step 6 complete: four specialized agents, all registered and tested

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/agents/__init__.py

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+"""Import all agent modules to trigger @registry.register decorators."""
+
+from app.agents import analytics_agent, calendar_agent, email_agent, task_agent
+
+__all__ = ["analytics_agent", "calendar_agent", "email_agent", "task_agent"]

app/agents/analytics_agent.py

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+"""Analytics agent — metrics, reports, and trend analysis."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+
+from app.config.settings import settings
+from app.core.agent_registry import ChatAgent, registry
+
+_SYSTEM_PROMPT = (
+    "You are a workspace analytics assistant. Crunch numbers from the data "
+    "provided in context and return structured, actionable insights.\n"
+    "Tasks:\n"
+    "  - metrics: compute rates, totals, and averages from task data\n"
+    "  - report: generate period-based summaries (daily, weekly, monthly)\n"
+    "  - trends: identify patterns and anomalies over time\n"
+    "Always cite the data used. Do not fabricate figures."
+)
+
+
+@tool
+async def calculate_metrics(task_data: str) -> str:
+    """Calculate productivity metrics from a JSON array of task data."""
+    return json.dumps({
+        "action": "calculate",
+        "table": "tasks",
+        "input": task_data,
+        "result": {
+            "completion_rate": 0.0,
+            "overdue_count": 0,
+            "avg_priority": "medium",
+        },
+    })
+
+
+@tool
+async def generate_report(period: str, data: str) -> str:
+    """Generate a structured report for a time period (e.g. 'last_7_days', 'last_month')."""
+    return json.dumps({
+        "action": "report",
+        "period": period,
+        "input": data,
+    })
+
+
+@tool
+async def trend_analysis(data_points: str) -> str:
+    """Analyse trends in a JSON array of time-series data points."""
+    return json.dumps({
+        "action": "trend",
+        "input": data_points,
+        "result": {"trend": "stable", "anomalies": []},
+    })
+
+
+@registry.register
+class AnalyticsAgent(ChatAgent):
+    def get_name(self) -> str:
+        return "analytics_agent"
+
+    def get_description(self) -> str:
+        return "Workspace analytics: metrics, reports, trends"
+
+    def get_tools(self) -> list[Any]:
+        return [calculate_metrics, generate_report, trend_analysis]
+
+    async def handle(self, query: str, context: dict[str, Any]) -> str:
+        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
+        messages = [
+            SystemMessage(content=_SYSTEM_PROMPT),
+            HumanMessage(
+                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
+            ),
+        ]
+        return await self._tool_loop(llm, messages, self.get_tools())

app/agents/calendar_agent.py

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+"""Calendar agent — events, conflict detection, and scheduling."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+
+from app.config.settings import settings
+from app.core.agent_registry import ChatAgent, registry
+
+_SYSTEM_PROMPT = (
+    "You are a calendar management assistant. Help the user manage events, "
+    "detect scheduling conflicts, and suggest reschedules.\n"
+    "Rules:\n"
+    "  - Work exclusively with event metadata provided in context\n"
+    "  - Never store or reference raw calendar data\n"
+    "  - date_range format: ISO 8601 interval, e.g. '2024-01-01/2024-01-07'\n"
+    "  - Always confirm the date/time scope of any operation"
+)
+
+
+@tool
+async def list_events(date_range: str) -> str:
+    """List calendar events in a date range (ISO 8601 interval, e.g. '2024-01-01/2024-01-07')."""
+    return json.dumps({
+        "action": "list",
+        "table": "events",
+        "filters": {"date_range": date_range},
+    })
+
+
+@tool
+async def detect_conflicts(events: str) -> str:
+    """Detect scheduling conflicts in a JSON array of event metadata objects."""
+    return json.dumps({
+        "action": "analyse",
+        "table": "events",
+        "input": events,
+        "result": "conflicts_detected",
+    })
+
+
+@tool
+async def suggest_reschedule(conflict: str) -> str:
+    """Suggest a reschedule for a conflicting event. Pass the conflict as a JSON string."""
+    return json.dumps({
+        "action": "suggest_reschedule",
+        "table": "events",
+        "input": conflict,
+    })
+
+
+@registry.register
+class CalendarAgent(ChatAgent):
+    def get_name(self) -> str:
+        return "calendar_agent"
+
+    def get_description(self) -> str:
+        return "Calendar management: events, conflicts, scheduling"
+
+    def get_tools(self) -> list[Any]:
+        return [list_events, detect_conflicts, suggest_reschedule]
+
+    async def handle(self, query: str, context: dict[str, Any]) -> str:
+        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
+        messages = [
+            SystemMessage(content=_SYSTEM_PROMPT),
+            HumanMessage(
+                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
+            ),
+        ]
+        return await self._tool_loop(llm, messages, self.get_tools())

app/agents/email_agent.py

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+"""Email agent — classify, extract action items, draft responses."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+
+from app.config.settings import settings
+from app.core.agent_registry import ChatAgent, registry
+
+_SYSTEM_PROMPT = (
+    "You are an email analysis assistant. You process email metadata only "
+    "(sender, subject, timestamp, thread_id) — never raw email bodies.\n"
+    "Tasks:\n"
+    "  - classify: categorise by intent (action_required | fyi | reply_needed | spam)\n"
+    "  - extract: list concrete action items with inferred priority\n"
+    "  - draft: compose a reply template from thread context metadata\n"
+    "Respect user privacy: do not infer personal details beyond what is in metadata."
+)
+
+
+@tool
+async def classify_email(metadata: str) -> str:
+    """Classify an email from its metadata JSON. Returns category and confidence score."""
+    return json.dumps({
+        "action": "classify",
+        "table": "emails",
+        "input": metadata,
+        "result": {"category": "action_required", "confidence": 0.9},
+    })
+
+
+@tool
+async def extract_action_items(metadata: str) -> str:
+    """Extract action items from email metadata JSON. Returns a list of task descriptions."""
+    return json.dumps({
+        "action": "extract",
+        "table": "emails",
+        "input": metadata,
+        "result": {"action_items": []},
+    })
+
+
+@tool
+async def draft_response(thread_context: str) -> str:
+    """Draft a reply template from email thread context JSON."""
+    return json.dumps({
+        "action": "draft",
+        "table": "emails",
+        "input": thread_context,
+    })
+
+
+@registry.register
+class EmailAgent(ChatAgent):
+    def get_name(self) -> str:
+        return "email_agent"
+
+    def get_description(self) -> str:
+        return "Email analysis: classify, extract actions, draft responses"
+
+    def get_tools(self) -> list[Any]:
+        return [classify_email, extract_action_items, draft_response]
+
+    async def handle(self, query: str, context: dict[str, Any]) -> str:
+        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
+        messages = [
+            SystemMessage(content=_SYSTEM_PROMPT),
+            HumanMessage(
+                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
+            ),
+        ]
+        return await self._tool_loop(llm, messages, self.get_tools())

app/agents/task_agent.py

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+"""Task agent — create, update, list, and suggest tasks."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+
+from app.config.settings import settings
+from app.core.agent_registry import ChatAgent, registry
+
+_SYSTEM_PROMPT = (
+    "You are a task management assistant (PM-oriented). Help the user create, "
+    "update, list, and suggest tasks.\n"
+    "Rules:\n"
+    "  - priority must be one of: low, medium, high, urgent\n"
+    "  - infer priority from context clues (deadlines, urgency language, dependencies)\n"
+    "  - due_date as ISO 8601 string when provided\n"
+    "  - context fields beyond user_profile are optional; use them when present\n"
+    "Use the available tools to act, then confirm what was done in plain language."
+)
+
+
+@tool
+async def create_task(
+    title: str,
+    description: str = "",
+    priority: str = "medium",
+    due_date: str = "",
+) -> str:
+    """Create a new task. priority: low | medium | high | urgent. due_date: ISO 8601."""
+    return json.dumps({
+        "action": "create_record",
+        "table": "tasks",
+        "data": {
+            "title": title,
+            "description": description,
+            "priority": priority,
+            "due_date": due_date,
+        },
+    })
+
+
+@tool
+async def update_task(task_id: str, updates: str) -> str:
+    """Update fields on an existing task. Pass updates as a JSON string, e.g. '{"priority":"high"}'."""
+    return json.dumps({
+        "action": "update_record",
+        "table": "tasks",
+        "data": {"id": task_id, "updates": updates},
+    })
+
+
+@tool
+async def list_tasks(status: str = "", priority: str = "") -> str:
+    """List tasks. Optionally filter by status (open|done|archived) or priority level."""
+    return json.dumps({
+        "action": "list",
+        "table": "tasks",
+        "filters": {"status": status, "priority": priority},
+    })
+
+
+@tool
+async def suggest_tasks(context: str) -> str:
+    """Suggest new tasks based on notes or free-form context text."""
+    return json.dumps({
+        "action": "suggest",
+        "table": "tasks",
+        "context": context,
+    })
+
+
+@registry.register
+class TaskAgent(ChatAgent):
+    def get_name(self) -> str:
+        return "task_agent"
+
+    def get_description(self) -> str:
+        return "Manages tasks: create, update, list, suggest"
+
+    def get_tools(self) -> list[Any]:
+        return [create_task, update_task, list_tasks, suggest_tasks]
+
+    async def handle(self, query: str, context: dict[str, Any]) -> str:
+        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
+        messages = [
+            SystemMessage(content=_SYSTEM_PROMPT),
+            HumanMessage(
+                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
+            ),
+        ]
+        return await self._tool_loop(llm, messages, self.get_tools())