Add task brief research agent: Stage 1 deep-research + canvas draft emission
- run_task_brief_research() runner with brief-specific tool set and max_steps=12 - New agents: client_agent (list_clients, get_client) and relations_agent (query_relations) - search_associative tool wrapping MemoryMiddleware semantic search - BRIEF_RESEARCH_TOOLS constant: read-only task/project/note/timeline + memory + client/relations - canvas block extraction in output_formatter (splits visible text from <canvas> draft) - device_ws.py: task_brief_research request type; emits canvas_draft mutation on stream_end - Stage 2 briefMode: briefing_context injected into floating system prompt when present - briefingContext kwarg wired through compile_prompt call chain Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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@@ -28,8 +28,9 @@ class Settings(BaseSettings):
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LLM_MODEL_FLOATING_AGENT: str = "" # floating-agent (contextual chat)
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LLM_MODEL_UNIFIED_PROCESSOR: str = "" # unified-processor (agent_runner)
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LLM_MODEL_CLOUD_PROCESSOR: str = "" # cloud-processor (agent_runner)
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LLM_MODEL_BRIEF_AGENT: str = "" # brief-agent (home + project text briefs)
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LLM_MODEL_SETUP_AGENT: str = "" # agent-setup journey
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LLM_MODEL_BRIEF_AGENT: str = "" # brief-agent (home + project text briefs)
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LLM_MODEL_TASK_BRIEF_AGENT: str = "" # task-brief-agent (per-task deep research)
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LLM_MODEL_SETUP_AGENT: str = "" # agent-setup journey
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LLM_MODEL_MEMORY_EXTRACTOR: str = "" # memory-extractor (Phase 2 extract/decide)
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LLM_MODEL_MEMORY_MINER: str = "" # memory-miner (Phase 5 proactive mining)
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LLM_MODEL_MEMORY_AUDITOR: str = "" # memory-auditor (Phase 7 weekly audit)
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