OSIL Layered Stack
Six layers, each higher layer consumes lower-layer artifacts. L1 is foundational; L6 is observe-only.
graph LR subgraph L6["Layer 6 Track-only research-tier"] L6A[HyperAgents] L6B[AlphaEvolve / SWE-RL] L6C[EvoAgentX firehose] L6D[GenericAgent / Evolver] end subgraph L5["Layer 5 Memory Evolution"] L5A[Honcho dialectic] L5B[Mem0 production] L5C[Letta MemGPT] end subgraph L4["Layer 4 Skill Induction + Autoresearch"] L4A[Voyager pattern<br/>auto-skill discovery] L4B[Karpathy autoresearch<br/>overnight loops] L4C[GenericAgent reference] end subgraph L3["Layer 3 Prompt Optimization"] L3A[DSPy + GEPA] L3B[TextGrad] L3C[OPRO pattern] L3D[PromptBreeder] L3E[MIPROv2 fallback] end subgraph L2["Layer 2 Failure Loops"] L2A[Reflexion runner] L2B[Self-Refine pattern] L2C[CRITIC for code] end subgraph L1["Layer 1 Capture"] L1A[peterskoett self-improvement skill] L1B[AGENTS.md formalization] L1C[Trajectory capture ShareGPT] end L1A --> L2A L1B --> L3A L1C --> L4B L1C --> L4A L1C --> L3A L2A --> L3A L3A --> L4A L4A --> L5A L5A -.feedback.-> L4A L6A -.observe.-> L4A
Layer descriptions
- L1 Capture — passive collection of learnings, AGENTS.md notes, trajectory traces. Foundation; everything depends on this.
- L2 Failure Loops — generate reflections from failures, store and retrieve. Reduces error rate.
- L3 Prompt Optimization — GEPA / TextGrad / OPRO / PromptBreeder / MIPROv2. Mutates prompts based on eval data.
- L4 Skill Induction + Autoresearch — auto-generates new skills from successful traces; runs autoresearch loops on measurable workflows.
- L5 Memory Evolution — alternative memory backends (Honcho/Mem0/Letta) evaluated against current vec0+FTS5+Voyage-4 hybrid.
- L6 Track-only — frontier research observed quarterly; not implemented unless validated.
Cross-links
- Parent plan: openclaw-self-improvement-layer-2026-05-03
- Related maps: vm-osil-overview · vm-osil-dataflow · vm-osil-decision-tree · vm-osil-vendor-map