Reliable local AI workflows without cloud-first orchestration
Run durable agent workflows on your own machines, close to the data, tools, and compute they need. When something fails, MirrorNeuron helps the workflow remember where it was, try again, and continue.
Reliable execution. Durable recovery. Lower cost.
These results show why MirrorNeuron is a runtime for real AI workflows, not just an agent demo: workflows finish, failures recover, and optimized runs can cost less than a naive agent chain.
Fault recovery rate
124 / 125 injected failures recovered
Recovers from worker, tool, loop, and approval failures.
Workflow completion rate
19 / 20 golden workflows completed
Completes real multi-step workflows reliably.
Cost vs naive agent chain
Optimized vs naive GPT-5.4 mini workflow
Cuts cost per successful workflow by over half.
Note: based on benchmarks using real blueprints with context compression enabled.
Start with normal code.
Start with Python or a simple manifest, reuse your existing agents and tools, and let MirrorNeuron handle waiting, retries, recovery, and repeatable runs around normal code.
import json from mn_sdk import RetryPolicy, RunnerConfig, agent, workflow RETRY = RetryPolicy(max_attempts=2, backoff_ms=250)RUNNER = RunnerConfig.host_local() class HelloAgents: @agent.defn(name="hello", type="map", runner=RUNNER, retries=RETRY, timeout_seconds=10) def hello(self, name: str): return {"message_type": "hello_result", "text": f"Hello, {name}!"} @workflow.defn(name="hello_world_v1", recovery_mode="cluster_recover")class HelloWorldFlow: def __init__(self): self.agents = HelloAgents() @workflow.run def run(self): name = workflow.input("name", default="world") return self.agents.hello(name) def run_local(name: str = "world") -> dict: agents = HelloAgents() return agents.hello(name) if __name__ == "__main__": print(json.dumps(run_local(), indent=2, sort_keys=True))For teams building durable AI workflows.
MirrorNeuron is a good fit when AI workflows need background execution, recovery, and a first-run path that stays easy to adopt.
Private or local data
Your workflow should run close to files, databases, research systems, financial signals, devices, or internal tools.
Long-running workflow state
The workflow needs to wait, retry, sleep, resume, call tools, or keep background state instead of finishing in one request.
Blueprint-led adoption
You want a working example first, then a path to replace mock inputs with your own adapters, code, and data.
Run AI near sensitive systems without losing control.
MirrorNeuron can run fully self-hosted, inside your own governance boundary, and still support cloud deployment when that is the right operational fit. Give agents room to reason, search, and use tools, while keeping execution sandboxed, observable, and recoverable.
Run on-edge first
Run MirrorNeuron on your own machine, edge node, private cluster, or cloud account. Agent work can stay inside infrastructure you control.
Keep data under governance
Your workflows can operate where your data already lives, so sensitive inputs and outputs do not need to leave your security boundary.
Sandbox exploratory AI
Let agents research, call tools, and explore broadly, while execution stays bounded by explicit workflow stages, policies, and recovery.
The wedge is durable AI where cloud-first orchestration is not the starting point.
MirrorNeuron's product narrative is simple: make durable AI workflows easy to adopt where the work already lives, then let teams carry the same workflow into larger deployments.
AI work is moving closer to data
Agents are becoming useful around private systems, local tools, devices, and domain data that teams may not want to ship to a hosted workflow layer first.
Durability is the missing step
A demo agent can answer once. A useful workflow needs retries, checkpoints, background execution, recovery, and a way to run again.
Blueprints lower the first-run cost
MirrorNeuron can start as a runnable example, then expand into normal-code workflows that move from laptop to edge, private cluster, or cloud.
If the problem sounds familiar, start with a blueprint.
Blueprints show the product in the way developers care about most: runnable workflows with manifests, agents, recovery modes, and code you can change.