Merge branch 'main' into pr-1581

nanobot/agent/context.py

@@ -10,6 +10,7 @@ from typing import Any
 
 from nanobot.agent.memory import MemoryStore
 from nanobot.agent.skills import SkillsLoader
+from nanobot.utils.helpers import detect_image_mime
 
 
 class ContextBuilder:
@@ -136,10 +137,14 @@ Reply directly with text for conversations. Only use the 'message' tool to send
         images = []
         for path in media:
             p = Path(path)
-            mime, _ = mimetypes.guess_type(path)
-            if not p.is_file() or not mime or not mime.startswith("image/"):
+            if not p.is_file():
                 continue
-            b64 = base64.b64encode(p.read_bytes()).decode()
+            raw = p.read_bytes()
+            # Detect real MIME type from magic bytes; fallback to filename guess
+            mime = detect_image_mime(raw) or mimetypes.guess_type(path)[0]
+            if not mime or not mime.startswith("image/"):
+                continue
+            b64 = base64.b64encode(raw).decode()
             images.append({"type": "image_url", "image_url": {"url": f"data:{mime};base64,{b64}"}})
 
         if not images:

nanobot/agent/loop.py

@@ -202,18 +202,9 @@ class AgentLoop:
 
             if response.has_tool_calls:
                 if on_progress:
-                    thoughts = [
-                        self._strip_think(response.content),
-                        response.reasoning_content,
-                        *(
-                            f"Thinking [{b.get('signature', '...')}]:\n{b.get('thought', '...')}"
-                            for b in (response.thinking_blocks or [])
-                            if isinstance(b, dict) and "signature" in b
-                        ),
-                    ]
-                    combined_thoughts = "\n\n".join(filter(None, thoughts))
-                    if combined_thoughts:
-                        await on_progress(combined_thoughts)
+                    thought = self._strip_think(response.content)
+                    if thought:
+                        await on_progress(thought)
                     await on_progress(self._tool_hint(response.tool_calls), tool_hint=True)
 
                 tool_call_dicts = [

nanobot/agent/memory.py

@@ -128,6 +128,13 @@ class MemoryStore:
             # Some providers return arguments as a JSON string instead of dict
             if isinstance(args, str):
                 args = json.loads(args)
+            # Some providers return arguments as a list (handle edge case)
+            if isinstance(args, list):
+                if args and isinstance(args[0], dict):
+                    args = args[0]
+                else:
+                    logger.warning("Memory consolidation: unexpected arguments as empty or non-dict list")
+                    return False
             if not isinstance(args, dict):
                 logger.warning("Memory consolidation: unexpected arguments type {}", type(args).__name__)
                 return False

nanobot/agent/tools/base.py

@@ -52,6 +52,75 @@ class Tool(ABC):
         """
         pass
 
+    def cast_params(self, params: dict[str, Any]) -> dict[str, Any]:
+        """Apply safe schema-driven casts before validation."""
+        schema = self.parameters or {}
+        if schema.get("type", "object") != "object":
+            return params
+
+        return self._cast_object(params, schema)
+
+    def _cast_object(self, obj: Any, schema: dict[str, Any]) -> dict[str, Any]:
+        """Cast an object (dict) according to schema."""
+        if not isinstance(obj, dict):
+            return obj
+
+        props = schema.get("properties", {})
+        result = {}
+
+        for key, value in obj.items():
+            if key in props:
+                result[key] = self._cast_value(value, props[key])
+            else:
+                result[key] = value
+
+        return result
+
+    def _cast_value(self, val: Any, schema: dict[str, Any]) -> Any:
+        """Cast a single value according to schema."""
+        target_type = schema.get("type")
+
+        if target_type == "boolean" and isinstance(val, bool):
+            return val
+        if target_type == "integer" and isinstance(val, int) and not isinstance(val, bool):
+            return val
+        if target_type in self._TYPE_MAP and target_type not in ("boolean", "integer", "array", "object"):
+            expected = self._TYPE_MAP[target_type]
+            if isinstance(val, expected):
+                return val
+
+        if target_type == "integer" and isinstance(val, str):
+            try:
+                return int(val)
+            except ValueError:
+                return val
+
+        if target_type == "number" and isinstance(val, str):
+            try:
+                return float(val)
+            except ValueError:
+                return val
+
+        if target_type == "string":
+            return val if val is None else str(val)
+
+        if target_type == "boolean" and isinstance(val, str):
+            val_lower = val.lower()
+            if val_lower in ("true", "1", "yes"):
+                return True
+            if val_lower in ("false", "0", "no"):
+                return False
+            return val
+
+        if target_type == "array" and isinstance(val, list):
+            item_schema = schema.get("items")
+            return [self._cast_value(item, item_schema) for item in val] if item_schema else val
+
+        if target_type == "object" and isinstance(val, dict):
+            return self._cast_object(val, schema)
+
+        return val
+
     def validate_params(self, params: dict[str, Any]) -> list[str]:
         """Validate tool parameters against JSON schema. Returns error list (empty if valid)."""
         if not isinstance(params, dict):
@@ -63,7 +132,13 @@ class Tool(ABC):
 
     def _validate(self, val: Any, schema: dict[str, Any], path: str) -> list[str]:
         t, label = schema.get("type"), path or "parameter"
-        if t in self._TYPE_MAP and not isinstance(val, self._TYPE_MAP[t]):
+        if t == "integer" and (not isinstance(val, int) or isinstance(val, bool)):
+            return [f"{label} should be integer"]
+        if t == "number" and (
+            not isinstance(val, self._TYPE_MAP[t]) or isinstance(val, bool)
+        ):
+            return [f"{label} should be number"]
+        if t in self._TYPE_MAP and t not in ("integer", "number") and not isinstance(val, self._TYPE_MAP[t]):
             return [f"{label} should be {t}"]
 
         errors = []

nanobot/agent/tools/message.py

@@ -96,7 +96,7 @@ class MessageTool(Tool):
             media=media or [],
             metadata={
                 "message_id": message_id,
-            }
+            },
         )
 
         try:

nanobot/agent/tools/registry.py

@@ -44,6 +44,10 @@ class ToolRegistry:
             return f"Error: Tool '{name}' not found. Available: {', '.join(self.tool_names)}"
 
         try:
+            # Attempt to cast parameters to match schema types
+            params = tool.cast_params(params)
+            
+            # Validate parameters
             errors = tool.validate_params(params)
             if errors:
                 return f"Error: Invalid parameters for tool '{name}': " + "; ".join(errors) + _HINT

nanobot/channels/base.py

@@ -66,10 +66,7 @@ class BaseChannel(ABC):
             return False
         if "*" in allow_list:
             return True
-        sender_str = str(sender_id)
-        return sender_str in allow_list or any(
-            p in allow_list for p in sender_str.split("|") if p
-        )
+        return str(sender_id) in allow_list
 
     async def _handle_message(
         self,

nanobot/channels/dingtalk.py

@@ -70,12 +70,24 @@ class NanobotDingTalkHandler(CallbackHandler):
             sender_id = chatbot_msg.sender_staff_id or chatbot_msg.sender_id
             sender_name = chatbot_msg.sender_nick or "Unknown"
 
+            conversation_type = message.data.get("conversationType")
+            conversation_id = (
+                message.data.get("conversationId")
+                or message.data.get("openConversationId")
+            )
+
             logger.info("Received DingTalk message from {} ({}): {}", sender_name, sender_id, content)
 
             # Forward to Nanobot via _on_message (non-blocking).
             # Store reference to prevent GC before task completes.
             task = asyncio.create_task(
-                self.channel._on_message(content, sender_id, sender_name)
+                self.channel._on_message(
+                    content,
+                    sender_id,
+                    sender_name,
+                    conversation_type,
+                    conversation_id,
+                )
             )
             self.channel._background_tasks.add(task)
             task.add_done_callback(self.channel._background_tasks.discard)
@@ -95,8 +107,8 @@ class DingTalkChannel(BaseChannel):
     Uses WebSocket to receive events via `dingtalk-stream` SDK.
     Uses direct HTTP API to send messages (SDK is mainly for receiving).
 
-    Note: Currently only supports private (1:1) chat. Group messages are
-    received but replies are sent back as private messages to the sender.
+    Supports both private (1:1) and group chats.
+    Group chat_id is stored with a "group:" prefix to route replies back.
     """
 
     name = "dingtalk"
@@ -301,14 +313,25 @@ class DingTalkChannel(BaseChannel):
             logger.warning("DingTalk HTTP client not initialized, cannot send")
             return False
 
-        url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
         headers = {"x-acs-dingtalk-access-token": token}
-        payload = {
-            "robotCode": self.config.client_id,
-            "userIds": [chat_id],
-            "msgKey": msg_key,
-            "msgParam": json.dumps(msg_param, ensure_ascii=False),
-        }
+        if chat_id.startswith("group:"):
+            # Group chat
+            url = "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+            payload = {
+                "robotCode": self.config.client_id,
+                "openConversationId": chat_id[6:],  # Remove "group:" prefix,
+                "msgKey": msg_key,
+                "msgParam": json.dumps(msg_param, ensure_ascii=False),
+            }
+        else:
+            # Private chat
+            url = "https://api.dingtalk.com/v1.0/robot/oToMessages/batchSend"
+            payload = {
+                "robotCode": self.config.client_id,
+                "userIds": [chat_id],
+                "msgKey": msg_key,
+                "msgParam": json.dumps(msg_param, ensure_ascii=False),
+            }
 
         try:
             resp = await self._http.post(url, json=payload, headers=headers)
@@ -417,7 +440,14 @@ class DingTalkChannel(BaseChannel):
                 f"[Attachment send failed: {filename}]",
             )
 
-    async def _on_message(self, content: str, sender_id: str, sender_name: str) -> None:
+    async def _on_message(
+        self,
+        content: str,
+        sender_id: str,
+        sender_name: str,
+        conversation_type: str | None = None,
+        conversation_id: str | None = None,
+    ) -> None:
         """Handle incoming message (called by NanobotDingTalkHandler).
 
         Delegates to BaseChannel._handle_message() which enforces allow_from
@@ -425,13 +455,16 @@ class DingTalkChannel(BaseChannel):
         """
         try:
             logger.info("DingTalk inbound: {} from {}", content, sender_name)
+            is_group = conversation_type == "2" and conversation_id
+            chat_id = f"group:{conversation_id}" if is_group else sender_id
             await self._handle_message(
                 sender_id=sender_id,
-                chat_id=sender_id,  # For private chat, chat_id == sender_id
+                chat_id=chat_id,
                 content=str(content),
                 metadata={
                     "sender_name": sender_name,
                     "platform": "dingtalk",
+                    "conversation_type": conversation_type,
                 },
             )
         except Exception as e:

nanobot/channels/discord.py

@@ -13,34 +13,13 @@ from nanobot.bus.events import OutboundMessage
 from nanobot.bus.queue import MessageBus
 from nanobot.channels.base import BaseChannel
 from nanobot.config.schema import DiscordConfig
+from nanobot.utils.helpers import split_message
 
 DISCORD_API_BASE = "https://discord.com/api/v10"
 MAX_ATTACHMENT_BYTES = 20 * 1024 * 1024  # 20MB
 MAX_MESSAGE_LEN = 2000  # Discord message character limit
 
 
-def _split_message(content: str, max_len: int = MAX_MESSAGE_LEN) -> list[str]:
-    """Split content into chunks within max_len, preferring line breaks."""
-    if not content:
-        return []
-    if len(content) <= max_len:
-        return [content]
-    chunks: list[str] = []
-    while content:
-        if len(content) <= max_len:
-            chunks.append(content)
-            break
-        cut = content[:max_len]
-        pos = cut.rfind('\n')
-        if pos <= 0:
-            pos = cut.rfind(' ')
-        if pos <= 0:
-            pos = max_len
-        chunks.append(content[:pos])
-        content = content[pos:].lstrip()
-    return chunks
-
-
 class DiscordChannel(BaseChannel):
     """Discord channel using Gateway websocket."""
 
@@ -96,7 +75,7 @@ class DiscordChannel(BaseChannel):
             self._http = None
 
     async def send(self, msg: OutboundMessage) -> None:
-        """Send a message through Discord REST API."""
+        """Send a message through Discord REST API, including file attachments."""
         if not self._http:
             logger.warning("Discord HTTP client not initialized")
             return
@@ -105,15 +84,31 @@ class DiscordChannel(BaseChannel):
         headers = {"Authorization": f"Bot {self.config.token}"}
 
         try:
-            chunks = _split_message(msg.content or "")
+            sent_media = False
+            failed_media: list[str] = []
+
+            # Send file attachments first
+            for media_path in msg.media or []:
+                if await self._send_file(url, headers, media_path, reply_to=msg.reply_to):
+                    sent_media = True
+                else:
+                    failed_media.append(Path(media_path).name)
+
+            # Send text content
+            chunks = split_message(msg.content or "", MAX_MESSAGE_LEN)
+            if not chunks and failed_media and not sent_media:
+                chunks = split_message(
+                    "\n".join(f"[attachment: {name} - send failed]" for name in failed_media),
+                    MAX_MESSAGE_LEN,
+                )
             if not chunks:
                 return
 
             for i, chunk in enumerate(chunks):
                 payload: dict[str, Any] = {"content": chunk}
 
-                # Only set reply reference on the first chunk
-                if i == 0 and msg.reply_to:
+                # Let the first successful attachment carry the reply if present.
+                if i == 0 and msg.reply_to and not sent_media:
                     payload["message_reference"] = {"message_id": msg.reply_to}
                     payload["allowed_mentions"] = {"replied_user": False}
 
@@ -144,6 +139,54 @@ class DiscordChannel(BaseChannel):
                     await asyncio.sleep(1)
         return False
 
+    async def _send_file(
+        self,
+        url: str,
+        headers: dict[str, str],
+        file_path: str,
+        reply_to: str | None = None,
+    ) -> bool:
+        """Send a file attachment via Discord REST API using multipart/form-data."""
+        path = Path(file_path)
+        if not path.is_file():
+            logger.warning("Discord file not found, skipping: {}", file_path)
+            return False
+
+        if path.stat().st_size > MAX_ATTACHMENT_BYTES:
+            logger.warning("Discord file too large (>20MB), skipping: {}", path.name)
+            return False
+
+        payload_json: dict[str, Any] = {}
+        if reply_to:
+            payload_json["message_reference"] = {"message_id": reply_to}
+            payload_json["allowed_mentions"] = {"replied_user": False}
+
+        for attempt in range(3):
+            try:
+                with open(path, "rb") as f:
+                    files = {"files[0]": (path.name, f, "application/octet-stream")}
+                    data: dict[str, Any] = {}
+                    if payload_json:
+                        data["payload_json"] = json.dumps(payload_json)
+                    response = await self._http.post(
+                        url, headers=headers, files=files, data=data
+                    )
+                if response.status_code == 429:
+                    resp_data = response.json()
+                    retry_after = float(resp_data.get("retry_after", 1.0))
+                    logger.warning("Discord rate limited, retrying in {}s", retry_after)
+                    await asyncio.sleep(retry_after)
+                    continue
+                response.raise_for_status()
+                logger.info("Discord file sent: {}", path.name)
+                return True
+            except Exception as e:
+                if attempt == 2:
+                    logger.error("Error sending Discord file {}: {}", path.name, e)
+                else:
+                    await asyncio.sleep(1)
+        return False
+
     async def _gateway_loop(self) -> None:
         """Main gateway loop: identify, heartbeat, dispatch events."""
         if not self._ws:

nanobot/channels/feishu.py

@@ -244,15 +244,22 @@ class FeishuChannel(BaseChannel):
 
     name = "feishu"
 
-    def __init__(self, config: FeishuConfig, bus: MessageBus):
+    def __init__(self, config: FeishuConfig, bus: MessageBus, groq_api_key: str = ""):
         super().__init__(config, bus)
         self.config: FeishuConfig = config
+        self.groq_api_key = groq_api_key
         self._client: Any = None
         self._ws_client: Any = None
         self._ws_thread: threading.Thread | None = None
         self._processed_message_ids: OrderedDict[str, None] = OrderedDict()  # Ordered dedup cache
         self._loop: asyncio.AbstractEventLoop | None = None
 
+    @staticmethod
+    def _register_optional_event(builder: Any, method_name: str, handler: Any) -> Any:
+        """Register an event handler only when the SDK supports it."""
+        method = getattr(builder, method_name, None)
+        return method(handler) if callable(method) else builder
+
     async def start(self) -> None:
         """Start the Feishu bot with WebSocket long connection."""
         if not FEISHU_AVAILABLE:
@@ -273,14 +280,24 @@ class FeishuChannel(BaseChannel):
             .app_secret(self.config.app_secret) \
             .log_level(lark.LogLevel.INFO) \
             .build()
-
-        # Create event handler (only register message receive, ignore other events)
-        event_handler = lark.EventDispatcherHandler.builder(
+        builder = lark.EventDispatcherHandler.builder(
             self.config.encrypt_key or "",
             self.config.verification_token or "",
         ).register_p2_im_message_receive_v1(
             self._on_message_sync
-        ).build()
+        )
+        builder = self._register_optional_event(
+            builder, "register_p2_im_message_reaction_created_v1", self._on_reaction_created
+        )
+        builder = self._register_optional_event(
+            builder, "register_p2_im_message_message_read_v1", self._on_message_read
+        )
+        builder = self._register_optional_event(
+            builder,
+            "register_p2_im_chat_access_event_bot_p2p_chat_entered_v1",
+            self._on_bot_p2p_chat_entered,
+        )
+        event_handler = builder.build()
 
         # Create WebSocket client for long connection
         self._ws_client = lark.ws.Client(
@@ -472,8 +489,124 @@ class FeishuChannel(BaseChannel):
 
         return elements or [{"tag": "markdown", "content": content}]
 
+    # ── Smart format detection ──────────────────────────────────────────
+    # Patterns that indicate "complex" markdown needing card rendering
+    _COMPLEX_MD_RE = re.compile(
+        r"```"                        # fenced code block
+        r"|^\|.+\|.*\n\s*\|[-:\s|]+\|"  # markdown table (header + separator)
+        r"|^#{1,6}\s+"                # headings
+        , re.MULTILINE,
+    )
+
+    # Simple markdown patterns (bold, italic, strikethrough)
+    _SIMPLE_MD_RE = re.compile(
+        r"\*\*.+?\*\*"               # **bold**
+        r"|__.+?__"                   # __bold__
+        r"|(?<!\*)\*(?!\*)(.+?)(?<!\*)\*(?!\*)"  # *italic* (single *)
+        r"|~~.+?~~"                   # ~~strikethrough~~
+        , re.DOTALL,
+    )
+
+    # Markdown link: [text](url)
+    _MD_LINK_RE = re.compile(r"\[([^\]]+)\]\((https?://[^\)]+)\)")
+
+    # Unordered list items
+    _LIST_RE = re.compile(r"^[\s]*[-*+]\s+", re.MULTILINE)
+
+    # Ordered list items
+    _OLIST_RE = re.compile(r"^[\s]*\d+\.\s+", re.MULTILINE)
+
+    # Max length for plain text format
+    _TEXT_MAX_LEN = 200
+
+    # Max length for post (rich text) format; beyond this, use card
+    _POST_MAX_LEN = 2000
+
+    @classmethod
+    def _detect_msg_format(cls, content: str) -> str:
+        """Determine the optimal Feishu message format for *content*.
+
+        Returns one of:
+        - ``"text"``        – plain text, short and no markdown
+        - ``"post"``        – rich text (links only, moderate length)
+        - ``"interactive"`` – card with full markdown rendering
+        """
+        stripped = content.strip()
+
+        # Complex markdown (code blocks, tables, headings) → always card
+        if cls._COMPLEX_MD_RE.search(stripped):
+            return "interactive"
+
+        # Long content → card (better readability with card layout)
+        if len(stripped) > cls._POST_MAX_LEN:
+            return "interactive"
+
+        # Has bold/italic/strikethrough → card (post format can't render these)
+        if cls._SIMPLE_MD_RE.search(stripped):
+            return "interactive"
+
+        # Has list items → card (post format can't render list bullets well)
+        if cls._LIST_RE.search(stripped) or cls._OLIST_RE.search(stripped):
+            return "interactive"
+
+        # Has links → post format (supports <a> tags)
+        if cls._MD_LINK_RE.search(stripped):
+            return "post"
+
+        # Short plain text → text format
+        if len(stripped) <= cls._TEXT_MAX_LEN:
+            return "text"
+
+        # Medium plain text without any formatting → post format
+        return "post"
+
+    @classmethod
+    def _markdown_to_post(cls, content: str) -> str:
+        """Convert markdown content to Feishu post message JSON.
+
+        Handles links ``[text](url)`` as ``a`` tags; everything else as ``text`` tags.
+        Each line becomes a paragraph (row) in the post body.
+        """
+        lines = content.strip().split("\n")
+        paragraphs: list[list[dict]] = []
+
+        for line in lines:
+            elements: list[dict] = []
+            last_end = 0
+
+            for m in cls._MD_LINK_RE.finditer(line):
+                # Text before this link
+                before = line[last_end:m.start()]
+                if before:
+                    elements.append({"tag": "text", "text": before})
+                elements.append({
+                    "tag": "a",
+                    "text": m.group(1),
+                    "href": m.group(2),
+                })
+                last_end = m.end()
+
+            # Remaining text after last link
+            remaining = line[last_end:]
+            if remaining:
+                elements.append({"tag": "text", "text": remaining})
+
+            # Empty line → empty paragraph for spacing
+            if not elements:
+                elements.append({"tag": "text", "text": ""})
+
+            paragraphs.append(elements)
+
+        post_body = {
+            "zh_cn": {
+                "content": paragraphs,
+            }
+        }
+        return json.dumps(post_body, ensure_ascii=False)
+
     _IMAGE_EXTS = {".png", ".jpg", ".jpeg", ".gif", ".bmp", ".webp", ".ico", ".tiff", ".tif"}
     _AUDIO_EXTS = {".opus"}
+    _VIDEO_EXTS = {".mp4", ".mov", ".avi"}
     _FILE_TYPE_MAP = {
         ".opus": "opus", ".mp4": "mp4", ".pdf": "pdf", ".doc": "doc", ".docx": "doc",
         ".xls": "xls", ".xlsx": "xls", ".ppt": "ppt", ".pptx": "ppt",
@@ -682,25 +815,50 @@ class FeishuChannel(BaseChannel):
                 else:
                     key = await loop.run_in_executor(None, self._upload_file_sync, file_path)
                     if key:
-                        media_type = "audio" if ext in self._AUDIO_EXTS else "file"
+                        # Use msg_type "media" for audio/video so users can play inline;
+                        # "file" for everything else (documents, archives, etc.)
+                        if ext in self._AUDIO_EXTS or ext in self._VIDEO_EXTS:
+                            media_type = "media"
+                        else:
+                            media_type = "file"
                         await loop.run_in_executor(
                             None, self._send_message_sync,
                             receive_id_type, msg.chat_id, media_type, json.dumps({"file_key": key}, ensure_ascii=False),
                         )
 
             if msg.content and msg.content.strip():
-                elements = self._build_card_elements(msg.content)
-                for chunk in self._split_elements_by_table_limit(elements):
-                    card = {"config": {"wide_screen_mode": True}, "elements": chunk}
+                fmt = self._detect_msg_format(msg.content)
+
+                if fmt == "text":
+                    # Short plain text – send as simple text message
+                    text_body = json.dumps({"text": msg.content.strip()}, ensure_ascii=False)
                     await loop.run_in_executor(
                         None, self._send_message_sync,
-                        receive_id_type, msg.chat_id, "interactive", json.dumps(card, ensure_ascii=False),
+                        receive_id_type, msg.chat_id, "text", text_body,
                     )
 
+                elif fmt == "post":
+                    # Medium content with links – send as rich-text post
+                    post_body = self._markdown_to_post(msg.content)
+                    await loop.run_in_executor(
+                        None, self._send_message_sync,
+                        receive_id_type, msg.chat_id, "post", post_body,
+                    )
+
+                else:
+                    # Complex / long content – send as interactive card
+                    elements = self._build_card_elements(msg.content)
+                    for chunk in self._split_elements_by_table_limit(elements):
+                        card = {"config": {"wide_screen_mode": True}, "elements": chunk}
+                        await loop.run_in_executor(
+                            None, self._send_message_sync,
+                            receive_id_type, msg.chat_id, "interactive", json.dumps(card, ensure_ascii=False),
+                        )
+
         except Exception as e:
             logger.error("Error sending Feishu message: {}", e)
 
-    def _on_message_sync(self, data: "P2ImMessageReceiveV1") -> None:
+    def _on_message_sync(self, data: Any) -> None:
         """
         Sync handler for incoming messages (called from WebSocket thread).
         Schedules async handling in the main event loop.
@@ -708,7 +866,7 @@ class FeishuChannel(BaseChannel):
         if self._loop and self._loop.is_running():
             asyncio.run_coroutine_threadsafe(self._on_message(data), self._loop)
 
-    async def _on_message(self, data: "P2ImMessageReceiveV1") -> None:
+    async def _on_message(self, data: Any) -> None:
         """Handle incoming message from Feishu."""
         try:
             event = data.event
@@ -768,6 +926,18 @@ class FeishuChannel(BaseChannel):
                 file_path, content_text = await self._download_and_save_media(msg_type, content_json, message_id)
                 if file_path:
                     media_paths.append(file_path)
+
+                # Transcribe audio using Groq Whisper
+                if msg_type == "audio" and file_path and self.groq_api_key:
+                    try:
+                        from nanobot.providers.transcription import GroqTranscriptionProvider
+                        transcriber = GroqTranscriptionProvider(api_key=self.groq_api_key)
+                        transcription = await transcriber.transcribe(file_path)
+                        if transcription:
+                            content_text = f"[transcription: {transcription}]"
+                    except Exception as e:
+                        logger.warning("Failed to transcribe audio: {}", e)
+
                 content_parts.append(content_text)
 
             elif msg_type in ("share_chat", "share_user", "interactive", "share_calendar_event", "system", "merge_forward"):
@@ -800,3 +970,16 @@ class FeishuChannel(BaseChannel):
 
         except Exception as e:
             logger.error("Error processing Feishu message: {}", e)
+
+    def _on_reaction_created(self, data: Any) -> None:
+        """Ignore reaction events so they do not generate SDK noise."""
+        pass
+
+    def _on_message_read(self, data: Any) -> None:
+        """Ignore read events so they do not generate SDK noise."""
+        pass
+
+    def _on_bot_p2p_chat_entered(self, data: Any) -> None:
+        """Ignore p2p-enter events when a user opens a bot chat."""
+        logger.debug("Bot entered p2p chat (user opened chat window)")
+        pass

nanobot/channels/manager.py

@@ -74,7 +74,8 @@ class ChannelManager:
             try:
                 from nanobot.channels.feishu import FeishuChannel
                 self.channels["feishu"] = FeishuChannel(
-                    self.config.channels.feishu, self.bus
+                    self.config.channels.feishu, self.bus,
+                    groq_api_key=self.config.providers.groq.api_key,
                 )
                 logger.info("Feishu channel enabled")
             except ImportError as e:

nanobot/channels/qq.py

@@ -13,16 +13,17 @@ from nanobot.config.schema import QQConfig
 
 try:
     import botpy
-    from botpy.message import C2CMessage
+    from botpy.message import C2CMessage, GroupMessage
 
     QQ_AVAILABLE = True
 except ImportError:
     QQ_AVAILABLE = False
     botpy = None
     C2CMessage = None
+    GroupMessage = None
 
 if TYPE_CHECKING:
-    from botpy.message import C2CMessage
+    from botpy.message import C2CMessage, GroupMessage
 
 
 def _make_bot_class(channel: "QQChannel") -> "type[botpy.Client]":
@@ -38,10 +39,13 @@ def _make_bot_class(channel: "QQChannel") -> "type[botpy.Client]":
             logger.info("QQ bot ready: {}", self.robot.name)
 
         async def on_c2c_message_create(self, message: "C2CMessage"):
-            await channel._on_message(message)
+            await channel._on_message(message, is_group=False)
+
+        async def on_group_at_message_create(self, message: "GroupMessage"):
+            await channel._on_message(message, is_group=True)
 
         async def on_direct_message_create(self, message):
-            await channel._on_message(message)
+            await channel._on_message(message, is_group=False)
 
     return _Bot
 
@@ -57,6 +61,7 @@ class QQChannel(BaseChannel):
         self._client: "botpy.Client | None" = None
         self._processed_ids: deque = deque(maxlen=1000)
         self._msg_seq: int = 1  # 消息序列号，避免被 QQ API 去重
+        self._chat_type_cache: dict[str, str] = {}
 
     async def start(self) -> None:
         """Start the QQ bot."""
@@ -71,8 +76,7 @@ class QQChannel(BaseChannel):
         self._running = True
         BotClass = _make_bot_class(self)
         self._client = BotClass()
-
-        logger.info("QQ bot started (C2C private message)")
+        logger.info("QQ bot started (C2C & Group supported)")
         await self._run_bot()
 
     async def _run_bot(self) -> None:
@@ -101,20 +105,31 @@ class QQChannel(BaseChannel):
         if not self._client:
             logger.warning("QQ client not initialized")
             return
+
         try:
             msg_id = msg.metadata.get("message_id")
-            self._msg_seq += 1  # 递增序列号
-            await self._client.api.post_c2c_message(
-                openid=msg.chat_id,
-                msg_type=0,
-                content=msg.content,
-                msg_id=msg_id,
-                msg_seq=self._msg_seq,  # 添加序列号避免去重
-            )
+            self._msg_seq += 1
+            msg_type = self._chat_type_cache.get(msg.chat_id, "c2c")
+            if msg_type == "group":
+                await self._client.api.post_group_message(
+                    group_openid=msg.chat_id,
+                    msg_type=0,
+                    content=msg.content,
+                    msg_id=msg_id,
+                    msg_seq=self._msg_seq,
+                )
+            else:
+                await self._client.api.post_c2c_message(
+                    openid=msg.chat_id,
+                    msg_type=0,
+                    content=msg.content,
+                    msg_id=msg_id,
+                    msg_seq=self._msg_seq,
+                )
         except Exception as e:
             logger.error("Error sending QQ message: {}", e)
 
-    async def _on_message(self, data: "C2CMessage") -> None:
+    async def _on_message(self, data: "C2CMessage | GroupMessage", is_group: bool = False) -> None:
         """Handle incoming message from QQ."""
         try:
             # Dedup by message ID
@@ -122,18 +137,24 @@ class QQChannel(BaseChannel):
                 return
             self._processed_ids.append(data.id)
 
-            author = data.author
-            user_id = str(getattr(author, 'id', None) or getattr(author, 'user_openid', 'unknown'))
             content = (data.content or "").strip()
             if not content:
                 return
 
+            if is_group:
+                chat_id = data.group_openid
+                user_id = data.author.member_openid
+                self._chat_type_cache[chat_id] = "group"
+            else:
+                chat_id = str(getattr(data.author, 'id', None) or getattr(data.author, 'user_openid', 'unknown'))
+                user_id = chat_id
+                self._chat_type_cache[chat_id] = "c2c"
+
             await self._handle_message(
                 sender_id=user_id,
-                chat_id=user_id,
+                chat_id=chat_id,
                 content=content,
                 metadata={"message_id": data.id},
             )
         except Exception:
             logger.exception("Error handling QQ message")
-

nanobot/channels/slack.py

@@ -82,13 +82,14 @@ class SlackChannel(BaseChannel):
             thread_ts = slack_meta.get("thread_ts")
             channel_type = slack_meta.get("channel_type")
             # Only reply in thread for channel/group messages; DMs don't use threads
-            use_thread = thread_ts and channel_type != "im"
             thread_ts_param = thread_ts if use_thread else None
 
-            if msg.content:
+            # Slack rejects empty text payloads. Keep media-only messages media-only,
+            # but send a single blank message when the bot has no text or files to send.
+            if msg.content or not (msg.media or []):
                 await self._web_client.chat_postMessage(
                     channel=msg.chat_id,
-                    text=self._to_mrkdwn(msg.content),
+                    text=self._to_mrkdwn(msg.content) if msg.content else " ",
                     thread_ts=thread_ts_param,
                 )
 

nanobot/channels/telegram.py

@@ -4,6 +4,8 @@ from __future__ import annotations
 
 import asyncio
 import re
+import time
+import unicodedata
 
 from loguru import logger
 from telegram import BotCommand, ReplyParameters, Update
@@ -14,6 +16,50 @@ from nanobot.bus.events import OutboundMessage
 from nanobot.bus.queue import MessageBus
 from nanobot.channels.base import BaseChannel
 from nanobot.config.schema import TelegramConfig
+from nanobot.utils.helpers import split_message
+
+TELEGRAM_MAX_MESSAGE_LEN = 4000  # Telegram message character limit
+
+
+def _strip_md(s: str) -> str:
+    """Strip markdown inline formatting from text."""
+    s = re.sub(r'\*\*(.+?)\*\*', r'\1', s)
+    s = re.sub(r'__(.+?)__', r'\1', s)
+    s = re.sub(r'~~(.+?)~~', r'\1', s)
+    s = re.sub(r'`([^`]+)`', r'\1', s)
+    return s.strip()
+
+
+def _render_table_box(table_lines: list[str]) -> str:
+    """Convert markdown pipe-table to compact aligned text for <pre> display."""
+
+    def dw(s: str) -> int:
+        return sum(2 if unicodedata.east_asian_width(c) in ('W', 'F') else 1 for c in s)
+
+    rows: list[list[str]] = []
+    has_sep = False
+    for line in table_lines:
+        cells = [_strip_md(c) for c in line.strip().strip('|').split('|')]
+        if all(re.match(r'^:?-+:?$', c) for c in cells if c):
+            has_sep = True
+            continue
+        rows.append(cells)
+    if not rows or not has_sep:
+        return '\n'.join(table_lines)
+
+    ncols = max(len(r) for r in rows)
+    for r in rows:
+        r.extend([''] * (ncols - len(r)))
+    widths = [max(dw(r[c]) for r in rows) for c in range(ncols)]
+
+    def dr(cells: list[str]) -> str:
+        return '  '.join(f'{c}{" " * (w - dw(c))}' for c, w in zip(cells, widths))
+
+    out = [dr(rows[0])]
+    out.append('  '.join('─' * w for w in widths))
+    for row in rows[1:]:
+        out.append(dr(row))
+    return '\n'.join(out)
 
 
 def _markdown_to_telegram_html(text: str) -> str:
@@ -31,6 +77,27 @@ def _markdown_to_telegram_html(text: str) -> str:
 
     text = re.sub(r'```[\w]*\n?([\s\S]*?)```', save_code_block, text)
 
+    # 1.5. Convert markdown tables to box-drawing (reuse code_block placeholders)
+    lines = text.split('\n')
+    rebuilt: list[str] = []
+    li = 0
+    while li < len(lines):
+        if re.match(r'^\s*\|.+\|', lines[li]):
+            tbl: list[str] = []
+            while li < len(lines) and re.match(r'^\s*\|.+\|', lines[li]):
+                tbl.append(lines[li])
+                li += 1
+            box = _render_table_box(tbl)
+            if box != '\n'.join(tbl):
+                code_blocks.append(box)
+                rebuilt.append(f"\x00CB{len(code_blocks) - 1}\x00")
+            else:
+                rebuilt.extend(tbl)
+        else:
+            rebuilt.append(lines[li])
+            li += 1
+    text = '\n'.join(rebuilt)
+
     # 2. Extract and protect inline code
     inline_codes: list[str] = []
     def save_inline_code(m: re.Match) -> str:
@@ -79,26 +146,6 @@ def _markdown_to_telegram_html(text: str) -> str:
     return text
 
 
-def _split_message(content: str, max_len: int = 4000) -> list[str]:
-    """Split content into chunks within max_len, preferring line breaks."""
-    if len(content) <= max_len:
-        return [content]
-    chunks: list[str] = []
-    while content:
-        if len(content) <= max_len:
-            chunks.append(content)
-            break
-        cut = content[:max_len]
-        pos = cut.rfind('\n')
-        if pos == -1:
-            pos = cut.rfind(' ')
-        if pos == -1:
-            pos = max_len
-        chunks.append(content[:pos])
-        content = content[pos:].lstrip()
-    return chunks
-
-
 class TelegramChannel(BaseChannel):
     """
     Telegram channel using long polling.
@@ -130,6 +177,26 @@ class TelegramChannel(BaseChannel):
         self._typing_tasks: dict[str, asyncio.Task] = {}  # chat_id -> typing loop task
         self._media_group_buffers: dict[str, dict] = {}
         self._media_group_tasks: dict[str, asyncio.Task] = {}
+        self._message_threads: dict[tuple[str, int], int] = {}
+
+    def is_allowed(self, sender_id: str) -> bool:
+        """Preserve Telegram's legacy id|username allowlist matching."""
+        if super().is_allowed(sender_id):
+            return True
+
+        allow_list = getattr(self.config, "allow_from", [])
+        if not allow_list or "*" in allow_list:
+            return False
+
+        sender_str = str(sender_id)
+        if sender_str.count("|") != 1:
+            return False
+
+        sid, username = sender_str.split("|", 1)
+        if not sid.isdigit() or not username:
+            return False
+
+        return sid in allow_list or username in allow_list
 
     async def start(self) -> None:
         """Start the Telegram bot with long polling."""
@@ -140,16 +207,21 @@ class TelegramChannel(BaseChannel):
         self._running = True
 
         # Build the application with larger connection pool to avoid pool-timeout on long runs
-        req = HTTPXRequest(connection_pool_size=16, pool_timeout=5.0, connect_timeout=30.0, read_timeout=30.0)
+        req = HTTPXRequest(
+            connection_pool_size=16,
+            pool_timeout=5.0,
+            connect_timeout=30.0,
+            read_timeout=30.0,
+            proxy=self.config.proxy if self.config.proxy else None,
+        )
         builder = Application.builder().token(self.config.token).request(req).get_updates_request(req)
-        if self.config.proxy:
-            builder = builder.proxy(self.config.proxy).get_updates_proxy(self.config.proxy)
         self._app = builder.build()
         self._app.add_error_handler(self._on_error)
 
         # Add command handlers
         self._app.add_handler(CommandHandler("start", self._on_start))
         self._app.add_handler(CommandHandler("new", self._forward_command))
+        self._app.add_handler(CommandHandler("stop", self._forward_command))
         self._app.add_handler(CommandHandler("help", self._on_help))
 
         # Add message handler for text, photos, voice, documents
@@ -234,10 +306,16 @@ class TelegramChannel(BaseChannel):
         except ValueError:
             logger.error("Invalid chat_id: {}", msg.chat_id)
             return
+        reply_to_message_id = msg.metadata.get("message_id")
+        message_thread_id = msg.metadata.get("message_thread_id")
+        if message_thread_id is None and reply_to_message_id is not None:
+            message_thread_id = self._message_threads.get((msg.chat_id, reply_to_message_id))
+        thread_kwargs = {}
+        if message_thread_id is not None:
+            thread_kwargs["message_thread_id"] = message_thread_id
 
         reply_params = None
         if self.config.reply_to_message:
-            reply_to_message_id = msg.metadata.get("message_id")
             if reply_to_message_id:
                 reply_params = ReplyParameters(
                     message_id=reply_to_message_id,
@@ -258,7 +336,8 @@ class TelegramChannel(BaseChannel):
                     await sender(
                         chat_id=chat_id,
                         **{param: f},
-                        reply_parameters=reply_params
+                        reply_parameters=reply_params,
+                        **thread_kwargs,
                     )
             except Exception as e:
                 filename = media_path.rsplit("/", 1)[-1]
@@ -266,48 +345,71 @@ class TelegramChannel(BaseChannel):
                 await self._app.bot.send_message(
                     chat_id=chat_id,
                     text=f"[Failed to send: {filename}]",
-                    reply_parameters=reply_params
+                    reply_parameters=reply_params,
+                    **thread_kwargs,
                 )
 
         # Send text content
         if msg.content and msg.content != "[empty message]":
             is_progress = msg.metadata.get("_progress", False)
-            draft_id = msg.metadata.get("message_id")
-            
-            for chunk in _split_message(msg.content):
-                try:
-                    html = _markdown_to_telegram_html(chunk)
-                    if is_progress and draft_id:
-                        await self._app.bot.send_message_draft(
-                            chat_id=chat_id,
-                            draft_id=draft_id,
-                            text=html,
-                            parse_mode="HTML"
-                        )
-                    else:
-                        await self._app.bot.send_message(
-                            chat_id=chat_id,
-                            text=html,
-                            parse_mode="HTML",
-                            reply_parameters=reply_params
-                        )
-                except Exception as e:
-                    logger.warning("HTML parse failed, falling back to plain text: {}", e)
-                    try:
-                        if is_progress and draft_id:
-                            await self._app.bot.send_message_draft(
-                                chat_id=chat_id,
-                                draft_id=draft_id,
-                                text=chunk
-                            )
-                        else:
-                            await self._app.bot.send_message(
-                                chat_id=chat_id,
-                                text=chunk,
-                                reply_parameters=reply_params
-                            )
-                    except Exception as e2:
-                        logger.error("Error sending Telegram message: {}", e2)
+
+            for chunk in split_message(msg.content, TELEGRAM_MAX_MESSAGE_LEN):
+                # Final response: simulate streaming via draft, then persist
+                if not is_progress:
+                    await self._send_with_streaming(chat_id, chunk, reply_params, thread_kwargs)
+                else:
+                    await self._send_text(chat_id, chunk, reply_params, thread_kwargs)
+
+    async def _send_text(
+        self,
+        chat_id: int,
+        text: str,
+        reply_params=None,
+        thread_kwargs: dict | None = None,
+    ) -> None:
+        """Send a plain text message with HTML fallback."""
+        try:
+            html = _markdown_to_telegram_html(text)
+            await self._app.bot.send_message(
+                chat_id=chat_id, text=html, parse_mode="HTML",
+                reply_parameters=reply_params,
+                **(thread_kwargs or {}),
+            )
+        except Exception as e:
+            logger.warning("HTML parse failed, falling back to plain text: {}", e)
+            try:
+                await self._app.bot.send_message(
+                    chat_id=chat_id,
+                    text=text,
+                    reply_parameters=reply_params,
+                    **(thread_kwargs or {}),
+                )
+            except Exception as e2:
+                logger.error("Error sending Telegram message: {}", e2)
+
+    async def _send_with_streaming(
+        self,
+        chat_id: int,
+        text: str,
+        reply_params=None,
+        thread_kwargs: dict | None = None,
+    ) -> None:
+        """Simulate streaming via send_message_draft, then persist with send_message."""
+        draft_id = int(time.time() * 1000) % (2**31)
+        try:
+            step = max(len(text) // 8, 40)
+            for i in range(step, len(text), step):
+                await self._app.bot.send_message_draft(
+                    chat_id=chat_id, draft_id=draft_id, text=text[:i],
+                )
+                await asyncio.sleep(0.04)
+            await self._app.bot.send_message_draft(
+                chat_id=chat_id, draft_id=draft_id, text=text,
+            )
+            await asyncio.sleep(0.15)
+        except Exception:
+            pass
+        await self._send_text(chat_id, text, reply_params, thread_kwargs)
 
     async def _on_start(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
         """Handle /start command."""
@@ -338,14 +440,50 @@ class TelegramChannel(BaseChannel):
         sid = str(user.id)
         return f"{sid}|{user.username}" if user.username else sid
 
+    @staticmethod
+    def _derive_topic_session_key(message) -> str | None:
+        """Derive topic-scoped session key for non-private Telegram chats."""
+        message_thread_id = getattr(message, "message_thread_id", None)
+        if message.chat.type == "private" or message_thread_id is None:
+            return None
+        return f"telegram:{message.chat_id}:topic:{message_thread_id}"
+
+    @staticmethod
+    def _build_message_metadata(message, user) -> dict:
+        """Build common Telegram inbound metadata payload."""
+        return {
+            "message_id": message.message_id,
+            "user_id": user.id,
+            "username": user.username,
+            "first_name": user.first_name,
+            "is_group": message.chat.type != "private",
+            "message_thread_id": getattr(message, "message_thread_id", None),
+            "is_forum": bool(getattr(message.chat, "is_forum", False)),
+        }
+
+    def _remember_thread_context(self, message) -> None:
+        """Cache topic thread id by chat/message id for follow-up replies."""
+        message_thread_id = getattr(message, "message_thread_id", None)
+        if message_thread_id is None:
+            return
+        key = (str(message.chat_id), message.message_id)
+        self._message_threads[key] = message_thread_id
+        if len(self._message_threads) > 1000:
+            self._message_threads.pop(next(iter(self._message_threads)))
+
     async def _forward_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
         """Forward slash commands to the bus for unified handling in AgentLoop."""
         if not update.message or not update.effective_user:
             return
+        message = update.message
+        user = update.effective_user
+        self._remember_thread_context(message)
         await self._handle_message(
-            sender_id=self._sender_id(update.effective_user),
-            chat_id=str(update.message.chat_id),
-            content=update.message.text,
+            sender_id=self._sender_id(user),
+            chat_id=str(message.chat_id),
+            content=message.text,
+            metadata=self._build_message_metadata(message, user),
+            session_key=self._derive_topic_session_key(message),
         )
 
     async def _on_message(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
@@ -357,6 +495,7 @@ class TelegramChannel(BaseChannel):
         user = update.effective_user
         chat_id = message.chat_id
         sender_id = self._sender_id(user)
+        self._remember_thread_context(message)
 
         # Store chat_id for replies
         self._chat_ids[sender_id] = chat_id
@@ -392,8 +531,11 @@ class TelegramChannel(BaseChannel):
         if media_file and self._app:
             try:
                 file = await self._app.bot.get_file(media_file.file_id)
-                ext = self._get_extension(media_type, getattr(media_file, 'mime_type', None))
-
+                ext = self._get_extension(
+                    media_type,
+                    getattr(media_file, 'mime_type', None),
+                    getattr(media_file, 'file_name', None),
+                )
                 # Save to workspace/media/
                 from pathlib import Path
                 media_dir = Path.home() / ".nanobot" / "media"
@@ -427,6 +569,8 @@ class TelegramChannel(BaseChannel):
         logger.debug("Telegram message from {}: {}...", sender_id, content[:50])
 
         str_chat_id = str(chat_id)
+        metadata = self._build_message_metadata(message, user)
+        session_key = self._derive_topic_session_key(message)
 
         # Telegram media groups: buffer briefly, forward as one aggregated turn.
         if media_group_id := getattr(message, "media_group_id", None):
@@ -435,11 +579,8 @@ class TelegramChannel(BaseChannel):
                 self._media_group_buffers[key] = {
                     "sender_id": sender_id, "chat_id": str_chat_id,
                     "contents": [], "media": [],
-                    "metadata": {
-                        "message_id": message.message_id, "user_id": user.id,
-                        "username": user.username, "first_name": user.first_name,
-                        "is_group": message.chat.type != "private",
-                    },
+                    "metadata": metadata,
+                    "session_key": session_key,
                 }
                 self._start_typing(str_chat_id)
             buf = self._media_group_buffers[key]
@@ -459,13 +600,8 @@ class TelegramChannel(BaseChannel):
             chat_id=str_chat_id,
             content=content,
             media=media_paths,
-            metadata={
-                "message_id": message.message_id,
-                "user_id": user.id,
-                "username": user.username,
-                "first_name": user.first_name,
-                "is_group": message.chat.type != "private"
-            }
+            metadata=metadata,
+            session_key=session_key,
         )
 
     async def _flush_media_group(self, key: str) -> None:
@@ -479,6 +615,7 @@ class TelegramChannel(BaseChannel):
                 sender_id=buf["sender_id"], chat_id=buf["chat_id"],
                 content=content, media=list(dict.fromkeys(buf["media"])),
                 metadata=buf["metadata"],
+                session_key=buf.get("session_key"),
             )
         finally:
             self._media_group_tasks.pop(key, None)
@@ -510,8 +647,13 @@ class TelegramChannel(BaseChannel):
         """Log polling / handler errors instead of silently swallowing them."""
         logger.error("Telegram error: {}", context.error)
 
-    def _get_extension(self, media_type: str, mime_type: str | None) -> str:
-        """Get file extension based on media type."""
+    def _get_extension(
+        self,
+        media_type: str,
+        mime_type: str | None,
+        filename: str | None = None,
+    ) -> str:
+        """Get file extension based on media type or original filename."""
         if mime_type:
             ext_map = {
                 "image/jpeg": ".jpg", "image/png": ".png", "image/gif": ".gif",
@@ -521,4 +663,12 @@ class TelegramChannel(BaseChannel):
                 return ext_map[mime_type]
 
         type_map = {"image": ".jpg", "voice": ".ogg", "audio": ".mp3", "file": ""}
-        return type_map.get(media_type, "")
+        if ext := type_map.get(media_type, ""):
+            return ext
+
+        if filename:
+            from pathlib import Path
+
+            return "".join(Path(filename).suffixes)
+
+        return ""

nanobot/channels/whatsapp.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import json
+import mimetypes
 from collections import OrderedDict
 
 from loguru import logger
@@ -128,10 +129,22 @@ class WhatsAppChannel(BaseChannel):
                 logger.info("Voice message received from {}, but direct download from bridge is not yet supported.", sender_id)
                 content = "[Voice Message: Transcription not available for WhatsApp yet]"
 
+            # Extract media paths (images/documents/videos downloaded by the bridge)
+            media_paths = data.get("media") or []
+
+            # Build content tags matching Telegram's pattern: [image: /path] or [file: /path]
+            if media_paths:
+                for p in media_paths:
+                    mime, _ = mimetypes.guess_type(p)
+                    media_type = "image" if mime and mime.startswith("image/") else "file"
+                    media_tag = f"[{media_type}: {p}]"
+                    content = f"{content}\n{media_tag}" if content else media_tag
+
             await self._handle_message(
                 sender_id=sender_id,
                 chat_id=sender,  # Use full LID for replies
                 content=content,
+                media=media_paths,
                 metadata={
                     "message_id": message_id,
                     "timestamp": data.get("timestamp"),

nanobot/cli/commands.py

@@ -7,6 +7,18 @@ import signal
 import sys
 from pathlib import Path
 
+# Force UTF-8 encoding for Windows console
+if sys.platform == "win32":
+    import locale
+    if sys.stdout.encoding != "utf-8":
+        os.environ["PYTHONIOENCODING"] = "utf-8"
+        # Re-open stdout/stderr with UTF-8 encoding
+        try:
+            sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+            sys.stderr.reconfigure(encoding="utf-8", errors="replace")
+        except Exception:
+            pass
+
 import typer
 from prompt_toolkit import PromptSession
 from prompt_toolkit.formatted_text import HTML
@@ -200,9 +212,8 @@ def onboard():
 
 def _make_provider(config: Config):
     """Create the appropriate LLM provider from config."""
-    from nanobot.providers.custom_provider import CustomProvider
-    from nanobot.providers.litellm_provider import LiteLLMProvider
     from nanobot.providers.openai_codex_provider import OpenAICodexProvider
+    from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
 
     model = config.agents.defaults.model
     provider_name = config.get_provider_name(model)
@@ -213,6 +224,7 @@ def _make_provider(config: Config):
         return OpenAICodexProvider(default_model=model)
 
     # Custom: direct OpenAI-compatible endpoint, bypasses LiteLLM
+    from nanobot.providers.custom_provider import CustomProvider
     if provider_name == "custom":
         return CustomProvider(
             api_key=p.api_key if p else "no-key",
@@ -220,6 +232,21 @@ def _make_provider(config: Config):
             default_model=model,
         )
 
+    # Azure OpenAI: direct Azure OpenAI endpoint with deployment name
+    if provider_name == "azure_openai":
+        if not p or not p.api_key or not p.api_base:
+            console.print("[red]Error: Azure OpenAI requires api_key and api_base.[/red]")
+            console.print("Set them in ~/.nanobot/config.json under providers.azure_openai section")
+            console.print("Use the model field to specify the deployment name.")
+            raise typer.Exit(1)
+        
+        return AzureOpenAIProvider(
+            api_key=p.api_key,
+            api_base=p.api_base,
+            default_model=model,
+        )
+
+    from nanobot.providers.litellm_provider import LiteLLMProvider
     from nanobot.providers.registry import find_by_name
     spec = find_by_name(provider_name)
     if not model.startswith("bedrock/") and not (p and p.api_key) and not (spec and spec.is_oauth):
@@ -530,9 +557,13 @@ def agent(
 
         signal.signal(signal.SIGINT, _handle_signal)
         signal.signal(signal.SIGTERM, _handle_signal)
-        signal.signal(signal.SIGHUP, _handle_signal)
+        # SIGHUP is not available on Windows
+        if hasattr(signal, 'SIGHUP'):
+            signal.signal(signal.SIGHUP, _handle_signal)
         # Ignore SIGPIPE to prevent silent process termination when writing to closed pipes
-        signal.signal(signal.SIGPIPE, signal.SIG_IGN)
+        # SIGPIPE is not available on Windows
+        if hasattr(signal, 'SIGPIPE'):
+            signal.signal(signal.SIGPIPE, signal.SIG_IGN)
 
         async def run_interactive():
             bus_task = asyncio.create_task(agent_loop.run())

nanobot/config/schema.py

@@ -199,21 +199,6 @@ class QQConfig(Base):
     )  # Allowed user openids (empty = public access)
 
 
-class MatrixConfig(Base):
-    """Matrix (Element) channel configuration."""
-
-    enabled: bool = False
-    homeserver: str = "https://matrix.org"
-    access_token: str = ""
-    user_id: str = ""  # e.g. @bot:matrix.org
-    device_id: str = ""
-    e2ee_enabled: bool = True  # end-to-end encryption support
-    sync_stop_grace_seconds: int = 2  # graceful sync_forever shutdown timeout
-    max_media_bytes: int = 20 * 1024 * 1024  # inbound + outbound attachment limit
-    allow_from: list[str] = Field(default_factory=list)
-    group_policy: Literal["open", "mention", "allowlist"] = "open"
-    group_allow_from: list[str] = Field(default_factory=list)
-    allow_room_mentions: bool = False
 
 
 class ChannelsConfig(Base):
@@ -266,6 +251,7 @@ class ProvidersConfig(Base):
     """Configuration for LLM providers."""
 
     custom: ProviderConfig = Field(default_factory=ProviderConfig)  # Any OpenAI-compatible endpoint
+    azure_openai: ProviderConfig = Field(default_factory=ProviderConfig)  # Azure OpenAI (model = deployment name)
     anthropic: ProviderConfig = Field(default_factory=ProviderConfig)
     openai: ProviderConfig = Field(default_factory=ProviderConfig)
     openrouter: ProviderConfig = Field(default_factory=ProviderConfig)
@@ -278,12 +264,8 @@ class ProvidersConfig(Base):
     moonshot: ProviderConfig = Field(default_factory=ProviderConfig)
     minimax: ProviderConfig = Field(default_factory=ProviderConfig)
     aihubmix: ProviderConfig = Field(default_factory=ProviderConfig)  # AiHubMix API gateway
-    siliconflow: ProviderConfig = Field(
-        default_factory=ProviderConfig
-    )  # SiliconFlow (硅基流动) API gateway
-    volcengine: ProviderConfig = Field(
-        default_factory=ProviderConfig
-    )  # VolcEngine (火山引擎) API gateway
+    siliconflow: ProviderConfig = Field(default_factory=ProviderConfig)  # SiliconFlow (硅基流动)
+    volcengine: ProviderConfig = Field(default_factory=ProviderConfig)  # VolcEngine (火山引擎)
     openai_codex: ProviderConfig = Field(default_factory=ProviderConfig)  # OpenAI Codex (OAuth)
     github_copilot: ProviderConfig = Field(default_factory=ProviderConfig)  # Github Copilot (OAuth)
 

nanobot/providers/__init__.py

@@ -3,5 +3,6 @@
 from nanobot.providers.base import LLMProvider, LLMResponse
 from nanobot.providers.litellm_provider import LiteLLMProvider
 from nanobot.providers.openai_codex_provider import OpenAICodexProvider
+from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
 
-__all__ = ["LLMProvider", "LLMResponse", "LiteLLMProvider", "OpenAICodexProvider"]
+__all__ = ["LLMProvider", "LLMResponse", "LiteLLMProvider", "OpenAICodexProvider", "AzureOpenAIProvider"]

nanobot/providers/azure_openai_provider.py

@@ -0,0 +1,210 @@
+"""Azure OpenAI provider implementation with API version 2024-10-21."""
+
+from __future__ import annotations
+
+import uuid
+from typing import Any
+from urllib.parse import urljoin
+
+import httpx
+import json_repair
+
+from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
+
+_AZURE_MSG_KEYS = frozenset({"role", "content", "tool_calls", "tool_call_id", "name"})
+
+
+class AzureOpenAIProvider(LLMProvider):
+    """
+    Azure OpenAI provider with API version 2024-10-21 compliance.
+    
+    Features:
+    - Hardcoded API version 2024-10-21
+    - Uses model field as Azure deployment name in URL path
+    - Uses api-key header instead of Authorization Bearer
+    - Uses max_completion_tokens instead of max_tokens
+    - Direct HTTP calls, bypasses LiteLLM
+    """
+
+    def __init__(
+        self,
+        api_key: str = "",
+        api_base: str = "",
+        default_model: str = "gpt-5.2-chat",
+    ):
+        super().__init__(api_key, api_base)
+        self.default_model = default_model
+        self.api_version = "2024-10-21"
+        
+        # Validate required parameters
+        if not api_key:
+            raise ValueError("Azure OpenAI api_key is required")
+        if not api_base:
+            raise ValueError("Azure OpenAI api_base is required")
+        
+        # Ensure api_base ends with /
+        if not api_base.endswith('/'):
+            api_base += '/'
+        self.api_base = api_base
+
+    def _build_chat_url(self, deployment_name: str) -> str:
+        """Build the Azure OpenAI chat completions URL."""
+        # Azure OpenAI URL format:
+        # https://{resource}.openai.azure.com/openai/deployments/{deployment}/chat/completions?api-version={version}
+        base_url = self.api_base
+        if not base_url.endswith('/'):
+            base_url += '/'
+        
+        url = urljoin(
+            base_url, 
+            f"openai/deployments/{deployment_name}/chat/completions"
+        )
+        return f"{url}?api-version={self.api_version}"
+
+    def _build_headers(self) -> dict[str, str]:
+        """Build headers for Azure OpenAI API with api-key header."""
+        return {
+            "Content-Type": "application/json",
+            "api-key": self.api_key,  # Azure OpenAI uses api-key header, not Authorization
+            "x-session-affinity": uuid.uuid4().hex,  # For cache locality
+        }
+
+    @staticmethod
+    def _supports_temperature(
+        deployment_name: str,
+        reasoning_effort: str | None = None,
+    ) -> bool:
+        """Return True when temperature is likely supported for this deployment."""
+        if reasoning_effort:
+            return False
+        name = deployment_name.lower()
+        return not any(token in name for token in ("gpt-5", "o1", "o3", "o4"))
+
+    def _prepare_request_payload(
+        self,
+        deployment_name: str,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
+        reasoning_effort: str | None = None,
+    ) -> dict[str, Any]:
+        """Prepare the request payload with Azure OpenAI 2024-10-21 compliance."""
+        payload: dict[str, Any] = {
+            "messages": self._sanitize_request_messages(
+                self._sanitize_empty_content(messages),
+                _AZURE_MSG_KEYS,
+            ),
+            "max_completion_tokens": max(1, max_tokens),  # Azure API 2024-10-21 uses max_completion_tokens
+        }
+
+        if self._supports_temperature(deployment_name, reasoning_effort):
+            payload["temperature"] = temperature
+
+        if reasoning_effort:
+            payload["reasoning_effort"] = reasoning_effort
+
+        if tools:
+            payload["tools"] = tools
+            payload["tool_choice"] = "auto"
+
+        return payload
+
+    async def chat(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]] | None = None,
+        model: str | None = None,
+        max_tokens: int = 4096,
+        temperature: float = 0.7,
+        reasoning_effort: str | None = None,
+    ) -> LLMResponse:
+        """
+        Send a chat completion request to Azure OpenAI.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content'.
+            tools: Optional list of tool definitions in OpenAI format.
+            model: Model identifier (used as deployment name).
+            max_tokens: Maximum tokens in response (mapped to max_completion_tokens).
+            temperature: Sampling temperature.
+            reasoning_effort: Optional reasoning effort parameter.
+
+        Returns:
+            LLMResponse with content and/or tool calls.
+        """
+        deployment_name = model or self.default_model
+        url = self._build_chat_url(deployment_name)
+        headers = self._build_headers()
+        payload = self._prepare_request_payload(
+            deployment_name, messages, tools, max_tokens, temperature, reasoning_effort
+        )
+
+        try:
+            async with httpx.AsyncClient(timeout=60.0, verify=True) as client:
+                response = await client.post(url, headers=headers, json=payload)
+                if response.status_code != 200:
+                    return LLMResponse(
+                        content=f"Azure OpenAI API Error {response.status_code}: {response.text}",
+                        finish_reason="error",
+                    )
+                
+                response_data = response.json()
+                return self._parse_response(response_data)
+
+        except Exception as e:
+            return LLMResponse(
+                content=f"Error calling Azure OpenAI: {repr(e)}",
+                finish_reason="error",
+            )
+
+    def _parse_response(self, response: dict[str, Any]) -> LLMResponse:
+        """Parse Azure OpenAI response into our standard format."""
+        try:
+            choice = response["choices"][0]
+            message = choice["message"]
+
+            tool_calls = []
+            if message.get("tool_calls"):
+                for tc in message["tool_calls"]:
+                    # Parse arguments from JSON string if needed
+                    args = tc["function"]["arguments"]
+                    if isinstance(args, str):
+                        args = json_repair.loads(args)
+
+                    tool_calls.append(
+                        ToolCallRequest(
+                            id=tc["id"],
+                            name=tc["function"]["name"],
+                            arguments=args,
+                        )
+                    )
+
+            usage = {}
+            if response.get("usage"):
+                usage_data = response["usage"]
+                usage = {
+                    "prompt_tokens": usage_data.get("prompt_tokens", 0),
+                    "completion_tokens": usage_data.get("completion_tokens", 0),
+                    "total_tokens": usage_data.get("total_tokens", 0),
+                }
+
+            reasoning_content = message.get("reasoning_content") or None
+
+            return LLMResponse(
+                content=message.get("content"),
+                tool_calls=tool_calls,
+                finish_reason=choice.get("finish_reason", "stop"),
+                usage=usage,
+                reasoning_content=reasoning_content,
+            )
+
+        except (KeyError, IndexError) as e:
+            return LLMResponse(
+                content=f"Error parsing Azure OpenAI response: {str(e)}",
+                finish_reason="error",
+            )
+
+    def get_default_model(self) -> str:
+        """Get the default model (also used as default deployment name)."""
+        return self.default_model

nanobot/providers/base.py

@@ -87,6 +87,20 @@ class LLMProvider(ABC):
             result.append(msg)
         return result
 
+    @staticmethod
+    def _sanitize_request_messages(
+        messages: list[dict[str, Any]],
+        allowed_keys: frozenset[str],
+    ) -> list[dict[str, Any]]:
+        """Keep only provider-safe message keys and normalize assistant content."""
+        sanitized = []
+        for msg in messages:
+            clean = {k: v for k, v in msg.items() if k in allowed_keys}
+            if clean.get("role") == "assistant" and "content" not in clean:
+                clean["content"] = None
+            sanitized.append(clean)
+        return sanitized
+
     @abstractmethod
     async def chat(
         self,

nanobot/providers/litellm_provider.py

@@ -1,5 +1,6 @@
 """LiteLLM provider implementation for multi-provider support."""
 
+import hashlib
 import os
 import secrets
 import string
@@ -8,6 +9,7 @@ from typing import Any
 import json_repair
 import litellm
 from litellm import acompletion
+from loguru import logger
 
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
 from nanobot.providers.registry import find_by_model, find_gateway
@@ -165,17 +167,43 @@ class LiteLLMProvider(LLMProvider):
             return _ANTHROPIC_EXTRA_KEYS
         return frozenset()
 
+    @staticmethod
+    def _normalize_tool_call_id(tool_call_id: Any) -> Any:
+        """Normalize tool_call_id to a provider-safe 9-char alphanumeric form."""
+        if not isinstance(tool_call_id, str):
+            return tool_call_id
+        if len(tool_call_id) == 9 and tool_call_id.isalnum():
+            return tool_call_id
+        return hashlib.sha1(tool_call_id.encode()).hexdigest()[:9]
+
     @staticmethod
     def _sanitize_messages(messages: list[dict[str, Any]], extra_keys: frozenset[str] = frozenset()) -> list[dict[str, Any]]:
         """Strip non-standard keys and ensure assistant messages have a content key."""
         allowed = _ALLOWED_MSG_KEYS | extra_keys
-        sanitized = []
-        for msg in messages:
-            clean = {k: v for k, v in msg.items() if k in allowed}
-            # Strict providers require "content" even when assistant only has tool_calls
-            if clean.get("role") == "assistant" and "content" not in clean:
-                clean["content"] = None
-            sanitized.append(clean)
+        sanitized = LLMProvider._sanitize_request_messages(messages, allowed)
+        id_map: dict[str, str] = {}
+
+        def map_id(value: Any) -> Any:
+            if not isinstance(value, str):
+                return value
+            return id_map.setdefault(value, LiteLLMProvider._normalize_tool_call_id(value))
+
+        for clean in sanitized:
+            # Keep assistant tool_calls[].id and tool tool_call_id in sync after
+            # shortening, otherwise strict providers reject the broken linkage.
+            if isinstance(clean.get("tool_calls"), list):
+                normalized_tool_calls = []
+                for tc in clean["tool_calls"]:
+                    if not isinstance(tc, dict):
+                        normalized_tool_calls.append(tc)
+                        continue
+                    tc_clean = dict(tc)
+                    tc_clean["id"] = map_id(tc_clean.get("id"))
+                    normalized_tool_calls.append(tc_clean)
+                clean["tool_calls"] = normalized_tool_calls
+
+            if "tool_call_id" in clean and clean["tool_call_id"]:
+                clean["tool_call_id"] = map_id(clean["tool_call_id"])
         return sanitized
 
     async def chat(
@@ -255,20 +283,37 @@ class LiteLLMProvider(LLMProvider):
         """Parse LiteLLM response into our standard format."""
         choice = response.choices[0]
         message = choice.message
+        content = message.content
+        finish_reason = choice.finish_reason
+
+        # Some providers (e.g. GitHub Copilot) split content and tool_calls
+        # across multiple choices. Merge them so tool_calls are not lost.
+        raw_tool_calls = []
+        for ch in response.choices:
+            msg = ch.message
+            if hasattr(msg, "tool_calls") and msg.tool_calls:
+                raw_tool_calls.extend(msg.tool_calls)
+                if ch.finish_reason in ("tool_calls", "stop"):
+                    finish_reason = ch.finish_reason
+            if not content and msg.content:
+                content = msg.content
+
+        if len(response.choices) > 1:
+            logger.debug("LiteLLM response has {} choices, merged {} tool_calls",
+                         len(response.choices), len(raw_tool_calls))
 
         tool_calls = []
-        if hasattr(message, "tool_calls") and message.tool_calls:
-            for tc in message.tool_calls:
-                # Parse arguments from JSON string if needed
-                args = tc.function.arguments
-                if isinstance(args, str):
-                    args = json_repair.loads(args)
+        for tc in raw_tool_calls:
+            # Parse arguments from JSON string if needed
+            args = tc.function.arguments
+            if isinstance(args, str):
+                args = json_repair.loads(args)
 
-                tool_calls.append(ToolCallRequest(
-                    id=_short_tool_id(),
-                    name=tc.function.name,
-                    arguments=args,
-                ))
+            tool_calls.append(ToolCallRequest(
+                id=_short_tool_id(),
+                name=tc.function.name,
+                arguments=args,
+            ))
 
         usage = {}
         if hasattr(response, "usage") and response.usage:
@@ -280,11 +325,11 @@ class LiteLLMProvider(LLMProvider):
 
         reasoning_content = getattr(message, "reasoning_content", None) or None
         thinking_blocks = getattr(message, "thinking_blocks", None) or None
-        
+
         return LLMResponse(
-            content=message.content,
+            content=content,
             tool_calls=tool_calls,
-            finish_reason=choice.finish_reason or "stop",
+            finish_reason=finish_reason or "stop",
             usage=usage,
             reasoning_content=reasoning_content,
             thinking_blocks=thinking_blocks,

nanobot/providers/registry.py

@@ -26,33 +26,33 @@ class ProviderSpec:
     """
 
     # identity
-    name: str                       # config field name, e.g. "dashscope"
-    keywords: tuple[str, ...]       # model-name keywords for matching (lowercase)
-    env_key: str                    # LiteLLM env var, e.g. "DASHSCOPE_API_KEY"
-    display_name: str = ""          # shown in `nanobot status`
+    name: str  # config field name, e.g. "dashscope"
+    keywords: tuple[str, ...]  # model-name keywords for matching (lowercase)
+    env_key: str  # LiteLLM env var, e.g. "DASHSCOPE_API_KEY"
+    display_name: str = ""  # shown in `nanobot status`
 
     # model prefixing
-    litellm_prefix: str = ""                 # "dashscope" → model becomes "dashscope/{model}"
-    skip_prefixes: tuple[str, ...] = ()      # don't prefix if model already starts with these
+    litellm_prefix: str = ""  # "dashscope" → model becomes "dashscope/{model}"
+    skip_prefixes: tuple[str, ...] = ()  # don't prefix if model already starts with these
 
     # extra env vars, e.g. (("ZHIPUAI_API_KEY", "{api_key}"),)
     env_extras: tuple[tuple[str, str], ...] = ()
 
     # gateway / local detection
-    is_gateway: bool = False                 # routes any model (OpenRouter, AiHubMix)
-    is_local: bool = False                   # local deployment (vLLM, Ollama)
-    detect_by_key_prefix: str = ""           # match api_key prefix, e.g. "sk-or-"
-    detect_by_base_keyword: str = ""         # match substring in api_base URL
-    default_api_base: str = ""               # fallback base URL
+    is_gateway: bool = False  # routes any model (OpenRouter, AiHubMix)
+    is_local: bool = False  # local deployment (vLLM, Ollama)
+    detect_by_key_prefix: str = ""  # match api_key prefix, e.g. "sk-or-"
+    detect_by_base_keyword: str = ""  # match substring in api_base URL
+    default_api_base: str = ""  # fallback base URL
 
     # gateway behavior
-    strip_model_prefix: bool = False         # strip "provider/" before re-prefixing
+    strip_model_prefix: bool = False  # strip "provider/" before re-prefixing
 
     # per-model param overrides, e.g. (("kimi-k2.5", {"temperature": 1.0}),)
     model_overrides: tuple[tuple[str, dict[str, Any]], ...] = ()
 
     # OAuth-based providers (e.g., OpenAI Codex) don't use API keys
-    is_oauth: bool = False                   # if True, uses OAuth flow instead of API key
+    is_oauth: bool = False  # if True, uses OAuth flow instead of API key
 
     # Direct providers bypass LiteLLM entirely (e.g., CustomProvider)
     is_direct: bool = False
@@ -70,7 +70,6 @@ class ProviderSpec:
 # ---------------------------------------------------------------------------
 
 PROVIDERS: tuple[ProviderSpec, ...] = (
-
     # === Custom (direct OpenAI-compatible endpoint, bypasses LiteLLM) ======
     ProviderSpec(
         name="custom",
@@ -81,16 +80,24 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         is_direct=True,
     ),
 
+    # === Azure OpenAI (direct API calls with API version 2024-10-21) =====
+    ProviderSpec(
+        name="azure_openai",
+        keywords=("azure", "azure-openai"),
+        env_key="",
+        display_name="Azure OpenAI",
+        litellm_prefix="",
+        is_direct=True,
+    ),
     # === Gateways (detected by api_key / api_base, not model name) =========
     # Gateways can route any model, so they win in fallback.
-
     # OpenRouter: global gateway, keys start with "sk-or-"
     ProviderSpec(
         name="openrouter",
         keywords=("openrouter",),
         env_key="OPENROUTER_API_KEY",
         display_name="OpenRouter",
-        litellm_prefix="openrouter",        # claude-3 → openrouter/claude-3
+        litellm_prefix="openrouter",  # claude-3 → openrouter/claude-3
         skip_prefixes=(),
         env_extras=(),
         is_gateway=True,
@@ -102,16 +109,15 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         model_overrides=(),
         supports_prompt_caching=True,
     ),
-
     # AiHubMix: global gateway, OpenAI-compatible interface.
     # strip_model_prefix=True: it doesn't understand "anthropic/claude-3",
     # so we strip to bare "claude-3" then re-prefix as "openai/claude-3".
     ProviderSpec(
         name="aihubmix",
         keywords=("aihubmix",),
-        env_key="OPENAI_API_KEY",           # OpenAI-compatible
+        env_key="OPENAI_API_KEY",  # OpenAI-compatible
         display_name="AiHubMix",
-        litellm_prefix="openai",            # → openai/{model}
+        litellm_prefix="openai",  # → openai/{model}
         skip_prefixes=(),
         env_extras=(),
         is_gateway=True,
@@ -119,10 +125,9 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         detect_by_key_prefix="",
         detect_by_base_keyword="aihubmix",
         default_api_base="https://aihubmix.com/v1",
-        strip_model_prefix=True,            # anthropic/claude-3 → claude-3 → openai/claude-3
+        strip_model_prefix=True,  # anthropic/claude-3 → claude-3 → openai/claude-3
         model_overrides=(),
     ),
-
     # SiliconFlow (硅基流动): OpenAI-compatible gateway, model names keep org prefix
     ProviderSpec(
         name="siliconflow",
@@ -140,7 +145,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # VolcEngine (火山引擎): OpenAI-compatible gateway
     ProviderSpec(
         name="volcengine",
@@ -158,9 +162,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # === Standard providers (matched by model-name keywords) ===============
-
     # Anthropic: LiteLLM recognizes "claude-*" natively, no prefix needed.
     ProviderSpec(
         name="anthropic",
@@ -179,7 +181,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         model_overrides=(),
         supports_prompt_caching=True,
     ),
-
     # OpenAI: LiteLLM recognizes "gpt-*" natively, no prefix needed.
     ProviderSpec(
         name="openai",
@@ -197,14 +198,13 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # OpenAI Codex: uses OAuth, not API key.
     ProviderSpec(
         name="openai_codex",
         keywords=("openai-codex",),
-        env_key="",                         # OAuth-based, no API key
+        env_key="",  # OAuth-based, no API key
         display_name="OpenAI Codex",
-        litellm_prefix="",                  # Not routed through LiteLLM
+        litellm_prefix="",  # Not routed through LiteLLM
         skip_prefixes=(),
         env_extras=(),
         is_gateway=False,
@@ -214,16 +214,15 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         default_api_base="https://chatgpt.com/backend-api",
         strip_model_prefix=False,
         model_overrides=(),
-        is_oauth=True,                      # OAuth-based authentication
+        is_oauth=True,  # OAuth-based authentication
     ),
-
     # Github Copilot: uses OAuth, not API key.
     ProviderSpec(
         name="github_copilot",
         keywords=("github_copilot", "copilot"),
-        env_key="",                         # OAuth-based, no API key
+        env_key="",  # OAuth-based, no API key
         display_name="Github Copilot",
-        litellm_prefix="github_copilot",   # github_copilot/model → github_copilot/model
+        litellm_prefix="github_copilot",  # github_copilot/model → github_copilot/model
         skip_prefixes=("github_copilot/",),
         env_extras=(),
         is_gateway=False,
@@ -233,17 +232,16 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         default_api_base="",
         strip_model_prefix=False,
         model_overrides=(),
-        is_oauth=True,                      # OAuth-based authentication
+        is_oauth=True,  # OAuth-based authentication
     ),
-
     # DeepSeek: needs "deepseek/" prefix for LiteLLM routing.
     ProviderSpec(
         name="deepseek",
         keywords=("deepseek",),
         env_key="DEEPSEEK_API_KEY",
         display_name="DeepSeek",
-        litellm_prefix="deepseek",          # deepseek-chat → deepseek/deepseek-chat
-        skip_prefixes=("deepseek/",),       # avoid double-prefix
+        litellm_prefix="deepseek",  # deepseek-chat → deepseek/deepseek-chat
+        skip_prefixes=("deepseek/",),  # avoid double-prefix
         env_extras=(),
         is_gateway=False,
         is_local=False,
@@ -253,15 +251,14 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # Gemini: needs "gemini/" prefix for LiteLLM.
     ProviderSpec(
         name="gemini",
         keywords=("gemini",),
         env_key="GEMINI_API_KEY",
         display_name="Gemini",
-        litellm_prefix="gemini",            # gemini-pro → gemini/gemini-pro
-        skip_prefixes=("gemini/",),         # avoid double-prefix
+        litellm_prefix="gemini",  # gemini-pro → gemini/gemini-pro
+        skip_prefixes=("gemini/",),  # avoid double-prefix
         env_extras=(),
         is_gateway=False,
         is_local=False,
@@ -271,7 +268,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # Zhipu: LiteLLM uses "zai/" prefix.
     # Also mirrors key to ZHIPUAI_API_KEY (some LiteLLM paths check that).
     # skip_prefixes: don't add "zai/" when already routed via gateway.
@@ -280,11 +276,9 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("zhipu", "glm", "zai"),
         env_key="ZAI_API_KEY",
         display_name="Zhipu AI",
-        litellm_prefix="zai",              # glm-4 → zai/glm-4
+        litellm_prefix="zai",  # glm-4 → zai/glm-4
         skip_prefixes=("zhipu/", "zai/", "openrouter/", "hosted_vllm/"),
-        env_extras=(
-            ("ZHIPUAI_API_KEY", "{api_key}"),
-        ),
+        env_extras=(("ZHIPUAI_API_KEY", "{api_key}"),),
         is_gateway=False,
         is_local=False,
         detect_by_key_prefix="",
@@ -293,14 +287,13 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # DashScope: Qwen models, needs "dashscope/" prefix.
     ProviderSpec(
         name="dashscope",
         keywords=("qwen", "dashscope"),
         env_key="DASHSCOPE_API_KEY",
         display_name="DashScope",
-        litellm_prefix="dashscope",         # qwen-max → dashscope/qwen-max
+        litellm_prefix="dashscope",  # qwen-max → dashscope/qwen-max
         skip_prefixes=("dashscope/", "openrouter/"),
         env_extras=(),
         is_gateway=False,
@@ -311,7 +304,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # Moonshot: Kimi models, needs "moonshot/" prefix.
     # LiteLLM requires MOONSHOT_API_BASE env var to find the endpoint.
     # Kimi K2.5 API enforces temperature >= 1.0.
@@ -320,22 +312,17 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("moonshot", "kimi"),
         env_key="MOONSHOT_API_KEY",
         display_name="Moonshot",
-        litellm_prefix="moonshot",          # kimi-k2.5 → moonshot/kimi-k2.5
+        litellm_prefix="moonshot",  # kimi-k2.5 → moonshot/kimi-k2.5
         skip_prefixes=("moonshot/", "openrouter/"),
-        env_extras=(
-            ("MOONSHOT_API_BASE", "{api_base}"),
-        ),
+        env_extras=(("MOONSHOT_API_BASE", "{api_base}"),),
         is_gateway=False,
         is_local=False,
         detect_by_key_prefix="",
         detect_by_base_keyword="",
-        default_api_base="https://api.moonshot.ai/v1",   # intl; use api.moonshot.cn for China
+        default_api_base="https://api.moonshot.ai/v1",  # intl; use api.moonshot.cn for China
         strip_model_prefix=False,
-        model_overrides=(
-            ("kimi-k2.5", {"temperature": 1.0}),
-        ),
+        model_overrides=(("kimi-k2.5", {"temperature": 1.0}),),
     ),
-
     # MiniMax: needs "minimax/" prefix for LiteLLM routing.
     # Uses OpenAI-compatible API at api.minimax.io/v1.
     ProviderSpec(
@@ -343,7 +330,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("minimax",),
         env_key="MINIMAX_API_KEY",
         display_name="MiniMax",
-        litellm_prefix="minimax",            # MiniMax-M2.1 → minimax/MiniMax-M2.1
+        litellm_prefix="minimax",  # MiniMax-M2.1 → minimax/MiniMax-M2.1
         skip_prefixes=("minimax/", "openrouter/"),
         env_extras=(),
         is_gateway=False,
@@ -354,9 +341,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # === Local deployment (matched by config key, NOT by api_base) =========
-
     # vLLM / any OpenAI-compatible local server.
     # Detected when config key is "vllm" (provider_name="vllm").
     ProviderSpec(
@@ -364,20 +349,18 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("vllm",),
         env_key="HOSTED_VLLM_API_KEY",
         display_name="vLLM/Local",
-        litellm_prefix="hosted_vllm",      # Llama-3-8B → hosted_vllm/Llama-3-8B
+        litellm_prefix="hosted_vllm",  # Llama-3-8B → hosted_vllm/Llama-3-8B
         skip_prefixes=(),
         env_extras=(),
         is_gateway=False,
         is_local=True,
         detect_by_key_prefix="",
         detect_by_base_keyword="",
-        default_api_base="",                # user must provide in config
+        default_api_base="",  # user must provide in config
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # === Auxiliary (not a primary LLM provider) ============================
-
     # Groq: mainly used for Whisper voice transcription, also usable for LLM.
     # Needs "groq/" prefix for LiteLLM routing. Placed last — it rarely wins fallback.
     ProviderSpec(
@@ -385,8 +368,8 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         keywords=("groq",),
         env_key="GROQ_API_KEY",
         display_name="Groq",
-        litellm_prefix="groq",              # llama3-8b-8192 → groq/llama3-8b-8192
-        skip_prefixes=("groq/",),           # avoid double-prefix
+        litellm_prefix="groq",  # llama3-8b-8192 → groq/llama3-8b-8192
+        skip_prefixes=("groq/",),  # avoid double-prefix
         env_extras=(),
         is_gateway=False,
         is_local=False,
@@ -403,6 +386,7 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
 # Lookup helpers
 # ---------------------------------------------------------------------------
 
+
 def find_by_model(model: str) -> ProviderSpec | None:
     """Match a standard provider by model-name keyword (case-insensitive).
     Skips gateways/local — those are matched by api_key/api_base instead."""
@@ -418,7 +402,9 @@ def find_by_model(model: str) -> ProviderSpec | None:
             return spec
 
     for spec in std_specs:
-        if any(kw in model_lower or kw.replace("-", "_") in model_normalized for kw in spec.keywords):
+        if any(
+            kw in model_lower or kw.replace("-", "_") in model_normalized for kw in spec.keywords
+        ):
             return spec
     return None
 

nanobot/utils/helpers.py

@@ -5,6 +5,19 @@ from datetime import datetime
 from pathlib import Path
 
 
+def detect_image_mime(data: bytes) -> str | None:
+    """Detect image MIME type from magic bytes, ignoring file extension."""
+    if data[:8] == b"\x89PNG\r\n\x1a\n":
+        return "image/png"
+    if data[:3] == b"\xff\xd8\xff":
+        return "image/jpeg"
+    if data[:6] in (b"GIF87a", b"GIF89a"):
+        return "image/gif"
+    if data[:4] == b"RIFF" and data[8:12] == b"WEBP":
+        return "image/webp"
+    return None
+
+
 def ensure_dir(path: Path) -> Path:
     """Ensure directory exists, return it."""
     path.mkdir(parents=True, exist_ok=True)
@@ -35,6 +48,38 @@ def safe_filename(name: str) -> str:
     return _UNSAFE_CHARS.sub("_", name).strip()
 
 
+def split_message(content: str, max_len: int = 2000) -> list[str]:
+    """
+    Split content into chunks within max_len, preferring line breaks.
+
+    Args:
+        content: The text content to split.
+        max_len: Maximum length per chunk (default 2000 for Discord compatibility).
+
+    Returns:
+        List of message chunks, each within max_len.
+    """
+    if not content:
+        return []
+    if len(content) <= max_len:
+        return [content]
+    chunks: list[str] = []
+    while content:
+        if len(content) <= max_len:
+            chunks.append(content)
+            break
+        cut = content[:max_len]
+        # Try to break at newline first, then space, then hard break
+        pos = cut.rfind('\n')
+        if pos <= 0:
+            pos = cut.rfind(' ')
+        if pos <= 0:
+            pos = max_len
+        chunks.append(content[:pos])
+        content = content[pos:].lstrip()
+    return chunks
+
+
 def sync_workspace_templates(workspace: Path, silent: bool = False) -> list[str]:
     """Sync bundled templates to workspace. Only creates missing files."""
     from importlib.resources import files as pkg_files