DataGrout
The Unified AI Operating Layer for Agentic Intelligence
90 followers
The Unified AI Operating Layer for Agentic Intelligence
90 followers
DataGrout gives your AI agents memory, reliability, and cost control. Most agent frameworks fall apart at scale, context windows explode, workflows fail silently, and token costs spiral before you notice. DataGrout fixes the plumbing: connect to any system, remember across sessions, and run continuously without burning your budget. Built for teams shipping production agents, not just demos.
This is the 4th launch from DataGrout . View more
DataGrout Data
Launching today
DataGrout Data is a suite of deterministic JSON manipulation tools built for AI agents. It filters, sorts, aggregates, merges, flattens, and maps out JSON payloads through MCP tools. Agents handling raw API responses often waste context window space or rely on Python sidecars just to reshape data. Data removes both problems with deterministic operations, no AI generation, and zero DataGrout credit cost. Built into DataGroutβs intelligence layer with cache ref support for large datasets.
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Launch Team
Hey Product Hunt! π
I'm part of the DataGrout team on the marketing side and so excited to bring Data to you today!
Working closely with our engineering team, I've seen how painful raw JSON handling is in real agent workflows. Every API call returns a messy nested payload and reshaping it without burning tokens or spinning up a Python runtime has always been a gap.
Data fills that gap, filter, sort, aggregate, merge, flatten, and fan-out as MCP tools. No extra infrastructure, zero credit cost.
Here's what Data can do for your agents:
β data.filter β declarative row filtering with 10+ operators
β data.sort β multi-field sorting with direction control
β data.aggregate β counts, sums, averages by key
β data.merge β deep-merge two JSON objects
β data.flatten β simplify deeply nested payloads in one step
β data.map β split large arrays for parallel processing
Using Data, early users are already noticing meaningful reductions in token usage. Drop your questions below, our team is standing by!
@murali_gour1Β Congrats on the launch guys! π Quick question, does data.flatten work on any nested JSON structure or does it need a specific format?
@deepaksabharwalΒ Thanks so much! π
It works on any nested map or array, no specific format needed. It flattens into a single-level map using dot-path keys. You can also customize the separator if you prefer a different delimiter than the default dot. Just pass the payload or a cache ref and it handles the rest!
DataGrout
Hello Product Hunt!
I'm Nick and I built the Data suite. Murali covered what it does, so let me add how I actually think about it.
Data is a small standard library for JSON that an agent reaches into mid-task. Whatever shape an API hands back, there's a verb for it: data.get to pull a value out of a deeply nested structure, data.pick and data.omit to keep or drop fields, data.filter and data.sort to narrow and order, data.aggregate to reduce a field to a number, data.flatten to collapse nesting, data.unique to dedupe, or data.map to fan an mcp tool out across a list. None of them are clever on their own. The point is that the agent composes them, the way you'd chain shell commands, to turn a raw payload into exactly the shape the task needs.
What makes that practical is where it runs. Every step happens server-side and hands back a short rc_... reference, so the agent chains filter -> sort -> aggregate (through flow.into) by passing references, not by pulling the data back into its context at each step. You can also turn on auto-head, and any response over ~200 records comes back as a preview plus a reference instead of the full list, so even the first hop never floods the context window. The model reasons about the shape and orchestrates the verbs while the bytes stay on the server.
Because every operation is pure and deterministic, no LLM ever touches the data itself. The agent decides which verbs to run and Data executes them exactly. No reshaping hallucinations, zero credit cost. It pairs with Frame too: Data for nested JSON from an API, Frame for columnar, tabular rows.
I'll be around in the thread all day. Happy to go deep on the cache_ref design, auto-head, or how the pieces compose. What's the messiest payload your agents have to deal with?