versioning: 0.0.6, ux: move buttons, feat: add cloud providers, feat: increese tool call limit

traderai/agent.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import json
 import re
 from collections.abc import AsyncIterator
+from contextlib import nullcontext
 from typing import Any
 
 import httpx
@@ -38,6 +39,8 @@ class OllamaAgent:
         memory: MemoryStore | None = None,
         user_name: str | None = None,
         num_ctx: int | None = None,
+        provider: str = "ollama",
+        api_key: str | None = None,
     ) -> None:
         self.base_url = base_url.rstrip("/")
         self.model = model
@@ -45,9 +48,13 @@ class OllamaAgent:
         self.memory = memory
         self.user_name = user_name
         self.num_ctx = num_ctx
+        self.provider = provider.strip().casefold() or "ollama"
+        self.api_key = api_key
         self.thread_messages: dict[str, list[dict[str, Any]]] = {}
 
     async def health(self) -> dict[str, Any]:
+        if self.provider == "openai":
+            return await self._openai_health()
         try:
             async with httpx.AsyncClient(timeout=3) as client:
                 response = await client.get(f"{self.base_url}/api/tags")
@@ -77,60 +84,74 @@ class OllamaAgent:
         if not health["online"]:
             raise OllamaUnavailable(health["message"])
 
-    async def chat(self, content: str, thread_id: str | None = DEFAULT_THREAD_ID) -> dict[str, Any]:
+    async def chat(
+        self,
+        content: str,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+        images: list[dict[str, Any]] | None = None,
+    ) -> dict[str, Any]:
         await self.ensure_available()
         resolved_thread_id = self._thread_id(thread_id)
         messages = self._messages_for_thread(resolved_thread_id)
         previous_interaction = self.memory.last_interaction(resolved_thread_id) if self.memory else None
+        normalized_images = self._normalize_images(images)
+        prompt_text = self._prompt_text(content, len(normalized_images))
+        memory_content = self._conversation_content(content, len(normalized_images))
         if self.memory:
-            self.memory.add_conversation("user", content, resolved_thread_id)
-            await self._title_first_message(resolved_thread_id, content, previous_interaction)
-        messages.append({"role": "user", "content": content})
+            self.memory.add_conversation("user", memory_content, resolved_thread_id)
+            await self._title_first_message(resolved_thread_id, prompt_text, previous_interaction)
+        messages.append(self._user_message(prompt_text, normalized_images))
         last_tool_results: list[dict[str, Any]] = []
-        for _ in range(5):
-            try:
-                response = await self._ollama_chat(
-                    content,
-                    messages,
-                    previous_interaction=previous_interaction,
-                    thread_id=resolved_thread_id,
-                )
-            except Exception as exc:
-                if not last_tool_results:
-                    raise
-                answer = self._tool_result_fallback(
-                    last_tool_results,
-                    f"The local model stopped after the tool call: {exc}",
-                )
-                messages.append({"role": "assistant", "content": answer})
-                if self.memory:
-                    self.memory.add_conversation("assistant", answer, resolved_thread_id)
-                return {"message": answer, "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
-            message = response.get("message") or {}
-            tool_calls = message.get("tool_calls") or []
-            if not tool_calls:
-                answer = message.get("content", "")
-                if not answer.strip():
-                    answer = self._empty_response_fallback(last_tool_results)
-                messages.append({"role": "assistant", "content": answer})
-                if self.memory:
-                    self.memory.add_conversation("assistant", answer, resolved_thread_id)
-                return {"message": answer, "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
-
-            messages.append(message)
-            for call in tool_calls:
-                name, arguments = self._extract_call(call)
-                result = await self.tools.execute(name, arguments)
-                last_tool_results.append({"tool": name, "result": result})
-                messages.append({"role": "tool", "tool_name": name, "content": json.dumps(result)})
+        image_scope = self.tools.chat_image_scope(normalized_images) if hasattr(self.tools, "chat_image_scope") else nullcontext()
+        with image_scope:
+            for _ in range(10):
+                try:
+                    response = await self._chat_once(
+                        prompt_text,
+                        messages,
+                        previous_interaction=previous_interaction,
+                        thread_id=resolved_thread_id,
+                    )
+                except Exception as exc:
+                    if not last_tool_results:
+                        raise
+                    answer = self._tool_result_fallback(
+                        last_tool_results,
+                        f"The {self._provider_label()} stopped after the tool call: {exc}",
+                    )
+                    messages.append({"role": "assistant", "content": answer})
+                    if self.memory:
+                        self.memory.add_conversation("assistant", answer, resolved_thread_id)
+                    return {"message": answer, "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
+                message = response.get("message") or {}
+                tool_calls = message.get("tool_calls") or []
+                if not tool_calls:
+                    answer = message.get("content", "")
+                    if not answer.strip():
+                        answer = self._empty_response_fallback(last_tool_results)
+                    messages.append({"role": "assistant", "content": answer})
+                    if self.memory:
+                        self.memory.add_conversation("assistant", answer, resolved_thread_id)
+                    return {"message": answer, "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
 
+                messages.append(message)
+                for call in tool_calls:
+                    name, arguments = self._extract_call(call)
+                    result = await self.tools.execute(name, arguments)
+                    last_tool_results.append({"tool": name, "result": result})
+                    messages.append({"role": "tool", "tool_name": name, "tool_call_id": call.get("id"), "content": json.dumps(result)})
         fallback = "I hit the tool-call limit while working on that. Try narrowing the request or approve any pending action first."
         messages.append({"role": "assistant", "content": fallback})
         if self.memory:
             self.memory.add_conversation("assistant", fallback, resolved_thread_id)
         return {"message": fallback, "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
 
-    async def chat_events(self, content: str, thread_id: str | None = DEFAULT_THREAD_ID) -> AsyncIterator[dict[str, Any]]:
+    async def chat_events(
+        self,
+        content: str,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+        images: list[dict[str, Any]] | None = None,
+    ) -> AsyncIterator[dict[str, Any]]:
         health = await self.health()
         if not health["online"]:
             yield {"type": "warning", "message": health["message"]}
@@ -140,74 +161,77 @@ class OllamaAgent:
         resolved_thread_id = self._thread_id(thread_id)
         messages = self._messages_for_thread(resolved_thread_id)
         previous_interaction = self.memory.last_interaction(resolved_thread_id) if self.memory else None
+        normalized_images = self._normalize_images(images)
+        prompt_text = self._prompt_text(content, len(normalized_images))
+        memory_content = self._conversation_content(content, len(normalized_images))
         if self.memory:
-            self.memory.add_conversation("user", content, resolved_thread_id)
-            await self._title_first_message(resolved_thread_id, content, previous_interaction)
-        messages.append({"role": "user", "content": content})
+            self.memory.add_conversation("user", memory_content, resolved_thread_id)
+            await self._title_first_message(resolved_thread_id, prompt_text, previous_interaction)
+        messages.append(self._user_message(prompt_text, normalized_images))
         yield {"type": "status", "message": "Thinking"}
         last_tool_results: list[dict[str, Any]] = []
+        image_scope = self.tools.chat_image_scope(normalized_images) if hasattr(self.tools, "chat_image_scope") else nullcontext()
+        with image_scope:
+            for _ in range(10):
+                assistant_message: dict[str, Any] = {"role": "assistant", "content": ""}
+                tool_calls: list[dict[str, Any]] = []
 
-        for _ in range(5):
-            assistant_message: dict[str, Any] = {"role": "assistant", "content": ""}
-            tool_calls: list[dict[str, Any]] = []
-
-            try:
-                async for event in self._ollama_chat_stream(
-                    content,
-                    messages,
-                    previous_interaction=previous_interaction,
-                    thread_id=resolved_thread_id,
-                ):
-                    message = event.get("message") or {}
-                    chunk = message.get("content") or ""
-                    if chunk:
-                        assistant_message["content"] += chunk
-                        yield {"type": "token", "content": chunk}
-                    if message.get("tool_calls"):
-                        tool_calls.extend(message["tool_calls"])
-                    if event.get("done"):
-                        metrics = self._stream_metrics(event)
-                        if metrics:
-                            yield {"type": "metrics", **metrics}
-            except Exception as exc:
-                if not last_tool_results:
-                    yield {"type": "warning", "message": f"Chat failed before any tool result was available: {exc}"}
+                try:
+                    async for event in self._chat_stream_once(
+                        prompt_text,
+                        messages,
+                        previous_interaction=previous_interaction,
+                        thread_id=resolved_thread_id,
+                    ):
+                        message = event.get("message") or {}
+                        chunk = message.get("content") or ""
+                        if chunk:
+                            assistant_message["content"] += chunk
+                            yield {"type": "token", "content": chunk}
+                        if message.get("tool_calls"):
+                            tool_calls.extend(message["tool_calls"])
+                        if event.get("done"):
+                            metrics = self._stream_metrics(event)
+                            if metrics:
+                                yield {"type": "metrics", **metrics}
+                except Exception as exc:
+                    if not last_tool_results:
+                        yield {"type": "warning", "message": f"Chat failed before any tool result was available: {exc}"}
+                        yield {"type": "done", "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
+                        return
+                    fallback = self._tool_result_fallback(
+                        last_tool_results,
+                        f"The {self._provider_label()} stopped after the tool call: {exc}",
+                    )
+                    assistant_message["content"] = fallback
+                    messages.append(assistant_message)
+                    if self.memory:
+                        self.memory.add_conversation("assistant", fallback, resolved_thread_id)
+                    yield {"type": "token", "content": fallback}
                     yield {"type": "done", "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
                     return
-                fallback = self._tool_result_fallback(
-                    last_tool_results,
-                    f"The local model stopped after the tool call: {exc}",
-                )
-                assistant_message["content"] = fallback
+
+                if not tool_calls:
+                    if not assistant_message.get("content", "").strip():
+                        fallback = self._empty_response_fallback(last_tool_results)
+                        assistant_message["content"] = fallback
+                        yield {"type": "token", "content": fallback}
+                    messages.append(assistant_message)
+                    if self.memory:
+                        self.memory.add_conversation("assistant", assistant_message.get("content", ""), resolved_thread_id)
+                    yield {"type": "done", "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
+                    return
+
+                assistant_message["tool_calls"] = tool_calls
                 messages.append(assistant_message)
-                if self.memory:
-                    self.memory.add_conversation("assistant", fallback, resolved_thread_id)
-                yield {"type": "token", "content": fallback}
-                yield {"type": "done", "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
-                return
-
-            if not tool_calls:
-                if not assistant_message.get("content", "").strip():
-                    fallback = self._empty_response_fallback(last_tool_results)
-                    assistant_message["content"] = fallback
-                    yield {"type": "token", "content": fallback}
-                messages.append(assistant_message)
-                if self.memory:
-                    self.memory.add_conversation("assistant", assistant_message.get("content", ""), resolved_thread_id)
-                yield {"type": "done", "pending_actions": self._pending_payloads(), "thread_id": resolved_thread_id}
-                return
-
-            assistant_message["tool_calls"] = tool_calls
-            messages.append(assistant_message)
-            for call in tool_calls:
-                name, arguments = self._extract_call(call)
-                yield {"type": "status", "message": self._tool_status(name)}
-                result = await self.tools.execute(name, arguments)
-                last_tool_results.append({"tool": name, "result": result})
-                messages.append({"role": "tool", "tool_name": name, "content": json.dumps(result)})
-
-            yield {"type": "status", "message": "Writing response"}
+                for call in tool_calls:
+                    name, arguments = self._extract_call(call)
+                    yield {"type": "status", "message": self._tool_status(name)}
+                    result = await self.tools.execute(name, arguments)
+                    last_tool_results.append({"tool": name, "result": result})
+                    messages.append({"role": "tool", "tool_name": name, "tool_call_id": call.get("id"), "content": json.dumps(result)})
 
+                yield {"type": "status", "message": "Writing response"}
         fallback = "I hit the tool-call limit while working on that. Try narrowing the request or approve any pending action first."
         messages.append({"role": "assistant", "content": fallback})
         if self.memory:
@@ -221,9 +245,9 @@ class OllamaAgent:
         previous_interaction = self.memory.last_interaction("wake") if self.memory else None
         messages.append({"role": "user", "content": wake_message})
         last_tool_results: list[dict[str, Any]] = []
-        for _ in range(5):
+        for _ in range(10):
             try:
-                response = await self._ollama_chat(
+                response = await self._chat_once(
                     wake_message,
                     messages,
                     previous_interaction=previous_interaction,
@@ -234,7 +258,7 @@ class OllamaAgent:
                     raise
                 content = self._tool_result_fallback(
                     last_tool_results,
-                    f"The local model stopped after the wake-job tool call: {exc}",
+                    f"The {self._provider_label()} stopped after the wake-job tool call: {exc}",
                 )
                 messages.append({"role": "assistant", "content": content})
                 if self.memory:
@@ -258,8 +282,7 @@ class OllamaAgent:
                 name, arguments = self._extract_call(call)
                 result = await self.tools.execute(name, arguments)
                 last_tool_results.append({"tool": name, "result": result})
-                messages.append({"role": "tool", "tool_name": name, "content": json.dumps(result)})
-
+                messages.append({"role": "tool", "tool_name": name, "tool_call_id": call.get("id"), "content": json.dumps(result)})
         content = "I hit the tool-call limit while running this scheduled wake job. Check the job prompt or pending approvals."
         messages.append({"role": "assistant", "content": content})
         if self.memory:
@@ -267,6 +290,51 @@ class OllamaAgent:
             self.memory.add_conversation("assistant", content, "wake")
         return content
 
+    async def _chat_once(
+        self,
+        query: str = "",
+        messages: list[dict[str, Any]] | None = None,
+        previous_interaction: dict[str, Any] | None = None,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+    ) -> dict[str, Any]:
+        if self.provider == "openai":
+            return await self._openai_chat(
+                query,
+                messages,
+                previous_interaction=previous_interaction,
+                thread_id=thread_id,
+            )
+        return await self._ollama_chat(
+            query,
+            messages,
+            previous_interaction=previous_interaction,
+            thread_id=thread_id,
+        )
+
+    async def _chat_stream_once(
+        self,
+        query: str = "",
+        messages: list[dict[str, Any]] | None = None,
+        previous_interaction: dict[str, Any] | None = None,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+    ) -> AsyncIterator[dict[str, Any]]:
+        if self.provider == "openai":
+            async for event in self._openai_chat_stream(
+                query,
+                messages,
+                previous_interaction=previous_interaction,
+                thread_id=thread_id,
+            ):
+                yield event
+            return
+        async for event in self._ollama_chat_stream(
+            query,
+            messages,
+            previous_interaction=previous_interaction,
+            thread_id=thread_id,
+        ):
+            yield event
+
     async def _ollama_chat(
         self,
         query: str = "",
@@ -322,6 +390,103 @@ class OllamaAgent:
                     if line:
                         yield json.loads(line)
 
+    async def _openai_chat(
+        self,
+        query: str = "",
+        messages: list[dict[str, Any]] | None = None,
+        previous_interaction: dict[str, Any] | None = None,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+    ) -> dict[str, Any]:
+        async with httpx.AsyncClient(timeout=120) as client:
+            response = await client.post(
+                f"{self.base_url}/chat/completions",
+                headers=self._openai_headers(),
+                json={
+                    "model": self.model,
+                    "messages": self._openai_messages(
+                        query,
+                        messages or self._messages_for_thread(thread_id),
+                        previous_interaction=previous_interaction,
+                        thread_id=thread_id,
+                    ),
+                    "tools": self.tools.schemas,
+                    "stream": False,
+                },
+            )
+        response.raise_for_status()
+        body = response.json()
+        choice = (body.get("choices") or [{}])[0]
+        message = choice.get("message") or {}
+        return {
+            "message": {
+                "role": message.get("role", "assistant"),
+                "content": message.get("content") or "",
+                "tool_calls": message.get("tool_calls") or [],
+            }
+        }
+
+    async def _openai_chat_stream(
+        self,
+        query: str = "",
+        messages: list[dict[str, Any]] | None = None,
+        previous_interaction: dict[str, Any] | None = None,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+    ) -> AsyncIterator[dict[str, Any]]:
+        tool_calls: dict[int, dict[str, Any]] = {}
+        async with httpx.AsyncClient(timeout=120) as client:
+            async with client.stream(
+                "POST",
+                f"{self.base_url}/chat/completions",
+                headers=self._openai_headers(),
+                json={
+                    "model": self.model,
+                    "messages": self._openai_messages(
+                        query,
+                        messages or self._messages_for_thread(thread_id),
+                        previous_interaction=previous_interaction,
+                        thread_id=thread_id,
+                    ),
+                    "tools": self.tools.schemas,
+                    "stream": True,
+                },
+            ) as response:
+                response.raise_for_status()
+                async for line in response.aiter_lines():
+                    if not line or not line.startswith("data:"):
+                        continue
+                    payload = line.removeprefix("data:").strip()
+                    if not payload:
+                        continue
+                    if payload == "[DONE]":
+                        break
+                    event = json.loads(payload)
+                    choice = (event.get("choices") or [{}])[0]
+                    delta = choice.get("delta") or {}
+                    content = delta.get("content") or ""
+                    if content:
+                        yield {"message": {"role": "assistant", "content": content}}
+                    for tool_call in delta.get("tool_calls") or []:
+                        self._merge_openai_tool_call(tool_calls, tool_call)
+                    finish_reason = choice.get("finish_reason")
+                    if finish_reason:
+                        yield {
+                            "message": {
+                                "role": "assistant",
+                                "content": "",
+                                "tool_calls": self._ordered_tool_calls(tool_calls),
+                            },
+                            "done": True,
+                        }
+                        return
+        yield {
+            "message": {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": self._ordered_tool_calls(tool_calls),
+            },
+            "done": True,
+        }
+
     def _messages_with_context(
         self,
         query: str,
@@ -329,21 +494,146 @@ class OllamaAgent:
         previous_interaction: dict[str, Any] | None = None,
         thread_id: str | None = DEFAULT_THREAD_ID,
     ) -> list[dict[str, Any]]:
-        context = self._runtime_context(query, previous_interaction=previous_interaction, thread_id=thread_id)
+        attached_image_count = 0
+        for message in reversed(messages):
+            if message.get("role") != "user":
+                continue
+            attached_image_count = len(message.get("images") or [])
+            break
+        context = self._runtime_context(
+            query,
+            previous_interaction=previous_interaction,
+            thread_id=thread_id,
+            attached_image_count=attached_image_count,
+        )
         if not context:
             return messages
         return [messages[0], {"role": "system", "content": context}, *messages[1:]]
 
+    async def _openai_health(self) -> dict[str, Any]:
+        if not self.api_key:
+            return {
+                "online": False,
+                "model": self.model,
+                "base_url": self.base_url,
+                "provider": "openai",
+                "model_available": False,
+                "models": [],
+                "message": "OpenAI is selected, but no OpenAI API key is configured.",
+                "detail": "",
+            }
+        try:
+            async with httpx.AsyncClient(timeout=10) as client:
+                response = await client.get(f"{self.base_url}/models", headers=self._openai_headers())
+            response.raise_for_status()
+            body = response.json()
+        except (httpx.HTTPError, ValueError) as exc:
+            return {
+                "online": False,
+                "model": self.model,
+                "base_url": self.base_url,
+                "provider": "openai",
+                "model_available": False,
+                "models": [],
+                "message": f"OpenAI is unreachable at {self.base_url} or rejected the API key.",
+                "detail": str(exc),
+            }
+        models = sorted(item.get("id") for item in body.get("data", []) if item.get("id"))
+        return {
+            "online": True,
+            "model": self.model,
+            "base_url": self.base_url,
+            "provider": "openai",
+            "model_available": self.model in models,
+            "models": models,
+            "message": "OpenAI is online.",
+        }
+
+    def _openai_headers(self) -> dict[str, str]:
+        return {
+            "Authorization": f"Bearer {self.api_key or ''}",
+            "Content-Type": "application/json",
+        }
+
+    def _openai_messages(
+        self,
+        query: str,
+        messages: list[dict[str, Any]],
+        previous_interaction: dict[str, Any] | None = None,
+        thread_id: str | None = DEFAULT_THREAD_ID,
+    ) -> list[dict[str, Any]]:
+        normalized: list[dict[str, Any]] = []
+        for message in self._messages_with_context(
+            query,
+            messages,
+            previous_interaction=previous_interaction,
+            thread_id=thread_id,
+        ):
+            role = message.get("role")
+            if role not in {"system", "user", "assistant", "tool"}:
+                continue
+            entry: dict[str, Any] = {"role": role, "content": message.get("content", "")}
+            if role == "user" and message.get("images"):
+                text_content = message.get("content", "")
+                content_parts: list[dict[str, Any]] = []
+                content_types = list(message.get("image_content_types") or [])
+                if text_content:
+                    content_parts.append({"type": "text", "text": text_content})
+                for index, image_data in enumerate(message.get("images") or []):
+                    content_type = content_types[index] if index < len(content_types) else "image/png"
+                    content_parts.append(
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:{content_type};base64,{image_data}"},
+                        }
+                    )
+                entry["content"] = content_parts
+            if role == "assistant" and message.get("tool_calls"):
+                entry["tool_calls"] = message["tool_calls"]
+            if role == "tool":
+                entry["tool_call_id"] = message.get("tool_call_id") or message.get("tool_name") or "tool"
+            normalized.append(entry)
+        return normalized
+
+    def _provider_label(self) -> str:
+        return "OpenAI model" if self.provider == "openai" else "local model"
+
+    @staticmethod
+    def _merge_openai_tool_call(target: dict[int, dict[str, Any]], delta: dict[str, Any]) -> None:
+        index = int(delta.get("index") or 0)
+        current = target.setdefault(index, {"id": delta.get("id"), "type": "function", "function": {"name": "", "arguments": ""}})
+        if delta.get("id"):
+            current["id"] = delta["id"]
+        function = delta.get("function") or {}
+        current_function = current.setdefault("function", {"name": "", "arguments": ""})
+        if function.get("name"):
+            current_function["name"] += function["name"]
+        if function.get("arguments"):
+            current_function["arguments"] += function["arguments"]
+
+    @staticmethod
+    def _ordered_tool_calls(tool_calls: dict[int, dict[str, Any]]) -> list[dict[str, Any]]:
+        return [tool_calls[index] for index in sorted(tool_calls)]
+
     def _runtime_context(
         self,
         query: str,
         previous_interaction: dict[str, Any] | None = None,
         thread_id: str | None = DEFAULT_THREAD_ID,
+        attached_image_count: int = 0,
     ) -> str:
         local_zone = get_localzone()
         parts = [
             f"Current local date/time: {iso_now()} UTC; {iso_now_in_zone(local_zone)} {local_zone}.",
         ]
+        if attached_image_count:
+            label = "image" if attached_image_count == 1 else "images"
+            parts.append(
+                f"Current user message includes {attached_image_count} pasted {label}. "
+                "You can inspect them visually. If the user wants one reused in a marketplace listing draft, "
+                "call draft_marketplace_listing or draft_marketplace_listing_with_cornerstone_image with "
+                "use_attached_image=true and attached_image_index when needed."
+            )
         uex = getattr(self.tools, "uex", None)
         if uex:
             auth_methods = []
@@ -433,6 +723,24 @@ class OllamaAgent:
             f"Message: {first_message[:800]}"
         )
         try:
+            if self.provider == "openai":
+                async with httpx.AsyncClient(timeout=20) as client:
+                    response = await client.post(
+                        f"{self.base_url}/chat/completions",
+                        headers=self._openai_headers(),
+                        json={
+                            "model": self.model,
+                            "messages": [
+                                {"role": "system", "content": "You write short chat titles."},
+                                {"role": "user", "content": prompt},
+                            ],
+                            "stream": False,
+                        },
+                    )
+                response.raise_for_status()
+                choice = (response.json().get("choices") or [{}])[0]
+                message = choice.get("message") or {}
+                return self._clean_generated_title(message.get("content", ""))
             async with httpx.AsyncClient(timeout=20) as client:
                 response = await client.post(
                     f"{self.base_url}/api/chat",
@@ -489,10 +797,10 @@ class OllamaAgent:
     @staticmethod
     def _empty_response_fallback(tool_results: list[dict[str, Any]]) -> str:
         if not tool_results:
-            return "I did not get a usable response from the local model. Please try again, or narrow the request a bit."
+            return "I did not get a usable response from the model. Please try again, or narrow the request a bit."
         return OllamaAgent._tool_result_fallback(
             tool_results,
-            "I completed the tool call, but the local model did not write a final answer.",
+            "I completed the tool call, but the model did not write a final answer.",
         )
 
     @staticmethod
@@ -633,6 +941,47 @@ class OllamaAgent:
             arguments = json.loads(arguments or "{}")
         return name, arguments
 
+    @staticmethod
+    def _normalize_images(images: list[dict[str, Any]] | None) -> list[dict[str, Any]]:
+        normalized: list[dict[str, Any]] = []
+        for image in images or []:
+            if not isinstance(image, dict):
+                continue
+            image_data = str(image.get("image_data") or "").strip()
+            if not image_data:
+                continue
+            normalized.append(
+                {
+                    "name": str(image.get("name") or "").strip() or "pasted-image.png",
+                    "content_type": str(image.get("content_type") or "image/png").strip() or "image/png",
+                    "image_data": image_data,
+                }
+            )
+        return normalized
+
+    @staticmethod
+    def _prompt_text(content: str, image_count: int) -> str:
+        text = content.strip()
+        if text:
+            return text
+        return "Please analyze the attached image." if image_count == 1 else "Please analyze the attached images."
+
+    @staticmethod
+    def _conversation_content(content: str, image_count: int) -> str:
+        text = content.strip()
+        if not image_count:
+            return text
+        note = f"[Attached {image_count} pasted image{'s' if image_count != 1 else ''}]"
+        return f"{text}\n\n{note}" if text else note
+
+    @staticmethod
+    def _user_message(content: str, images: list[dict[str, Any]]) -> dict[str, Any]:
+        message: dict[str, Any] = {"role": "user", "content": content}
+        if images:
+            message["images"] = [image["image_data"] for image in images]
+            message["image_content_types"] = [image["content_type"] for image in images]
+        return message
+
 
 class OllamaUnavailable(RuntimeError):
     pass