Reliable agent runtime for anyone
Define your multi-agent workflow in normal code. MirrorNeuron handles the running, waiting, retries, recovery, and repeatability without making you build a workflow platform first.
from mn_sdk import agent, workflowfrom existing_research import collect_research_summary, save_summary TOPIC = "electric vehicle charging adoption in New England" # Reuse your existing code. MirrorNeuron only wires it into a durable run.class ResearchAgents: @agent.defn(name="ingress", type="map") def ingress(self, topic: str): return { "message_type": "research_request", "topic": topic, "text": "Collect a short research summary.", } @agent.defn(name="retriever", type="map") def retriever(self, request): summary = collect_research_summary( topic=request["topic"], instructions=request["text"], ) return {"message_type": "research_request", "summary": summary} @agent.defn(name="reviewer", type="reduce") def reviewer(self, result): save_summary(result["summary"]) return {"status": "saved"} @workflow.defn(name="marketing_research_flow_v1")class MarketingResearchFlow: def __init__(self): self.agents = ResearchAgents() @workflow.run def run(self): request = self.agents.ingress(TOPIC) result = self.agents.retriever(request) return self.agents.reviewer(result)When orchestration is too heavy, but agent scripts are too fragile.
MirrorNeuron is for builders who want durable multi-agent workflows without slow platform setup, high operational cost, or unpredictable tool-use loops.
Airflow feels like too much ceremony.
Great for scheduled pipelines, but agent workflows need tool calls, waiting, retries, and background work without turning every idea into DAG operations.
Temporal feels like a platform project.
Powerful for broad workflow infrastructure, but often slow and costly to adopt when a solo builder or small team just needs reliable agents today.
OpenClaw-style agents need guardrails.
Tool-using agents are useful, but long-running actions can be unpredictable without durable state, checkpoints, recovery, and explicit workflow boundaries.
Explore with AI without losing control of your data.
MirrorNeuron can run fully self-hosted, inside your own governance boundary. Give agents room to reason, search, and use tools, while keeping execution sandboxed, observable, and recoverable.
Self-host the runtime
Run MirrorNeuron on your own machine, edge node, cloud account, or cluster. Agent work stays 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.
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.