- web/media_server.py: FastAPI service owning the permanent library
(library.json + ./media volume), yt-dlp YouTube Music search, queued
download jobs with mp3 transcode (ffmpeg), per-room player desired
state, and an HTTPS proxy for external live-stream URLs.
- agent/media_mcp.py: Hope tools — search_music, play_song,
offer_song_choices, pause/resume/stop/skip/seek, now_playing,
list_library, add_stream.
- agent/agent.py: media toolset wiring + conversation rule (music
playing disables the agent mic; re-engaging pauses music).
- web UI: slide-out Music panel (search, suggestions, library, live
stream add, play/pause, ±10s, seek, volume) with a reconciling poller
and media_state data-channel reports.
- token server returns the minted room name; nginx serves /media/files
as static (native Range) and proxies /media/api to the media server.
- supervisord runs media-server; compose mounts ./media and adds
ffmpeg to the image.
- Point the agent at OpenWebUI's OpenAI-compatible API (LLM_BASE_URL,
LLM_API_KEY, LLM_MODEL) with GEMMA_* env fallbacks
- Add list_available_models() fetching {base_url}/models, filtering
non-chat ids (embedding/reranker)
- Rename GemmaLLM -> HopeLLM with update_options(model=...) for runtime
model switching (mutates _opts.model) and instance-based compaction
- Data channel: agent publishes {type: models} after connect and on
get_models; set_model switches live and acks model_changed
- Web UI: Brain section in the settings sheet with a model picker that
persists the choice in localStorage
- Update tests, CI workflow env, and docs for the new endpoint
- 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.
- agent/web_mcp.py: stdio MCP server exposing web_search and web_scrape,
backed by the self-hosted Firecrawl stack on xNAS (no API key needed)
- agent.py: Agent now attaches mcp_servers built from config; EXTRA_MCP_SERVERS
env var allows adding arbitrary HTTP/SSE MCP servers as JSON
- Dockerfile: installs livekit-agents[mcp], copies web_mcp.py
- .env.example: WEB_MCP_ENABLED, FIRECRAWL_BASE, EXTRA_MCP_SERVERS documented
snowspeeder
Shane