we're two engineers/friends based in Amsterdam, NL. We just launched the project, so it's rough around the edges ofc, but we're very eager to get some feedback!

feel free to reach out to me personally if you like this! - https://www.linkedin.com/in/lennertjansen/ - https://x.com/lennertjansen

We built Airweave, an open-source dev tool that turns your users' app data into agent knowledge by making any source (app, db, api) searchable for AI agents. It handles authentication, ingestion, and keeping sync with your vector store + graph db. You can do other nice stuff like white labeling and monitoring your sources, destinations, and sync jobs in a ui.

We believe agent builders should be able to focus on their verticals, and not have to worry about infra like this. So we're building all the necessary tooling and integrations to enable that.

Feel free to try it out and we'd love to hear your thoughts!

Repo: https://github.com/airweave-ai/airweave

Comments URL: https://news.ycombinator.com/item?id=42936521

Points: 2

# Comments: 2