DataGrout

The Unified AI Operating Layer for Agentic Intelligence

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The Unified AI Operating Layer for Agentic Intelligence

85 followers

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LLM Developer Tools

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AI Workflow Automation

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.

SeaTicketAl agent that resolves issues across all your channels

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DataGrout Launches

DataGrout Frame Columnar data ops for AI agents — filter, pivot, join

Launched on June 4th, 2026

DataGrout InvariantSemantic code analysis for the AI era

Launched on May 27th, 2026

DataGrout Enterprise AI Platform for Agentic AI & MCP Integration

Launched on April 15th, 2026

Forum Threads

p/datagrout-ai

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1d ago

We're launching DataGrout Data tomorrow. What raw JSON challenges are slowing down your agents?

Hey DataGrout community!

Launching something tomorrow and wanted to share here first.

After shipping Frame for columnar data ops, the next biggest pain point we kept seeing was raw JSON handling. Agents calling Salesforce, HubSpot, or any REST API get back messy nested payloads and reshaping them without burning tokens or spinning up Python has always been a gap.

We built DataGrout Data to fix this, filter, sort, aggregate, merge, flatten, fanout as pure MCP tools. Zero credit cost, pure deterministic, cache ref support for large payloads.

p/datagrout-ai

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16d ago

We built a diff analyzer that scores whether a code change matched the stated goal

One of the core tools in Invariant is diff_analyzer.

You give it:

p/datagrout-ai

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17d ago

Your coding agent ships code that works but does the wrong thing. How are you catching it?

We kept running into this while building DatGrout Invariant.

The agent makes a change. Tests pass. No errors. But the behaviour doesn't match what you actually asked for. No crash to debug, no obvious failure, just silent goal drift that shows up later.

We called this the "plausible but wrong" problem.

The frustrating part is that existing tools don't catch it, linters check syntax, test suites check output, but nothing checks whether the change matched the original intent.

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