Not another code generator.
You can write agent code faster than ever. The problem is everything that has to happen before it runs reliably at scale. Three panes on one engine cover it: The Workstation, The Harness and The Benchmarking.
Three panes on one engine.
The AI industry has delivered two things well. mimik delivers the third.
Intelligence is solved and compute is solved. The third is operationalization: making a system of agents run in parallel, execute across the continuum, and mesh together by discovering each other at runtime. That is what no other stack does, and what the suite is built to build, prove and measure.
Every node, every model, one surface.
Developers keep writing code in the IDE of their choice and point inference calls at a mimOE runtime URL. The Workstation is the visual control and management surface built on mimOE's APIs.
Where Finder and File Explorer navigate files, it navigates AI models, agents, images and traces across every node in your mesh: Local Network, Account, Proximity and Multi-cloud, from one workstation.
- Load, unload and switch models on demand, between My Device and Network
- Benchmark CPU against GPU and pick the most efficient execution path
- Validate any model or agent live, via chat or direct API, before anything reaches production
- Mesh-wide visibility and live workload management
- Trace every call end to end: routing decisions, latency, inputs, outputs, token usage
- Watch live runtime metrics, memory included, from desktop or mobile in the field
macOS · Windows · iOS · AndroidProve the workflow before it ships.
A workflow is only as reliable as its weakest chained step. Agents that pass in the lab can fail in the field: a different chipset, a dropped connection, a slower model. The Harness builds and runs the whole chain for real, through the conditions it will actually meet. And because inference runs locally at zero marginal cost, every test and every debug session is free.
Real Runs, Real Tools
Fleets of agents executing actual tool calls on your target compute, not simulated requests.
Reproducible by Design
The same run, repeated across devices, models and network conditions, produces comparable results.
Every Run Is Evidence
Each decision, tool call and hand-off is captured as a full trace: replay, compare and validate behavior.
Measure it by running it.
An oscilloscope shows the live waveform, not the datasheet's prediction. The Benchmarking does the same for agent fleets: it answers with a run, not a spreadsheet. Fleet size 1 to N, real models, real tool calls, on your own hardware.
Real Density
How many agents your box sustains for a given workload, measured by running them.
Verified Completion
Output checked against the expected result of the task, for every executed agent.
The Breakpoint
Sweep the fleet size upward and watch where the run bends: queue depth, agents in flight, per-agent latency.
Cost and Latency
Wall time per verified agent, completion rate and cost per verified task.
From a real run: mimOE memory held flat at 96 MB while 80 agents queued for 4 GPU slots. A waiting agent costs the box almost nothing.
The benchmark fleet and the production fleet run on the same engine: testing and production share one system of record.
One story. Shipping in packages.
The panes above arrive as packages. Pick the one that matches your setup; new packages and platforms land here as they ship.
| Package | Windows | macOS | iOS | Android | |
|---|---|---|---|---|---|
| mimOE StudioThe Workstation, standalone | Available | Available | Available | Available | Download |
| Integrated SuiteAll three panes: Workstation, Harness & Benchmarking | Available | Coming soon | Coming soon | Coming soon | Download Integrated |
mimOE Studio is on the Apple App Store and Google Play for mobile, and on the mimik developer portal for macOS and Windows. The Integrated Suite ships on Windows and macOS today; iOS and Android are coming soon.
Powering Workflow Operations, Automation, and Autonomous Decision-Making.
mimik powers agentix-native systems to function as they should, rather than being force-fit into cloud-native models designed for the mobile app era.