tracking: remove memorystore, ux: change streaming area, more agent markdown formating.

.env.example

@@ -0,0 +1,8 @@
+OLLAMA_BASE_URL=http://localhost:11434
+OLLAMA_MODEL=qwen3.5:9b
+UEX_BASE_URL=https://api.uexcorp.space/2.0
+UEX_SECRET_KEY=
+UEX_BEARER_TOKEN=
+TRADERAI_USER_NAME=
+TRADERAI_MEMORY_PATH=data/traderai.sqlite3
+REQUIRE_WRITE_APPROVAL=true

.gitignore

@@ -0,0 +1,59 @@
+# Environment and secrets
+.env
+.env.*
+!.env.example
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+.python-version
+.venv/
+venv/
+env/
+ENV/
+
+# Python packaging and build output
+build/
+dist/
+*.egg-info/
+.eggs/
+pip-wheel-metadata/
+
+# Test and coverage output
+.pytest_cache/
+.coverage
+.coverage.*
+htmlcov/
+coverage.xml
+
+# Type checker and linter caches
+.mypy_cache/
+.ruff_cache/
+.pyre/
+.pytype/
+
+# FastAPI/Uvicorn/runtime files
+*.log
+*.pid
+data/
+*.sqlite3
+*.sqlite3-*
+
+# Frontend dependencies and build artifacts
+node_modules/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+web/dist/
+web/build/
+
+# IDE and OS files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+Thumbs.db
+desktop.ini

INSTRUCTIONS.md

@@ -1,2 +0,0 @@
-Use the UEX API spefications at https://app.swaggerhub.com/apis-docs/dolejska-daniel/UEX-API/v2.1#/Static/get_data_parameters.
-I want a ollama powered chat bot that can search marketplace listings on UEX, respond to messages regarding them, make offers etc. I will use a local model for this. 

README.md

@@ -0,0 +1,65 @@
+# TraderAI
+
+Local Ollama-powered chat for UEX marketplace workflows.
+
+## What It Does
+
+- Searches active UEX marketplace listings through `GET /marketplace_listings/`.
+- Reads authenticated marketplace negotiations and negotiation messages when `UEX_SECRET_KEY` or `UEX_BEARER_TOKEN` is set.
+- Drafts negotiation messages and marketplace listings as pending actions.
+- Requires browser approval before sending authenticated write requests to UEX.
+- Maintains local SQLite memory with searchable recall for user facts, preferences, and prior chat context.
+- Can create one-time or recurring wake jobs that prompt the assistant later and surface the result in the UI.
+- Loads the configured UEX user profile from `GET /user` so the assistant knows the current account username, display name, timezone, language, and marketplace-relevant profile details.
+
+## Setup
+
+1. Install Python 3.11+.
+2. Install Ollama and pull a tool-capable model, for example:
+
+   ```powershell
+   ollama pull qwen3.5:9b
+   ```
+
+3. Create `.env` from `.env.example` and set `UEX_SECRET_KEY` and/or `UEX_BEARER_TOKEN` if you want authenticated actions.
+4. Install and run:
+
+   ```powershell
+   pip install -e .
+   uvicorn traderai.server:app --reload
+   ```
+
+5. Open `http://127.0.0.1:8000`.
+
+## Notes
+
+Ollama runs locally at `http://localhost:11434` by default. This app talks to Ollama's native chat API with tool schemas, then executes approved UEX calls in the FastAPI backend.
+
+UEX marketplace posting and negotiation messages are guarded because they are account-affecting write actions. The model can draft them, but the UI approval button performs the final API call.
+
+The assistant gets runtime context on every chat: current date/time, authenticated UEX identity when credentials are configured, remembered user profile, last interaction time, relevant memories, and recent conversation excerpts. Memory is stored locally at `TRADERAI_MEMORY_PATH`.
+
+Wake jobs can be created from chat, for example:
+
+```text
+At 9 PM remind yourself to check my open Polaris Bit negotiations.
+```
+
+or:
+
+```text
+Every day at 9 AM, wake up and check whether I have marketplace followups.
+```
+
+The scheduler accepts one-time ISO datetimes and five-field cron expressions through the `schedule_wake_job` tool. When a wake job fires, the assistant receives context like the current time and last interaction time, then places its response into the UI notification queue.
+
+## Sources Used
+
+- UEX SwaggerHub OpenAPI v2.1: https://app.swaggerhub.com/apis-docs/dolejska-daniel/UEX-API/v2.1
+- UEX marketplace listings docs: https://uexcorp.space/api/documentation/id/get_marketplace_listings/?is_kiosk=1
+- UEX negotiation message docs: https://uexcorp.space/api/documentation/id/post_marketplace_negotiations_messages/?is_kiosk=1
+- Ollama tool calling docs: https://docs.ollama.com/capabilities/tool-calling
+- Ollama API streaming/tool-call reference: https://github.com/ollama/ollama/blob/main/docs/api.md
+- SQLite FTS5 docs: https://www.sqlite.org/fts5.html
+- APScheduler AsyncIO scheduler docs: https://apscheduler.readthedocs.io/en/stable/modules/schedulers/asyncio.html
+- Letta/MemGPT memory hierarchy background: https://docs.letta.com/concepts/letta

pyproject.toml

@@ -0,0 +1,26 @@
+[project]
+name = "traderai"
+version = "0.1.0"
+description = "Local Ollama-powered assistant for UEX marketplace workflows."
+requires-python = ">=3.11"
+dependencies = [
+  "apscheduler>=3.10.4",
+  "fastapi>=0.115.0",
+  "httpx>=0.27.0",
+  "pydantic>=2.8.0",
+  "pydantic-settings>=2.4.0",
+  "python-dotenv>=1.0.1",
+  "tzlocal>=5.2",
+  "uvicorn[standard]>=0.30.0",
+]
+
+[project.optional-dependencies]
+dev = [
+  "pytest>=8.3.0",
+  "pytest-asyncio>=0.23.8",
+  "respx>=0.21.1",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["."]

tests/test_agent.py

@@ -0,0 +1,79 @@
+import pytest
+
+from traderai.agent import OllamaAgent
+from traderai.memory import MemoryStore
+
+
+class EmptyTools:
+    schemas = []
+
+    @property
+    def pending_actions(self):
+        return {}
+
+
+@pytest.mark.asyncio
+async def test_chat_events_warns_when_ollama_offline():
+    agent = OllamaAgent("http://127.0.0.1:1", "missing-model", EmptyTools())
+    events = []
+    async for event in agent.chat_events("hello"):
+        events.append(event)
+
+    assert events[0]["type"] == "warning"
+    assert "Ollama is offline" in events[0]["message"]
+    assert events[-1]["type"] == "done"
+
+
+def test_runtime_context_uses_previous_interaction_not_current_message(tmp_path):
+    memory = MemoryStore(str(tmp_path / "memory.sqlite3"))
+    memory.add_conversation("assistant", "older answer")
+    previous = memory.last_interaction()
+    assert previous is not None
+
+    memory.add_conversation("user", "current question")
+    current = memory.last_interaction()
+
+    agent = OllamaAgent("http://127.0.0.1:1", "missing-model", EmptyTools(), memory=memory)
+    context = agent._runtime_context("current question", previous_interaction=previous)
+
+    assert f"Previous interaction before this message: {previous['created_at']}" in context
+    assert f"Previous interaction before this message: {current['created_at']}" not in context
+    assert "Current local date/time:" in context
+
+
+def test_runtime_context_includes_uex_user_identity(tmp_path):
+    memory = MemoryStore(str(tmp_path / "memory.sqlite3"))
+    memory.set_profile(
+        "uex_user",
+        {
+            "username": "pilot_hudson",
+            "name": "Hudson",
+            "email": "hudson@example.test",
+            "timezone": "America/New_York",
+            "specializations": "trading,hauling",
+        },
+    )
+
+    agent = OllamaAgent("http://127.0.0.1:1", "missing-model", EmptyTools(), memory=memory)
+    context = agent._runtime_context("")
+
+    assert "You are speaking with UEX user pilot_hudson (Hudson)." in context
+    assert "timezone: America/New_York" in context
+    assert "specializations: trading,hauling" in context
+    assert "hudson@example.test" not in context
+
+
+def test_stream_metrics_include_reading_and_writing_rates():
+    metrics = OllamaAgent._stream_metrics(
+        {
+            "prompt_eval_count": 20,
+            "prompt_eval_duration": 2_000_000_000,
+            "eval_count": 30,
+            "eval_duration": 3_000_000_000,
+        }
+    )
+
+    assert metrics["reading_tokens"] == 20
+    assert metrics["reading_tokens_per_second"] == 10
+    assert metrics["writing_tokens"] == 30
+    assert metrics["writing_tokens_per_second"] == 10

tests/test_memory.py

@@ -0,0 +1,27 @@
+from traderai.memory import MemoryStore
+
+
+def test_memory_store_recalls_saved_fact(tmp_path):
+    store = MemoryStore(str(tmp_path / "memory.sqlite3"))
+    store.remember("preference", "The user prefers Polaris Bits searches to include barter listings.", importance=5)
+
+    results = store.recall("Polaris barter")
+
+    assert results
+    assert "Polaris Bits" in results[0]["content"]
+
+
+def test_memory_store_clear_selected_sections(tmp_path):
+    store = MemoryStore(str(tmp_path / "memory.sqlite3"))
+    store.remember("note", "Forgettable note")
+    store.add_conversation("user", "hello")
+    store.set_profile("configured_name", "Hudson")
+
+    deleted = store.clear(include_profile=False)
+    snapshot = store.inspect()
+
+    assert deleted["memories"] == 1
+    assert deleted["conversations"] == 1
+    assert snapshot["memories"] == []
+    assert snapshot["conversations"] == []
+    assert snapshot["profile"][0]["key"] == "configured_name"

tests/test_tools.py

@@ -0,0 +1,83 @@
+import pytest
+import respx
+from httpx import Response
+
+from traderai.tools import ToolRegistry
+from traderai.uex_client import UEXClient
+
+
+class FakeUEX:
+    async def get(self, path, params=None, authenticated=False):
+        assert path == "marketplace_listings"
+        return {
+            "data": [
+                {
+                    "id": 1,
+                    "slug": "gold-haul",
+                    "title": "Gold haul escort",
+                    "description": "Escort service",
+                    "operation": "sell",
+                    "type": "service",
+                    "price": 5000,
+                    "currency": "UEC",
+                    "unit": "run",
+                    "location": "Port Tressler",
+                    "user_username": "pilot_a",
+                    "date_expiration": 123,
+                },
+                {
+                    "id": 2,
+                    "slug": "armor-set",
+                    "title": "Armor set",
+                    "description": "Clean set",
+                    "operation": "sell",
+                    "type": "item",
+                    "price": 15000,
+                    "currency": "UEC",
+                    "unit": "set",
+                    "location": "Area18",
+                    "user_username": "pilot_b",
+                    "date_expiration": 456,
+                },
+            ]
+        }
+
+
+@pytest.mark.asyncio
+async def test_search_marketplace_listings_filters_locally():
+    registry = ToolRegistry(FakeUEX())
+    result = await registry.search_marketplace_listings(query="gold", type="service", max_price=6000)
+    assert result["count"] == 1
+    assert result["listings"][0]["slug"] == "gold-haul"
+
+
+@pytest.mark.asyncio
+async def test_draft_message_creates_pending_action():
+    registry = ToolRegistry(FakeUEX())
+    result = await registry.draft_negotiation_message(hash="abc", message="Would you take 4500 UEC?")
+    pending = result["pending_action"]
+    assert pending["endpoint"] == "marketplace_negotiations_messages"
+    assert pending["payload"]["message"] == "Would you take 4500 UEC?"
+    assert pending["id"] in registry.pending_actions
+
+
+def test_uex_client_uses_bearer_and_secret_headers():
+    client = UEXClient("https://api.uexcorp.space/2.0", secret_key="secret", bearer_token="bearer")
+
+    headers = client._headers(authenticated=True)
+
+    assert headers["secret-key"] == "secret"
+    assert headers["Authorization"] == "Bearer bearer"
+
+
+@pytest.mark.asyncio
+@respx.mock
+async def test_uex_client_get_user_normalizes_user_payload():
+    respx.get("https://api.uexcorp.space/2.0/user/").mock(
+        return_value=Response(200, json={"status": "ok", "data": [{"username": "pilot_hudson"}]})
+    )
+    client = UEXClient("https://api.uexcorp.space/2.0", bearer_token="bearer")
+
+    result = await client.get_user(authenticated=True)
+
+    assert result == {"status": "ok", "user": {"username": "pilot_hudson"}}

traderai/__init__.py

@@ -0,0 +1 @@
+"""TraderAI application package."""

traderai/agent.py

@@ -0,0 +1,367 @@
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncIterator
+from typing import Any
+
+import httpx
+from tzlocal import get_localzone
+
+from traderai.memory import MemoryStore, iso_now, iso_now_in_zone, time_since
+from traderai.tools import ToolRegistry
+
+
+SYSTEM_PROMPT = """You are TraderAI, a local assistant for UEX marketplace work.
+Use tools when the user asks about listings, negotiations, messages, offers, or posting ads.
+For marketplace writes, draft the exact pending action and tell the user what will be sent; never claim it was sent until approval succeeds.
+Keep prices, listing ids, slugs, users, and UEX status codes precise. If data is missing, say what you need next."""
+
+
+class OllamaAgent:
+    def __init__(
+        self,
+        base_url: str,
+        model: str,
+        tools: ToolRegistry,
+        memory: MemoryStore | None = None,
+        user_name: str | None = None,
+    ) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.tools = tools
+        self.memory = memory
+        self.user_name = user_name
+        self.messages: list[dict[str, Any]] = [{"role": "system", "content": SYSTEM_PROMPT}]
+
+    async def health(self) -> dict[str, Any]:
+        try:
+            async with httpx.AsyncClient(timeout=3) as client:
+                response = await client.get(f"{self.base_url}/api/tags")
+            response.raise_for_status()
+            body = response.json()
+        except (httpx.HTTPError, ValueError) as exc:
+            return {
+                "online": False,
+                "model": self.model,
+                "base_url": self.base_url,
+                "message": f"Ollama is offline or unreachable at {self.base_url}. Start Ollama and make sure the model is pulled.",
+                "detail": str(exc),
+            }
+
+        models = [model.get("name") or model.get("model") for model in body.get("models", [])]
+        return {
+            "online": True,
+            "model": self.model,
+            "base_url": self.base_url,
+            "model_available": self.model in models,
+            "models": models,
+            "message": "Ollama is online.",
+        }
+
+    async def ensure_available(self) -> None:
+        health = await self.health()
+        if not health["online"]:
+            raise OllamaUnavailable(health["message"])
+
+    async def chat(self, content: str) -> dict[str, Any]:
+        await self.ensure_available()
+        previous_interaction = self.memory.last_interaction() if self.memory else None
+        if self.memory:
+            self.memory.add_conversation("user", content)
+        self.messages.append({"role": "user", "content": content})
+        for _ in range(5):
+            response = await self._ollama_chat(content, previous_interaction=previous_interaction)
+            message = response.get("message") or {}
+            tool_calls = message.get("tool_calls") or []
+            if not tool_calls:
+                self.messages.append({"role": "assistant", "content": message.get("content", "")})
+                if self.memory:
+                    self.memory.add_conversation("assistant", message.get("content", ""))
+                return {"message": message.get("content", ""), "pending_actions": self._pending_payloads()}
+
+            self.messages.append(message)
+            for call in tool_calls:
+                name, arguments = self._extract_call(call)
+                result = await self.tools.execute(name, arguments)
+                self.messages.append({"role": "tool", "tool_name": name, "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."
+        self.messages.append({"role": "assistant", "content": fallback})
+        if self.memory:
+            self.memory.add_conversation("assistant", fallback)
+        return {"message": fallback, "pending_actions": self._pending_payloads()}
+
+    async def chat_events(self, content: str) -> AsyncIterator[dict[str, Any]]:
+        health = await self.health()
+        if not health["online"]:
+            yield {"type": "warning", "message": health["message"]}
+            yield {"type": "done", "pending_actions": self._pending_payloads()}
+            return
+
+        previous_interaction = self.memory.last_interaction() if self.memory else None
+        if self.memory:
+            self.memory.add_conversation("user", content)
+        self.messages.append({"role": "user", "content": content})
+        yield {"type": "status", "message": "Thinking"}
+
+        for _ in range(5):
+            assistant_message: dict[str, Any] = {"role": "assistant", "content": ""}
+            tool_calls: list[dict[str, Any]] = []
+
+            async for event in self._ollama_chat_stream(content, previous_interaction=previous_interaction):
+                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}
+
+            if not tool_calls:
+                self.messages.append(assistant_message)
+                if self.memory:
+                    self.memory.add_conversation("assistant", assistant_message.get("content", ""))
+                yield {"type": "done", "pending_actions": self._pending_payloads()}
+                return
+
+            assistant_message["tool_calls"] = tool_calls
+            self.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)
+                self.messages.append({"role": "tool", "tool_name": name, "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."
+        self.messages.append({"role": "assistant", "content": fallback})
+        if self.memory:
+            self.memory.add_conversation("assistant", fallback)
+        yield {"type": "token", "content": fallback}
+        yield {"type": "done", "pending_actions": self._pending_payloads()}
+
+    async def generate_wake_response(self, wake_message: str) -> str:
+        await self.ensure_available()
+        self.messages.append({"role": "user", "content": wake_message})
+        response = await self._ollama_chat(wake_message)
+        message = response.get("message") or {}
+        content = message.get("content", "")
+        self.messages.append({"role": "assistant", "content": content})
+        if self.memory:
+            self.memory.add_conversation("system", wake_message)
+            self.memory.add_conversation("assistant", content)
+        return content or wake_message
+
+    async def _ollama_chat(self, query: str = "", previous_interaction: dict[str, Any] | None = None) -> dict[str, Any]:
+        async with httpx.AsyncClient(timeout=120) as client:
+            response = await client.post(
+                f"{self.base_url}/api/chat",
+                json={
+                    "model": self.model,
+                    "messages": self._messages_with_context(query, previous_interaction=previous_interaction),
+                    "tools": self.tools.schemas,
+                    "stream": False,
+                },
+            )
+        response.raise_for_status()
+        return response.json()
+
+    async def _ollama_chat_stream(
+        self,
+        query: str = "",
+        previous_interaction: dict[str, Any] | None = None,
+    ) -> AsyncIterator[dict[str, Any]]:
+        async with httpx.AsyncClient(timeout=120) as client:
+            async with client.stream(
+                "POST",
+                f"{self.base_url}/api/chat",
+                json={
+                    "model": self.model,
+                    "messages": self._messages_with_context(query, previous_interaction=previous_interaction),
+                    "tools": self.tools.schemas,
+                    "stream": True,
+                },
+            ) as response:
+                response.raise_for_status()
+                async for line in response.aiter_lines():
+                    if line:
+                        yield json.loads(line)
+
+    def _messages_with_context(
+        self,
+        query: str,
+        previous_interaction: dict[str, Any] | None = None,
+    ) -> list[dict[str, Any]]:
+        context = self._runtime_context(query, previous_interaction=previous_interaction)
+        if not context:
+            return self.messages
+        return [self.messages[0], {"role": "system", "content": context}, *self.messages[1:]]
+
+    def _runtime_context(self, query: str, previous_interaction: dict[str, Any] | None = None) -> str:
+        local_zone = get_localzone()
+        parts = [
+            f"Current local date/time: {iso_now()} UTC; {iso_now_in_zone(local_zone)} {local_zone}.",
+        ]
+        if self.user_name:
+            parts.append(f"Known user name/handle: {self.user_name}.")
+
+        if self.memory is None:
+            return "\n".join(parts)
+
+        profile = self.memory.get_profile()
+        if profile:
+            identity = self._profile_identity(profile)
+            if identity:
+                parts.append(identity)
+            parts.append(f"Known user profile JSON: {json.dumps(self._profile_for_prompt(profile), ensure_ascii=True)}.")
+
+        last = previous_interaction if previous_interaction is not None else self.memory.last_interaction()
+        if last:
+            parts.append(
+                f"Previous interaction before this message: {last['created_at']} "
+                f"({time_since(last['created_at'])}, role {last['role']})."
+            )
+        else:
+            parts.append("Previous interaction before this message: none recorded.")
+
+        memories = self.memory.recall(query, limit=6)
+        if memories:
+            memory_text = "\n".join(
+                f"- [{item['kind']}, importance {item['importance']}] {item['content']}"
+                for item in memories
+            )
+            parts.append(f"Relevant long-term memories:\n{memory_text}")
+
+        recent = self.memory.recent_conversation(limit=6)
+        if recent:
+            recent_text = "\n".join(
+                f"- {item['created_at']} {item['role']}: {item['content'][:500]}"
+                for item in recent
+            )
+            parts.append(f"Recent conversation excerpts:\n{recent_text}")
+
+        return "\n".join(parts)
+
+    def _pending_payloads(self) -> list[dict[str, Any]]:
+        return [
+            {
+                "id": action.id,
+                "label": action.label,
+                "endpoint": action.endpoint,
+                "payload": action.payload,
+            }
+            for action in self.tools.pending_actions.values()
+        ]
+
+    @staticmethod
+    def _tool_status(name: str) -> str:
+        labels = {
+            "search_marketplace_listings": "Searching UEX listings",
+            "get_marketplace_listing": "Fetching listing details",
+            "list_marketplace_negotiations": "Checking negotiations",
+            "get_negotiation_messages": "Reading negotiation messages",
+            "draft_negotiation_message": "Drafting message for approval",
+            "draft_marketplace_listing": "Drafting listing for approval",
+        }
+        return labels.get(name, f"Running {name}")
+
+    @staticmethod
+    def _stream_metrics(event: dict[str, Any]) -> dict[str, Any]:
+        prompt_tokens = int(event.get("prompt_eval_count") or 0)
+        prompt_duration = int(event.get("prompt_eval_duration") or 0)
+        output_tokens = int(event.get("eval_count") or 0)
+        output_duration = int(event.get("eval_duration") or 0)
+
+        def rate(tokens: int, duration_ns: int) -> float | None:
+            if not tokens or not duration_ns:
+                return None
+            return tokens / (duration_ns / 1_000_000_000)
+
+        return {
+            "reading_tokens": prompt_tokens,
+            "reading_tokens_per_second": rate(prompt_tokens, prompt_duration),
+            "writing_tokens": output_tokens,
+            "writing_tokens_per_second": rate(output_tokens, output_duration),
+        }
+
+    @staticmethod
+    def _profile_identity(profile: dict[str, Any]) -> str:
+        user = profile.get("uex_user")
+        if not isinstance(user, dict):
+            configured = profile.get("configured_name")
+            return f"You are speaking with {configured}." if configured else ""
+
+        username = user.get("username") or user.get("user_username")
+        name = user.get("name")
+        fields = []
+        if username and name and username != name:
+            fields.append(f"You are speaking with UEX user {username} ({name}).")
+        elif username or name:
+            fields.append(f"You are speaking with UEX user {username or name}.")
+
+        details = []
+        for key, label in [
+            ("timezone", "timezone"),
+            ("language", "preferred language"),
+            ("specializations", "specializations"),
+            ("languages", "languages"),
+            ("archetypes", "archetypes"),
+        ]:
+            value = user.get(key)
+            if value:
+                details.append(f"{label}: {value}")
+        if details:
+            fields.append("UEX profile details: " + "; ".join(details) + ".")
+        return " ".join(fields)
+
+    @staticmethod
+    def _profile_for_prompt(profile: dict[str, Any]) -> dict[str, Any]:
+        user = profile.get("uex_user")
+        if not isinstance(user, dict):
+            return profile
+
+        useful_user_fields = [
+            "id",
+            "name",
+            "username",
+            "avatar",
+            "bio",
+            "website_url",
+            "timezone",
+            "language",
+            "day_availability",
+            "time_availability",
+            "specializations",
+            "languages",
+            "archetypes",
+            "is_datarunner",
+            "is_staff",
+            "is_away_game",
+            "date_rsi_verified",
+            "date_twitch_verified",
+        ]
+        prompt_profile = dict(profile)
+        prompt_profile["uex_user"] = {
+            key: user[key]
+            for key in useful_user_fields
+            if key in user and user[key] not in (None, "")
+        }
+        return prompt_profile
+
+    @staticmethod
+    def _extract_call(call: dict[str, Any]) -> tuple[str, dict[str, Any]]:
+        function = call.get("function") or {}
+        name = function.get("name") or call.get("name")
+        arguments = function.get("arguments") or call.get("arguments") or {}
+        if isinstance(arguments, str):
+            arguments = json.loads(arguments or "{}")
+        return name, arguments
+
+
+class OllamaUnavailable(RuntimeError):
+    pass

traderai/config.py

@@ -0,0 +1,22 @@
+from functools import lru_cache
+
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+    ollama_base_url: str = "http://localhost:11434"
+    ollama_model: str = "qwen3.5:9b"
+    uex_base_url: str = "https://api.uexcorp.space/2.0"
+    uex_secret_key: str | None = Field(default=None)
+    uex_bearer_token: str | None = Field(default=None)
+    traderai_user_name: str | None = Field(default=None)
+    traderai_memory_path: str = "data/traderai.sqlite3"
+    require_write_approval: bool = True
+
+
+@lru_cache
+def get_settings() -> Settings:
+    return Settings()

traderai/memory.py

@@ -0,0 +1,378 @@
+from __future__ import annotations
+
+import json
+import sqlite3
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+from zoneinfo import ZoneInfo
+
+
+def utc_now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
+def iso_now() -> str:
+    return utc_now().isoformat()
+
+
+def iso_now_in_zone(zone: ZoneInfo) -> str:
+    return utc_now().astimezone(zone).isoformat()
+
+
+def parse_iso(value: str) -> datetime:
+    parsed = datetime.fromisoformat(value)
+    if parsed.tzinfo is None:
+        return parsed.replace(tzinfo=timezone.utc)
+    return parsed
+
+
+def time_since(value: str, now: datetime | None = None) -> str:
+    then = parse_iso(value)
+    current = now or utc_now()
+    if current.tzinfo is None:
+        current = current.replace(tzinfo=timezone.utc)
+    seconds = max(0, int((current - then).total_seconds()))
+    if seconds < 60:
+        return f"{seconds} seconds ago"
+    minutes = seconds // 60
+    if minutes < 60:
+        return _plural(minutes, "minute") + " ago"
+    hours = minutes // 60
+    if hours < 24:
+        return _plural(hours, "hour") + " ago"
+    days = hours // 24
+    return _plural(days, "day") + " ago"
+
+
+def _plural(value: int, unit: str) -> str:
+    suffix = "" if value == 1 else "s"
+    return f"{value} {unit}{suffix}"
+
+
+class MemoryStore:
+    def __init__(self, path: str) -> None:
+        self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self._init_db()
+
+    def _connect(self) -> sqlite3.Connection:
+        connection = sqlite3.connect(self.path)
+        connection.row_factory = sqlite3.Row
+        return connection
+
+    def _init_db(self) -> None:
+        with self._connect() as db:
+            db.executescript(
+                """
+                CREATE TABLE IF NOT EXISTS conversations (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    role TEXT NOT NULL,
+                    content TEXT NOT NULL,
+                    created_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS memories (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    kind TEXT NOT NULL,
+                    content TEXT NOT NULL,
+                    importance INTEGER NOT NULL DEFAULT 3,
+                    metadata TEXT NOT NULL DEFAULT '{}',
+                    created_at TEXT NOT NULL,
+                    updated_at TEXT NOT NULL
+                );
+
+                CREATE VIRTUAL TABLE IF NOT EXISTS memories_fts USING fts5(
+                    content,
+                    kind UNINDEXED,
+                    content='memories',
+                    content_rowid='id'
+                );
+
+                CREATE TRIGGER IF NOT EXISTS memories_ai AFTER INSERT ON memories BEGIN
+                    INSERT INTO memories_fts(rowid, content, kind) VALUES (new.id, new.content, new.kind);
+                END;
+
+                CREATE TRIGGER IF NOT EXISTS memories_ad AFTER DELETE ON memories BEGIN
+                    INSERT INTO memories_fts(memories_fts, rowid, content, kind)
+                    VALUES('delete', old.id, old.content, old.kind);
+                END;
+
+                CREATE TRIGGER IF NOT EXISTS memories_au AFTER UPDATE ON memories BEGIN
+                    INSERT INTO memories_fts(memories_fts, rowid, content, kind)
+                    VALUES('delete', old.id, old.content, old.kind);
+                    INSERT INTO memories_fts(rowid, content, kind) VALUES (new.id, new.content, new.kind);
+                END;
+
+                CREATE TABLE IF NOT EXISTS user_profile (
+                    key TEXT PRIMARY KEY,
+                    value TEXT NOT NULL,
+                    updated_at TEXT NOT NULL
+                );
+
+                CREATE TABLE IF NOT EXISTS scheduled_jobs (
+                    id TEXT PRIMARY KEY,
+                    prompt TEXT NOT NULL,
+                    trigger_type TEXT NOT NULL,
+                    trigger_value TEXT NOT NULL,
+                    next_run_at TEXT,
+                    created_at TEXT NOT NULL,
+                    last_run_at TEXT,
+                    enabled INTEGER NOT NULL DEFAULT 1
+                );
+
+                CREATE TABLE IF NOT EXISTS outbox (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    content TEXT NOT NULL,
+                    created_at TEXT NOT NULL,
+                    delivered_at TEXT
+                );
+                """
+            )
+
+    def add_conversation(self, role: str, content: str) -> None:
+        with self._connect() as db:
+            db.execute(
+                "INSERT INTO conversations(role, content, created_at) VALUES (?, ?, ?)",
+                (role, content, iso_now()),
+            )
+
+    def last_interaction(self) -> dict[str, Any] | None:
+        with self._connect() as db:
+            row = db.execute(
+                "SELECT role, content, created_at FROM conversations ORDER BY id DESC LIMIT 1"
+            ).fetchone()
+        return dict(row) if row else None
+
+    def recent_conversation(self, limit: int = 8) -> list[dict[str, Any]]:
+        with self._connect() as db:
+            rows = db.execute(
+                "SELECT role, content, created_at FROM conversations ORDER BY id DESC LIMIT ?",
+                (limit,),
+            ).fetchall()
+        return [dict(row) for row in reversed(rows)]
+
+    def remember(self, kind: str, content: str, importance: int = 3, metadata: dict[str, Any] | None = None) -> dict[str, Any]:
+        now = iso_now()
+        with self._connect() as db:
+            cursor = db.execute(
+                """
+                INSERT INTO memories(kind, content, importance, metadata, created_at, updated_at)
+                VALUES (?, ?, ?, ?, ?, ?)
+                """,
+                (kind, content, importance, json.dumps(metadata or {}), now, now),
+            )
+            memory_id = cursor.lastrowid
+        return {"id": memory_id, "kind": kind, "content": content, "importance": importance, "created_at": now}
+
+    def recall(self, query: str, limit: int = 6) -> list[dict[str, Any]]:
+        if not query.strip():
+            return self.top_memories(limit)
+
+        with self._connect() as db:
+            try:
+                rows = db.execute(
+                    """
+                    SELECT m.id, m.kind, m.content, m.importance, m.metadata, m.created_at,
+                           bm25(memories_fts) AS rank
+                    FROM memories_fts
+                    JOIN memories m ON m.id = memories_fts.rowid
+                    WHERE memories_fts MATCH ?
+                    ORDER BY rank, m.importance DESC
+                    LIMIT ?
+                    """,
+                    (self._fts_query(query), limit),
+                ).fetchall()
+            except sqlite3.OperationalError:
+                rows = db.execute(
+                    """
+                    SELECT id, kind, content, importance, metadata, created_at
+                    FROM memories
+                    WHERE content LIKE ?
+                    ORDER BY importance DESC, id DESC
+                    LIMIT ?
+                    """,
+                    (f"%{query}%", limit),
+                ).fetchall()
+        return [self._memory_row(row) for row in rows]
+
+    def top_memories(self, limit: int = 6) -> list[dict[str, Any]]:
+        with self._connect() as db:
+            rows = db.execute(
+                """
+                SELECT id, kind, content, importance, metadata, created_at
+                FROM memories
+                ORDER BY importance DESC, updated_at DESC
+                LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+        return [self._memory_row(row) for row in rows]
+
+    def inspect(self, limit: int = 50) -> dict[str, Any]:
+        with self._connect() as db:
+            memories = db.execute(
+                """
+                SELECT id, kind, content, importance, metadata, created_at, updated_at
+                FROM memories
+                ORDER BY importance DESC, updated_at DESC
+                LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+            conversations = db.execute(
+                """
+                SELECT id, role, content, created_at
+                FROM conversations
+                ORDER BY id DESC
+                LIMIT ?
+                """,
+                (limit,),
+            ).fetchall()
+            profile_rows = db.execute(
+                "SELECT key, value, updated_at FROM user_profile ORDER BY key"
+            ).fetchall()
+            jobs = db.execute(
+                "SELECT * FROM scheduled_jobs ORDER BY enabled DESC, next_run_at"
+            ).fetchall()
+            outbox = db.execute(
+                "SELECT id, content, created_at, delivered_at FROM outbox ORDER BY id DESC LIMIT ?",
+                (limit,),
+            ).fetchall()
+
+        profile = []
+        for row in profile_rows:
+            item = dict(row)
+            try:
+                item["value"] = json.loads(item["value"])
+            except json.JSONDecodeError:
+                pass
+            profile.append(item)
+
+        return {
+            "path": str(self.path),
+            "memories": [self._memory_row(row) for row in memories],
+            "conversations": [dict(row) for row in conversations],
+            "profile": profile,
+            "scheduled_jobs": [dict(row) for row in jobs],
+            "outbox": [dict(row) for row in outbox],
+        }
+
+    def clear(
+        self,
+        include_memories: bool = True,
+        include_conversations: bool = True,
+        include_profile: bool = False,
+        include_jobs: bool = False,
+        include_outbox: bool = True,
+    ) -> dict[str, int]:
+        deleted: dict[str, int] = {}
+        with self._connect() as db:
+            if include_memories:
+                deleted["memories"] = db.execute("DELETE FROM memories").rowcount
+                db.execute("INSERT INTO memories_fts(memories_fts) VALUES('rebuild')")
+            if include_conversations:
+                deleted["conversations"] = db.execute("DELETE FROM conversations").rowcount
+            if include_profile:
+                deleted["profile"] = db.execute("DELETE FROM user_profile").rowcount
+            if include_jobs:
+                deleted["scheduled_jobs"] = db.execute("DELETE FROM scheduled_jobs").rowcount
+            if include_outbox:
+                deleted["outbox"] = db.execute("DELETE FROM outbox").rowcount
+        return deleted
+
+    def set_profile(self, key: str, value: Any) -> None:
+        with self._connect() as db:
+            db.execute(
+                """
+                INSERT INTO user_profile(key, value, updated_at) VALUES (?, ?, ?)
+                ON CONFLICT(key) DO UPDATE SET value=excluded.value, updated_at=excluded.updated_at
+                """,
+                (key, json.dumps(value), iso_now()),
+            )
+
+    def get_profile(self) -> dict[str, Any]:
+        with self._connect() as db:
+            rows = db.execute("SELECT key, value FROM user_profile").fetchall()
+        profile = {}
+        for row in rows:
+            try:
+                profile[row["key"]] = json.loads(row["value"])
+            except json.JSONDecodeError:
+                profile[row["key"]] = row["value"]
+        return profile
+
+    def add_job(
+        self,
+        job_id: str,
+        prompt: str,
+        trigger_type: str,
+        trigger_value: str,
+        next_run_at: str | None = None,
+    ) -> dict[str, Any]:
+        with self._connect() as db:
+            db.execute(
+                """
+                INSERT INTO scheduled_jobs(id, prompt, trigger_type, trigger_value, next_run_at, created_at, enabled)
+                VALUES (?, ?, ?, ?, ?, ?, 1)
+                ON CONFLICT(id) DO UPDATE SET
+                    prompt=excluded.prompt,
+                    trigger_type=excluded.trigger_type,
+                    trigger_value=excluded.trigger_value,
+                    next_run_at=excluded.next_run_at,
+                    enabled=1
+                """,
+                (job_id, prompt, trigger_type, trigger_value, next_run_at, iso_now()),
+            )
+        return {
+            "id": job_id,
+            "prompt": prompt,
+            "trigger_type": trigger_type,
+            "trigger_value": trigger_value,
+            "next_run_at": next_run_at,
+        }
+
+    def list_jobs(self) -> list[dict[str, Any]]:
+        with self._connect() as db:
+            rows = db.execute(
+                "SELECT * FROM scheduled_jobs WHERE enabled = 1 ORDER BY next_run_at IS NULL, next_run_at"
+            ).fetchall()
+        return [dict(row) for row in rows]
+
+    def mark_job_run(self, job_id: str, next_run_at: str | None = None) -> None:
+        with self._connect() as db:
+            db.execute(
+                "UPDATE scheduled_jobs SET last_run_at = ?, next_run_at = ? WHERE id = ?",
+                (iso_now(), next_run_at, job_id),
+            )
+
+    def add_outbox(self, content: str) -> None:
+        with self._connect() as db:
+            db.execute("INSERT INTO outbox(content, created_at) VALUES (?, ?)", (content, iso_now()))
+
+    def undelivered_outbox(self) -> list[dict[str, Any]]:
+        now = iso_now()
+        with self._connect() as db:
+            rows = db.execute(
+                "SELECT id, content, created_at FROM outbox WHERE delivered_at IS NULL ORDER BY id"
+            ).fetchall()
+            db.execute(
+                "UPDATE outbox SET delivered_at = ? WHERE delivered_at IS NULL",
+                (now,),
+            )
+        return [dict(row) for row in rows]
+
+    @staticmethod
+    def _fts_query(query: str) -> str:
+        tokens = [token.replace('"', "") for token in query.split() if token.strip()]
+        return " OR ".join(f'"{token}"' for token in tokens) or '""'
+
+    @staticmethod
+    def _memory_row(row: sqlite3.Row) -> dict[str, Any]:
+        data = dict(row)
+        if "metadata" in data:
+            try:
+                data["metadata"] = json.loads(data["metadata"])
+            except json.JSONDecodeError:
+                data["metadata"] = {}
+        return data

traderai/scheduler.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+from uuid import uuid4
+
+from apscheduler.schedulers.asyncio import AsyncIOScheduler
+from apscheduler.triggers.cron import CronTrigger
+from apscheduler.triggers.date import DateTrigger
+from tzlocal import get_localzone
+
+from traderai.memory import MemoryStore, iso_now, time_since
+
+
+class WakeScheduler:
+    def __init__(self, memory: MemoryStore) -> None:
+        self.memory = memory
+        self.scheduler = AsyncIOScheduler(timezone=get_localzone())
+        self.agent = None
+
+    def bind_agent(self, agent: Any) -> None:
+        self.agent = agent
+
+    def start(self) -> None:
+        if not self.scheduler.running:
+            self.scheduler.start()
+        for job in self.memory.list_jobs():
+            self._schedule_existing(job)
+
+    def shutdown(self) -> None:
+        if self.scheduler.running:
+            self.scheduler.shutdown(wait=False)
+
+    def schedule_date(self, run_at: str, prompt: str, job_id: str | None = None) -> dict[str, Any]:
+        parsed = datetime.fromisoformat(run_at)
+        job_id = job_id or f"wake-{uuid4()}"
+        trigger = DateTrigger(run_date=parsed)
+        self.scheduler.add_job(self._run_job, trigger=trigger, id=job_id, args=[job_id, prompt], replace_existing=True)
+        return self.memory.add_job(job_id, prompt, "date", run_at, parsed.isoformat())
+
+    def schedule_cron(self, cron: str, prompt: str, job_id: str | None = None) -> dict[str, Any]:
+        job_id = job_id or f"wake-{uuid4()}"
+        trigger = CronTrigger.from_crontab(cron)
+        self.scheduler.add_job(self._run_job, trigger=trigger, id=job_id, args=[job_id, prompt], replace_existing=True)
+        next_run = self.scheduler.get_job(job_id).next_run_time
+        return self.memory.add_job(job_id, prompt, "cron", cron, next_run.isoformat() if next_run else None)
+
+    def list_jobs(self) -> list[dict[str, Any]]:
+        return self.memory.list_jobs()
+
+    def _schedule_existing(self, job: dict[str, Any]) -> None:
+        if job["trigger_type"] == "cron":
+            trigger = CronTrigger.from_crontab(job["trigger_value"])
+        elif job["trigger_type"] == "date":
+            trigger = DateTrigger(run_date=datetime.fromisoformat(job["trigger_value"]))
+        else:
+            return
+        self.scheduler.add_job(
+            self._run_job,
+            trigger=trigger,
+            id=job["id"],
+            args=[job["id"], job["prompt"]],
+            replace_existing=True,
+        )
+
+    async def _run_job(self, job_id: str, prompt: str) -> None:
+        last = self.memory.last_interaction()
+        last_text = f"{last['created_at']} ({time_since(last['created_at'])})" if last else "never"
+        wake_message = (
+            f"Scheduled wake job fired. Current time is {iso_now()}. "
+            f"The last chat interaction was {last_text}. Job instruction: {prompt}"
+        )
+        if self.agent is None:
+            self.memory.add_outbox(wake_message)
+            return
+
+        text = await self.agent.generate_wake_response(wake_message)
+        self.memory.add_outbox(text)
+        self.memory.mark_job_run(job_id)

traderai/server.py

@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+from pathlib import Path
+import json
+
+from fastapi import FastAPI
+from fastapi import HTTPException
+from fastapi.responses import FileResponse, StreamingResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+
+from traderai.agent import OllamaAgent, OllamaUnavailable
+from traderai.config import get_settings
+from traderai.memory import MemoryStore
+from traderai.scheduler import WakeScheduler
+from traderai.tools import ToolRegistry
+from traderai.uex_client import UEXClient
+
+
+class ChatRequest(BaseModel):
+    message: str
+
+
+class ClearMemoryRequest(BaseModel):
+    include_memories: bool = True
+    include_conversations: bool = True
+    include_profile: bool = False
+    include_jobs: bool = False
+    include_outbox: bool = True
+
+
+def create_app() -> FastAPI:
+    settings = get_settings()
+    memory = MemoryStore(settings.traderai_memory_path)
+    scheduler = WakeScheduler(memory)
+    uex = UEXClient(settings.uex_base_url, settings.uex_secret_key, settings.uex_bearer_token)
+    tools = ToolRegistry(uex, settings.require_write_approval, memory=memory, scheduler=scheduler)
+    agent = OllamaAgent(settings.ollama_base_url, settings.ollama_model, tools, memory=memory, user_name=settings.traderai_user_name)
+    scheduler.bind_agent(agent)
+
+    app = FastAPI(title="TraderAI")
+    static_dir = Path(__file__).resolve().parent.parent / "web"
+    app.mount("/static", StaticFiles(directory=static_dir), name="static")
+
+    @app.on_event("startup")
+    async def startup() -> None:
+        await refresh_user_profile()
+        scheduler.start()
+
+    @app.on_event("shutdown")
+    async def shutdown() -> None:
+        scheduler.shutdown()
+
+    async def refresh_user_profile() -> None:
+        if settings.traderai_user_name:
+            memory.set_profile("configured_name", settings.traderai_user_name)
+            agent.user_name = agent.user_name or settings.traderai_user_name
+
+        try:
+            response = await uex.get_user(authenticated=True)
+        except Exception as exc:
+            memory.set_profile("uex_user_error", str(exc))
+            if settings.traderai_user_name:
+                try:
+                    response = await uex.get_user(username=settings.traderai_user_name)
+                except Exception:
+                    return
+            else:
+                return
+
+        data = response.get("user")
+        if data:
+            memory.set_profile("uex_user", data)
+            username = data.get("username") or data.get("user_username") or data.get("name")
+            if username:
+                agent.user_name = username
+
+    @app.get("/")
+    async def index() -> FileResponse:
+        return FileResponse(static_dir / "index.html")
+
+    @app.get("/api/health")
+    async def health() -> dict:
+        return {
+            "ollama": await agent.health(),
+            "user": memory.get_profile(),
+            "jobs": scheduler.list_jobs(),
+        }
+
+    @app.post("/api/chat")
+    async def chat(request: ChatRequest) -> dict:
+        try:
+            return await agent.chat(request.message)
+        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):
+                yield f"data: {json.dumps(event)}\n\n"
+
+        return StreamingResponse(events(), media_type="text/event-stream")
+
+    @app.get("/api/pending-actions")
+    async def pending_actions() -> dict:
+        return {"pending_actions": agent._pending_payloads()}
+
+    @app.get("/api/notifications")
+    async def notifications() -> dict:
+        return {"notifications": memory.undelivered_outbox()}
+
+    @app.get("/api/wake-jobs")
+    async def wake_jobs() -> dict:
+        return {"scheduled_jobs": scheduler.list_jobs()}
+
+    @app.get("/api/memory")
+    async def inspect_memory(limit: int = 50) -> dict:
+        return memory.inspect(max(1, min(limit, 200)))
+
+    @app.post("/api/memory/clear")
+    async def clear_memory(request: ClearMemoryRequest) -> dict:
+        if request.include_jobs:
+            scheduler.shutdown()
+        deleted = memory.clear(
+            include_memories=request.include_memories,
+            include_conversations=request.include_conversations,
+            include_profile=request.include_profile,
+            include_jobs=request.include_jobs,
+            include_outbox=request.include_outbox,
+        )
+        if request.include_jobs:
+            scheduler.start()
+        return {"deleted": deleted, "memory": memory.inspect(50)}
+
+    @app.post("/api/approve/{action_id}")
+    async def approve(action_id: str) -> dict:
+        return await tools.approve(action_id)
+
+    return app
+
+
+app = create_app()

traderai/tools.py

@@ -0,0 +1,357 @@
+from __future__ import annotations
+
+import uuid
+from dataclasses import dataclass
+from typing import Any, Awaitable, Callable
+
+from traderai.memory import MemoryStore
+from traderai.scheduler import WakeScheduler
+from traderai.uex_client import UEXClient
+
+
+ToolHandler = Callable[..., Awaitable[dict[str, Any]]]
+
+
+@dataclass
+class PendingAction:
+    id: str
+    label: str
+    endpoint: str
+    payload: dict[str, Any]
+
+
+class ToolRegistry:
+    def __init__(
+        self,
+        uex: UEXClient,
+        require_write_approval: bool = True,
+        memory: MemoryStore | None = None,
+        scheduler: WakeScheduler | None = None,
+    ) -> None:
+        self.uex = uex
+        self.require_write_approval = require_write_approval
+        self.memory = memory
+        self.scheduler = scheduler
+        self.pending_actions: dict[str, PendingAction] = {}
+        self.handlers: dict[str, ToolHandler] = {
+            "search_marketplace_listings": self.search_marketplace_listings,
+            "get_marketplace_listing": self.get_marketplace_listing,
+            "list_marketplace_negotiations": self.list_marketplace_negotiations,
+            "get_negotiation_messages": self.get_negotiation_messages,
+            "draft_negotiation_message": self.draft_negotiation_message,
+            "draft_marketplace_listing": self.draft_marketplace_listing,
+            "remember_user_fact": self.remember_user_fact,
+            "recall_memory": self.recall_memory,
+            "schedule_wake_job": self.schedule_wake_job,
+            "list_wake_jobs": self.list_wake_jobs,
+        }
+
+    @property
+    def schemas(self) -> list[dict[str, Any]]:
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "search_marketplace_listings",
+                    "description": "Search active UEX marketplace listings. UEX returns up to 100 active listings; filters are applied locally.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "query": {"type": "string", "description": "Text to search in title, description, location, advertiser, or slug."},
+                            "operation": {"type": "string", "enum": ["buy", "sell"]},
+                            "type": {"type": "string", "enum": ["item", "service", "contract"]},
+                            "username": {"type": "string", "description": "Advertiser IGN."},
+                            "location": {"type": "string"},
+                            "min_price": {"type": "number"},
+                            "max_price": {"type": "number"},
+                            "limit": {"type": "integer", "minimum": 1, "maximum": 25},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_marketplace_listing",
+                    "description": "Fetch a specific UEX marketplace listing by id or slug.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "id": {"type": "integer"},
+                            "slug": {"type": "string"},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "list_marketplace_negotiations",
+                    "description": "List authenticated marketplace negotiations for the configured UEX user.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "id": {"type": "integer"},
+                            "id_listing": {"type": "integer"},
+                            "hash": {"type": "string"},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_negotiation_messages",
+                    "description": "Fetch authenticated messages from a marketplace negotiation by hash or id_negotiation.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "hash": {"type": "string"},
+                            "id_negotiation": {"type": "integer"},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "draft_negotiation_message",
+                    "description": "Draft a message or offer to a UEX negotiation. This creates a pending action that must be approved before sending.",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["message"],
+                        "properties": {
+                            "message": {"type": "string"},
+                            "hash": {"type": "string"},
+                            "id_negotiation": {"type": "integer"},
+                            "is_production": {"type": "integer", "enum": [0, 1], "default": 0},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "draft_marketplace_listing",
+                    "description": "Draft a new UEX marketplace listing. This creates a pending action that must be approved before posting.",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["id_category", "operation", "type", "unit", "title", "description", "price", "currency", "language"],
+                        "properties": {
+                            "id_item": {"type": "integer"},
+                            "id_star_system": {"type": "integer"},
+                            "id_organization": {"type": "integer"},
+                            "id_category": {"type": "integer"},
+                            "operation": {"type": "string", "enum": ["buy", "sell"]},
+                            "type": {"type": "string", "enum": ["item", "service", "contract"]},
+                            "unit": {"type": "string"},
+                            "title": {"type": "string"},
+                            "description": {"type": "string"},
+                            "price": {"type": "number"},
+                            "currency": {"type": "string", "enum": ["UEC", "WIF"]},
+                            "language": {"type": "string", "default": "en_US"},
+                            "location": {"type": "string"},
+                            "source": {"type": "string"},
+                            "availability": {"type": "string"},
+                            "in_stock": {"type": "integer"},
+                            "hours_expiration": {"type": "integer"},
+                            "is_hidden": {"type": "integer", "enum": [0, 1]},
+                            "is_production": {"type": "integer", "enum": [0, 1], "default": 0},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "remember_user_fact",
+                    "description": "Persist a durable user preference, identity detail, trading rule, or long-term note for future chats.",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["content"],
+                        "properties": {
+                            "content": {"type": "string"},
+                            "kind": {"type": "string", "enum": ["user", "preference", "trading", "project", "note"], "default": "note"},
+                            "importance": {"type": "integer", "minimum": 1, "maximum": 5, "default": 3},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "recall_memory",
+                    "description": "Search long-term memory for relevant prior facts, preferences, and chat context.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "query": {"type": "string"},
+                            "limit": {"type": "integer", "minimum": 1, "maximum": 10, "default": 6},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "schedule_wake_job",
+                    "description": "Create a scheduled wake-up job for the assistant. Use either run_at for one-time jobs or cron for recurring jobs.",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["prompt"],
+                        "properties": {
+                            "prompt": {"type": "string", "description": "What the AI should consider or do when it wakes."},
+                            "run_at": {"type": "string", "description": "ISO datetime for a one-time wake job, such as 2026-05-05T20:30:00-04:00."},
+                            "cron": {"type": "string", "description": "Five-field cron expression for recurring jobs, such as 0 9 * * *."},
+                        },
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "list_wake_jobs",
+                    "description": "List currently enabled scheduled assistant wake jobs.",
+                    "parameters": {"type": "object", "properties": {}},
+                },
+            },
+        ]
+
+    async def execute(self, name: str, arguments: dict[str, Any]) -> dict[str, Any]:
+        handler = self.handlers.get(name)
+        if not handler:
+            return {"error": f"Unknown tool: {name}"}
+        try:
+            return await handler(**arguments)
+        except Exception as exc:
+            return {"error": str(exc)}
+
+    async def approve(self, action_id: str) -> dict[str, Any]:
+        action = self.pending_actions.pop(action_id, None)
+        if not action:
+            return {"error": f"Pending action not found: {action_id}"}
+        return await self.uex.post(action.endpoint, action.payload, authenticated=True)
+
+    async def search_marketplace_listings(
+        self,
+        query: str | None = None,
+        operation: str | None = None,
+        type: str | None = None,
+        username: str | None = None,
+        location: str | None = None,
+        min_price: float | None = None,
+        max_price: float | None = None,
+        limit: int = 10,
+    ) -> dict[str, Any]:
+        response = await self.uex.get("marketplace_listings", {"username": username})
+        listings = response.get("data") or []
+        filtered = []
+        q = (query or "").casefold()
+        loc = (location or "").casefold()
+        for listing in listings:
+            if operation and listing.get("operation") != operation:
+                continue
+            if type and listing.get("type") != type:
+                continue
+            if min_price is not None and float(listing.get("price") or 0) < min_price:
+                continue
+            if max_price is not None and float(listing.get("price") or 0) > max_price:
+                continue
+            if loc and loc not in str(listing.get("location") or "").casefold():
+                continue
+            haystack = " ".join(str(listing.get(k) or "") for k in ["title", "description", "location", "user_username", "slug"]).casefold()
+            if q and q not in haystack:
+                continue
+            filtered.append(self._summarize_listing(listing))
+            if len(filtered) >= max(1, min(limit, 25)):
+                break
+        return {"count": len(filtered), "listings": filtered}
+
+    async def get_marketplace_listing(self, id: int | None = None, slug: str | None = None) -> dict[str, Any]:
+        response = await self.uex.get("marketplace_listings", {"id": id, "slug": slug})
+        return {"listing": response.get("data")}
+
+    async def list_marketplace_negotiations(
+        self,
+        id: int | None = None,
+        id_listing: int | None = None,
+        hash: str | None = None,
+    ) -> dict[str, Any]:
+        return await self.uex.get("marketplace_negotiations", {"id": id, "id_listing": id_listing, "hash": hash}, authenticated=True)
+
+    async def get_negotiation_messages(self, hash: str | None = None, id_negotiation: int | None = None) -> dict[str, Any]:
+        return await self.uex.get("marketplace_negotiations_messages", {"hash": hash, "id_negotiation": id_negotiation}, authenticated=True)
+
+    async def draft_negotiation_message(
+        self,
+        message: str,
+        hash: str | None = None,
+        id_negotiation: int | None = None,
+        is_production: int = 0,
+    ) -> dict[str, Any]:
+        payload = {"message": message, "hash": hash, "id_negotiation": id_negotiation, "is_production": is_production}
+        return self._pending("Send negotiation message", "marketplace_negotiations_messages", payload)
+
+    async def draft_marketplace_listing(self, **payload: Any) -> dict[str, Any]:
+        return self._pending("Post marketplace listing", "marketplace_advertise", payload)
+
+    async def remember_user_fact(self, content: str, kind: str = "note", importance: int = 3) -> dict[str, Any]:
+        if self.memory is None:
+            return {"error": "Memory store is not configured."}
+        return {"memory": self.memory.remember(kind, content, importance)}
+
+    async def recall_memory(self, query: str = "", limit: int = 6) -> dict[str, Any]:
+        if self.memory is None:
+            return {"error": "Memory store is not configured."}
+        return {"memories": self.memory.recall(query, max(1, min(limit, 10)))}
+
+    async def schedule_wake_job(
+        self,
+        prompt: str,
+        run_at: str | None = None,
+        cron: str | None = None,
+    ) -> dict[str, Any]:
+        if self.scheduler is None:
+            return {"error": "Scheduler is not configured."}
+        if bool(run_at) == bool(cron):
+            return {"error": "Provide exactly one of run_at or cron."}
+        if run_at:
+            return {"scheduled_job": self.scheduler.schedule_date(run_at, prompt)}
+        return {"scheduled_job": self.scheduler.schedule_cron(cron or "", prompt)}
+
+    async def list_wake_jobs(self) -> dict[str, Any]:
+        if self.scheduler is None:
+            return {"error": "Scheduler is not configured."}
+        return {"scheduled_jobs": self.scheduler.list_jobs()}
+
+    def _pending(self, label: str, endpoint: str, payload: dict[str, Any]) -> dict[str, Any]:
+        action_id = str(uuid.uuid4())
+        payload = {key: value for key, value in payload.items() if value is not None}
+        self.pending_actions[action_id] = PendingAction(action_id, label, endpoint, payload)
+        return {
+            "pending_action": {
+                "id": action_id,
+                "label": label,
+                "endpoint": endpoint,
+                "payload": payload,
+                "approval_required": self.require_write_approval,
+            }
+        }
+
+    @staticmethod
+    def _summarize_listing(listing: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "id": listing.get("id"),
+            "slug": listing.get("slug"),
+            "title": listing.get("title"),
+            "operation": listing.get("operation"),
+            "type": listing.get("type"),
+            "price": listing.get("price"),
+            "currency": listing.get("currency"),
+            "unit": listing.get("unit"),
+            "location": listing.get("location"),
+            "availability": listing.get("availability"),
+            "in_stock": listing.get("in_stock"),
+            "advertiser": listing.get("user_username"),
+            "expires_at": listing.get("date_expiration"),
+        }

traderai/uex_client.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+from typing import Any
+
+import httpx
+
+
+class UEXError(RuntimeError):
+    pass
+
+
+class UEXClient:
+    def __init__(self, base_url: str, secret_key: str | None = None, bearer_token: str | None = None) -> None:
+        self.base_url = base_url.rstrip("/")
+        self.secret_key = secret_key
+        self.bearer_token = bearer_token
+
+    def _headers(self, authenticated: bool = False) -> dict[str, str]:
+        headers = {"Accept": "application/json"}
+        if authenticated:
+            if not self.secret_key and not self.bearer_token:
+                raise UEXError("UEX_SECRET_KEY or UEX_BEARER_TOKEN is required for this action.")
+            if self.secret_key:
+                headers["secret-key"] = self.secret_key
+            if self.bearer_token:
+                headers["Authorization"] = f"Bearer {self.bearer_token}"
+        return headers
+
+    async def get(self, path: str, params: dict[str, Any] | None = None, authenticated: bool = False) -> dict[str, Any]:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.get(
+                f"{self.base_url}/{path.strip('/')}/",
+                params={k: v for k, v in (params or {}).items() if v is not None},
+                headers=self._headers(authenticated),
+            )
+        return self._handle_response(response)
+
+    async def get_user(self, username: str | None = None, authenticated: bool = False) -> dict[str, Any]:
+        body = await self.get("user", {"username": username}, authenticated=authenticated)
+        data = body.get("data")
+        if isinstance(data, list):
+            data = data[0] if data else None
+        return {"status": body.get("status"), "user": data}
+
+    async def post(self, path: str, payload: dict[str, Any], authenticated: bool = True) -> dict[str, Any]:
+        async with httpx.AsyncClient(timeout=30) as client:
+            response = await client.post(
+                f"{self.base_url}/{path.strip('/')}/",
+                json=payload,
+                headers=self._headers(authenticated),
+            )
+        return self._handle_response(response)
+
+    @staticmethod
+    def _handle_response(response: httpx.Response) -> dict[str, Any]:
+        try:
+            body = response.json()
+        except ValueError as exc:
+            raise UEXError(f"UEX returned non-JSON response: HTTP {response.status_code}") from exc
+        if response.status_code >= 400:
+            raise UEXError(f"UEX HTTP {response.status_code}: {body}")
+        return body

uv.lock

@@ -0,0 +1,747 @@
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+
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+]
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+]

web/app.js

@@ -0,0 +1,505 @@
+const form = document.getElementById("chat-form");
+const input = document.getElementById("message-input");
+const messages = document.getElementById("messages");
+const statusEl = document.getElementById("status");
+const pendingEl = document.getElementById("pending-actions");
+const warningEl = document.getElementById("warning");
+const memoryInspectorEl = document.getElementById("memory-inspector");
+const memoryRefreshButton = document.getElementById("memory-refresh");
+const memoryClearButton = document.getElementById("memory-clear");
+
+let ollamaOnline = true;
+
+function addMessage(role, text) {
+  const node = document.createElement("div");
+  node.className = `message ${role}`;
+  setMessageMarkdown(node, text);
+  messages.appendChild(node);
+  messages.scrollTop = messages.scrollHeight;
+  return node;
+}
+
+function setMessageMarkdown(node, text) {
+  const body = node.querySelector(".message-body") || node;
+  body.innerHTML = renderMarkdown(text);
+}
+
+function setMessageActivity(node, text, active = false) {
+  const activity = node.querySelector(".message-activity");
+  if (!activity) return;
+  const phase = activity.querySelector(".message-phase");
+  phase.innerHTML = "";
+  if (text) {
+    const label = document.createElement("span");
+    label.textContent = text;
+    phase.appendChild(label);
+  }
+  if (active) {
+    const dots = document.createElement("span");
+    dots.className = "working-dots";
+    dots.innerHTML = "<i></i><i></i><i></i>";
+    phase.appendChild(dots);
+  }
+}
+
+function setMessageMetrics(node, metrics) {
+  const metricsEl = node.querySelector(".message-metrics");
+  if (!metricsEl) return;
+  metricsEl.textContent = metrics || "";
+}
+
+function ensureStreamingChrome(node) {
+  if (node.querySelector(".message-activity")) return;
+  node.innerHTML = "";
+  const activity = document.createElement("div");
+  activity.className = "message-activity";
+  const phase = document.createElement("span");
+  phase.className = "message-phase";
+  const metrics = document.createElement("span");
+  metrics.className = "message-metrics";
+  const body = document.createElement("div");
+  body.className = "message-body";
+  activity.append(phase, metrics);
+  node.append(activity, body);
+}
+
+function renderMarkdown(text) {
+  const lines = text.replace(/\r\n/g, "\n").split(/\n/);
+  const output = [];
+  let inList = false;
+  let inOrderedList = false;
+  let inCode = false;
+  let codeLines = [];
+
+  const closeLists = () => {
+    if (inList) {
+      output.push("</ul>");
+      inList = false;
+    }
+    if (inOrderedList) {
+      output.push("</ol>");
+      inOrderedList = false;
+    }
+  };
+
+  const flushCode = () => {
+    output.push(`<pre><code>${escapeHtml(codeLines.join("\n"))}</code></pre>`);
+    codeLines = [];
+    inCode = false;
+  };
+
+  const isTableAt = (index) => {
+    if (index + 1 >= lines.length) return false;
+    return isTableRow(lines[index]) && isTableDivider(lines[index + 1]);
+  };
+
+  for (let index = 0; index < lines.length; index += 1) {
+    const line = lines[index];
+    const trimmed = line.trim();
+
+    if (/^```/.test(trimmed)) {
+      if (inCode) {
+        flushCode();
+      } else {
+        closeLists();
+        inCode = true;
+      }
+      continue;
+    }
+
+    if (inCode) {
+      codeLines.push(line);
+      continue;
+    }
+
+    if (isTableAt(index)) {
+      closeLists();
+      const headers = splitTableRow(lines[index]);
+      const aligns = splitTableRow(lines[index + 1]).map((cell) => tableAlignment(cell));
+      const rows = [];
+      index += 2;
+      while (index < lines.length && isTableRow(lines[index])) {
+        rows.push(splitTableRow(lines[index]));
+        index += 1;
+      }
+      index -= 1;
+      output.push(renderTable(headers, aligns, rows));
+      continue;
+    }
+
+    const unorderedItem = line.match(/^(\s*)[-*+]\s+(.+)$/);
+    if (unorderedItem) {
+      if (inOrderedList) {
+        output.push("</ol>");
+        inOrderedList = false;
+      }
+      if (!inList) {
+        output.push("<ul>");
+        inList = true;
+      }
+      const nestedClass = unorderedItem[1].length >= 2 ? ' class="nested"' : "";
+      output.push(`<li${nestedClass}>${inlineMarkdown(unorderedItem[2])}</li>`);
+      continue;
+    }
+
+    const orderedItem = line.match(/^(\s*)\d+\.\s+(.+)$/);
+    if (orderedItem) {
+      if (inList) {
+        output.push("</ul>");
+        inList = false;
+      }
+      if (!inOrderedList) {
+        output.push("<ol>");
+        inOrderedList = true;
+      }
+      const nestedClass = orderedItem[1].length >= 2 ? ' class="nested"' : "";
+      output.push(`<li${nestedClass}>${inlineMarkdown(orderedItem[2])}</li>`);
+      continue;
+    }
+
+    closeLists();
+
+    if (/^>\s?/.test(trimmed)) {
+      output.push(`<blockquote>${inlineMarkdown(trimmed.replace(/^>\s?/, ""))}</blockquote>`);
+    } else if (/^---+$/.test(trimmed)) {
+      output.push("<hr>");
+    } else if (/^#{1,6}\s+/.test(trimmed)) {
+      const level = Math.min(4, trimmed.match(/^#+/)[0].length + 2);
+      output.push(`<h${level}>${inlineMarkdown(trimmed.replace(/^#{1,6}\s+/, ""))}</h${level}>`);
+    } else if (trimmed) {
+      output.push(`<p>${inlineMarkdown(line)}</p>`);
+    } else {
+      output.push("<br>");
+    }
+  }
+
+  if (inCode) flushCode();
+  closeLists();
+  return output.join("");
+}
+
+function inlineMarkdown(text) {
+  return escapeHtml(text)
+    .replace(/`([^`]+)`/g, "<code>$1</code>")
+    .replace(/\*\*(.+?)\*\*/g, "<strong>$1</strong>")
+    .replace(/\*([^*]+)\*/g, "<em>$1</em>")
+    .replace(/\[([^\]]+)\]\((https?:\/\/[^)\s]+)\)/g, '<a href="$2" target="_blank" rel="noreferrer">$1</a>');
+}
+
+function isTableRow(line) {
+  const trimmed = line.trim();
+  return trimmed.includes("|") && /^\|?.+\|.+\|?$/.test(trimmed);
+}
+
+function isTableDivider(line) {
+  return splitTableRow(line).every((cell) => /^:?-{3,}:?$/.test(cell.trim()));
+}
+
+function splitTableRow(line) {
+  return line
+    .trim()
+    .replace(/^\|/, "")
+    .replace(/\|$/, "")
+    .split("|")
+    .map((cell) => cell.trim());
+}
+
+function tableAlignment(cell) {
+  const trimmed = cell.trim();
+  if (trimmed.startsWith(":") && trimmed.endsWith(":")) return "center";
+  if (trimmed.endsWith(":")) return "right";
+  return "left";
+}
+
+function renderTable(headers, aligns, rows) {
+  const head = headers
+    .map((cell, index) => `<th style="text-align:${aligns[index] || "left"}">${inlineMarkdown(cell)}</th>`)
+    .join("");
+  const body = rows
+    .map((row) => {
+      const cells = headers
+        .map((_, index) => `<td style="text-align:${aligns[index] || "left"}">${inlineMarkdown(row[index] || "")}</td>`)
+        .join("");
+      return `<tr>${cells}</tr>`;
+    })
+    .join("");
+  return `<div class="table-wrap"><table><thead><tr>${head}</tr></thead><tbody>${body}</tbody></table></div>`;
+}
+
+function escapeHtml(text) {
+  return text
+    .replace(/&/g, "&amp;")
+    .replace(/</g, "&lt;")
+    .replace(/>/g, "&gt;")
+    .replace(/"/g, "&quot;")
+    .replace(/'/g, "&#039;");
+}
+
+function formatMetrics(event) {
+  const read = formatTokenMetric(event.reading_tokens, event.reading_tokens_per_second);
+  const wrote = formatTokenMetric(event.writing_tokens, event.writing_tokens_per_second);
+  return [read && `read ${read}`, wrote && `wrote ${wrote}`].filter(Boolean).join(" | ");
+}
+
+function formatTokenMetric(tokens, speed) {
+  if (!tokens) return "";
+  const speedText = typeof speed === "number" ? ` @ ${speed.toFixed(1)}/s` : "";
+  return `${tokens} tok${speedText}`;
+}
+
+function setWarning(text) {
+  warningEl.hidden = !text;
+  warningEl.textContent = text || "";
+}
+
+async function checkHealth() {
+  try {
+    const response = await fetch("/api/health");
+    const result = await response.json();
+    const health = result.ollama || {};
+    ollamaOnline = Boolean(health.online);
+    if (!ollamaOnline) {
+      statusEl.textContent = "Offline";
+      setWarning(health.message || "Ollama is offline. Start Ollama before chatting.");
+      return false;
+    }
+    if (health.model_available === false) {
+      setWarning(`Ollama is online, but model "${health.model}" is not pulled. Run: ollama pull ${health.model}`);
+    } else {
+      setWarning("");
+    }
+    statusEl.textContent = "Ready";
+    return true;
+  } catch (error) {
+    ollamaOnline = false;
+    statusEl.textContent = "Offline";
+    setWarning(`Could not check Ollama health: ${error.message}`);
+    return false;
+  }
+}
+
+function renderPending(actions) {
+  pendingEl.innerHTML = "";
+  if (!actions.length) {
+    pendingEl.className = "pending-empty";
+    pendingEl.textContent = "No pending actions";
+    return;
+  }
+  pendingEl.className = "";
+  for (const action of actions) {
+    const card = document.createElement("div");
+    card.className = "pending-card";
+    const title = document.createElement("strong");
+    title.textContent = action.label;
+    const endpoint = document.createElement("p");
+    endpoint.className = "muted";
+    endpoint.textContent = action.endpoint;
+    const payload = document.createElement("pre");
+    payload.textContent = JSON.stringify(action.payload, null, 2);
+    const approve = document.createElement("button");
+    approve.textContent = "Approve";
+    approve.addEventListener("click", () => approveAction(action.id));
+    card.append(title, endpoint, payload, approve);
+    pendingEl.appendChild(card);
+  }
+}
+
+async function approveAction(id) {
+  statusEl.textContent = "Sending";
+  try {
+    const response = await fetch(`/api/approve/${id}`, { method: "POST" });
+    const result = await response.json();
+    addMessage("assistant", `Approval result:\n${JSON.stringify(result, null, 2)}`);
+    await refreshPending();
+  } catch (error) {
+    addMessage("assistant", `Approval failed: ${error.message}`);
+  } finally {
+    statusEl.textContent = "Ready";
+  }
+}
+
+async function refreshPending() {
+  const response = await fetch("/api/pending-actions");
+  const result = await response.json();
+  renderPending(result.pending_actions || []);
+}
+
+function renderMemory(data) {
+  memoryInspectorEl.innerHTML = "";
+  const counts = document.createElement("div");
+  counts.className = "memory-counts";
+  counts.textContent = `${data.memories.length} memories | ${data.conversations.length} chat rows | ${data.profile.length} profile keys | ${data.scheduled_jobs.length} jobs`;
+  memoryInspectorEl.appendChild(counts);
+
+  const path = document.createElement("div");
+  path.className = "memory-path";
+  path.textContent = data.path;
+  memoryInspectorEl.appendChild(path);
+
+  memoryInspectorEl.appendChild(memoryGroup("Memories", data.memories, (item) => `${item.kind} (${item.importance})\n${item.content}`));
+  memoryInspectorEl.appendChild(memoryGroup("Profile", data.profile, (item) => `${item.key}\n${JSON.stringify(item.value, null, 2)}`));
+  memoryInspectorEl.appendChild(memoryGroup("Recent Chat", data.conversations, (item) => `${item.created_at} ${item.role}\n${item.content}`));
+  memoryInspectorEl.appendChild(memoryGroup("Wake Jobs", data.scheduled_jobs, (item) => `${item.id}\n${item.trigger_type}: ${item.trigger_value}\n${item.prompt}`));
+}
+
+function memoryGroup(title, items, formatter) {
+  const group = document.createElement("details");
+  group.className = "memory-group";
+  const summary = document.createElement("summary");
+  summary.textContent = `${title} (${items.length})`;
+  group.appendChild(summary);
+  if (!items.length) {
+    const empty = document.createElement("p");
+    empty.className = "muted";
+    empty.textContent = "Empty";
+    group.appendChild(empty);
+    return group;
+  }
+  for (const item of items.slice(0, 20)) {
+    const entry = document.createElement("pre");
+    entry.textContent = formatter(item);
+    group.appendChild(entry);
+  }
+  return group;
+}
+
+async function refreshMemory() {
+  try {
+    const response = await fetch("/api/memory?limit=50");
+    const data = await response.json();
+    renderMemory(data);
+  } catch (error) {
+    memoryInspectorEl.textContent = `Memory load failed: ${error.message}`;
+  }
+}
+
+async function clearMemory() {
+  const payload = {
+    include_memories: document.getElementById("clear-memories").checked,
+    include_conversations: document.getElementById("clear-conversations").checked,
+    include_profile: document.getElementById("clear-profile").checked,
+    include_jobs: document.getElementById("clear-jobs").checked,
+    include_outbox: document.getElementById("clear-outbox").checked,
+  };
+  if (!Object.values(payload).some(Boolean)) return;
+  const confirmed = window.confirm("Clear the selected TraderAI memory? This cannot be undone.");
+  if (!confirmed) return;
+  try {
+    const response = await fetch("/api/memory/clear", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(payload),
+    });
+    const result = await response.json();
+    renderMemory(result.memory);
+    addMessage("assistant", `Memory cleared:\n${JSON.stringify(result.deleted, null, 2)}`);
+  } catch (error) {
+    addMessage("assistant warning-message", `Memory clear failed: ${error.message}`);
+  }
+}
+
+async function pollNotifications() {
+  try {
+    const response = await fetch("/api/notifications");
+    const result = await response.json();
+    for (const notification of result.notifications || []) {
+      addMessage("assistant", notification.content);
+    }
+  } catch {
+    // Notification polling should never interrupt chat.
+  }
+}
+
+form.addEventListener("submit", async (event) => {
+  event.preventDefault();
+  await sendMessage();
+});
+
+input.addEventListener("keydown", async (event) => {
+  if (event.key === "Enter" && !event.shiftKey) {
+    event.preventDefault();
+    await sendMessage();
+  }
+});
+
+memoryRefreshButton.addEventListener("click", refreshMemory);
+memoryClearButton.addEventListener("click", clearMemory);
+
+async function sendMessage() {
+  const message = input.value.trim();
+  if (!message || input.disabled) return;
+  const healthy = await checkHealth();
+  if (!healthy) {
+    addMessage("assistant warning-message", "Ollama is offline. Start Ollama, then try again.");
+    return;
+  }
+  input.value = "";
+  input.disabled = true;
+  addMessage("user", message);
+  const assistantNode = addMessage("assistant streaming", "");
+  ensureStreamingChrome(assistantNode);
+  let assistantText = "";
+  statusEl.textContent = "Working";
+  setMessageActivity(assistantNode, "Thinking", true);
+  setMessageMetrics(assistantNode, "");
+  try {
+    const response = await fetch("/api/chat/stream", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ message }),
+    });
+    if (!response.ok || !response.body) {
+      throw new Error(`HTTP ${response.status}`);
+    }
+    const reader = response.body.getReader();
+    const decoder = new TextDecoder();
+    let buffer = "";
+    while (true) {
+      const { value, done } = await reader.read();
+      if (done) break;
+      buffer += decoder.decode(value, { stream: true });
+      const events = buffer.split("\n\n");
+      buffer = events.pop() || "";
+      for (const rawEvent of events) {
+        const line = rawEvent.split("\n").find((entry) => entry.startsWith("data: "));
+        if (!line) continue;
+        const event = JSON.parse(line.slice(6));
+        if (event.type === "status") {
+          setMessageActivity(assistantNode, event.message, true);
+        } else if (event.type === "metrics") {
+          setMessageMetrics(assistantNode, formatMetrics(event));
+        } else if (event.type === "warning") {
+          setWarning(event.message);
+          assistantText += event.message;
+          setMessageMarkdown(assistantNode, assistantText);
+        } else if (event.type === "token") {
+          assistantText += event.content;
+          setMessageMarkdown(assistantNode, assistantText);
+          messages.scrollTop = messages.scrollHeight;
+        } else if (event.type === "done") {
+          renderPending(event.pending_actions || []);
+        }
+      }
+    }
+  } catch (error) {
+    const message = error.message.includes("503")
+      ? "Ollama is offline or unreachable. Start Ollama, then try again."
+      : `Chat failed: ${error.message}`;
+    setWarning(message);
+    setMessageMarkdown(assistantNode, message);
+  } finally {
+    assistantNode.classList.remove("streaming");
+    input.disabled = false;
+    input.focus();
+    statusEl.textContent = "Ready";
+    setMessageActivity(assistantNode, "");
+  }
+}
+
+addMessage("assistant", "Tell me what to find or draft on UEX. I will ask for approval before sending anything.");
+refreshPending();
+refreshMemory();
+pollNotifications();
+checkHealth();
+setInterval(checkHealth, 30000);
+setInterval(pollNotifications, 15000);

web/index.html

@@ -0,0 +1,52 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <title>TraderAI</title>
+    <link rel="stylesheet" href="/static/styles.css">
+  </head>
+  <body>
+    <main class="shell">
+      <section class="workspace">
+        <header class="topbar">
+          <div>
+            <h1>TraderAI</h1>
+            <p>Local Ollama chat for UEX marketplace work</p>
+          </div>
+          <div class="status" id="status">Ready</div>
+        </header>
+        <div class="warning" id="warning" hidden></div>
+        <div class="messages" id="messages"></div>
+        <div class="composer-wrap">
+          <form class="composer" id="chat-form">
+            <textarea id="message-input" rows="2" placeholder="Search listings, draft a reply, prepare an offer..."></textarea>
+            <button type="submit">Send</button>
+          </form>
+        </div>
+      </section>
+      <aside class="actions">
+        <section class="side-section">
+          <h2>Pending Approval</h2>
+          <div id="pending-actions" class="pending-empty">No pending actions</div>
+        </section>
+        <section class="side-section">
+          <div class="section-title-row">
+            <h2>Memory</h2>
+            <button class="secondary small-button" id="memory-refresh" type="button">Refresh</button>
+          </div>
+          <div class="memory-controls">
+            <label><input type="checkbox" id="clear-memories" checked> Memories</label>
+            <label><input type="checkbox" id="clear-conversations" checked> Chat</label>
+            <label><input type="checkbox" id="clear-outbox" checked> Notices</label>
+            <label><input type="checkbox" id="clear-profile"> Profile</label>
+            <label><input type="checkbox" id="clear-jobs"> Jobs</label>
+          </div>
+          <button class="danger-button" id="memory-clear" type="button">Clear Selected</button>
+          <div id="memory-inspector" class="memory-inspector"></div>
+        </section>
+      </aside>
+    </main>
+    <script src="/static/app.js"></script>
+  </body>
+</html>

web/styles.css

@@ -0,0 +1,478 @@
+:root {
+  color-scheme: dark;
+  --bg: #111316;
+  --panel: #191d22;
+  --panel-2: #20262d;
+  --text: #edf1f5;
+  --muted: #97a1ad;
+  --accent: #44c2a5;
+  --accent-2: #e6b94d;
+  --border: #303842;
+  --danger: #e66b6b;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  min-height: 100vh;
+  background: var(--bg);
+  color: var(--text);
+  font-family: Inter, Segoe UI, Arial, sans-serif;
+}
+
+.shell {
+  display: grid;
+  grid-template-columns: minmax(0, 1fr) 360px;
+  gap: 1px;
+  min-height: 100vh;
+  background: var(--border);
+}
+
+.workspace,
+.actions {
+  background: var(--panel);
+}
+
+.workspace {
+  display: grid;
+  grid-template-rows: auto 1fr auto;
+  min-width: 0;
+}
+
+.topbar {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 16px;
+  padding: 18px 22px;
+  border-bottom: 1px solid var(--border);
+}
+
+h1,
+h2,
+p {
+  margin: 0;
+}
+
+h1 {
+  font-size: 22px;
+  font-weight: 700;
+}
+
+h2 {
+  font-size: 16px;
+  margin-bottom: 14px;
+}
+
+.topbar p,
+.muted {
+  color: var(--muted);
+  font-size: 13px;
+}
+
+.status {
+  min-width: 76px;
+  padding: 7px 10px;
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  color: var(--muted);
+  font-size: 13px;
+  text-align: center;
+}
+
+.warning {
+  padding: 10px 22px;
+  border-bottom: 1px solid rgba(230, 185, 77, 0.32);
+  background: rgba(230, 185, 77, 0.11);
+  color: #f0d28a;
+  font-size: 13px;
+}
+
+.messages {
+  overflow: auto;
+  padding: 22px;
+}
+
+.message {
+  max-width: 900px;
+  margin-bottom: 14px;
+  padding: 13px 14px;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  line-height: 1.45;
+}
+
+.message p {
+  margin: 0 0 8px;
+}
+
+.message p:last-child,
+.message ul:last-child,
+.message ol:last-child,
+.message blockquote:last-child,
+.message .table-wrap:last-child,
+.message pre:last-child {
+  margin-bottom: 0;
+}
+
+.message ul,
+.message ol {
+  margin: 0 0 10px 20px;
+  padding: 0;
+}
+
+.message li {
+  margin: 3px 0;
+}
+
+.message li.nested {
+  margin-left: 20px;
+  color: #d7dde4;
+}
+
+.message strong {
+  color: #ffffff;
+}
+
+.message h3 {
+  margin: 12px 0 8px;
+  font-size: 15px;
+  line-height: 1.3;
+}
+
+.message h4,
+.message h5,
+.message h6 {
+  margin: 10px 0 7px;
+  font-size: 14px;
+  line-height: 1.3;
+}
+
+.message hr {
+  height: 1px;
+  margin: 12px 0;
+  border: 0;
+  background: var(--border);
+}
+
+.message code {
+  padding: 1px 5px;
+  border: 1px solid var(--border);
+  border-radius: 4px;
+  background: #0e1114;
+  color: #dfe5eb;
+  font-family: Consolas, Monaco, monospace;
+  font-size: 0.92em;
+}
+
+.message pre {
+  max-height: none;
+  margin: 0 0 10px;
+  white-space: pre;
+}
+
+.message pre code {
+  display: block;
+  padding: 0;
+  border: 0;
+  background: transparent;
+}
+
+.message blockquote {
+  margin: 0 0 10px;
+  padding: 8px 10px;
+  border-left: 3px solid var(--accent);
+  background: rgba(68, 194, 165, 0.08);
+  color: #d7dde4;
+}
+
+.message a {
+  color: #69d7bd;
+}
+
+.table-wrap {
+  max-width: 100%;
+  margin: 0 0 10px;
+  overflow-x: auto;
+}
+
+.message table {
+  width: 100%;
+  min-width: 420px;
+  border-collapse: collapse;
+  font-size: 13px;
+}
+
+.message th,
+.message td {
+  padding: 8px 9px;
+  border: 1px solid var(--border);
+  vertical-align: top;
+}
+
+.message th {
+  background: #20262d;
+  color: #ffffff;
+  font-weight: 700;
+}
+
+.message.user {
+  margin-left: auto;
+  background: var(--panel-2);
+}
+
+.message.assistant {
+  background: #15191e;
+}
+
+.message.warning-message {
+  border-color: rgba(230, 185, 77, 0.42);
+  background: rgba(230, 185, 77, 0.1);
+}
+
+.composer-wrap {
+  border-top: 1px solid var(--border);
+}
+
+.message-activity {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 12px;
+  min-height: 20px;
+  margin-bottom: 6px;
+  color: rgba(151, 161, 173, 0.82);
+  font-size: 12px;
+}
+
+.message-phase {
+  display: inline-flex;
+  align-items: center;
+  min-width: 0;
+}
+
+.message-metrics {
+  color: rgba(151, 161, 173, 0.56);
+  text-align: right;
+  white-space: nowrap;
+}
+
+.working-dots {
+  display: inline-flex;
+  align-items: center;
+  gap: 4px;
+  margin-left: 8px;
+  transform: translateY(-1px);
+}
+
+.working-dots i {
+  width: 4px;
+  height: 4px;
+  border-radius: 50%;
+  background: rgba(151, 161, 173, 0.8);
+  animation: pulse-dot 1.2s infinite ease-in-out;
+}
+
+.working-dots i:nth-child(2) {
+  animation-delay: 0.16s;
+}
+
+.working-dots i:nth-child(3) {
+  animation-delay: 0.32s;
+}
+
+@keyframes pulse-dot {
+  0%,
+  80%,
+  100% {
+    opacity: 0.3;
+    transform: translateY(0);
+  }
+  40% {
+    opacity: 1;
+    transform: translateY(-2px);
+  }
+}
+
+.composer {
+  display: grid;
+  grid-template-columns: 1fr auto;
+  gap: 12px;
+  padding: 16px;
+}
+
+textarea {
+  width: 100%;
+  resize: vertical;
+  min-height: 48px;
+  max-height: 180px;
+  padding: 12px;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  outline: none;
+  background: #101316;
+  color: var(--text);
+  font: inherit;
+}
+
+textarea:disabled {
+  opacity: 0.62;
+}
+
+button {
+  min-width: 92px;
+  border: 0;
+  border-radius: 8px;
+  background: var(--accent);
+  color: #07110f;
+  font-weight: 700;
+  cursor: pointer;
+}
+
+button.secondary {
+  min-width: 0;
+  padding: 9px 12px;
+  border: 1px solid var(--border);
+  background: transparent;
+  color: var(--text);
+}
+
+.small-button {
+  min-width: 0;
+  padding: 6px 9px;
+  font-size: 12px;
+}
+
+.danger-button {
+  width: 100%;
+  min-width: 0;
+  padding: 9px 10px;
+  margin: 10px 0;
+  border: 1px solid rgba(230, 107, 107, 0.45);
+  background: rgba(230, 107, 107, 0.14);
+  color: #f0b1b1;
+}
+
+.actions {
+  padding: 18px;
+  overflow: auto;
+}
+
+.side-section {
+  margin-bottom: 22px;
+}
+
+.section-title-row {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 10px;
+}
+
+.memory-controls {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 6px 10px;
+  color: var(--muted);
+  font-size: 12px;
+}
+
+.memory-controls label {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+
+.memory-inspector {
+  font-size: 12px;
+}
+
+.memory-counts {
+  color: var(--text);
+  margin-bottom: 6px;
+}
+
+.memory-path {
+  overflow-wrap: anywhere;
+  color: var(--muted);
+  margin-bottom: 10px;
+}
+
+.memory-group {
+  margin-bottom: 8px;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  background: #15191e;
+}
+
+.memory-group summary {
+  cursor: pointer;
+  padding: 9px 10px;
+  color: var(--text);
+}
+
+.memory-group pre {
+  margin: 0 10px 10px;
+  white-space: pre-wrap;
+}
+
+.pending-empty {
+  color: var(--muted);
+  font-size: 13px;
+}
+
+.pending-card {
+  padding: 12px;
+  margin-bottom: 12px;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  background: #15191e;
+}
+
+.pending-card strong {
+  display: block;
+  margin-bottom: 8px;
+}
+
+pre {
+  overflow: auto;
+  max-height: 240px;
+  padding: 10px;
+  border-radius: 6px;
+  background: #0e1114;
+  color: #dfe5eb;
+  font-size: 12px;
+}
+
+.pending-card button {
+  width: 100%;
+  padding: 10px;
+}
+
+@media (max-width: 860px) {
+  .shell {
+    grid-template-columns: 1fr;
+  }
+
+  .actions {
+    border-top: 1px solid var(--border);
+  }
+}
+
+@media (max-width: 560px) {
+  .composer {
+    grid-template-columns: 1fr;
+  }
+
+  .composer button {
+    width: 100%;
+    min-height: 42px;
+  }
+
+  .message-metrics {
+    display: none;
+  }
+}