feat(multimodal): re-implement audio/video support on main

Re-implements PR #2908's generalized multimodal support on the
post-FSM main branch:

- Audio input: detects WAV/MP3/OGG/FLAC via magic bytes, sends
  input_audio blocks to compatible providers, falls back to
  [audio: path] placeholder when unsupported.
- Video input: sends video_url data-URI blocks to compatible
  providers, falls back to [video: path] placeholder.
- InputLimitsConfig: count limits (images/audios/videos) and byte
  limits per media type.
- AgentDefaults: pattern-matched vision_models, audio_models,
  video_models with supports_*() helpers.
- Provider retry: strips all media types (image_url, input_audio,
  video_url) on non-transient errors and retries once.
- Feishu: extracts media tags from post messages.
- Anthropic & OpenAI Responses converters handle audio/video.

Tests: 17 new multimodal tests + existing suite passes.

nanobot/agent/context.py

@@ -2,6 +2,7 @@
 
 import base64
 import mimetypes
+import os
 import platform
 from contextlib import suppress
 from importlib.resources import files as pkg_files
@@ -10,11 +11,16 @@ from typing import Any, Mapping, Sequence
 
 from nanobot.agent.memory import MemoryStore
 from nanobot.agent.skills import SkillsLoader
+from nanobot.config.schema import InputLimitsConfig
 from nanobot.session.goal_state import goal_state_runtime_lines
 from nanobot.utils.helpers import (
+    audio_format_for_api,
+    audio_mime_compat,
     current_time_str,
+    detect_audio_mime,
     detect_image_mime,
     truncate_text,
+    video_mime_compat,
 )
 from nanobot.utils.prompt_templates import render_template
 
@@ -28,11 +34,12 @@ class ContextBuilder:
     _MAX_HISTORY_CHARS = 32_000  # hard cap on recent history section size
     _RUNTIME_CONTEXT_END = "[/Runtime Context]"
 
-    def __init__(self, workspace: Path, timezone: str | None = None, disabled_skills: list[str] | None = None):
+    def __init__(self, workspace: Path, timezone: str | None = None, disabled_skills: list[str] | None = None, input_limits: InputLimitsConfig | None = None):
         self.workspace = workspace
         self.timezone = timezone
         self.memory = MemoryStore(workspace)
         self.skills = SkillsLoader(workspace, disabled_skills=set(disabled_skills) if disabled_skills else None)
+        self.input_limits = input_limits or InputLimitsConfig()
 
     def build_system_prompt(
         self,
@@ -142,6 +149,28 @@ class ContextBuilder:
                 return content.strip() == tpl.read_text(encoding="utf-8").strip()
         return False
 
+    @staticmethod
+    def _file_size_ok(p: Path, max_bytes: int) -> bool | None:
+        """Check file size via stat without reading into memory.
+
+        Returns True if size is within limit, False if oversized,
+        None if file cannot be stat'd (caller should try read_bytes instead).
+        """
+        try:
+            return os.stat(p).st_size <= max_bytes
+        except OSError:
+            return None
+
+    @staticmethod
+    def _encode_image_block(raw: bytes, mime: str, path: Path) -> dict[str, Any]:
+        """Base64-encode file bytes into an image_url content block."""
+        b64 = base64.b64encode(raw).decode()
+        return {
+            "type": "image_url",
+            "image_url": {"url": f"data:{mime};base64,{b64}"},
+            "_meta": {"path": str(path)},
+        }
+
     def build_messages(
         self,
         history: list[dict[str, Any]],
@@ -154,6 +183,9 @@ class ContextBuilder:
         sender_id: str | None = None,
         session_summary: str | None = None,
         session_metadata: Mapping[str, Any] | None = None,
+        supports_vision: bool | None = None,
+        supports_audio: bool | None = None,
+        supports_video: bool | None = None,
     ) -> list[dict[str, Any]]:
         """Build the complete message list for an LLM call."""
         extra = goal_state_runtime_lines(session_metadata)
@@ -164,7 +196,12 @@ class ContextBuilder:
             sender_id=sender_id,
             supplemental_lines=extra or None,
         )
-        user_content = self._build_user_content(current_message, media)
+        user_content = self._build_user_content(
+            current_message, media,
+            supports_vision=supports_vision,
+            supports_audio=supports_audio,
+            supports_video=supports_video,
+        )
 
         # Merge runtime context and user content into a single user message
         # to avoid consecutive same-role messages that some providers reject.
@@ -186,28 +223,171 @@ class ContextBuilder:
         messages.append({"role": current_role, "content": merged})
         return messages
 
-    def _build_user_content(self, text: str, media: list[str] | None) -> str | list[dict[str, Any]]:
-        """Build user message content with optional base64-encoded images."""
+    def _build_user_content(
+        self,
+        text: str,
+        media: list[str] | None,
+        *,
+        supports_vision: bool | None = None,
+        supports_audio: bool | None = None,
+        supports_video: bool | None = None,
+    ) -> str | list[dict[str, Any]]:
+        """Build user message content with optional media blocks.
+
+        Args:
+            text: The user text message.
+            media: List of file paths to media files.
+            supports_vision: True=model supports images, False=use placeholder,
+                             None=unconfigured (send images as before, let
+                             provider/retry handle degradation).
+            supports_audio: True=model supports native audio, False/None=skip
+                            (channel layer already transcribed).
+            supports_video: True=model supports native video, False/None=use
+                            [file: path] placeholder.
+        """
         if not media:
             return text
 
-        images = []
+        blocks: list[dict[str, Any]] = []
+        notes: list[str] = []
+        limits = self.input_limits
+
+        # Enforce image count limit
+        max_images = limits.max_input_images
+        image_count = 0
+        image_media = []
+        non_image_media = []
         for path in media:
+            p = Path(path)
+            guessed_mime = mimetypes.guess_type(path)[0] or ""
+            if guessed_mime.startswith("image/"):
+                image_count += 1
+                if image_count <= max_images:
+                    image_media.append(path)
+            else:
+                non_image_media.append(path)
+
+        if image_count > max_images:
+            extra = image_count - max_images
+            noun = "image" if extra == 1 else "images"
+            notes.append(
+                f"[Skipped {extra} {noun}: "
+                f"only the first {max_images} images are included]"
+            )
+
+        # Process images
+        for path in image_media:
             p = Path(path)
             if not p.is_file():
                 continue
-            raw = p.read_bytes()
-            mime = detect_image_mime(raw) or mimetypes.guess_type(path)[0]
-            if not mime or not mime.startswith("image/"):
+
+            # When explicitly marked as non-vision, downgrade to text placeholder
+            if supports_vision is False:
+                blocks.append({"type": "text", "text": f"[image: {p}]"})
                 continue
-            b64 = base64.b64encode(raw).decode()
-            images.append({
-                "type": "image_url",
-                "image_url": {"url": f"data:{mime};base64,{b64}"},
-                "_meta": {"path": str(p)},
-            })
 
-        if not images:
-            return text
-        return images + [{"type": "text", "text": text}]
+            size_ok = self._file_size_ok(p, limits.max_input_image_bytes)
+            if size_ok is False:
+                size_mb = limits.max_input_image_bytes // (1024 * 1024)
+                notes.append(f"[Skipped image: file too large ({p.name}, limit {size_mb} MB)]")
+                continue
+            try:
+                raw = p.read_bytes()
+            except OSError:
+                notes.append(f"[Skipped image: unable to read ({p.name or path})]")
+                continue
+            img_mime = detect_image_mime(raw[:32]) or mimetypes.guess_type(path)[0]
+            if not img_mime or not img_mime.startswith("image/"):
+                notes.append(f"[Skipped image: unsupported or invalid image format ({p.name})]")
+                continue
+            blocks.append(self._encode_image_block(raw, img_mime, p))
+
+        # Process non-image media (audio, video, unknown)
+        audio_count = 0
+        video_count = 0
+        for path in non_image_media:
+            p = Path(path)
+            if not p.is_file():
+                continue
+            guessed_mime = mimetypes.guess_type(path)[0] or ""
+            is_audio = guessed_mime.startswith("audio/")
+            is_video = guessed_mime.startswith("video/")
+
+            # Pre-check file size via stat to avoid reading oversized files into memory.
+            # Determine the relevant byte limit based on detected media type.
+            _size_limit = 0
+            if is_audio or is_video:
+                _size_limit = limits.max_input_audio_bytes if is_audio else limits.max_input_video_bytes
+            _stat_size_ok = self._file_size_ok(p, _size_limit) if _size_limit else None
+            if _stat_size_ok is False:
+                size_mb = _size_limit // (1024 * 1024)
+                label = "audio" if is_audio else "video"
+                notes.append(f"[Skipped {label}: file too large ({p.name}, limit {size_mb} MB)]")
+                continue
+
+            try:
+                raw = p.read_bytes()
+            except OSError:
+                notes.append(f"[Skipped file: unable to read ({p.name or path})]")
+                continue
+
+            # Audio detection: by magic bytes or by filename
+            # Always pass filename so fallback can match when magic bytes fail
+            audio_mime = detect_audio_mime(raw[:32], filename=path)
+            if audio_mime or is_audio:
+                if supports_audio is True and audio_mime_compat(audio_mime):
+                    audio_count += 1
+                    if audio_count > limits.max_input_audios:
+                        if audio_count == limits.max_input_audios + 1:
+                            notes.append(
+                                f"[Skipped audio: only {limits.max_input_audios} audio file(s) allowed]"
+                            )
+                        continue
+                    if len(raw) > limits.max_input_audio_bytes:
+                        size_mb = limits.max_input_audio_bytes // (1024 * 1024)
+                        notes.append(f"[Skipped audio: file too large ({p.name}, limit {size_mb} MB)]")
+                        continue
+                    b64 = base64.b64encode(raw).decode()
+                    blocks.append({
+                        "type": "input_audio",
+                        "input_audio": {"data": b64, "format": audio_format_for_api(audio_mime)},
+                        "_meta": {"path": str(p)},
+                    })
+                else:
+                    blocks.append({"type": "text", "text": f"[audio: {p}]"})
+                continue
+
+            # Video detection (already classified above)
+            if is_video:
+                if supports_video is True and video_mime_compat(guessed_mime):
+                    video_count += 1
+                    if video_count > limits.max_input_videos:
+                        if video_count == limits.max_input_videos + 1:
+                            notes.append(
+                                f"[Skipped video: only {limits.max_input_videos} video file(s) allowed]"
+                            )
+                        continue
+                    if len(raw) > limits.max_input_video_bytes:
+                        size_mb = limits.max_input_video_bytes // (1024 * 1024)
+                        notes.append(f"[Skipped video: file too large ({p.name}, limit {size_mb} MB)]")
+                        continue
+                    b64 = base64.b64encode(raw).decode()
+                    blocks.append({
+                        "type": "video_url",
+                        "video_url": {"url": f"data:{guessed_mime};base64,{b64}"},
+                        "_meta": {"path": str(p)},
+                    })
+                else:
+                    blocks.append({"type": "text", "text": f"[video: {p}]"})
+                continue
+
+            # Unknown files are silently ignored (preserves pre-multimodal behaviour)
+            continue
+
+        note_text = "\n".join(notes).strip()
+        text_block = text if not note_text else (f"{note_text}\n\n{text}" if text else note_text)
+
+        if not blocks:
+            return text_block
+        return blocks + [{"type": "text", "text": text_block}]
 

nanobot/agent/loop.py

@@ -190,6 +190,10 @@ class AgentLoop:
         model_preset: str | None = None,
         preset_snapshot_loader: preset_helpers.PresetSnapshotLoader | None = None,
         runtime_model_publisher: Callable[[str, str | None], None] | None = None,
+        input_limits: Any = None,
+        supports_vision: bool | None = None,
+        supports_audio: bool | None = None,
+        supports_video: bool | None = None,
     ):
         from nanobot.config.schema import ToolsConfig
 
@@ -227,6 +231,10 @@ class AgentLoop:
         self.tools_config = _tc
         self.web_config = _tc.web
         self.exec_config = _tc.exec
+        self.input_limits = input_limits or _tc.input_limits
+        self._supports_vision = supports_vision
+        self._supports_audio = supports_audio
+        self._supports_video = supports_video
         self._image_generation_provider_configs = dict(image_generation_provider_configs or {})
         if (
             image_generation_provider_config is not None
@@ -240,7 +248,7 @@ class AgentLoop:
         self._pending_turn_latency_ms: dict[str, int] = {}
         self._extra_hooks: list[AgentHook] = hooks or []
 
-        self.context = ContextBuilder(workspace, timezone=timezone, disabled_skills=disabled_skills)
+        self.context = ContextBuilder(workspace, timezone=timezone, disabled_skills=disabled_skills, input_limits=self.input_limits)
         self.sessions = session_manager or SessionManager(workspace)
         self._webui_turns = WebuiTurnCoordinator(
             bus=self.bus,
@@ -366,6 +374,10 @@ class AgentLoop:
             model_preset=defaults.model_preset,
             provider_snapshot_loader=provider_snapshot_loader,
             preset_snapshot_loader=preset_snapshot_loader,
+            input_limits=config.tools.input_limits,
+            supports_vision=defaults.supports_vision(defaults.model),
+            supports_audio=defaults.supports_audio(defaults.model),
+            supports_video=defaults.supports_video(defaults.model),
             **extra,
         )
 
@@ -594,6 +606,9 @@ class AgentLoop:
             sender_id=msg.sender_id,
             session_summary=pending_summary,
             session_metadata=session.metadata,
+            supports_vision=self._supports_vision,
+            supports_audio=self._supports_audio,
+            supports_video=self._supports_video,
         )
 
     async def _dispatch_command_inline(
@@ -1059,6 +1074,9 @@ class AgentLoop:
             sender_id=msg.sender_id,
             session_summary=pending,
             session_metadata=session.metadata,
+            supports_vision=self._supports_vision,
+            supports_audio=self._supports_audio,
+            supports_video=self._supports_video,
         )
         t_wall = time.time()
         final_content, _, all_msgs, stop_reason, _ = await self._run_agent_loop(

nanobot/channels/feishu.py

@@ -172,19 +172,22 @@ def _extract_element_content(element: dict) -> list[str]:
     return parts
 
 
-def _extract_post_content(content_json: dict) -> tuple[str, list[str]]:
-    """Extract text and image keys from Feishu post (rich text) message.
+def _extract_post_content(content_json: dict) -> tuple[str, list[str], list[dict]]:
+    """Extract text and media info from Feishu post (rich text) message.
 
     Handles three payload shapes:
     - Direct:    {"title": "...", "content": [[...]]}
     - Localized: {"zh_cn": {"title": "...", "content": [...]}}
     - Wrapped:   {"post": {"zh_cn": {"title": "...", "content": [...]}}}
+
+    Returns (text, image_keys, media_items) where media_items is a list of
+    {"tag": "media", "file_key": "..."} dicts for video/file attachments.
     """
 
-    def _parse_block(block: dict) -> tuple[str | None, list[str]]:
+    def _parse_block(block: dict) -> tuple[str | None, list[str], list[dict]]:
         if not isinstance(block, dict) or not isinstance(block.get("content"), list):
-            return None, []
-        texts, images = [], []
+            return None, [], []
+        texts, images, medias = [], [], []
         if title := block.get("title"):
             texts.append(title)
         for row in block["content"]:
@@ -204,43 +207,36 @@ def _extract_post_content(content_json: dict) -> tuple[str, list[str]]:
                     texts.append(f"\n```{lang}\n{code_text}\n```\n")
                 elif tag == "img" and (key := el.get("image_key")):
                     images.append(key)
-        return (" ".join(texts).strip() or None), images
+                elif tag == "media" and el.get("file_key"):
+                    medias.append({"tag": "media", "file_key": el["file_key"]})
+        return (" ".join(texts).strip() or None), images, medias
 
     # Unwrap optional {"post": ...} envelope
     root = content_json
     if isinstance(root, dict) and isinstance(root.get("post"), dict):
         root = root["post"]
     if not isinstance(root, dict):
-        return "", []
+        return "", [], []
 
     # Direct format
     if "content" in root:
-        text, imgs = _parse_block(root)
-        if text or imgs:
-            return text or "", imgs
+        text, imgs, medias = _parse_block(root)
+        if text or imgs or medias:
+            return text or "", imgs, medias
 
     # Localized: prefer known locales, then fall back to any dict child
     for key in ("zh_cn", "en_us", "ja_jp"):
         if key in root:
-            text, imgs = _parse_block(root[key])
-            if text or imgs:
-                return text or "", imgs
+            text, imgs, medias = _parse_block(root[key])
+            if text or imgs or medias:
+                return text or "", imgs, medias
     for val in root.values():
         if isinstance(val, dict):
-            text, imgs = _parse_block(val)
-            if text or imgs:
-                return text or "", imgs
+            text, imgs, medias = _parse_block(val)
+            if text or imgs or medias:
+                return text or "", imgs, medias
 
-    return "", []
-
-
-def _extract_post_text(content_json: dict) -> str:
-    """Extract plain text from Feishu post (rich text) message content.
-
-    Legacy wrapper for _extract_post_content, returns only text.
-    """
-    text, _ = _extract_post_content(content_json)
-    return text
+    return "", [], []
 
 
 class FeishuConfig(Base):
@@ -1156,7 +1152,7 @@ class FeishuChannel(BaseChannel):
             if msg_type == "text":
                 text = content_json.get("text", "").strip()
             elif msg_type == "post":
-                text, _ = _extract_post_content(content_json)
+                text, _, _ = _extract_post_content(content_json)
                 text = text.strip()
             else:
                 text = ""
@@ -1751,7 +1747,7 @@ class FeishuChannel(BaseChannel):
                     content_parts.append(text)
 
             elif msg_type == "post":
-                text, image_keys = _extract_post_content(content_json)
+                text, image_keys, media_items = _extract_post_content(content_json)
                 if text:
                     content_parts.append(text)
                 # Download images embedded in post
@@ -1762,6 +1758,14 @@ class FeishuChannel(BaseChannel):
                     if file_path:
                         media_paths.append(file_path)
                     content_parts.append(content_text)
+                # Download media (video/file) embedded in post
+                for media_item in media_items:
+                    file_path, content_text = await self._download_and_save_media(
+                        "media", media_item, message_id
+                    )
+                    if file_path:
+                        media_paths.append(file_path)
+                    content_parts.append(content_text)
 
             elif msg_type in ("image", "audio", "file", "media"):
                 file_path, content_text = await self._download_and_save_media(

nanobot/config/schema.py

@@ -155,8 +155,35 @@ class AgentDefaults(Base):
         validation_alias=AliasChoices("consolidationRatio"),
         serialization_alias="consolidationRatio",
     )  # Consolidation target ratio (0.5 = 50% of budget retained after compression)
+    vision_models: list[str] = Field(default_factory=list)  # Models that support image input
+    audio_models: list[str] = Field(default_factory=list)  # Models that support native audio input
+    video_models: list[str] = Field(default_factory=list)  # Models that support native video input
     dream: DreamConfig = Field(default_factory=DreamConfig)
 
+    @staticmethod
+    def _bare_model(model: str) -> str:
+        """Strip provider prefix, e.g. 'openai/gpt-4o' -> 'gpt-4o'."""
+        return model.split("/", 1)[-1].lower() if "/" in model else model.lower()
+
+    def _supports_capability(self, model: str, patterns: list[str]) -> bool | None:
+        """Check if model matches any pattern. Returns None if patterns is empty."""
+        if not patterns:
+            return None
+        bare = self._bare_model(model)
+        return any(p.lower() in bare for p in patterns)
+
+    def supports_vision(self, model: str) -> bool | None:
+        """Check if model supports vision. None if unconfigured."""
+        return self._supports_capability(model, self.vision_models)
+
+    def supports_audio(self, model: str) -> bool | None:
+        """Check if model supports native audio. None if unconfigured."""
+        return self._supports_capability(model, self.audio_models)
+
+    def supports_video(self, model: str) -> bool | None:
+        """Check if model supports native video. None if unconfigured."""
+        return self._supports_capability(model, self.video_models)
+
 
 class AgentsConfig(Base):
     """Agent configuration."""
@@ -258,6 +285,17 @@ class MCPServerConfig(Base):
     enabled_tools: list[str] = Field(default_factory=lambda: ["*"])  # Only register these tools; accepts raw MCP names or wrapped mcp_<server>_<tool> names; ["*"] = all tools; [] = no tools
 
 
+class InputLimitsConfig(Base):
+    """Limits for user-provided multimodal inputs."""
+
+    max_input_images: int = 3
+    max_input_image_bytes: int = 10 * 1024 * 1024  # 10 MB
+    max_input_audios: int = 1
+    max_input_audio_bytes: int = 10 * 1024 * 1024  # 10 MB
+    max_input_videos: int = 1
+    max_input_video_bytes: int = 20 * 1024 * 1024  # 20 MB
+
+
 def _lazy_default(module_path: str, class_name: str) -> Any:
     """Deferred import helper for ToolsConfig default factories."""
     import importlib
@@ -279,6 +317,7 @@ class ToolsConfig(Base):
     image_generation: ImageGenerationToolConfig = Field(
         default_factory=lambda: _lazy_default("nanobot.agent.tools.image_generation", "ImageGenerationToolConfig"),
     )
+    input_limits: InputLimitsConfig = Field(default_factory=InputLimitsConfig)
     restrict_to_workspace: bool = False  # restrict all tool access to workspace directory
     mcp_servers: dict[str, MCPServerConfig] = Field(default_factory=dict)
     ssrf_whitelist: list[str] = Field(default_factory=list)  # CIDR ranges to exempt from SSRF blocking (e.g. ["100.64.0.0/10"] for Tailscale)

nanobot/providers/anthropic_provider.py

@@ -212,7 +212,7 @@ class AnthropicProvider(LLMProvider):
 
     @staticmethod
     def _convert_user_content(content: Any) -> Any:
-        """Convert user message content, translating image_url blocks."""
+        """Convert user message content, translating image_url and input_audio blocks."""
         if isinstance(content, str) or content is None:
             return content or "(empty)"
         if not isinstance(content, list):
@@ -228,6 +228,14 @@ class AnthropicProvider(LLMProvider):
                 if converted:
                     result.append(converted)
                 continue
+            if item.get("type") == "input_audio":
+                # Anthropic doesn't support native audio → text placeholder
+                result.append(LLMProvider._media_placeholder("input_audio", item))
+                continue
+            if item.get("type") == "video_url":
+                # Anthropic doesn't support native video → text placeholder
+                result.append(LLMProvider._media_placeholder("video_url", item))
+                continue
             result.append(item)
         return result or "(empty)"
 

nanobot/providers/base.py

@@ -13,8 +13,6 @@ from typing import Any
 
 from loguru import logger
 
-from nanobot.utils.helpers import image_placeholder_text
-
 
 @dataclass
 class ToolCallRequest:
@@ -439,9 +437,23 @@ class LLMProvider(ABC):
 
         return merged
 
+    _MEDIA_LABEL_MAP = {"image_url": "image", "input_audio": "audio", "video_url": "video"}
+    _STRIP_MEDIA_TYPES = frozenset({"image_url", "input_audio", "video_url"})
+
     @staticmethod
-    def _strip_image_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]] | None:
-        """Replace image_url blocks with text placeholder. Returns None if no images found."""
+    def _media_placeholder(btype: str, block: dict[str, Any]) -> dict[str, str]:
+        """Build a text placeholder for a media block."""
+        path = (block.get("_meta") or {}).get("path", "")
+        label = LLMProvider._MEDIA_LABEL_MAP.get(btype, "media")
+        text = f"[{label}: {path}]" if path else f"[{label}]"
+        return {"type": "text", "text": text}
+
+    @staticmethod
+    def _strip_media_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]] | None:
+        """Replace image_url, input_audio, and video_url blocks with text placeholders.
+
+        Returns None if no media blocks were found (no changes needed).
+        """
         found = False
         result = []
         for msg in messages:
@@ -449,10 +461,8 @@ class LLMProvider(ABC):
             if isinstance(content, list):
                 new_content = []
                 for b in content:
-                    if isinstance(b, dict) and b.get("type") == "image_url":
-                        path = (b.get("_meta") or {}).get("path", "")
-                        placeholder = image_placeholder_text(path, empty="[image omitted]")
-                        new_content.append({"type": "text", "text": placeholder})
+                    if isinstance(b, dict) and b.get("type") in LLMProvider._STRIP_MEDIA_TYPES:
+                        new_content.append(LLMProvider._media_placeholder(b["type"], b))
                         found = True
                     else:
                         new_content.append(b)
@@ -462,8 +472,13 @@ class LLMProvider(ABC):
         return result if found else None
 
     @staticmethod
-    def _strip_image_content_inplace(messages: list[dict[str, Any]]) -> bool:
-        """Replace image_url blocks with text placeholder *in-place*.
+    def _strip_image_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]] | None:
+        """Replace image_url blocks with text placeholder. Returns None if no images found."""
+        return LLMProvider._strip_media_content(messages)
+
+    @staticmethod
+    def _strip_media_content_inplace(messages: list[dict[str, Any]]) -> bool:
+        """Replace media blocks with text placeholder *in-place*.
 
         Mutates the content lists of the original message dicts so that
         callers holding references to those dicts also see the stripped
@@ -474,13 +489,16 @@ class LLMProvider(ABC):
             content = msg.get("content")
             if isinstance(content, list):
                 for i, b in enumerate(content):
-                    if isinstance(b, dict) and b.get("type") == "image_url":
-                        path = (b.get("_meta") or {}).get("path", "")
-                        placeholder = image_placeholder_text(path, empty="[image omitted]")
-                        content[i] = {"type": "text", "text": placeholder}
+                    if isinstance(b, dict) and b.get("type") in LLMProvider._STRIP_MEDIA_TYPES:
+                        content[i] = LLMProvider._media_placeholder(b["type"], b)
                         found = True
         return found
 
+    @staticmethod
+    def _strip_image_content_inplace(messages: list[dict[str, Any]]) -> bool:
+        """Replace image_url blocks with text placeholder *in-place*."""
+        return LLMProvider._strip_media_content_inplace(messages)
+
     async def _safe_chat(self, **kwargs: Any) -> LLMResponse:
         """Call chat() and convert unexpected exceptions to error responses."""
         try:
@@ -738,18 +756,18 @@ class LLMProvider(ABC):
                 identical_error_count = 1 if error_key else 0
 
             if not self._is_transient_response(response):
-                stripped = self._strip_image_content(original_messages)
+                stripped = self._strip_media_content(original_messages)
                 if stripped is not None and stripped != kw["messages"]:
                     logger.warning(
-                        "Non-transient LLM error with image content, retrying without images"
+                        "Non-transient LLM error with media content, retrying without media"
                     )
                     retry_kw = dict(kw)
                     retry_kw["messages"] = stripped
                     result = await call(**retry_kw)
-                    # Permanently strip images from the original messages so
+                    # Permanently strip media from the original messages so
                     # subsequent iterations do not repeat the error-retry cycle.
                     if result.finish_reason != "error":
-                        self._strip_image_content_inplace(original_messages)
+                        self._strip_media_content_inplace(original_messages)
                     return result
                 return response
 

nanobot/providers/openai_responses/converters.py

@@ -5,6 +5,8 @@ from __future__ import annotations
 import json
 from typing import Any
 
+from nanobot.providers.base import LLMProvider
+
 
 def convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]:
     """Convert Chat Completions messages to Responses API input items.
@@ -58,8 +60,10 @@ def convert_messages(messages: list[dict[str, Any]]) -> tuple[str, list[dict[str
 def convert_user_message(content: Any) -> dict[str, Any]:
     """Convert a user message's content to Responses API format.
 
-    Handles plain strings, ``text`` blocks -> ``input_text``, and
-    ``image_url`` blocks -> ``input_image``.
+    Handles plain strings, ``text`` blocks -> ``input_text``,
+    ``image_url`` blocks -> ``input_image``, and ``input_audio`` blocks.
+    ``video_url`` is downgraded to a text placeholder because Codex does
+    not support native video.
     """
     if isinstance(content, str):
         return {"role": "user", "content": [{"type": "input_text", "text": content}]}
@@ -74,6 +78,18 @@ def convert_user_message(content: Any) -> dict[str, Any]:
                 url = (item.get("image_url") or {}).get("url")
                 if url:
                     converted.append({"type": "input_image", "image_url": url, "detail": "auto"})
+            elif item.get("type") == "input_audio":
+                audio_info = item.get("input_audio") or {}
+                audio_data = audio_info.get("data")
+                if audio_data:
+                    converted.append({
+                        "type": "input_audio",
+                        "input_audio": {"data": audio_data, "format": audio_info.get("format", "wav")},
+                    })
+            elif item.get("type") == "video_url":
+                # Codex doesn't support native video → text placeholder
+                placeholder = LLMProvider._media_placeholder("video_url", item)
+                converted.append({"type": "input_text", "text": placeholder["text"]})
         if converted:
             return {"role": "user", "content": converted}
     return {"role": "user", "content": [{"type": "input_text", "text": ""}]}

nanobot/utils/helpers.py

@@ -171,6 +171,79 @@ def detect_image_mime(data: bytes) -> str | None:
     return None
 
 
+# Audio formats supported by OpenAI input_audio block
+_AUDIO_MIME_COMPAT = {"audio/wav", "audio/mpeg", "audio/mp3", "audio/aac",
+                      "audio/ogg", "audio/flac", "audio/x-m4a", "audio/mp4"}
+
+# Map MIME types to the format token expected by OpenAI-compatible input_audio APIs.
+_AUDIO_FORMAT_MAP: dict[str, str] = {
+    "audio/wav": "wav",
+    "audio/x-wav": "wav",
+    "audio/mpeg": "mp3",
+    "audio/mp3": "mp3",
+    "audio/aac": "aac",
+    "audio/ogg": "ogg",
+    "audio/flac": "flac",
+    "audio/x-m4a": "m4a",
+    "audio/mp4": "m4a",
+}
+
+
+def detect_audio_mime(data: bytes, filename: str = "") -> str | None:
+    """Detect audio MIME type from magic bytes; fallback to filename guess."""
+    if data[:4] == b"RIFF" and data[8:12] == b"WAVE":
+        return "audio/wav"
+    if data[:2] in (b"\xff\xfb", b"\xff\xf3", b"\xff\xf2", b"\xff\xfa"):
+        return "audio/mpeg"
+    if data[:4] == b"fLaC":
+        return "audio/flac"
+    if data[:4] == b"OggS":
+        return "audio/ogg"
+    if len(data) > 8 and data[4:8] == b"ftyp":
+        # Only claim audio for M4A-specific brands; avoid matching MP4 video.
+        brand = data[8:12]
+        if brand in (b"M4A ", b"M4AB", b"M4AC"):
+            return "audio/x-m4a"
+    if filename:
+        import mimetypes as _mt
+        guessed = _mt.guess_type(filename)[0]
+        if guessed and guessed.startswith("audio/"):
+            return guessed
+    return None
+
+
+def audio_mime_compat(mime: str | None) -> bool:
+    """Check if the audio MIME is compatible with OpenAI input_audio block."""
+    if not mime:
+        return False
+    return mime in _AUDIO_MIME_COMPAT
+
+
+def audio_format_for_api(mime: str) -> str:
+    """Convert an audio MIME type to the format token expected by the API.
+
+    Falls back to the subtype portion of the MIME (e.g. "x-m4a" from
+    "audio/x-m4a") when no explicit mapping exists.
+    """
+    if not mime:
+        return "wav"
+    return _AUDIO_FORMAT_MAP.get(mime, mime.split("/")[-1])
+
+
+# Video formats commonly supported by LLM APIs (data URI inline)
+_VIDEO_MIME_COMPAT = {
+    "video/mp4", "video/quicktime", "video/x-m4v",
+    "video/webm", "video/x-matroska",
+}
+
+
+def video_mime_compat(mime: str | None) -> bool:
+    """Check if the video MIME is in the commonly-supported set."""
+    if not mime:
+        return False
+    return mime in _VIDEO_MIME_COMPAT
+
+
 def build_image_content_blocks(
     raw: bytes, mime: str, path: str, label: str
 ) -> list[dict[str, Any]]:

tests/agent/test_multimodal.py

@@ -0,0 +1,178 @@
+from pathlib import Path
+
+from nanobot.agent.context import ContextBuilder
+from nanobot.config.schema import InputLimitsConfig
+from nanobot.utils.helpers import detect_audio_mime, video_mime_compat
+
+PNG_BYTES = (
+    b"\x89PNG\r\n\x1a\n"
+    b"\x00\x00\x00\rIHDR"
+    b"\x00\x00\x00\x01\x00\x00\x00\x01\x08\x02\x00\x00\x00"
+    b"\x90wS\xde"
+    b"\x00\x00\x00\x0cIDATx\x9cc``\x00\x00\x00\x04\x00\x01"
+    b"\x0b\x0e-\xb4"
+    b"\x00\x00\x00\x00IEND\xaeB`\x82"
+)
+
+WAV_BYTES = b"RIFF\x24\x00\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x01\x00"
+
+
+def _builder(tmp_path: Path, input_limits: InputLimitsConfig | None = None) -> ContextBuilder:
+    return ContextBuilder(tmp_path, input_limits=input_limits)
+
+
+class TestAudioDetection:
+    def test_detect_wav_from_magic_bytes(self) -> None:
+        assert detect_audio_mime(WAV_BYTES) == "audio/wav"
+
+    def test_detect_mp3_from_magic_bytes(self) -> None:
+        mp3 = b"\xff\xfb\x90\x00"
+        assert detect_audio_mime(mp3) == "audio/mpeg"
+
+    def test_detect_fallback_to_filename(self) -> None:
+        assert detect_audio_mime(b"unknown", filename="song.mp3") == "audio/mpeg"
+
+    def test_returns_none_for_non_audio(self) -> None:
+        assert detect_audio_mime(PNG_BYTES) is None
+
+
+class TestVideoMimeCompat:
+    def test_mp4_is_compatible(self) -> None:
+        assert video_mime_compat("video/mp4") is True
+
+    def test_unknown_is_not_compatible(self) -> None:
+        assert video_mime_compat("video/avi") is False
+
+    def test_none_is_not_compatible(self) -> None:
+        assert video_mime_compat(None) is False
+
+
+class TestBuildUserContentMultimodal:
+    def test_audio_block_when_supported(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        path = tmp_path / "voice.wav"
+        path.write_bytes(WAV_BYTES)
+
+        content = builder._build_user_content("transcribe", [str(path)], supports_audio=True)
+
+        assert isinstance(content, list)
+        audio_blocks = [b for b in content if b.get("type") == "input_audio"]
+        assert len(audio_blocks) == 1
+        assert audio_blocks[0]["input_audio"]["format"] == "wav"
+
+    def test_audio_placeholder_when_not_supported(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        path = tmp_path / "voice.wav"
+        path.write_bytes(WAV_BYTES)
+
+        content = builder._build_user_content("transcribe", [str(path)], supports_audio=False)
+
+        assert isinstance(content, list)
+        assert any("[audio:" in b.get("text", "") for b in content)
+
+    def test_video_block_when_supported(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        path = tmp_path / "clip.mp4"
+        path.write_bytes(b"\x00" * 64)
+
+        content = builder._build_user_content("describe", [str(path)], supports_video=True)
+
+        assert isinstance(content, list)
+        video_blocks = [b for b in content if b.get("type") == "video_url"]
+        assert len(video_blocks) == 1
+        assert video_blocks[0]["video_url"]["url"].startswith("data:video/mp4;base64,")
+
+    def test_video_placeholder_when_not_supported(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        path = tmp_path / "clip.mp4"
+        path.write_bytes(b"\x00" * 64)
+
+        content = builder._build_user_content("describe", [str(path)], supports_video=False)
+
+        assert isinstance(content, list)
+        assert any("[video:" in b.get("text", "") for b in content)
+
+    def test_vision_fallback_downgrades_image(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        path = tmp_path / "pic.png"
+        path.write_bytes(PNG_BYTES)
+
+        content = builder._build_user_content("look", [str(path)], supports_vision=False)
+
+        assert isinstance(content, list)
+        assert any("[image:" in b.get("text", "") for b in content)
+        assert not any(b.get("type") == "image_url" for b in content)
+
+    def test_image_limit_count(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        paths = []
+        for i in range(5):
+            path = tmp_path / f"img{i}.png"
+            path.write_bytes(PNG_BYTES)
+            paths.append(str(path))
+
+        content = builder._build_user_content("describe", paths)
+
+        assert isinstance(content, list)
+        image_count = sum(1 for b in content if b.get("type") == "image_url")
+        assert image_count == 3  # default max_input_images
+        assert any("only the first 3 images" in b.get("text", "") for b in content)
+
+    def test_image_limit_bytes(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        big = tmp_path / "big.png"
+        big.write_bytes(PNG_BYTES + b"x" * builder.input_limits.max_input_image_bytes)
+
+        content = builder._build_user_content("analyze", [str(big)])
+
+        assert isinstance(content, str)
+        assert "file too large" in content
+
+    def test_audio_limit_count(self, tmp_path: Path) -> None:
+        limits = InputLimitsConfig(max_input_audios=1)
+        builder = _builder(tmp_path, input_limits=limits)
+        for i in range(2):
+            path = tmp_path / f"snd{i}.wav"
+            path.write_bytes(WAV_BYTES)
+
+        content = builder._build_user_content(
+            "compare", [str(tmp_path / "snd0.wav"), str(tmp_path / "snd1.wav")], supports_audio=True
+        )
+
+        assert isinstance(content, list)
+        audio_count = sum(1 for b in content if b.get("type") == "input_audio")
+        assert audio_count == 1
+        assert any("only 1 audio" in b.get("text", "") for b in content)
+
+    def test_audio_limit_bytes(self, tmp_path: Path) -> None:
+        limits = InputLimitsConfig(max_input_audio_bytes=32)
+        builder = _builder(tmp_path, input_limits=limits)
+        path = tmp_path / "big.wav"
+        path.write_bytes(WAV_BYTES + b"x" * 64)
+
+        content = builder._build_user_content("analyze", [str(path)], supports_audio=True)
+
+        assert isinstance(content, str)
+        assert "file too large" in content
+
+    def test_mixed_media_types(self, tmp_path: Path) -> None:
+        builder = _builder(tmp_path)
+        img = tmp_path / "pic.png"
+        img.write_bytes(PNG_BYTES)
+        snd = tmp_path / "voice.wav"
+        snd.write_bytes(WAV_BYTES)
+        vid = tmp_path / "clip.mp4"
+        vid.write_bytes(b"\x00" * 64)
+
+        content = builder._build_user_content(
+            "analyze",
+            [str(img), str(snd), str(vid)],
+            supports_vision=True,
+            supports_audio=True,
+            supports_video=True,
+        )
+
+        assert isinstance(content, list)
+        assert any(b.get("type") == "image_url" for b in content)
+        assert any(b.get("type") == "input_audio" for b in content)
+        assert any(b.get("type") == "video_url" for b in content)

tests/channels/test_feishu_post_content.py

@@ -27,10 +27,11 @@ def test_extract_post_content_supports_post_wrapper_shape() -> None:
         }
     }
 
-    text, image_keys = _extract_post_content(payload)
+    text, image_keys, media_items = _extract_post_content(payload)
 
     assert text == "日报 完成"
     assert image_keys == ["img_1"]
+    assert media_items == []
 
 
 def test_extract_post_content_keeps_direct_shape_behavior() -> None:
@@ -45,10 +46,29 @@ def test_extract_post_content_keeps_direct_shape_behavior() -> None:
         ],
     }
 
-    text, image_keys = _extract_post_content(payload)
+    text, image_keys, media_items = _extract_post_content(payload)
 
     assert text == "Daily report"
     assert image_keys == ["img_a", "img_b"]
+    assert media_items == []
+
+
+def test_extract_post_content_extracts_media_tags() -> None:
+    payload = {
+        "title": "Video",
+        "content": [
+            [
+                {"tag": "text", "text": "see this"},
+                {"tag": "media", "file_key": "vid_1"},
+            ]
+        ],
+    }
+
+    text, image_keys, media_items = _extract_post_content(payload)
+
+    assert text == "Video see this"
+    assert image_keys == []
+    assert media_items == [{"tag": "media", "file_key": "vid_1"}]
 
 
 def test_register_optional_event_keeps_builder_when_method_missing() -> None:

tests/providers/test_provider_retry.py

@@ -242,7 +242,7 @@ async def test_image_fallback_returns_error_on_second_failure() -> None:
 
 @pytest.mark.asyncio
 async def test_image_fallback_without_meta_uses_default_placeholder() -> None:
-    """When _meta is absent, fallback placeholder is '[image omitted]'."""
+    """When _meta is absent, fallback placeholder is '[image]'."""
     provider = ScriptedProvider([
         LLMResponse(content="error", finish_reason="error"),
         LLMResponse(content="ok"),
@@ -256,7 +256,7 @@ async def test_image_fallback_without_meta_uses_default_placeholder() -> None:
     for msg in msgs_on_retry:
         content = msg.get("content")
         if isinstance(content, list):
-            assert any("[image omitted]" in (b.get("text") or "") for b in content)
+            assert any("[image]" in (b.get("text") or "") for b in content)
 
 
 @pytest.mark.asyncio