New endpoint: POST /api/v1/agent/sessions/:sessionId/voice-message
- Accepts multipart/form-data audio file (any format Whisper supports)
- Transcribes via OpenAI Whisper API (routed through existing proxy)
- If a task is currently running in the session → hard-interrupts it first
(same cancel+inject pattern as text inject, triggered by voice command)
- Otherwise → starts a fresh task with the transcript
- Returns { sessionId, taskId, transcript } so client can subscribe to WS stream
This enables WhatsApp-style push-to-talk and doubles as an async voice
interrupt into any active agent workflow, bypassing the need for speaker
diarization (whoever presses record owns the message).
New files:
infrastructure/stt/openai-stt.service.ts — OpenAI Whisper client,
manually builds multipart/form-data, supports self-signed proxy cert
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace the per-turn POST /tasks approach for voice calls with a
long-lived agent run loop tied to the call lifecycle:
agent-service:
- Add AsyncQueue<T> utility for blocking message relay
- Add VoiceSessionManager: spawns one background run loop per voice call,
accepts injected messages, terminates cleanly on hangup
- Add VoiceSessionController with 3 endpoints:
POST /api/v1/agent/sessions/voice/start (call start)
POST /api/v1/agent/sessions/:id/voice/inject (each speech turn)
DELETE /api/v1/agent/sessions/:id/voice (user hung up)
- Register VoiceSessionManager + VoiceSessionController in agent.module.ts
voice-agent:
- AgentServiceLLM: add start_voice_session(), terminate_voice_session(),
inject_text_message() (voice/inject-aware), _do_inject_voice()
- AgentServiceLLMStream._run(): use voice/inject path when voice session
is active; fall back to per-task POST for text-chat / non-SDK engines
- entrypoint(): call start_voice_session() after session.start();
register _on_room_disconnect that calls terminate_voice_session()
so the agent is always killed when the user hangs up
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously AgentSkillService wrote skills to public.agent_skills (TypeORM
entity with tenantId column filter), while ClaudeAgentSdkEngine read from
it0_t_{tenantId}.skills (per-tenant schema). The two tables were never
connected, so any skill added via the CRUD API was invisible to the agent.
This fix:
- Rewrites AgentSkillService to use DataSource + raw SQL against the
per-tenant schema it0_t_{tenantId}.skills
- Maps API fields: script→content, enabled→is_active
- Removes AgentSkillRepository and AgentSkill entity from module (no longer needed)
- CRUD API response shape is unchanged (fields mapped back to script/enabled)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Implement DB-based conversation message storage (engine-agnostic) that
works across both Claude API and Agent SDK engines. Add ChatGPT/Claude-style
conversation history drawer in Flutter with date-grouped session list,
session switching, and new chat functionality.
Backend: entity, repository, context service, migration 004, session/message
API endpoints. Flutter: ConversationDrawer, sessionId flow from backend
response via SessionInfoEvent, session list/switch/delete support.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement missing REST API endpoints that the web-admin frontend
pages were calling but had no backend support:
- GET/POST/PUT /api/v1/agent-config (engine, prompt, turns, budget, tools)
- GET/POST/PUT/DELETE /api/v1/agent/skills (CRUD for agent skills)
- GET/POST/PUT/DELETE /api/v1/agent/hooks (CRUD for hook scripts)
Each endpoint includes entity, repository, service, and controller
layers following the existing DDD + tenant-aware patterns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
hailin