feat: 新增 core/agents 模块和 nanobot

- 新增 agents 模块，包含 agent、api、skills 等子模块
- 新增 nanobot 项目，支持多渠道集成
- 添加启动脚本 start-all.bat 和 start-all.sh

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

core/agents/agent/team_agent.py

@@ -0,0 +1,225 @@
+"""Team agent for multi-agent collaboration."""
+
+import asyncio
+import logging
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+class TeamAgent:
+    """Team agent that manages multiple agents for collaborative problem solving.
+
+    Supports different strategies:
+    - parallel: All agents respond in parallel, results are aggregated
+    - sequential: Agents respond one by one in sequence
+    - supervisor: A supervisor agent coordinates the work
+    """
+
+    def __init__(self, provider: Any, model: str, workspace: Any):
+        """Initialize the team agent.
+
+        Args:
+            provider: LLM provider
+            model: Model name to use
+            workspace: Workspace path
+        """
+        self.provider = provider
+        self.model = model
+        self.workspace = workspace
+
+    async def chat(
+        self,
+        message: str,
+        session_id: str = "default",
+        supervisor_agent_id: int = 0,
+        member_agent_ids: list[int] | None = None,
+        strategy: str = "parallel",
+    ) -> dict[str, Any]:
+        """Process a team chat message.
+
+        Args:
+            message: User message
+            session_id: Session identifier
+            supervisor_agent_id: Supervisor agent ID (for future use)
+            member_agent_ids: List of member agent IDs to involve
+            strategy: Collaboration strategy (parallel/sequential/supervisor)
+
+        Returns:
+            Dict with response and subtask_results
+        """
+        member_agent_ids = member_agent_ids or []
+
+        logger.info(f"Team chat: strategy={strategy}, members={member_agent_ids}, message={message[:50]}...")
+
+        if strategy == "parallel":
+            return await self._parallel_chat(message, member_agent_ids, session_id)
+        elif strategy == "sequential":
+            return await self._sequential_chat(message, member_agent_ids, session_id)
+        else:
+            # Default to parallel
+            return await self._parallel_chat(message, member_agent_ids, session_id)
+
+    async def _parallel_chat(
+        self,
+        message: str,
+        member_agent_ids: list[int],
+        session_id: str,
+    ) -> dict[str, Any]:
+        """Execute parallel chat with multiple agents.
+
+        Args:
+            message: User message
+            member_agent_ids: List of member agent IDs
+            session_id: Session identifier
+
+        Returns:
+            Aggregated response from all agents
+        """
+        if not member_agent_ids:
+            return {
+                "response": "No member agents specified for team chat.",
+                "subtask_results": [],
+            }
+
+        # Create tasks for each agent
+        tasks = []
+        for agent_id in member_agent_ids:
+            task = self._call_agent(agent_id, message, session_id)
+            tasks.append(task)
+
+        # Execute all tasks in parallel
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        # Aggregate results
+        subtask_results = []
+        responses = []
+
+        for i, result in enumerate(results):
+            agent_id = member_agent_ids[i]
+
+            if isinstance(result, Exception):
+                error_msg = f"Agent {agent_id} error: {str(result)}"
+                logger.error(error_msg)
+                subtask_results.append({
+                    "agent_id": agent_id,
+                    "status": "error",
+                    "result": str(result),
+                })
+            else:
+                subtask_results.append({
+                    "agent_id": agent_id,
+                    "status": "success",
+                    "result": result,
+                })
+                responses.append(result)
+
+        # Combine responses
+        if responses:
+            combined_response = self._aggregate_responses(responses)
+        else:
+            combined_response = "All agents failed to respond."
+
+        return {
+            "response": combined_response,
+            "subtask_results": subtask_results,
+        }
+
+    async def _sequential_chat(
+        self,
+        message: str,
+        member_agent_ids: list[int],
+        session_id: str,
+    ) -> dict[str, Any]:
+        """Execute sequential chat with multiple agents.
+
+        Args:
+            message: User message
+            member_agent_ids: List of member agent IDs
+            session_id: Session identifier
+
+        Returns:
+            Aggregated response from all agents
+        """
+        if not member_agent_ids:
+            return {
+                "response": "No member agents specified for team chat.",
+                "subtask_results": [],
+            }
+
+        subtask_results = []
+        responses = []
+
+        for agent_id in member_agent_ids:
+            try:
+                result = await self._call_agent(agent_id, message, session_id)
+                subtask_results.append({
+                    "agent_id": agent_id,
+                    "status": "success",
+                    "result": result,
+                })
+                responses.append(result)
+            except Exception as e:
+                error_msg = f"Agent {agent_id} error: {str(e)}"
+                logger.error(error_msg)
+                subtask_results.append({
+                    "agent_id": agent_id,
+                    "status": "error",
+                    "result": str(e),
+                })
+
+        # Combine responses
+        if responses:
+            combined_response = self._aggregate_responses(responses)
+        else:
+            combined_response = "All agents failed to respond."
+
+        return {
+            "response": combined_response,
+            "subtask_results": subtask_results,
+        }
+
+    async def _call_agent(
+        self,
+        agent_id: int,
+        message: str,
+        session_id: str,
+    ) -> str:
+        """Call an individual agent.
+
+        For now, this is a placeholder that simulates agent responses.
+        In a real implementation, this would call the actual agent.
+
+        Args:
+            agent_id: Agent ID
+            message: User message
+            session_id: Session identifier
+
+        Returns:
+            Agent response
+        """
+        # Simulate agent processing delay
+        await asyncio.sleep(0.5)
+
+        # Return a simulated response
+        return f"Agent {agent_id} processed: {message[:30]}..."
+
+    def _aggregate_responses(self, responses: list[str]) -> str:
+        """Aggregate multiple agent responses into a single response.
+
+        Args:
+            responses: List of individual agent responses
+
+        Returns:
+            Combined response
+        """
+        if len(responses) == 1:
+            return responses[0]
+
+        header = f"【团队协作结果】共 {len(responses)} 位智能体参与了讨论:\n\n"
+        body = ""
+
+        for i, resp in enumerate(responses, 1):
+            body += f"--- 智能体 {i} ---\n{resp}\n\n"
+
+        return header + body