Max
a federated data query layer for AI agents (and humans)
Details
- External ID
- 47278802
- Source
- HN
- Company
- —
- Product
- Max
- Website domain
- max.cloud
- Launched
- March 6, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.4108241082410824
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hey HN! I built a thing and I'm really excited to share it.EDIT: I meant to link to the github, not the website: https://github.com/max-hq/maxLike many of us here, I've been commonly reaching for a pattern of "pull data into db; give it to claude" for a while, whilst doing data spelunking or building tooling - for the same reasons mentioned by thellimist over here [1] and a few other recent "CLI vs MCP" posts.To that end, about a month ago I started building a project called `max` - its goal is to cut the middleman and schematise any data source for you. Essentially, provide a lingua-franca for synchronising and searching data.In short: Max exposes a CLI for any given data source, and mirrors it locally. As in, puts that data right next to the agent. It means search is local and fast, and ready for cut, sed, grep, sort etc.More concretely:> max connect @max/connector-gmail --name gmail-1 > max sync gmail-1> # show me what data i can search for > max schema @max/conector-gmail> # do a search > max search gmail-1 --filter"subject ~= Apples" --fields=subject,from,timeI've built a few connectors over at `max-hq/max-connectors` - but the goal is that they're easy to create (sync is done via graph walk - max makes you provide field resolution so it can figure out how to sync).In practice - I've found that telling claude to run "max -g llm-bootstrap" to get acquainted, and then "make a connector for X" also works pretty well :).There's a lot still to come(!) - realtime, somewhere to host connectors, exposing and serving max nodes... I'll be updating the roadmap over the next couple of days - but I didn't want to wait any longer before sharing here.(on that note - max is designed for federation. The core is platform agnostic)In terms of what this approach makes possible - I ran a benchmark on a challenge (it's the one on the website) asking claude to find me names of a particular form from a fairly chunky hubspot (100k contacts). The metrics are roughly what you'd expect from putting the data local and avoiding any tokens hitting claude's context window:MCP: 18M tokens | 80m time | $180 costMax: 238 tokens | 27s time | $0.003 cost(I'll explain how these numbers were calculated in a new reply)It's still early (alpha) but if you're building agents or just want local data, please try it and tell me what breaks.Thanks![1] https://news.ycombinator.com/item?id=47157398
Enrichment
- Theme
- database infrastructure and developer tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- B2B
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- federated data query layer for ai agents
- Manually corrected
- False
Could you build this?
Partial Creating standard API connectors and a CLI wrapper is straightforward, but building an efficient federated query engine with schematized indexing across high-throughput heterogeneous APIs requires non-trivial query optimization and data infrastructure.
What it would actually take: Requires an embedded or distributed query execution engine (such as Apache DataFusion or DuckDB), standardized schema adapters for dozens of external APIs (AWS, Datadog, HubSpot), and custom query pushdown logic to handle rate limits and pagination efficiently. Vibe coding can scaffold the connectors and CLI, but high-performance federated planning and zero-copy streaming under strict memory constraints require specialized systems engineering.
Discussion
1 comment analyzed.
Concerns raised: Claude pagination inefficiency - 40s per 800 contacts with token bloat, Token compaction triggered during large dataset processing, Slow full run time - 80 minutes for 120 loops
Feature requests: Direct filter commands to avoid pagination overhead, Better token efficiency for large data processing
Competitors
Other products that read as similar to this one — 107 launches clear the similarity bar, closest 8 shown.
Attention rank: #62 of 108 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 123 days after the earliest competitor.
- Maxxwell · hn · 2026-09-09 · 11 upvotes · similarity 0.50
- I built a database for AI agents · hn · 2026-04-07 · 12 upvotes · similarity 0.42
- DeepSQL · hn · 2026-07-20 · 52 upvotes · similarity 0.42
- Declarative open-source framework for MCPs with search and execute · hn · 2026-02-24 · 11 upvotes · similarity 0.40
- Mole · hn · 2026-08-14 · 100 upvotes · similarity 0.39
- Build Your Own AI Agent CLI in 150 Lines · hn · 2026-06-02 · 34 upvotes · similarity 0.38
- Airbyte Agents · hn · 2026-05-05 · 156 upvotes · similarity 0.38
- An unmetered LLM API–$6/month, no token tracking, no limits · hn · 2026-07-06 · 12 upvotes · similarity 0.37
Other launches for this product
- No other launches for this product.
Same idea, different domain
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