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

traderai/config.py

@@ -11,12 +11,16 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
 CONFIG_FIELDS: dict[str, dict[str, Any]] = {
+    "model_provider": {"env": "MODEL_PROVIDER", "type": "string", "secret": False},
     "ollama_base_url": {"env": "OLLAMA_BASE_URL", "type": "string", "secret": False},
     "ollama_model": {"env": "OLLAMA_MODEL", "type": "string", "secret": False},
     "ollama_num_ctx": {"env": "OLLAMA_NUM_CTX", "type": "integer", "secret": False},
+    "openai_base_url": {"env": "OPENAI_BASE_URL", "type": "string", "secret": False},
+    "openai_model": {"env": "OPENAI_MODEL", "type": "string", "secret": False},
     "uex_base_url": {"env": "UEX_BASE_URL", "type": "string", "secret": False},
     "scmdb_base_url": {"env": "SCMDB_BASE_URL", "type": "string", "secret": False},
     "cornerstone_base_url": {"env": "CORNERSTONE_BASE_URL", "type": "string", "secret": False},
+    "openai_api_key": {"env": "OPENAI_API_KEY", "type": "string", "secret": True},
     "uex_secret_key": {"env": "UEX_SECRET_KEY", "type": "string", "secret": True},
     "uex_bearer_token": {"env": "UEX_BEARER_TOKEN", "type": "string", "secret": True},
     "traderai_user_name": {"env": "TRADERAI_USER_NAME", "type": "string", "secret": False},
@@ -62,12 +66,16 @@ class Settings(BaseSettings):
         env_file_encoding="utf-8",
     )
 
+    model_provider: str = "ollama"
     ollama_base_url: str = "http://localhost:11434"
     ollama_model: str = "qwen3.5:9b"
     ollama_num_ctx: int = 64512
+    openai_base_url: str = "https://api.openai.com/v1"
+    openai_model: str = "gpt-5.3-codex"
     uex_base_url: str = "https://api.uexcorp.space/2.0"
     scmdb_base_url: str = "https://scmdb.net"
     cornerstone_base_url: str = "https://finder.cstone.space"
+    openai_api_key: str | None = Field(default=None)
     uex_secret_key: str | None = Field(default=None)
     uex_bearer_token: str | None = Field(default=None)
     traderai_user_name: str | None = Field(default=None)
@@ -75,11 +83,17 @@ class Settings(BaseSettings):
     uex_notification_poll_seconds: int = 60
     require_write_approval: bool = True
 
-    @field_validator("uex_secret_key", "uex_bearer_token", "traderai_user_name", mode="before")
+    @field_validator("openai_api_key", "uex_secret_key", "uex_bearer_token", "traderai_user_name", mode="before")
     @classmethod
     def _blank_optional(cls, value: Any) -> Any:
         return None if value == "" else value
 
+    @field_validator("model_provider", mode="before")
+    @classmethod
+    def _normalize_model_provider(cls, value: Any) -> str:
+        text = str(value or "ollama").strip().casefold()
+        return text if text in {"ollama", "openai"} else "ollama"
+
     @field_validator("traderai_memory_path", mode="before")
     @classmethod
     def _blank_memory_path(cls, value: Any) -> Any:
@@ -137,7 +151,7 @@ def save_settings(values: dict[str, Any]) -> dict[str, Any]:
 def _coerce_value(key: str, value: Any) -> Any:
     field_type = CONFIG_FIELDS[key]["type"]
     if value == "":
-        return None if key in {"uex_secret_key", "uex_bearer_token", "traderai_user_name"} else ""
+        return None if key in {"openai_api_key", "uex_secret_key", "uex_bearer_token", "traderai_user_name"} else ""
     if field_type == "integer":
         return int(value)
     if field_type == "boolean":

traderai/server.py

@@ -39,6 +39,13 @@ def resource_path(*parts: str) -> Path:
 class ChatRequest(BaseModel):
     message: str
     thread_id: str | None = DEFAULT_THREAD_ID
+    images: list["ChatImageRequest"] = []
+
+
+class ChatImageRequest(BaseModel):
+    name: str = "pasted-image.png"
+    content_type: str = "image/png"
+    image_data: str
 
 
 class ChatThreadRequest(BaseModel):
@@ -114,12 +121,14 @@ def create_app() -> FastAPI:
     plan_runner = ContinualPlanRunner(plan_store, tools, memory)
     tools.plan_runner = plan_runner
     agent = OllamaAgent(
-        settings.ollama_base_url,
-        settings.ollama_model,
+        settings.openai_base_url if settings.model_provider == "openai" else settings.ollama_base_url,
+        settings.openai_model if settings.model_provider == "openai" else settings.ollama_model,
         tools,
         memory=memory,
         user_name=settings.traderai_user_name,
         num_ctx=settings.ollama_num_ctx,
+        provider=settings.model_provider,
+        api_key=settings.openai_api_key,
     )
     plan_runner.bind_agent(agent)
     scheduler.bind_agent(agent)
@@ -171,6 +180,7 @@ def create_app() -> FastAPI:
     async def health() -> dict:
         return {
             "ollama": await agent.health(),
+            "model_provider": settings.model_provider,
             "user": memory.get_profile(),
             "jobs": scheduler.list_jobs(),
             "app_data_dir": settings_payload()["app_data_dir"],
@@ -190,7 +200,19 @@ def create_app() -> FastAPI:
 
     @app.get("/api/ollama/status")
     async def ollama_status() -> dict:
-        return await inspect_ollama()
+        return await inspect_model_provider()
+
+    @app.get("/api/openai/models")
+    async def openai_models() -> dict:
+        status = await inspect_openai()
+        return {
+            "provider": "openai",
+            "configured_model": status.get("configured_model"),
+            "models": status.get("models", []),
+            "message": status.get("message", ""),
+            "detail": status.get("detail", ""),
+            "online": status.get("online", False),
+        }
 
     @app.post("/api/ollama/launch")
     async def launch_ollama() -> dict:
@@ -201,7 +223,7 @@ def create_app() -> FastAPI:
             popen_hidden(command)
         except OSError as exc:
             raise HTTPException(status_code=500, detail=f"Could not launch Ollama: {exc}") from exc
-        status = await inspect_ollama()
+        status = await inspect_model_provider()
         status["message"] = "Ollama launch requested."
         return status
 
@@ -218,7 +240,7 @@ def create_app() -> FastAPI:
             popen_hidden([str(cli), "pull", model])
         except OSError as exc:
             raise HTTPException(status_code=500, detail=f"Could not start model install: {exc}") from exc
-        status = await inspect_ollama()
+        status = await inspect_model_provider()
         status["message"] = f"Started installing model {model}."
         return status
 
@@ -298,14 +320,22 @@ def create_app() -> FastAPI:
     @app.post("/api/chat")
     async def chat(request: ChatRequest) -> dict:
         try:
-            return await agent.chat(request.message, thread_id=request.thread_id)
+            return await agent.chat(
+                request.message,
+                thread_id=request.thread_id,
+                images=[image.model_dump() for image in request.images],
+            )
         except OllamaUnavailable as exc:
             raise HTTPException(status_code=503, detail=str(exc)) from exc
 
     @app.post("/api/chat/stream")
     async def chat_stream(request: ChatRequest) -> StreamingResponse:
         async def events():
-            async for event in agent.chat_events(request.message, thread_id=request.thread_id):
+            async for event in agent.chat_events(
+                request.message,
+                thread_id=request.thread_id,
+                images=[image.model_dump() for image in request.images],
+            ):
                 yield f"data: {json.dumps(event)}\n\n"
 
         return StreamingResponse(events(), media_type="text/event-stream")
@@ -475,6 +505,60 @@ def negotiation_identifier_params(identifier: str) -> dict[str, Any]:
     return {"hash": value}
 
 
+async def inspect_model_provider() -> dict[str, Any]:
+    settings = get_settings()
+    if settings.model_provider == "openai":
+        return await inspect_openai()
+    return await inspect_ollama()
+
+
+async def inspect_openai() -> dict[str, Any]:
+    settings = get_settings()
+    models: list[str] = []
+    online = False
+    detail = ""
+    if not settings.openai_api_key:
+        return {
+            "installed": True,
+            "running": False,
+            "online": False,
+            "provider": "openai",
+            "model_available": False,
+            "configured_model": settings.openai_model,
+            "base_url": settings.openai_base_url,
+            "models": [],
+            "message": "OpenAI is selected, but no API key is configured.",
+            "detail": "",
+        }
+
+    try:
+        async with httpx.AsyncClient(timeout=10) as client:
+            response = await client.get(
+                f"{settings.openai_base_url.rstrip('/')}/models",
+                headers={"Authorization": f"Bearer {settings.openai_api_key}"},
+            )
+        response.raise_for_status()
+        body = response.json()
+        online = True
+        models = sorted(item.get("id") for item in body.get("data", []) if item.get("id"))
+    except (httpx.HTTPError, ValueError) as exc:
+        detail = str(exc)
+
+    model_available = settings.openai_model in models
+    return {
+        "installed": True,
+        "running": online,
+        "online": online,
+        "provider": "openai",
+        "model_available": model_available,
+        "configured_model": settings.openai_model,
+        "base_url": settings.openai_base_url,
+        "models": models,
+        "message": openai_status_message(online, bool(settings.openai_api_key), model_available, settings.openai_model),
+        "detail": detail,
+    }
+
+
 async def inspect_ollama() -> dict[str, Any]:
     settings = get_settings()
     executable = find_ollama_executable()
@@ -500,6 +584,7 @@ async def inspect_ollama() -> dict[str, Any]:
         "installed": installed,
         "running": online,
         "online": online,
+        "provider": "ollama",
         "model_available": model_available,
         "configured_model": settings.ollama_model,
         "base_url": settings.ollama_base_url,
@@ -514,6 +599,16 @@ async def inspect_ollama() -> dict[str, Any]:
     }
 
 
+def openai_status_message(running: bool, configured: bool, model_available: bool, model: str) -> str:
+    if not configured:
+        return "OpenAI API key is not configured."
+    if not running:
+        return "OpenAI is not reachable with the configured key."
+    if not model_available:
+        return f'OpenAI is reachable, but model "{model}" was not returned by the API.'
+    return "OpenAI is ready."
+
+
 def ollama_status_message(installed: bool, running: bool, model_available: bool, model: str) -> str:
     if not installed:
         return "Ollama is not installed."

traderai/tools.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import uuid
+from contextlib import contextmanager
+from contextvars import ContextVar
 from dataclasses import dataclass
 from typing import Any, Awaitable, Callable
 
@@ -172,6 +174,7 @@ class ToolRegistry:
         self.plan_store = plan_store
         self.plan_runner = plan_runner
         self.pending_actions: dict[str, PendingAction] = {}
+        self._chat_images_var: ContextVar[list[dict[str, Any]]] = ContextVar("chat_images", default=[])
         self.handlers: dict[str, ToolHandler] = {
             "search_marketplace_listings": self.search_marketplace_listings,
             "get_marketplace_listing": self.get_marketplace_listing,
@@ -334,10 +337,19 @@ class ToolRegistry:
                             "source": {"type": "string"},
                             "availability": {"type": "string"},
                             "in_stock": {"type": "integer"},
-                            "durability": {"type": "integer", "minimum": 0, "maximum": 100},
-                            "video_url": {"type": "string"},
-                            "image_data": {"type": "string", "description": "Base64 JPG or PNG image data for UEX upload."},
-                            "hours_expiration": {"type": "integer"},
+                              "durability": {"type": "integer", "minimum": 0, "maximum": 100},
+                              "video_url": {"type": "string"},
+                              "image_data": {"type": "string", "description": "Base64 JPG or PNG image data for UEX upload."},
+                              "use_attached_image": {
+                                  "type": "boolean",
+                                  "description": "When true, reuse an image pasted into the current chat as the listing image_data.",
+                              },
+                              "attached_image_index": {
+                                  "type": "integer",
+                                  "minimum": 0,
+                                  "description": "Zero-based pasted image index to reuse when use_attached_image is true.",
+                              },
+                              "hours_expiration": {"type": "integer"},
                             "is_hidden": {"type": "integer", "enum": [0, 1]},
                             "is_tv_allowed": {"type": "integer", "enum": [0, 1]},
                             "is_production": {"type": "integer", "enum": [0, 1], "default": 1},
@@ -495,6 +507,14 @@ class ToolRegistry:
         except Exception as exc:
             return {"error": str(exc)}
 
+    @contextmanager
+    def chat_image_scope(self, images: list[dict[str, Any]] | None):
+        token = self._chat_images_var.set(self._normalize_chat_images(images))
+        try:
+            yield
+        finally:
+            self._chat_images_var.reset(token)
+
     async def approve(self, action_id: str) -> dict[str, Any]:
         action = self.pending_actions.pop(action_id, None)
         if not action:
@@ -1020,11 +1040,21 @@ class ToolRegistry:
                             "language": {"type": "string", "default": "en_US"},
                             "location": {"type": "string"},
                             "source": {"type": "string", "enum": ["looted", "pledged", "purchased_in_game", "pirated", "gifted"]},
-                            "availability": {"type": "string"},
-                            "in_stock": {"type": "integer"},
-                            "durability": {"type": "integer", "minimum": 0, "maximum": 100},
-                            "video_url": {"type": "string"},
-                            "hours_expiration": {"type": "integer"},
+                              "availability": {"type": "string"},
+                              "in_stock": {"type": "integer"},
+                              "durability": {"type": "integer", "minimum": 0, "maximum": 100},
+                              "video_url": {"type": "string"},
+                              "image_data": {"type": "string", "description": "Base64 JPG or PNG image data for UEX upload."},
+                              "use_attached_image": {
+                                  "type": "boolean",
+                                  "description": "When true, reuse an image pasted into the current chat as the listing image_data instead of sourcing from Cornerstone.",
+                              },
+                              "attached_image_index": {
+                                  "type": "integer",
+                                  "minimum": 0,
+                                  "description": "Zero-based pasted image index to reuse when use_attached_image is true.",
+                              },
+                              "hours_expiration": {"type": "integer"},
                             "is_hidden": {"type": "integer", "enum": [0, 1]},
                             "is_tv_allowed": {"type": "integer", "enum": [0, 1]},
                             "is_production": {"type": "integer", "enum": [0, 1], "default": 1},
@@ -1225,7 +1255,15 @@ class ToolRegistry:
         return self._pending("Send negotiation message", "marketplace_negotiations_messages", payload, metadata=metadata)
 
     async def draft_marketplace_listing(self, **payload: Any) -> dict[str, Any]:
-        return self._pending("Post marketplace listing", "marketplace_advertise", payload)
+        attached_image = self._attach_chat_image(payload)
+        if attached_image.get("error"):
+            return {"error": attached_image["error"]}
+        return self._pending(
+            "Post marketplace listing",
+            "marketplace_advertise",
+            payload,
+            metadata=attached_image.get("metadata"),
+        )
 
     async def draft_marketplace_listing_with_cornerstone_image(
         self,
@@ -1234,6 +1272,9 @@ class ToolRegistry:
         **payload: Any,
     ) -> dict[str, Any]:
         require_image = bool(payload.pop("require_image", False))
+        attached_image = self._attach_chat_image(payload)
+        if attached_image.get("error"):
+            return {"error": attached_image["error"]}
         item = await self._resolve_cornerstone_item(id=cornerstone_id, query=item_query)
         if not item:
             return {"error": "No Cornerstone item matched. Provide cornerstone_id or a more specific item_query."}
@@ -1250,9 +1291,9 @@ class ToolRegistry:
             except Exception as exc:
                 image_error = str(exc)
 
-        if image_result:
+        if image_result and not payload.get("image_data"):
             payload["image_data"] = image_result["image_data"]
-        elif require_image:
+        elif require_image and not payload.get("image_data"):
             return {
                 "error": "Cornerstone item matched, but no usable JPG/PNG image could be sourced.",
                 "cornerstone": {
@@ -1271,9 +1312,11 @@ class ToolRegistry:
             "cornerstone_image_url": image_result.get("url") if image_result else None,
             "cornerstone_image_content_type": image_result.get("content_type") if image_result else None,
             "cornerstone_image_size_bytes": image_result.get("size_bytes") if image_result else None,
-            "cornerstone_image_status": "included" if image_result else "not_found",
+            "cornerstone_image_status": "user_attached" if attached_image.get("metadata") else ("included" if image_result else "not_found"),
             "cornerstone_image_error": image_error or None,
         }
+        if attached_image.get("metadata"):
+            metadata.update(attached_image["metadata"])
         return self._pending("Post marketplace listing with Cornerstone image", "marketplace_advertise", payload, metadata=metadata)
 
     async def remember_user_fact(self, content: str, kind: str = "note", importance: int = 3) -> dict[str, Any]:
@@ -1625,6 +1668,48 @@ class ToolRegistry:
             display["image_data"] = f"<base64 image data redacted; {len(image_data)} characters>"
         return display
 
+    def _attach_chat_image(self, payload: dict[str, Any]) -> dict[str, Any]:
+        attached_index = payload.pop("attached_image_index", None)
+        use_attached_image = bool(payload.pop("use_attached_image", False) or attached_index is not None)
+        if payload.get("image_data") or not use_attached_image:
+            return {}
+        image = self._chat_image(attached_index or 0)
+        if not image:
+            return {"error": "No pasted chat image is available at the requested attached_image_index."}
+        payload["image_data"] = image["image_data"]
+        return {
+            "metadata": {
+                "attached_chat_image_name": image.get("name"),
+                "attached_chat_image_content_type": image.get("content_type"),
+                "attached_chat_image_index": attached_index or 0,
+                "attached_chat_image_status": "included",
+            }
+        }
+
+    def _chat_image(self, index: int) -> dict[str, Any] | None:
+        images = self._chat_images_var.get()
+        if 0 <= index < len(images):
+            return images[index]
+        return None
+
+    @staticmethod
+    def _normalize_chat_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 _int_or_none(value: Any) -> int | None:
         try:

traderai/version.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-__version__ = "0.0.5"
+__version__ = "0.0.6"
 
 RELEASES_URL = "https://git.hudsonriggs.systems/LambdaBankingConglomerate/TraderAI/releases"
 RELEASES_API_URL = "https://git.hudsonriggs.systems/api/v1/repos/LambdaBankingConglomerate/TraderAI/releases"
@@ -11,3 +11,4 @@ RELEASES_API_URL = "https://git.hudsonriggs.systems/api/v1/repos/LambdaBankingCo
 
 
 
+