Merge branch 'main' into pr-1581

README.md

@@ -20,9 +20,20 @@
 
 ## 📢 News
 
+- **2026-03-07** 🚀 Azure OpenAI provider, WhatsApp media, QQ group chats, and more Telegram/Feishu polish.
+- **2026-03-06** 🪄 Lighter providers, smarter media handling, and sturdier memory and CLI compatibility.
+- **2026-03-05** ⚡️ Telegram draft streaming, MCP SSE support, and broader channel reliability fixes.
+- **2026-03-04** 🛠️ Dependency cleanup, safer file reads, and another round of test and Cron fixes.
+- **2026-03-03** 🧠 Cleaner user-message merging, safer multimodal saves, and stronger Cron guards.
+- **2026-03-02** 🛡️ Safer default access control, sturdier Cron reloads, and cleaner Matrix media handling.
+- **2026-03-01** 🌐 Web proxy support, smarter Cron reminders, and Feishu rich-text parsing improvements.
 - **2026-02-28** 🚀 Released **v0.1.4.post3** — cleaner context, hardened session history, and smarter agent. Please see [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4.post3) for details.
 - **2026-02-27** 🧠 Experimental thinking mode support, DingTalk media messages, Feishu and QQ channel fixes.
 - **2026-02-26** 🛡️ Session poisoning fix, WhatsApp dedup, Windows path guard, Mistral compatibility.
+
+<details>
+<summary>Earlier news</summary>
+
 - **2026-02-25** 🧹 New Matrix channel, cleaner session context, auto workspace template sync.
 - **2026-02-24** 🚀 Released **v0.1.4.post2** — a reliability-focused release with a redesigned heartbeat, prompt cache optimization, and hardened provider & channel stability. See [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4.post2) for details.
 - **2026-02-23** 🔧 Virtual tool-call heartbeat, prompt cache optimization, Slack mrkdwn fixes.
@@ -30,10 +41,6 @@
 - **2026-02-21** 🎉 Released **v0.1.4.post1** — new providers, media support across channels, and major stability improvements. See [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4.post1) for details.
 - **2026-02-20** 🐦 Feishu now receives multimodal files from users. More reliable memory under the hood.
 - **2026-02-19** ✨ Slack now sends files, Discord splits long messages, and subagents work in CLI mode.
-
-<details>
-<summary>Earlier news</summary>
-
 - **2026-02-18** ⚡️ nanobot now supports VolcEngine, MCP custom auth headers, and Anthropic prompt caching.
 - **2026-02-17** 🎉 Released **v0.1.4** — MCP support, progress streaming, new providers, and multiple channel improvements. Please see [release notes](https://github.com/HKUDS/nanobot/releases/tag/v0.1.4) for details.
 - **2026-02-16** 🦞 nanobot now integrates a [ClawHub](https://clawhub.ai) skill — search and install public agent skills.
@@ -420,6 +427,10 @@ nanobot channels login
 nanobot gateway
 ```
 
+> WhatsApp bridge updates are not applied automatically for existing installations.
+> If you upgrade nanobot and need the latest WhatsApp bridge, run:
+> `rm -rf ~/.nanobot/bridge && nanobot channels login`
+
 </details>
 
 <details>
@@ -664,12 +675,14 @@ Config file: `~/.nanobot/config.json`
 > - **Zhipu Coding Plan**: If you're on Zhipu's coding plan, set `"apiBase": "https://open.bigmodel.cn/api/coding/paas/v4"` in your zhipu provider config.
 > - **MiniMax (Mainland China)**: If your API key is from MiniMax's mainland China platform (minimaxi.com), set `"apiBase": "https://api.minimaxi.com/v1"` in your minimax provider config.
 > - **VolcEngine Coding Plan**: If you're on VolcEngine's coding plan, set `"apiBase": "https://ark.cn-beijing.volces.com/api/coding/v3"` in your volcengine provider config.
+> - **Alibaba Cloud Coding Plan**: If you're on the Alibaba Cloud Coding Plan (BaiLian), set `"apiBase": "https://coding.dashscope.aliyuncs.com/v1"` in your dashscope provider config.
 
 | Provider | Purpose | Get API Key |
 |----------|---------|-------------|
 | `custom` | Any OpenAI-compatible endpoint (direct, no LiteLLM) | — |
 | `openrouter` | LLM (recommended, access to all models) | [openrouter.ai](https://openrouter.ai) |
 | `anthropic` | LLM (Claude direct) | [console.anthropic.com](https://console.anthropic.com) |
+| `azure_openai` | LLM (Azure OpenAI) | [portal.azure.com](https://portal.azure.com) |
 | `openai` | LLM (GPT direct) | [platform.openai.com](https://platform.openai.com) |
 | `deepseek` | LLM (DeepSeek direct) | [platform.deepseek.com](https://platform.deepseek.com) |
 | `groq` | LLM + **Voice transcription** (Whisper) | [console.groq.com](https://console.groq.com) |

bridge/src/whatsapp.ts

@@ -9,11 +9,17 @@ import makeWASocket, {
   useMultiFileAuthState,
   fetchLatestBaileysVersion,
   makeCacheableSignalKeyStore,
+  downloadMediaMessage,
+  extractMessageContent as baileysExtractMessageContent,
 } from '@whiskeysockets/baileys';
 
 import { Boom } from '@hapi/boom';
 import qrcode from 'qrcode-terminal';
 import pino from 'pino';
+import { writeFile, mkdir } from 'fs/promises';
+import { join } from 'path';
+import { homedir } from 'os';
+import { randomBytes } from 'crypto';
 
 const VERSION = '0.1.0';
 
@@ -24,6 +30,7 @@ export interface InboundMessage {
   content: string;
   timestamp: number;
   isGroup: boolean;
+  media?: string[];
 }
 
 export interface WhatsAppClientOptions {
@@ -110,14 +117,33 @@ export class WhatsAppClient {
       if (type !== 'notify') return;
 
       for (const msg of messages) {
-        // Skip own messages
         if (msg.key.fromMe) continue;
-
-        // Skip status updates
         if (msg.key.remoteJid === 'status@broadcast') continue;
 
-        const content = this.extractMessageContent(msg);
-        if (!content) continue;
+        const unwrapped = baileysExtractMessageContent(msg.message);
+        if (!unwrapped) continue;
+
+        const content = this.getTextContent(unwrapped);
+        let fallbackContent: string | null = null;
+        const mediaPaths: string[] = [];
+
+        if (unwrapped.imageMessage) {
+          fallbackContent = '[Image]';
+          const path = await this.downloadMedia(msg, unwrapped.imageMessage.mimetype ?? undefined);
+          if (path) mediaPaths.push(path);
+        } else if (unwrapped.documentMessage) {
+          fallbackContent = '[Document]';
+          const path = await this.downloadMedia(msg, unwrapped.documentMessage.mimetype ?? undefined,
+            unwrapped.documentMessage.fileName ?? undefined);
+          if (path) mediaPaths.push(path);
+        } else if (unwrapped.videoMessage) {
+          fallbackContent = '[Video]';
+          const path = await this.downloadMedia(msg, unwrapped.videoMessage.mimetype ?? undefined);
+          if (path) mediaPaths.push(path);
+        }
+
+        const finalContent = content || (mediaPaths.length === 0 ? fallbackContent : '') || '';
+        if (!finalContent && mediaPaths.length === 0) continue;
 
         const isGroup = msg.key.remoteJid?.endsWith('@g.us') || false;
 
@@ -125,18 +151,45 @@ export class WhatsAppClient {
           id: msg.key.id || '',
           sender: msg.key.remoteJid || '',
           pn: msg.key.remoteJidAlt || '',
-          content,
+          content: finalContent,
           timestamp: msg.messageTimestamp as number,
           isGroup,
+          ...(mediaPaths.length > 0 ? { media: mediaPaths } : {}),
         });
       }
     });
   }
 
-  private extractMessageContent(msg: any): string | null {
-    const message = msg.message;
-    if (!message) return null;
+  private async downloadMedia(msg: any, mimetype?: string, fileName?: string): Promise<string | null> {
+    try {
+      const mediaDir = join(homedir(), '.nanobot', 'media');
+      await mkdir(mediaDir, { recursive: true });
 
+      const buffer = await downloadMediaMessage(msg, 'buffer', {}) as Buffer;
+
+      let outFilename: string;
+      if (fileName) {
+        // Documents have a filename — use it with a unique prefix to avoid collisions
+        const prefix = `wa_${Date.now()}_${randomBytes(4).toString('hex')}_`;
+        outFilename = prefix + fileName;
+      } else {
+        const mime = mimetype || 'application/octet-stream';
+        // Derive extension from mimetype subtype (e.g. "image/png" → ".png", "application/pdf" → ".pdf")
+        const ext = '.' + (mime.split('/').pop()?.split(';')[0] || 'bin');
+        outFilename = `wa_${Date.now()}_${randomBytes(4).toString('hex')}${ext}`;
+      }
+
+      const filepath = join(mediaDir, outFilename);
+      await writeFile(filepath, buffer);
+
+      return filepath;
+    } catch (err) {
+      console.error('Failed to download media:', err);
+      return null;
+    }
+  }
+
+  private getTextContent(message: any): string | null {
     // Text message
     if (message.conversation) {
       return message.conversation;
@@ -147,19 +200,19 @@ export class WhatsAppClient {
       return message.extendedTextMessage.text;
     }
 
-    // Image with caption
-    if (message.imageMessage?.caption) {
-      return `[Image] ${message.imageMessage.caption}`;
+    // Image with optional caption
+    if (message.imageMessage) {
+      return message.imageMessage.caption || '';
     }
 
-    // Video with caption
-    if (message.videoMessage?.caption) {
-      return `[Video] ${message.videoMessage.caption}`;
+    // Video with optional caption
+    if (message.videoMessage) {
+      return message.videoMessage.caption || '';
     }
 
-    // Document with caption
-    if (message.documentMessage?.caption) {
-      return `[Document] ${message.documentMessage.caption}`;
+    // Document with optional caption
+    if (message.documentMessage) {
+      return message.documentMessage.caption || '';
     }
 
     // Voice/Audio message

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

tests/test_azure_openai_provider.py

@@ -0,0 +1,399 @@
+"""Test Azure OpenAI provider implementation (updated for model-based deployment names)."""
+
+from unittest.mock import AsyncMock, Mock, patch
+
+import pytest
+
+from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
+from nanobot.providers.base import LLMResponse
+
+
+def test_azure_openai_provider_init():
+    """Test AzureOpenAIProvider initialization without deployment_name."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o-deployment",
+    )
+    
+    assert provider.api_key == "test-key"
+    assert provider.api_base == "https://test-resource.openai.azure.com/"
+    assert provider.default_model == "gpt-4o-deployment"
+    assert provider.api_version == "2024-10-21"
+
+
+def test_azure_openai_provider_init_validation():
+    """Test AzureOpenAIProvider initialization validation."""
+    # Missing api_key
+    with pytest.raises(ValueError, match="Azure OpenAI api_key is required"):
+        AzureOpenAIProvider(api_key="", api_base="https://test.com")
+    
+    # Missing api_base
+    with pytest.raises(ValueError, match="Azure OpenAI api_base is required"):
+        AzureOpenAIProvider(api_key="test", api_base="")
+
+
+def test_build_chat_url():
+    """Test Azure OpenAI URL building with different deployment names."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    # Test various deployment names
+    test_cases = [
+        ("gpt-4o-deployment", "https://test-resource.openai.azure.com/openai/deployments/gpt-4o-deployment/chat/completions?api-version=2024-10-21"),
+        ("gpt-35-turbo", "https://test-resource.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-10-21"),
+        ("custom-model", "https://test-resource.openai.azure.com/openai/deployments/custom-model/chat/completions?api-version=2024-10-21"),
+    ]
+    
+    for deployment_name, expected_url in test_cases:
+        url = provider._build_chat_url(deployment_name)
+        assert url == expected_url
+
+
+def test_build_chat_url_api_base_without_slash():
+    """Test URL building when api_base doesn't end with slash."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",  # No trailing slash
+        default_model="gpt-4o",
+    )
+    
+    url = provider._build_chat_url("test-deployment")
+    expected = "https://test-resource.openai.azure.com/openai/deployments/test-deployment/chat/completions?api-version=2024-10-21"
+    assert url == expected
+
+
+def test_build_headers():
+    """Test Azure OpenAI header building with api-key authentication."""
+    provider = AzureOpenAIProvider(
+        api_key="test-api-key-123",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    headers = provider._build_headers()
+    assert headers["Content-Type"] == "application/json"
+    assert headers["api-key"] == "test-api-key-123"  # Azure OpenAI specific header
+    assert "x-session-affinity" in headers
+
+
+def test_prepare_request_payload():
+    """Test request payload preparation with Azure OpenAI 2024-10-21 compliance."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    messages = [{"role": "user", "content": "Hello"}]
+    payload = provider._prepare_request_payload("gpt-4o", messages, max_tokens=1500, temperature=0.8)
+    
+    assert payload["messages"] == messages
+    assert payload["max_completion_tokens"] == 1500  # Azure API 2024-10-21 uses max_completion_tokens
+    assert payload["temperature"] == 0.8
+    assert "tools" not in payload
+    
+    # Test with tools
+    tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
+    payload_with_tools = provider._prepare_request_payload("gpt-4o", messages, tools=tools)
+    assert payload_with_tools["tools"] == tools
+    assert payload_with_tools["tool_choice"] == "auto"
+    
+    # Test with reasoning_effort
+    payload_with_reasoning = provider._prepare_request_payload(
+        "gpt-5-chat", messages, reasoning_effort="medium"
+    )
+    assert payload_with_reasoning["reasoning_effort"] == "medium"
+    assert "temperature" not in payload_with_reasoning
+
+
+def test_prepare_request_payload_sanitizes_messages():
+    """Test Azure payload strips non-standard message keys before sending."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+
+    messages = [
+        {
+            "role": "assistant",
+            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
+            "reasoning_content": "hidden chain-of-thought",
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "x",
+            "content": "ok",
+            "extra_field": "should be removed",
+        },
+    ]
+
+    payload = provider._prepare_request_payload("gpt-4o", messages)
+
+    assert payload["messages"] == [
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [{"id": "call_123", "type": "function", "function": {"name": "x"}}],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_123",
+            "name": "x",
+            "content": "ok",
+        },
+    ]
+
+
+@pytest.mark.asyncio
+async def test_chat_success():
+    """Test successful chat request using model as deployment name."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o-deployment",
+    )
+    
+    # Mock response data
+    mock_response_data = {
+        "choices": [{
+            "message": {
+                "content": "Hello! How can I help you today?",
+                "role": "assistant"
+            },
+            "finish_reason": "stop"
+        }],
+        "usage": {
+            "prompt_tokens": 12,
+            "completion_tokens": 18,
+            "total_tokens": 30
+        }
+    }
+    
+    with patch("httpx.AsyncClient") as mock_client:
+        mock_response = AsyncMock()
+        mock_response.status_code = 200
+        mock_response.json = Mock(return_value=mock_response_data)
+        
+        mock_context = AsyncMock()
+        mock_context.post = AsyncMock(return_value=mock_response)
+        mock_client.return_value.__aenter__.return_value = mock_context
+        
+        # Test with specific model (deployment name)
+        messages = [{"role": "user", "content": "Hello"}]
+        result = await provider.chat(messages, model="custom-deployment")
+        
+        assert isinstance(result, LLMResponse)
+        assert result.content == "Hello! How can I help you today?"
+        assert result.finish_reason == "stop"
+        assert result.usage["prompt_tokens"] == 12
+        assert result.usage["completion_tokens"] == 18
+        assert result.usage["total_tokens"] == 30
+        
+        # Verify URL was built with the provided model as deployment name
+        call_args = mock_context.post.call_args
+        expected_url = "https://test-resource.openai.azure.com/openai/deployments/custom-deployment/chat/completions?api-version=2024-10-21"
+        assert call_args[0][0] == expected_url
+
+
+@pytest.mark.asyncio
+async def test_chat_uses_default_model_when_no_model_provided():
+    """Test that chat uses default_model when no model is specified."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="default-deployment",
+    )
+    
+    mock_response_data = {
+        "choices": [{
+            "message": {"content": "Response", "role": "assistant"},
+            "finish_reason": "stop"
+        }],
+        "usage": {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
+    }
+    
+    with patch("httpx.AsyncClient") as mock_client:
+        mock_response = AsyncMock()
+        mock_response.status_code = 200
+        mock_response.json = Mock(return_value=mock_response_data)
+        
+        mock_context = AsyncMock()
+        mock_context.post = AsyncMock(return_value=mock_response)
+        mock_client.return_value.__aenter__.return_value = mock_context
+        
+        messages = [{"role": "user", "content": "Test"}]
+        await provider.chat(messages)  # No model specified
+        
+        # Verify URL was built with default model as deployment name
+        call_args = mock_context.post.call_args
+        expected_url = "https://test-resource.openai.azure.com/openai/deployments/default-deployment/chat/completions?api-version=2024-10-21"
+        assert call_args[0][0] == expected_url
+
+
+@pytest.mark.asyncio
+async def test_chat_with_tool_calls():
+    """Test chat request with tool calls in response."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    # Mock response with tool calls
+    mock_response_data = {
+        "choices": [{
+            "message": {
+                "content": None,
+                "role": "assistant",
+                "tool_calls": [{
+                    "id": "call_12345",
+                    "function": {
+                        "name": "get_weather",
+                        "arguments": '{"location": "San Francisco"}'
+                    }
+                }]
+            },
+            "finish_reason": "tool_calls"
+        }],
+        "usage": {
+            "prompt_tokens": 20,
+            "completion_tokens": 15,
+            "total_tokens": 35
+        }
+    }
+    
+    with patch("httpx.AsyncClient") as mock_client:
+        mock_response = AsyncMock()
+        mock_response.status_code = 200
+        mock_response.json = Mock(return_value=mock_response_data)
+        
+        mock_context = AsyncMock()
+        mock_context.post = AsyncMock(return_value=mock_response)
+        mock_client.return_value.__aenter__.return_value = mock_context
+        
+        messages = [{"role": "user", "content": "What's the weather?"}]
+        tools = [{"type": "function", "function": {"name": "get_weather", "parameters": {}}}]
+        result = await provider.chat(messages, tools=tools, model="weather-model")
+        
+        assert isinstance(result, LLMResponse)
+        assert result.content is None
+        assert result.finish_reason == "tool_calls"
+        assert len(result.tool_calls) == 1
+        assert result.tool_calls[0].name == "get_weather"
+        assert result.tool_calls[0].arguments == {"location": "San Francisco"}
+
+
+@pytest.mark.asyncio
+async def test_chat_api_error():
+    """Test chat request API error handling."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    with patch("httpx.AsyncClient") as mock_client:
+        mock_response = AsyncMock()
+        mock_response.status_code = 401
+        mock_response.text = "Invalid authentication credentials"
+        
+        mock_context = AsyncMock()
+        mock_context.post = AsyncMock(return_value=mock_response)
+        mock_client.return_value.__aenter__.return_value = mock_context
+        
+        messages = [{"role": "user", "content": "Hello"}]
+        result = await provider.chat(messages)
+        
+        assert isinstance(result, LLMResponse)
+        assert "Azure OpenAI API Error 401" in result.content
+        assert "Invalid authentication credentials" in result.content
+        assert result.finish_reason == "error"
+
+
+@pytest.mark.asyncio
+async def test_chat_connection_error():
+    """Test chat request connection error handling."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    with patch("httpx.AsyncClient") as mock_client:
+        mock_context = AsyncMock()
+        mock_context.post = AsyncMock(side_effect=Exception("Connection failed"))
+        mock_client.return_value.__aenter__.return_value = mock_context
+        
+        messages = [{"role": "user", "content": "Hello"}]
+        result = await provider.chat(messages)
+        
+        assert isinstance(result, LLMResponse)
+        assert "Error calling Azure OpenAI: Exception('Connection failed')" in result.content
+        assert result.finish_reason == "error"
+
+
+def test_parse_response_malformed():
+    """Test response parsing with malformed data."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o",
+    )
+    
+    # Test with missing choices
+    malformed_response = {"usage": {"prompt_tokens": 10}}
+    result = provider._parse_response(malformed_response)
+    
+    assert isinstance(result, LLMResponse)
+    assert "Error parsing Azure OpenAI response" in result.content
+    assert result.finish_reason == "error"
+
+
+def test_get_default_model():
+    """Test get_default_model method."""
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="my-custom-deployment",
+    )
+    
+    assert provider.get_default_model() == "my-custom-deployment"
+
+
+if __name__ == "__main__":
+    # Run basic tests
+    print("Running basic Azure OpenAI provider tests...")
+    
+    # Test initialization
+    provider = AzureOpenAIProvider(
+        api_key="test-key",
+        api_base="https://test-resource.openai.azure.com",
+        default_model="gpt-4o-deployment",
+    )
+    print("✅ Provider initialization successful")
+    
+    # Test URL building
+    url = provider._build_chat_url("my-deployment")
+    expected = "https://test-resource.openai.azure.com/openai/deployments/my-deployment/chat/completions?api-version=2024-10-21"
+    assert url == expected
+    print("✅ URL building works correctly")
+    
+    # Test headers
+    headers = provider._build_headers()
+    assert headers["api-key"] == "test-key"
+    assert headers["Content-Type"] == "application/json"
+    print("✅ Header building works correctly")
+    
+    # Test payload preparation
+    messages = [{"role": "user", "content": "Test"}]
+    payload = provider._prepare_request_payload("gpt-4o-deployment", messages, max_tokens=1000)
+    assert payload["max_completion_tokens"] == 1000  # Azure 2024-10-21 format
+    print("✅ Payload preparation works correctly")
+    
+    print("✅ All basic tests passed! Updated test file is working correctly.")

tests/test_base_channel.py

@@ -0,0 +1,25 @@
+from types import SimpleNamespace
+
+from nanobot.bus.events import OutboundMessage
+from nanobot.bus.queue import MessageBus
+from nanobot.channels.base import BaseChannel
+
+
+class _DummyChannel(BaseChannel):
+    name = "dummy"
+
+    async def start(self) -> None:
+        return None
+
+    async def stop(self) -> None:
+        return None
+
+    async def send(self, msg: OutboundMessage) -> None:
+        return None
+
+
+def test_is_allowed_requires_exact_match() -> None:
+    channel = _DummyChannel(SimpleNamespace(allow_from=["allow@email.com"]), MessageBus())
+
+    assert channel.is_allowed("allow@email.com") is True
+    assert channel.is_allowed("attacker|allow@email.com") is False

tests/test_dingtalk_channel.py

@@ -0,0 +1,66 @@
+from types import SimpleNamespace
+
+import pytest
+
+from nanobot.bus.queue import MessageBus
+from nanobot.channels.dingtalk import DingTalkChannel
+from nanobot.config.schema import DingTalkConfig
+
+
+class _FakeResponse:
+    def __init__(self, status_code: int = 200, json_body: dict | None = None) -> None:
+        self.status_code = status_code
+        self._json_body = json_body or {}
+        self.text = "{}"
+
+    def json(self) -> dict:
+        return self._json_body
+
+
+class _FakeHttp:
+    def __init__(self) -> None:
+        self.calls: list[dict] = []
+
+    async def post(self, url: str, json=None, headers=None):
+        self.calls.append({"url": url, "json": json, "headers": headers})
+        return _FakeResponse()
+
+
+@pytest.mark.asyncio
+async def test_group_message_keeps_sender_id_and_routes_chat_id() -> None:
+    config = DingTalkConfig(client_id="app", client_secret="secret", allow_from=["user1"])
+    bus = MessageBus()
+    channel = DingTalkChannel(config, bus)
+
+    await channel._on_message(
+        "hello",
+        sender_id="user1",
+        sender_name="Alice",
+        conversation_type="2",
+        conversation_id="conv123",
+    )
+
+    msg = await bus.consume_inbound()
+    assert msg.sender_id == "user1"
+    assert msg.chat_id == "group:conv123"
+    assert msg.metadata["conversation_type"] == "2"
+
+
+@pytest.mark.asyncio
+async def test_group_send_uses_group_messages_api() -> None:
+    config = DingTalkConfig(client_id="app", client_secret="secret", allow_from=["*"])
+    channel = DingTalkChannel(config, MessageBus())
+    channel._http = _FakeHttp()
+
+    ok = await channel._send_batch_message(
+        "token",
+        "group:conv123",
+        "sampleMarkdown",
+        {"text": "hello", "title": "Nanobot Reply"},
+    )
+
+    assert ok is True
+    call = channel._http.calls[0]
+    assert call["url"] == "https://api.dingtalk.com/v1.0/robot/groupMessages/send"
+    assert call["json"]["openConversationId"] == "conv123"
+    assert call["json"]["msgKey"] == "sampleMarkdown"

tests/test_feishu_post_content.py

@@ -1,4 +1,4 @@
-from nanobot.channels.feishu import _extract_post_content
+from nanobot.channels.feishu import FeishuChannel, _extract_post_content
 
 
 def test_extract_post_content_supports_post_wrapper_shape() -> None:
@@ -38,3 +38,28 @@ def test_extract_post_content_keeps_direct_shape_behavior() -> None:
 
     assert text == "Daily report"
     assert image_keys == ["img_a", "img_b"]
+
+
+def test_register_optional_event_keeps_builder_when_method_missing() -> None:
+    class Builder:
+        pass
+
+    builder = Builder()
+    same = FeishuChannel._register_optional_event(builder, "missing", object())
+    assert same is builder
+
+
+def test_register_optional_event_calls_supported_method() -> None:
+    called = []
+
+    class Builder:
+        def register_event(self, handler):
+            called.append(handler)
+            return self
+
+    builder = Builder()
+    handler = object()
+    same = FeishuChannel._register_optional_event(builder, "register_event", handler)
+
+    assert same is builder
+    assert called == [handler]

tests/test_memory_consolidation_types.py

@@ -145,3 +145,78 @@ class TestMemoryConsolidationTypeHandling:
 
         assert result is True
         provider.chat.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_extracts_first_dict(self, tmp_path: Path) -> None:
+        """Some providers return arguments as a list - extract first element if it's a dict."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        # Simulate arguments being a list containing a dict
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=[{
+                        "history_entry": "[2026-01-01] User discussed testing.",
+                        "memory_update": "# Memory\nUser likes testing.",
+                    }],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is True
+        assert "User discussed testing." in store.history_file.read_text()
+        assert "User likes testing." in store.memory_file.read_text()
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_empty_list_returns_false(self, tmp_path: Path) -> None:
+        """Empty list arguments should return False."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=[],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is False
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_non_dict_content_returns_false(self, tmp_path: Path) -> None:
+        """List with non-dict content should return False."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=["string", "content"],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is False

tests/test_message_tool_suppress.py

@@ -86,6 +86,35 @@ class TestMessageToolSuppressLogic:
         assert result is not None
         assert "Hello" in result.content
 
+    async def test_progress_hides_internal_reasoning(self, tmp_path: Path) -> None:
+        loop = _make_loop(tmp_path)
+        tool_call = ToolCallRequest(id="call1", name="read_file", arguments={"path": "foo.txt"})
+        calls = iter([
+            LLMResponse(
+                content="Visible<think>hidden</think>",
+                tool_calls=[tool_call],
+                reasoning_content="secret reasoning",
+                thinking_blocks=[{"signature": "sig", "thought": "secret thought"}],
+            ),
+            LLMResponse(content="Done", tool_calls=[]),
+        ])
+        loop.provider.chat = AsyncMock(side_effect=lambda *a, **kw: next(calls))
+        loop.tools.get_definitions = MagicMock(return_value=[])
+        loop.tools.execute = AsyncMock(return_value="ok")
+
+        progress: list[tuple[str, bool]] = []
+
+        async def on_progress(content: str, *, tool_hint: bool = False) -> None:
+            progress.append((content, tool_hint))
+
+        final_content, _, _ = await loop._run_agent_loop([], on_progress=on_progress)
+
+        assert final_content == "Done"
+        assert progress == [
+            ("Visible", False),
+            ('read_file("foo.txt")', True),
+        ]
+
 
 class TestMessageToolTurnTracking:
 

tests/test_qq_channel.py

@@ -0,0 +1,66 @@
+from types import SimpleNamespace
+
+import pytest
+
+from nanobot.bus.events import OutboundMessage
+from nanobot.bus.queue import MessageBus
+from nanobot.channels.qq import QQChannel
+from nanobot.config.schema import QQConfig
+
+
+class _FakeApi:
+    def __init__(self) -> None:
+        self.c2c_calls: list[dict] = []
+        self.group_calls: list[dict] = []
+
+    async def post_c2c_message(self, **kwargs) -> None:
+        self.c2c_calls.append(kwargs)
+
+    async def post_group_message(self, **kwargs) -> None:
+        self.group_calls.append(kwargs)
+
+
+class _FakeClient:
+    def __init__(self) -> None:
+        self.api = _FakeApi()
+
+
+@pytest.mark.asyncio
+async def test_on_group_message_routes_to_group_chat_id() -> None:
+    channel = QQChannel(QQConfig(app_id="app", secret="secret", allow_from=["user1"]), MessageBus())
+
+    data = SimpleNamespace(
+        id="msg1",
+        content="hello",
+        group_openid="group123",
+        author=SimpleNamespace(member_openid="user1"),
+    )
+
+    await channel._on_message(data, is_group=True)
+
+    msg = await channel.bus.consume_inbound()
+    assert msg.sender_id == "user1"
+    assert msg.chat_id == "group123"
+
+
+@pytest.mark.asyncio
+async def test_send_group_message_uses_group_api_with_msg_seq() -> None:
+    channel = QQChannel(QQConfig(app_id="app", secret="secret", allow_from=["*"]), MessageBus())
+    channel._client = _FakeClient()
+    channel._chat_type_cache["group123"] = "group"
+
+    await channel.send(
+        OutboundMessage(
+            channel="qq",
+            chat_id="group123",
+            content="hello",
+            metadata={"message_id": "msg1"},
+        )
+    )
+
+    assert len(channel._client.api.group_calls) == 1
+    call = channel._client.api.group_calls[0]
+    assert call["group_openid"] == "group123"
+    assert call["msg_id"] == "msg1"
+    assert call["msg_seq"] == 2
+    assert not channel._client.api.c2c_calls

tests/test_telegram_channel.py

@@ -0,0 +1,184 @@
+from types import SimpleNamespace
+
+import pytest
+
+from nanobot.bus.events import OutboundMessage
+from nanobot.bus.queue import MessageBus
+from nanobot.channels.telegram import TelegramChannel
+from nanobot.config.schema import TelegramConfig
+
+
+class _FakeHTTPXRequest:
+    instances: list["_FakeHTTPXRequest"] = []
+
+    def __init__(self, **kwargs) -> None:
+        self.kwargs = kwargs
+        self.__class__.instances.append(self)
+
+
+class _FakeUpdater:
+    def __init__(self, on_start_polling) -> None:
+        self._on_start_polling = on_start_polling
+
+    async def start_polling(self, **kwargs) -> None:
+        self._on_start_polling()
+
+
+class _FakeBot:
+    def __init__(self) -> None:
+        self.sent_messages: list[dict] = []
+
+    async def get_me(self):
+        return SimpleNamespace(username="nanobot_test")
+
+    async def set_my_commands(self, commands) -> None:
+        self.commands = commands
+
+    async def send_message(self, **kwargs) -> None:
+        self.sent_messages.append(kwargs)
+
+
+class _FakeApp:
+    def __init__(self, on_start_polling) -> None:
+        self.bot = _FakeBot()
+        self.updater = _FakeUpdater(on_start_polling)
+        self.handlers = []
+        self.error_handlers = []
+
+    def add_error_handler(self, handler) -> None:
+        self.error_handlers.append(handler)
+
+    def add_handler(self, handler) -> None:
+        self.handlers.append(handler)
+
+    async def initialize(self) -> None:
+        pass
+
+    async def start(self) -> None:
+        pass
+
+
+class _FakeBuilder:
+    def __init__(self, app: _FakeApp) -> None:
+        self.app = app
+        self.token_value = None
+        self.request_value = None
+        self.get_updates_request_value = None
+
+    def token(self, token: str):
+        self.token_value = token
+        return self
+
+    def request(self, request):
+        self.request_value = request
+        return self
+
+    def get_updates_request(self, request):
+        self.get_updates_request_value = request
+        return self
+
+    def proxy(self, _proxy):
+        raise AssertionError("builder.proxy should not be called when request is set")
+
+    def get_updates_proxy(self, _proxy):
+        raise AssertionError("builder.get_updates_proxy should not be called when request is set")
+
+    def build(self):
+        return self.app
+
+
+@pytest.mark.asyncio
+async def test_start_uses_request_proxy_without_builder_proxy(monkeypatch) -> None:
+    config = TelegramConfig(
+        enabled=True,
+        token="123:abc",
+        allow_from=["*"],
+        proxy="http://127.0.0.1:7890",
+    )
+    bus = MessageBus()
+    channel = TelegramChannel(config, bus)
+    app = _FakeApp(lambda: setattr(channel, "_running", False))
+    builder = _FakeBuilder(app)
+
+    monkeypatch.setattr("nanobot.channels.telegram.HTTPXRequest", _FakeHTTPXRequest)
+    monkeypatch.setattr(
+        "nanobot.channels.telegram.Application",
+        SimpleNamespace(builder=lambda: builder),
+    )
+
+    await channel.start()
+
+    assert len(_FakeHTTPXRequest.instances) == 1
+    assert _FakeHTTPXRequest.instances[0].kwargs["proxy"] == config.proxy
+    assert builder.request_value is _FakeHTTPXRequest.instances[0]
+    assert builder.get_updates_request_value is _FakeHTTPXRequest.instances[0]
+
+
+def test_derive_topic_session_key_uses_thread_id() -> None:
+    message = SimpleNamespace(
+        chat=SimpleNamespace(type="supergroup"),
+        chat_id=-100123,
+        message_thread_id=42,
+    )
+
+    assert TelegramChannel._derive_topic_session_key(message) == "telegram:-100123:topic:42"
+
+
+def test_get_extension_falls_back_to_original_filename() -> None:
+    channel = TelegramChannel(TelegramConfig(), MessageBus())
+
+    assert channel._get_extension("file", None, "report.pdf") == ".pdf"
+    assert channel._get_extension("file", None, "archive.tar.gz") == ".tar.gz"
+
+
+def test_is_allowed_accepts_legacy_telegram_id_username_formats() -> None:
+    channel = TelegramChannel(TelegramConfig(allow_from=["12345", "alice", "67890|bob"]), MessageBus())
+
+    assert channel.is_allowed("12345|carol") is True
+    assert channel.is_allowed("99999|alice") is True
+    assert channel.is_allowed("67890|bob") is True
+
+
+def test_is_allowed_rejects_invalid_legacy_telegram_sender_shapes() -> None:
+    channel = TelegramChannel(TelegramConfig(allow_from=["alice"]), MessageBus())
+
+    assert channel.is_allowed("attacker|alice|extra") is False
+    assert channel.is_allowed("not-a-number|alice") is False
+
+
+@pytest.mark.asyncio
+async def test_send_progress_keeps_message_in_topic() -> None:
+    config = TelegramConfig(enabled=True, token="123:abc", allow_from=["*"])
+    channel = TelegramChannel(config, MessageBus())
+    channel._app = _FakeApp(lambda: None)
+
+    await channel.send(
+        OutboundMessage(
+            channel="telegram",
+            chat_id="123",
+            content="hello",
+            metadata={"_progress": True, "message_thread_id": 42},
+        )
+    )
+
+    assert channel._app.bot.sent_messages[0]["message_thread_id"] == 42
+
+
+@pytest.mark.asyncio
+async def test_send_reply_infers_topic_from_message_id_cache() -> None:
+    config = TelegramConfig(enabled=True, token="123:abc", allow_from=["*"], reply_to_message=True)
+    channel = TelegramChannel(config, MessageBus())
+    channel._app = _FakeApp(lambda: None)
+    channel._message_threads[("123", 10)] = 42
+
+    await channel.send(
+        OutboundMessage(
+            channel="telegram",
+            chat_id="123",
+            content="hello",
+            metadata={"message_id": 10},
+        )
+    )
+
+    assert channel._app.bot.sent_messages[0]["message_thread_id"] == 42
+    assert channel._app.bot.sent_messages[0]["reply_parameters"].message_id == 10

tests/test_tool_validation.py

@@ -106,3 +106,234 @@ def test_exec_extract_absolute_paths_captures_posix_absolute_paths() -> None:
     paths = ExecTool._extract_absolute_paths(cmd)
     assert "/tmp/data.txt" in paths
     assert "/tmp/out.txt" in paths
+
+
+# --- cast_params tests ---
+
+
+class CastTestTool(Tool):
+    """Minimal tool for testing cast_params."""
+
+    def __init__(self, schema: dict[str, Any]) -> None:
+        self._schema = schema
+
+    @property
+    def name(self) -> str:
+        return "cast_test"
+
+    @property
+    def description(self) -> str:
+        return "test tool for casting"
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return self._schema
+
+    async def execute(self, **kwargs: Any) -> str:
+        return "ok"
+
+
+def test_cast_params_string_to_int() -> None:
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"count": {"type": "integer"}},
+        }
+    )
+    result = tool.cast_params({"count": "42"})
+    assert result["count"] == 42
+    assert isinstance(result["count"], int)
+
+
+def test_cast_params_string_to_number() -> None:
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"rate": {"type": "number"}},
+        }
+    )
+    result = tool.cast_params({"rate": "3.14"})
+    assert result["rate"] == 3.14
+    assert isinstance(result["rate"], float)
+
+
+def test_cast_params_string_to_bool() -> None:
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"enabled": {"type": "boolean"}},
+        }
+    )
+    assert tool.cast_params({"enabled": "true"})["enabled"] is True
+    assert tool.cast_params({"enabled": "false"})["enabled"] is False
+    assert tool.cast_params({"enabled": "1"})["enabled"] is True
+
+
+def test_cast_params_array_items() -> None:
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {
+                "nums": {"type": "array", "items": {"type": "integer"}},
+            },
+        }
+    )
+    result = tool.cast_params({"nums": ["1", "2", "3"]})
+    assert result["nums"] == [1, 2, 3]
+
+
+def test_cast_params_nested_object() -> None:
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {
+                "config": {
+                    "type": "object",
+                    "properties": {
+                        "port": {"type": "integer"},
+                        "debug": {"type": "boolean"},
+                    },
+                },
+            },
+        }
+    )
+    result = tool.cast_params({"config": {"port": "8080", "debug": "true"}})
+    assert result["config"]["port"] == 8080
+    assert result["config"]["debug"] is True
+
+
+def test_cast_params_bool_not_cast_to_int() -> None:
+    """Booleans should not be silently cast to integers."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"count": {"type": "integer"}},
+        }
+    )
+    result = tool.cast_params({"count": True})
+    assert result["count"] is True
+    errors = tool.validate_params(result)
+    assert any("count should be integer" in e for e in errors)
+
+
+def test_cast_params_preserves_empty_string() -> None:
+    """Empty strings should be preserved for string type."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"name": {"type": "string"}},
+        }
+    )
+    result = tool.cast_params({"name": ""})
+    assert result["name"] == ""
+
+
+def test_cast_params_bool_string_false() -> None:
+    """Test that 'false', '0', 'no' strings convert to False."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"flag": {"type": "boolean"}},
+        }
+    )
+    assert tool.cast_params({"flag": "false"})["flag"] is False
+    assert tool.cast_params({"flag": "False"})["flag"] is False
+    assert tool.cast_params({"flag": "0"})["flag"] is False
+    assert tool.cast_params({"flag": "no"})["flag"] is False
+    assert tool.cast_params({"flag": "NO"})["flag"] is False
+
+
+def test_cast_params_bool_string_invalid() -> None:
+    """Invalid boolean strings should not be cast."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"flag": {"type": "boolean"}},
+        }
+    )
+    # Invalid strings should be preserved (validation will catch them)
+    result = tool.cast_params({"flag": "random"})
+    assert result["flag"] == "random"
+    result = tool.cast_params({"flag": "maybe"})
+    assert result["flag"] == "maybe"
+
+
+def test_cast_params_invalid_string_to_int() -> None:
+    """Invalid strings should not be cast to integer."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"count": {"type": "integer"}},
+        }
+    )
+    result = tool.cast_params({"count": "abc"})
+    assert result["count"] == "abc"  # Original value preserved
+    result = tool.cast_params({"count": "12.5.7"})
+    assert result["count"] == "12.5.7"
+
+
+def test_cast_params_invalid_string_to_number() -> None:
+    """Invalid strings should not be cast to number."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"rate": {"type": "number"}},
+        }
+    )
+    result = tool.cast_params({"rate": "not_a_number"})
+    assert result["rate"] == "not_a_number"
+
+
+def test_validate_params_bool_not_accepted_as_number() -> None:
+    """Booleans should not pass number validation."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"rate": {"type": "number"}},
+        }
+    )
+    errors = tool.validate_params({"rate": False})
+    assert any("rate should be number" in e for e in errors)
+
+
+def test_cast_params_none_values() -> None:
+    """Test None handling for different types."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "count": {"type": "integer"},
+                "items": {"type": "array"},
+                "config": {"type": "object"},
+            },
+        }
+    )
+    result = tool.cast_params(
+        {
+            "name": None,
+            "count": None,
+            "items": None,
+            "config": None,
+        }
+    )
+    # None should be preserved for all types
+    assert result["name"] is None
+    assert result["count"] is None
+    assert result["items"] is None
+    assert result["config"] is None
+
+
+def test_cast_params_single_value_not_auto_wrapped_to_array() -> None:
+    """Single values should NOT be automatically wrapped into arrays."""
+    tool = CastTestTool(
+        {
+            "type": "object",
+            "properties": {"items": {"type": "array"}},
+        }
+    )
+    # Non-array values should be preserved (validation will catch them)
+    result = tool.cast_params({"items": 5})
+    assert result["items"] == 5  # Not wrapped to [5]
+    result = tool.cast_params({"items": "text"})
+    assert result["items"] == "text"  # Not wrapped to ["text"]