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Bootstrap: conversational onboarding + system discovery for day-zero memory #556
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agentchatChat SDK changesChat SDK changesdomain:agent-coreFramework, tools, registry, memory, skills, orchestrationFramework, tools, registry, memory, skills, orchestrationelectronElectron app changesElectron app changesenhancementNew feature or requestNew feature or requestp0high priorityhigh prioritytrack:consumer-appHermes-competitor consumer product — mobile-first, voice + messaging + memory + skillsHermes-competitor consumer product — mobile-first, voice + messaging + memory + skills
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agentchatChat SDK changesChat SDK changesdomain:agent-coreFramework, tools, registry, memory, skills, orchestrationFramework, tools, registry, memory, skills, orchestrationelectronElectron app changesElectron app changesenhancementNew feature or requestNew feature or requestp0high priorityhigh prioritytrack:consumer-appHermes-competitor consumer product — mobile-first, voice + messaging + memory + skillsHermes-competitor consumer product — mobile-first, voice + messaging + memory + skills
Summary
Solve the cold-start problem. When the agent has no memory (first launch), bootstrap it through a guided conversation and optional system discovery so it's useful from the first interaction.
Spec:
docs/spec/agent-memory-architecture.md→ "Bootstrap: Day-Zero Onboarding"Depends on: #542 (MemoryStore), #543 (MemoryMixin)
Phase 1: Conversational Onboarding
Guided question flow that runs on first launch or via
gaia memory bootstrap:fact(global)preference(global)fact(work context, with entity linking)Implementation:
run_bootstrap_conversation()method on MemoryMixin.Phase 2: System Discovery
File:
src/gaia/agents/base/discovery.pyOpt-in system scanning with user review before storing:
All discoveries returned as dicts with
source='discovery',confidence=0.4.User reviews and approves/rejects each item before storage.
Auto-classification into contexts via heuristics (~/Work/ → work, social media → personal).
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