 Omar SobhandClaude Opus 4.7
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9b5e63cbb7
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slice 8.5: per-agent + per-team level-up endpoints
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Level-up analyzes an agent's brain + recent run outcomes (or a
whole team's aggregate state), calls Gemini 2.5 Flash for structured
JSON proposals, and persists them as pending level_up_proposals
rows. Reviewer approves a subset via /apply; the applier commits
only those items.
Migration 0052 adds level_up_proposals (id, workspace_id, agent_id
XOR team_id via CHECK constraint, status, payload JSONB,
applied_items[], model, created_by, approved_by, created_at,
applied_at) + workspace/pending/agent/team indexes.
Rust surface:
- cm_db::repo::level_up::{insert, get, list_pending, mark_applied,
mark_rejected}
- cm_api::level_up::{propose_agent, propose_team, apply}
Item kinds handled by apply():
identity_refinement → UPDATE agents.system_prompt
skill_add → agent_skills_ext INSERT
skill_candidate → workspace-scoped skills INSERT
(deterministic id per (workspace, name))
brain_consolidation → set_agent_md on the brain (unlike
brain_seed::ingest, this overwrites)
roster_change / mcp_bundle_change — logged as
"not auto-applied, human runs
team-wizard" (structural changes need
human review of side effects).
API:
- POST /api/claws/{id}/level-up → { proposal_id }
- POST /api/teams/{id}/level-up → { proposal_id }
- GET /api/level-up-proposals → pending list
- GET /api/level-up-proposals/{id}
- POST /api/level-up-proposals/{id}/apply { approved_item_ids }
- POST /api/level-up-proposals/{id}/reject
Uses Gemini 2.5 Flash with response_mime_type: "application/json"
so the model returns structured JSON directly (no ```json fence
stripping needed). Configurable via CLAWMATES_LEVEL_UP_MODEL.
Follow-ups:
- Frontend diff-review UI (pick items, approve/reject)
- roster_change / mcp_bundle_change appliers (currently manual)
- Anthropic + OpenAI proposer variants
- Promote workspace-scoped skills to builtin via a curator flow
Co-Authored-By: Claude Opus 4.7 <[email protected]>
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2026-07-19 16:36:29 -07:00 |
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