Merge branch 'main' of https://github.com/kunalk16/nanobot into feat-support-azure-openai

README.md

@@ -664,6 +664,7 @@ 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 |
 |----------|---------|-------------|

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/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/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."""
 
@@ -105,7 +84,7 @@ class DiscordChannel(BaseChannel):
         headers = {"Authorization": f"Bot {self.config.token}"}
 
         try:
-            chunks = _split_message(msg.content or "")
+            chunks = split_message(msg.content or "", MAX_MESSAGE_LEN)
             if not chunks:
                 return
 

nanobot/channels/feishu.py

@@ -472,8 +472,124 @@ 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",
@@ -682,21 +798,46 @@ 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)
 

nanobot/channels/telegram.py

@@ -14,6 +14,9 @@ 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 _markdown_to_telegram_html(text: str) -> str:
@@ -79,26 +82,6 @@ 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.
@@ -273,8 +256,8 @@ class TelegramChannel(BaseChannel):
         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):
+
+            for chunk in split_message(msg.content, TELEGRAM_MAX_MESSAGE_LEN):
                 try:
                     html = _markdown_to_telegram_html(chunk)
                     if is_progress and draft_id:

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
@@ -200,8 +212,6 @@ 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
 
@@ -214,6 +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",
@@ -235,6 +246,7 @@ def _make_provider(config: Config):
             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):
@@ -540,9 +552,13 @@ 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):
@@ -279,12 +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/litellm_provider.py

@@ -8,6 +8,7 @@ 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
@@ -255,20 +256,37 @@ 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 +298,11 @@ 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",
@@ -90,17 +89,15 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         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,
@@ -112,16 +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,
@@ -129,10 +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",
@@ -150,7 +145,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         strip_model_prefix=False,
         model_overrides=(),
     ),
-
     # VolcEngine (火山引擎): OpenAI-compatible gateway
     ProviderSpec(
         name="volcengine",
@@ -168,9 +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",
@@ -189,7 +181,6 @@ PROVIDERS: tuple[ProviderSpec, ...] = (
         model_overrides=(),
         supports_prompt_caching=True,
     ),
-
     # OpenAI: LiteLLM recognizes "gpt-*" natively, no prefix needed.
     ProviderSpec(
         name="openai",
@@ -207,14 +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,
@@ -224,16 +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,
@@ -243,17 +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,
@@ -263,15 +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,
@@ -281,7 +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.
@@ -290,11 +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="",
@@ -303,14 +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,
@@ -321,7 +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.
@@ -330,22 +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(
@@ -353,7 +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,
@@ -364,9 +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(
@@ -374,20 +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(
@@ -395,8 +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,
@@ -413,6 +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."""
@@ -428,7 +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)
@@ -34,6 +47,38 @@ 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_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