* feat(feishu): add streaming support via CardKit PATCH API
Implement send_delta() for Feishu channel using interactive card
progressive editing:
- First delta creates a card with markdown content and typing cursor
- Subsequent deltas throttled at 0.5s to respect 5 QPS PATCH limit
- stream_end finalizes with full formatted card (tables, rich markdown)
Also refactors _send_message_sync to return message_id (str | None)
and adds _patch_card_sync for card updates.
Includes 17 new unit tests covering streaming lifecycle, config,
card building, and edge cases.
Made-with: Cursor
* feat(feishu): close CardKit streaming_mode on stream end
Call cardkit card.settings after final content update so chat preview
leaves default [生成中...] summary (Feishu streaming docs).
Made-with: Cursor
* style: polish Feishu streaming (PEP8 spacing, drop unused test imports)
Made-with: Cursor
* docs(feishu): document cardkit:card:write for streaming
- README: permissions, upgrade note for existing apps, streaming toggle
- CHANNEL_PLUGIN_GUIDE: Feishu CardKit scope and when to disable streaming
Made-with: Cursor
* docs: address PR 2382 review (test path, plugin guide, README, English docstrings)
- Move Feishu streaming tests to tests/channels/
- Remove Feishu CardKit scope from CHANNEL_PLUGIN_GUIDE (plugin-dev doc only)
- README Feishu permissions: consistent English
- feishu.py: replace Chinese in streaming docstrings/comments
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.
Read the default timezone from the agent context when wiring the cron tool so startup no longer depends on an out-of-scope local variable. Add a regression test to ensure AgentLoop passes the configured timezone through to cron.
Made-with: Cursor
Make cron list output render one-shot and run-state timestamps in the same timezone context used to interpret schedules. This keeps scheduling logic and user-facing time displays consistent.
Made-with: Cursor
Make cron use the configured agent timezone when a cron expression omits tz or a one-shot ISO time has no offset. This keeps runtime context, heartbeat, and scheduling aligned around the same notion of time.
Made-with: Cursor
Add agent-level timezone configuration with a UTC default, propagate it into runtime context and heartbeat prompts, and document valid IANA timezone usage in the README.
Replace the flatten/unflatten approach (merging extra_content.google.*
into provider_specific_fields then reconstructing) with direct pass-through:
parse extra_content as-is, store on ToolCallRequest.extra_content, serialize
back untouched. This is lossless, requires no hardcoded field names, and
covers all three parsing branches (str, dict, SDK object) plus streaming.
Handle string and dict-shaped responses from OpenAI-compatible backends so non-standard providers no longer crash on missing choices fields. Add regression tests to keep SDK, dict, and plain-text parsing paths aligned.
- Prevent repeated retries on expired sessions in the polling thread
- Stop sending messages to invalid agent sessions to eliminate noise logs and unnecessary requests
Keep the mypy-friendly optional execute signatures while returning clearer errors for missing arguments and locking that behavior with regression tests.
Made-with: Cursor
Keep the channel enhancements aligned with the current codebase while preserving a simpler product surface. This keeps QQ, Feishu, Telegram, and WhatsApp improvements together, removes the extra Telegram-only tool hint toggle, and makes WhatsApp mention-only groups actually work.
Keep cron state workspace-scoped while only migrating legacy jobs into the default workspace. This preserves seamless upgrades for existing installs without polluting intentionally new workspaces.
Move channel-specific login logic from CLI into each channel class via a
new `login(force=False)` method on BaseChannel. The `channels login <name>`
command now dynamically loads the channel and calls its login() method.
- WeixinChannel.login(): calls existing _qr_login(), with force to clear saved token
- WhatsAppChannel.login(): sets up bridge and spawns npm process for QR login
- CLI no longer contains duplicate login logic per channel
- Update CHANNEL_PLUGIN_GUIDE to document the login() hook
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Previously the WeChat channel's send() method only handled text messages,
completely ignoring msg.media. When the agent called message(media=[...]),
the file was never delivered to the user.
Implement the full WeChat CDN upload protocol following the reference
@tencent-weixin/openclaw-weixin v1.0.2:
1. Generate a client-side AES-128 key (16 random bytes)
2. Call getuploadurl with file metadata + hex-encoded AES key
3. AES-128-ECB encrypt the file and POST to CDN with filekey param
4. Read x-encrypted-param from CDN response header as download param
5. Send message with the media item (image/video/file) referencing
the CDN upload
Also adds:
- _encrypt_aes_ecb() for AES-128-ECB encryption (reverse of existing
_decrypt_aes_ecb)
- Media type detection from file extension (image/video/file)
- Graceful error handling: failed media sends notify the user via text
without blocking subsequent text delivery
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
During testing, we discovered that when a user requests the agent to
send a file (e.g., "send me IMG_1115.png"), the agent would call
read_file to view the content and then reply with text claiming
"file sent" — but never actually deliver the file to the user.
Root cause: The system prompt stated "Reply directly with text for
conversations. Only use the 'message' tool to send to a specific
chat channel", which led the LLM to believe text replies were
sufficient for all responses, including file delivery.
Fix: Add an explicit IMPORTANT instruction in the system prompt
telling the LLM it MUST use the 'message' tool with the 'media'
parameter to send files, and that read_file only reads content
for its own analysis.
Co-Authored-By: qulllee <qullkui@tencent.com>
