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>
Add a new WeChat (微信) channel that connects to personal WeChat using
the ilinkai.weixin.qq.com HTTP long-poll API. Protocol reverse-engineered
from @tencent-weixin/openclaw-weixin v1.0.2.
Features:
- QR code login flow (nanobot weixin login)
- HTTP long-poll message receiving (getupdates)
- Text message sending with proper WeixinMessage format
- Media download with AES-128-ECB decryption (image/voice/file/video)
- Voice-to-text from WeChat + Groq Whisper fallback
- Quoted message (ref_msg) support
- Session expiry detection and auto-pause
- Server-suggested poll timeout adaptation
- Context token caching for replies
- Auto-discovery via channel registry
No WebSocket, no Node.js bridge, no local WeChat client needed — pure
HTTP with a bot token obtained via QR code scan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace the single _processing_lock (asyncio.Lock) with per-session locks
so that different sessions can process LLM requests concurrently, while
messages within the same session remain serialised.
An optional global concurrency cap is available via the
NANOBOT_MAX_CONCURRENT_REQUESTS env var (default 3, <=0 for unlimited).
Also re-binds tool context before each tool execution round to prevent
concurrent sessions from clobbering each other's routing info.
Tested in production and manually reviewed.
(cherry picked from commit c397bb4229e8c3b7f99acea7ffe4bea15e73e957)
- Fix _read_media_bytes treating local paths as URLs: local file
handling code was dead code placed after an early return inside the
HTTP try/except block. Restructure to check for local paths (plain
path or file:// URI) before URL validation, so files like
/home/.../.nanobot/workspace/generated_image.svg can be read and
sent correctly.
- Add .svg to _IMAGE_EXTS so SVG files are uploaded as file_type=1
(image) instead of file_type=4 (file).
- Add tests for local path, file:// URI, and missing file cases.
Fixes: https://github.com/HKUDS/nanobot/pull/1667#issuecomment-4096400955
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Trigger token consolidation before prompt usage reaches the full context window so response tokens and tokenizer estimation drift still fit safely within the model budget.
Made-with: Cursor
Register Mistral as a first-class provider with LiteLLM routing,
MISTRAL_API_KEY env var, and https://api.mistral.ai/v1 default base.
Includes schema field, registry entry, and tests.
- Add strip_think() to helpers.py as single source of truth
- Filter deltas in agent loop before dispatching to consumers
- Implement send_delta in TelegramChannel with progressive edit_message_text
- Remove duplicate think filtering from CLI stream.py and telegram.py
- Remove legacy fake streaming (send_message_draft) from Telegram
- Default Telegram streaming to true
- Update CHANNEL_PLUGIN_GUIDE.md with streaming documentation
Made-with: Cursor
Move ThinkingSpinner and StreamRenderer into a dedicated module to keep
commands.py focused on orchestration. Uses Rich Live with manual refresh
(auto_refresh=False) and ellipsis overflow for stable streaming output.
Made-with: Cursor
Preserve the provider and agent-loop streaming primitives plus the CLI experiment scaffolding so this work can be resumed later without blocking urgent bug fixes on main.
Made-with: Cursor
Resolve conflict in context.py: keep main's build_messages which already
merges runtime context into user message (achieving the same cache goal).
The real value-add from this PR is the second cache breakpoint in
litellm_provider.py.
Made-with: Cursor
estimate_prompt_tokens() only counted the `content` text field, completely
missing tool_calls JSON (~72% of actual payload), reasoning_content,
tool_call_id, name, and per-message framing overhead. This caused the
memory consolidator to never trigger for tool-heavy sessions (e.g. cron
jobs), leading to context window overflow errors from the LLM provider.
Also adds reasoning_content counting and proper per-message overhead to
estimate_message_tokens() for consistent boundary detection.
Made-with: Cursor
Merge process_direct() and process_direct_outbound() into a single
interface returning OutboundMessage | None. This eliminates the
dual-path detection logic in CLI single-message mode that relied on
inspect.iscoroutinefunction to distinguish between the two APIs.
Extract status rendering into a pure function build_status_content()
in utils/helpers.py, decoupling it from AgentLoop internals.
Made-with: Cursor
Only use process_direct_outbound when the agent loop actually exposes it as an async method, and otherwise fall back to the legacy process_direct path. This keeps the new CLI render-metadata flow without breaking existing test doubles or older direct-call implementations.
Made-with: Cursor
Keep status output responsive while estimating current context from session history, dropping low-value queue/subagent counters, and marking command-style replies for plain-text rendering in CLI. Also route direct CLI calls through outbound metadata so help/status formatting stays explicit instead of relying on content heuristics.
Made-with: Cursor
Handle /status at the run-loop level so it can return immediately while the agent is busy, and reset last-usage stats when providers omit usage data. Also keep Telegram help/menu coverage for /status without changing the existing final-response send path.
Made-with: Cursor
Only normalize nullable MCP tool schemas for OpenAI-compatible providers so optional params still work without collapsing unrelated unions. Also teach local validation to honor nullable flags and add regression coverage for nullable and non-nullable schemas.
Made-with: Cursor
