The task queue was completely stuck: 82 tasks trapped in pending_approval,
4 zombie tasks frozen in running, and the worker loop unable to process
anything. This removes the approval gate as the default and adds startup
recovery for orphaned tasks.
- Auto-approve all tasks by default; only task_type="escalation" requires
human review (and escalations never block the processor)
- Add reconcile_zombie_tasks() to reset RUNNING→APPROVED on startup
- Use in-memory _current_task for concurrency check instead of DB status
so stale RUNNING rows from a crash can't block new work
- Update get_next_pending_task to only query APPROVED tasks
- Update all callsites (chat route, API, form) to match new defaults
Co-authored-by: Alexander Payne <apayne@MM.local>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add task queue system for Timmy - all work goes through the queue
- Add queue position tracking to task_queue models with task_type field
- Add TaskProcessor class that consumes tasks from queue one at a time
- Modify chat route to queue all messages for async processing
- Chat responses get 'high' priority to jump ahead of thought tasks
- Add queue status API endpoints for position polling
- Update UI to show queue position (x/y) and current task banner
- Replace thinking loop with task-based approach - thoughts are queued tasks
- Push responses to user via WebSocket instead of immediate HTTP response
- Add database migrations for existing tables
* feat: Timmy drains task queue on startup, backlogs unhandleable tasks
On spin-up, Timmy now iterates through all pending/approved tasks
immediately instead of waiting for the polling loop. Tasks without a
registered handler or with permanent errors are moved to a new
BACKLOGGED status with a reason, keeping the queue clear for work
Timmy can actually do.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Alexander Payne <apayne@MM.local>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Inspired by OpenClaw-RL's multi-model orchestration, this adds four
features for custom model management:
1. Custom model registry (infrastructure/models/registry.py) — SQLite-backed
registry for GGUF, safetensors, HF checkpoint, and Ollama models with
role-based lookups (general, reward, teacher, judge).
2. Per-agent model assignment — each swarm persona can use a different model
instead of sharing the global default. Resolved via registry assignment >
persona default > global default.
3. Runtime model management API (/api/v1/models) — REST endpoints to register,
list, assign, enable/disable, and remove custom models without restart.
Includes a dashboard page at /models.
4. Reward model scoring (PRM-style) — majority-vote quality evaluation of
agent outputs using a configurable reward model. Scores persist in SQLite
and feed into the swarm learner.
New config settings: custom_weights_dir, reward_model_enabled,
reward_model_name, reward_model_votes.
54 new tests covering registry CRUD, API endpoints, agent assignments,
role lookups, and reward scoring.
https://claude.ai/code/session_01V4iTozMwcE2gjfnCJdCugC
