DataGrout
Enterprise AI Platform for Agentic AI & MCP Integration
81 followers
Enterprise AI Platform for Agentic AI & MCP Integration
81 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 3rd launch from DataGrout . View more
DataGrout Frame
Launching today
Frame is DataGrout's columnar data operations tool for AI agents. It filters, sorts, groups, pivots, joins, and slices tabular records directly as MCP tools. Agents today bloat context windows or need Python sidecars just to reshape data, Frame eliminates that entirely. Pure deterministic operations, no AI generation, no extra runtime, zero credit cost. Composes natively in DataGrout intelligence layer with cache ref support for large datasets.
Free Options
Launch Team
DataGrout
Hello Product Hunt, I'm Nick 👋
I built Frame, and I just wanted to share some cool details that I'm quite proud of.
Your dataset never has to re-enter the model's context to get transformed. Every tool result, from any mcp tool in the system, is cached server-side and handed back as a tiny reference (a 19-char rc_... string).
Frame tools take that reference in place of the actual rows -- so a pipeline like fetch 10k invoices -> group by customer -> sort by total -> take top 10 passes a reference between steps, and only the final 10 rows ever come back to the agent. A table that'd be hundreds of thousands of tokens as JSON costs you ~20 characters. Token cost stops scaling with row count.
And because every op is pure and deterministic, no LLM touches the data itself -- the model decides what to do, Frame executes it exactly. No reshaping hallucinations, and it's all zero-credit and zero-token.
Under the hood the cache is an ETS layer with a 10-minute sliding TTL, AES-256-GCM at rest, per-user isolation, and content-addressable dedup (an identical fetch reuses the same ref instead of re-storing). The tools also compose natively via our flow.into workflows, so one tool's output feeds the next with no glue code.
I'll be in the thread all day -- genuinely happy to go deep on the cache design, the MCP surface, or how this fits into a larger agent loop. What are you building?