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Orpheus is a specialized cognitive runtime engineered to provide long-term persistence, coordination, and governance for autonomous AI agents.
Unlike traditional serverless functions or container orchestrators, it utilizes a three-layer architecture, coordination through workers, execution via lightweight runc containers, and persistence through dedicated workspaces.
The system optimizes for time and continuity rather than just latency, using queue-depth autoscaling to manage I/O-bound workloads that typically idle while waiting for LLM responses.
Key components like the ServiceManager and Registry enable seamless integration with local inference engines like Ollama and vLLM, providing a platform-agnostic environment for complex reasoning systems.
Recent technical refinements have introduced an actor model to eliminate race conditions and an ExecLog system for crash recovery and auditability.
Ultimately, Orpheus bridges the gap between simple script execution and heavyweight cloud orchestration, the essential substrate for industrial-grade LLM applications.
When to Use Orpheus
- AI agents that run for minutes (not milliseconds)
- Conversational agents that need context
- Agents that create or modify files
- Workflows that shouldn’t lose state on restart
