Architecture Diagram
Modern AI workloads do not run well on a single static model or a disconnected stack. Real production systems need deployment, model routing, external grounding, and performance evaluation working together. OpenMesh brings these layers into one platform so teams can run agentic inference with better cost, speed, and reliability.
Why OpenMesh
Lower cost
Faster performance
Higher efficiency
Built for production AI workflows
Generated Workflow
Turn a high-level task into a routed multi-step workflow for real use cases such as coding, research, customer support, and document analysis.
Agent Monitor
Observe and manage live AI agents in production with step-level visibility into quality, routing, and performance.
Router Playground
Preview how prompts are matched to the right route, skill, or workflow before deploying them in real traffic.
Evaluations
Run benchmarks across models and workflows to measure quality, speed, cost, and reliability on real-world tasks.
Skills
Discover the strongest models and routes for common AI tasks, from coding and summarization to tool use and translation.
Monitoring Dashboard
Get a real-time view of how OpenMesh is performing across production workloads, from traffic and latency to uptime and cost.
Powering the next billion AI agents with the world’s first task-level model routing
AI workflows increasingly rely on many smaller tasks executed in sequence or in parallel. Sending every step to a single model is often inefficient, costly, and poorly suited to the structure of real workloads. OpenMesh provides a task-aware routing layer that selects the right model for each step of the workflow. Developers can build applications through one unified platform while OpenMesh continuously optimizes model selection behind the scenes to improve cost, speed, and overall performance.