agent-runtimes

Self-evolving runtime infrastructure for Physical AI and embodied agents. Ground AI agents into robot bodies with e-URDF, sandbox safety, capability routing, praxis capture, physical memory, runtime intervention, and skill evolution.

A work-centered runtime for agentic software engineering. Work persists; agents, context, and graphs assemble around it.

Build AI agents you can trust in production. AgentFlow4J is a framework and runtime for governed, stateful multi-agent systems on the JVM. Human approvals · Checkpoints · Budget controls · Tool policies · Durable execution

ARES is a self-healing multi-agent runtime that combines event sourcing, memory distillation, chaos engineering, and evolutionary workflow optimization.

"Local LLM agent framework in Rust — native tool-role support, tmux session persistence, two-tier summarization, and ShareGPT logging. Works with any OpenAI-compatible backend."

Auto consolidate all your Agent Runtimes. Share memory across your agents.Auto highly structured task analyzer.

Local runtime self-learning engine for Hanako with policy gates, evidence chain, validation, rollback, and skill governance.

API platform info for agents