OpenSpace: Skill Ecosystems for Self-Evolving Agent Harness

Agent skills encapsulate reusable procedures imported from communities or abstracted from past executions. However, as libraries expand, overlapping scopes and uneven quality complicate retrieval, while static instructions fail to generalize across varying task contexts. We introduce OpenSpace, a self-evolving agent harness that transforms static skill collections into a dynamic ecosystem continually refined by execution evidence. OpenSpace operates through three coupled mechanisms: evidence-grounded acquisition (abstracting verified methods from raw trajectories), quality-driven maintenance (structurally integrating repairs and derivations while tracking empirical utility), and context-aware activation (dynamically binding optimal skills to the runtime environment). On EvoAgentBench, SkillFlow, and LiveMCPBench, OpenSpace delivers substantial gains in procedural transfer and continual task completion. Strictly without updating the foundation model's weights, it outperforms the strongest non-oracle baselines across all twelve backbone-domain settings on EvoAgentBench (by 5.4–34.5 percentage points) and boosts task completion on SkillFlow from 36.14% to 42.77%. Because OpenSpace evolves the external ecosystem rather than the internal model, it provides a highly effective, training-free paradigm to scale agent capabilities.