Keep dict-backed channel configs compatible with both allow_from and allowFrom without losing empty-list semantics, and add focused regression coverage for the allow-list boundary.
Made-with: Cursor
getattr() on a dict never finds custom keys — it only searches
object attributes, not dict keys. When channel config is loaded as
a Pydantic extra field (which is a plain dict), getattr(config,
'allow_from', []) always returns the default [], causing all access
to be denied regardless of the allowFrom configuration.
Fix both is_allowed() and _validate_allow_from() to use isinstance
checks, falling back to dict.get() for dict configs while preserving
getattr() for object-style configs.
When the primary transcription provider fails (bad key, API error, etc.),
automatically try the other provider if its API key is available.
Made-with: Cursor
When LLM generates faster than channel can process, asyncio.Queue
accumulates multiple _stream_delta messages. Each delta triggers a
separate API call (~700ms each), causing visible delay after LLM
finishes.
Solution: In _dispatch_outbound, drain all queued deltas for the same
(channel, chat_id) before sending, combining them into a single API
call. Non-matching messages are preserved in a pending buffer for
subsequent processing.
This reduces N API calls to 1 when queue has N accumulated deltas.
Make channel delivery failures raise consistently so retry policy lives in ChannelManager rather than being split across individual channels. Tighten Telegram stream finalization, clarify sendMaxRetries semantics, and align the docs with the behavior the system actually guarantees.
- Remove trailing whitespace and normalize blank lines
- Unify string quotes and line breaks for long lines
- Sort imports alphabetically across modules
Add a boolean config option `channels.sendProgress` (default: false) to
control whether progress messages (marked with `_progress` metadata) are
sent to chat channels. When disabled, progress messages are filtered
out in the outbound dispatcher.
Add official QQ platform support using botpy SDK with WebSocket connection.
Features:
- C2C (private message) support via QQ Open Platform
- WebSocket-based bot connection (no public IP required)
- Message deduplication with efficient deque-based LRU cache
- User whitelist support via allow_from configuration
- Clean async architecture using single event loop
Changes:
- Add QQChannel implementation in nanobot/channels/qq.py
- Add QQConfig schema with appId and secret fields
- Register QQ channel in ChannelManager
- Update README with QQ setup instructions
- Add qq-botpy dependency to pyproject.toml
- Add botpy.log to .gitignore
Setup:
1. Get AppID and Secret from q.qq.com
2. Configure in ~/.nanobot/config.json:
{
"channels": {
"qq": {
"enabled": true,
"appId": "YOUR_APP_ID",
"secret": "YOUR_APP_SECRET",
"allowFrom": []
}
}
}
3. Run: nanobot gateway
Note: Group chat support will be added in future updates.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Implement Discord channel functionality with websocket integration.
- Update configuration schema to include Discord settings.
- Enhance README with setup instructions for Discord integration.
- Modify channel manager to initialize Discord channel if enabled.
- Update CLI status command to display Discord channel status.
