4 Commits

Include renames

Author	SHA1	Message	Date
snowspeeder	d4ea4fc984	fix: background tasks now fetch real page content; add task-status tools ... Background research tasks were producing garbage or failing because the self-hosted Firecrawl /v1/scrape defaults to a ~30s timeout that's too short for heavy news/research sites, and the small LLM won't reliably chain a second scrape call after a search that only returns snippets. task_worker.py: - web_search now auto-fetches the top results directly from the container (which has working outbound internet) and returns their real text, so the model gets actual page content in one call instead of snippet echoes. - web_scrape requests timeout: 90000 (Firecrawl renders heavy sites but needs >30s), and falls back to a direct container fetch with tag-stripping if Firecrawl times out or returns nothing. task_registry.py / dispatch_mcp.py: - Add task_status(id) and list_tasks() tools so Hope can check the live state of any dispatched task (pending / running / completed / failed / STALLED). - Track last_activity per task and surface a stall heuristic. - Move the status summary into task_registry (no mcp dependency) so it's unit-testable. agent.py: prompt tells Hope about task_status / list_tasks. tests: add task-status summary and last-activity regression tests.	2026-08-30 06:54:57 -04:00
snowspeeder	321421e37f	fix: reliably dispatch and return background tasks	2026-08-29 07:29:38 -04:00
Shane	44e8d05e2c	feat: background task dispatch system with live UI panel ... - agent/task_registry.py: file-based JSONL event registry (cross-process) - agent/task_worker.py: autonomous LLM loop with weather/time/memory/web tools - agent/dispatch_mcp.py: MCP tool exposing dispatch_task to the main agent - agent/agent.py: registers dispatch toolset, polls task events → room data - web: slide-out task panel (FAB button + badge), live step streaming via data channel topic 'tasks', status dots (running/completed/failed) - Dockerfile: copies new task_*.py and dispatch_mcp.py files The dispatch MCP runs in its own process; events flow through /tmp/tasks/events.jsonl which the main agent tails every second and forwards to the browser. Tasks run up to 10 LLM iterations with tool calls.	2026-08-22 18:06:23 -04:00
Shane	d372adca7d	fix: strengthen memory tool prompt — Gemma 4B needs explicit examples ... The 4B model was responding conversationally instead of calling memory_save/ memory_recall. Added imperative language (MUST call), concrete examples of trigger phrases, and explicit instructions to never skip the tool call. Verified: model now reliably generates tool_calls for save/recall/list.	2026-08-22 17:42:26 -04:00