renjianbo
beff3fac8d
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
281 lines
11 KiB
Python
281 lines
11 KiB
Python
"""
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Agent 蜂群 API — Leader/Teammate 并行协作
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POST /api/v1/swarm/run
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{"message": "帮我做三件事: ...", "mode": "parallel", "max_teammates": 5}
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→ Leader 分解任务 → Teammates 并行执行 → Leader 汇总
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参考 Claude Code src/tools/AgentTool/ + forkSubagent.ts
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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from fastapi import APIRouter, Depends, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field
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from app.core.database import get_db
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from sqlalchemy.orm import Session
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from app.api.auth import get_current_user
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from app.models.user import User
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from app.models.agent import Agent
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from app.agent_runtime.swarm import (
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SwarmRuntime,
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SwarmConfig,
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SwarmMode,
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SwarmResult,
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SwarmTask,
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create_swarm,
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)
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from app.agent_runtime.schemas import AgentConfig, AgentLLMConfig, AgentToolConfig
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/swarm", tags=["swarm"])
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# ──────────────────────────── 请求/响应模型 ────────────────────────────
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class SwarmRunRequest(BaseModel):
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message: str = Field(..., description="用户输入")
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mode: str = Field(default="parallel", description="蜂群模式: parallel | pipeline | debate")
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max_teammates: int = Field(default=5, ge=1, le=20)
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leader_model: Optional[str] = Field(default=None, description="Leader 模型")
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teammate_model: Optional[str] = Field(default=None, description="Teammate 模型")
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mailbox_enabled: bool = Field(default=True, description="启用 Agent 间消息传递")
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agent_ids: Optional[List[str]] = Field(default=None, description="指定的 Agent ID 列表（作为 Teammates）")
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retry_failed: bool = Field(default=True, description="失败任务是否重试")
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class SwarmTaskItem(BaseModel):
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id: str
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description: str
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assigned_agent_id: Optional[str] = None
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status: str
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result: Optional[str] = None
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error: Optional[str] = None
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iterations_used: int = 0
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tool_calls_made: int = 0
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duration_ms: int = 0
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class SwarmTeammateItem(BaseModel):
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agent_id: str
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agent_name: str
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task_id: str
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success: bool
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output: str
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duration_ms: int
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iterations_used: int
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tool_calls_made: int
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error: Optional[str] = None
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class MailboxMessageItem(BaseModel):
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id: str
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from_: str = Field(alias="from")
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to: str
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content: str
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timestamp: float
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class SwarmRunResponse(BaseModel):
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success: bool
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final_answer: str
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mode: str
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tasks: List[SwarmTaskItem] = Field(default_factory=list)
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teammate_results: List[SwarmTeammateItem] = Field(default_factory=list)
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mailbox_messages: List[Dict[str, Any]] = Field(default_factory=list)
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total_duration_ms: int = 0
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total_iterations: int = 0
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total_tool_calls: int = 0
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error: Optional[str] = None
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# ──────────────────────────── 端点 ────────────────────────────
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@router.post("/run", response_model=SwarmRunResponse)
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async def swarm_run(
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req: SwarmRunRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""运行 Agent 蜂群 — Leader 分解任务 → Teammates 并行执行 → 汇总。
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支持三种模式:
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- parallel: 所有子任务并发执行（无依赖）
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- pipeline: 按依赖顺序执行
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- debate: 多个 Agent 独立回答后汇总
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Teammates 来源（优先级）:
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1. agent_ids 参数指定 → 从数据库加载 Agent 配置
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2. 自动生成 → 使用 teammate_model 创建轻量 Teammate
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"""
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uid = current_user.id
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# 解析模式
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mode = SwarmMode.PARALLEL
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if req.mode == "pipeline":
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mode = SwarmMode.PIPELINE
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elif req.mode == "debate":
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mode = SwarmMode.DEBATE
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# 构建 SwarmConfig
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config = SwarmConfig(
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mode=mode,
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max_teammates=req.max_teammates,
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leader_model=req.leader_model or "deepseek-v4-pro",
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teammate_model=req.teammate_model or "deepseek-v4-flash",
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mailbox_enabled=req.mailbox_enabled,
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retry_failed=req.retry_failed,
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)
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# 加载指定的 Agent 作为 Teammates
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teammate_configs: List[AgentConfig] = []
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if req.agent_ids:
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for aid in req.agent_ids:
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agent = db.query(Agent).filter(Agent.id == aid).first()
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if agent:
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wc = agent.workflow_config or {}
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nodes = wc.get("nodes", [])
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agent_node_cfg = {}
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for node in nodes:
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if node.get("type") in ("agent", "llm", "template"):
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agent_node_cfg = node.get("data") or {}
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break
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teammate_configs.append(AgentConfig(
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name=agent.name,
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system_prompt=agent_node_cfg.get("system_prompt") or agent.description or "你是一个有用的AI助手。",
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llm=AgentLLMConfig(
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model=agent_node_cfg.get("model", req.teammate_model or "deepseek-v4-flash"),
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provider=agent_node_cfg.get("provider", "deepseek"),
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temperature=float(agent_node_cfg.get("temperature", 0.7)),
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max_iterations=int(agent_node_cfg.get("max_iterations", 10)),
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),
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tools=AgentToolConfig(
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include_tools=agent_node_cfg.get("tools", []),
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),
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user_id=uid,
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))
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# 构建 Leader 配置
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leader_config = AgentConfig(
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name="SwarmLeader",
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system_prompt="你是一个AI任务协调者。将复杂问题分解为子任务，协调多个AI Agent并行处理，并汇总结果。",
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llm=AgentLLMConfig(model=config.leader_model, temperature=0.3, max_iterations=10),
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user_id=uid,
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)
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# 创建并运行 Swarm
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swarm = SwarmRuntime(
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config=config,
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leader_config=leader_config,
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teammate_configs=teammate_configs,
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)
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result = await swarm.run(req.message)
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return SwarmRunResponse(
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success=result.success,
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final_answer=result.final_answer,
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mode=result.mode.value,
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tasks=[
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SwarmTaskItem(
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id=t.id, description=t.description,
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assigned_agent_id=t.assigned_agent_id,
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status=t.status.value, result=t.result[:500] if t.result else None,
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error=t.error, iterations_used=t.iterations_used,
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tool_calls_made=t.tool_calls_made, duration_ms=t.duration_ms,
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)
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for t in result.tasks
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],
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teammate_results=[
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SwarmTeammateItem(
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agent_id=tr["agent_id"], agent_name=tr["agent_name"],
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task_id=tr["task_id"], success=tr["success"],
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output=tr["output"][:500], duration_ms=tr["duration_ms"],
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iterations_used=tr["iterations_used"], tool_calls_made=tr["tool_calls_made"],
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error=tr.get("error"),
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)
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for tr in result.teammate_results
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],
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mailbox_messages=result.mailbox_messages,
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total_duration_ms=result.total_duration_ms,
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total_iterations=result.total_iterations,
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total_tool_calls=result.total_tool_calls,
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error=result.error,
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)
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@router.post("/run/stream")
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async def swarm_run_stream(
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req: SwarmRunRequest,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db),
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):
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"""运行 Agent 蜂群（流式 SSE）— 实时推送任务分解、执行进度、汇总结果。"""
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import json as _json
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async def _stream():
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uid = current_user.id
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mode = {"parallel": SwarmMode.PARALLEL, "pipeline": SwarmMode.PIPELINE,
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"debate": SwarmMode.DEBATE}.get(req.mode, SwarmMode.PARALLEL)
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config = SwarmConfig(
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mode=mode, max_teammates=req.max_teammates,
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leader_model=req.leader_model or "deepseek-v4-pro",
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teammate_model=req.teammate_model or "deepseek-v4-flash",
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mailbox_enabled=req.mailbox_enabled, retry_failed=req.retry_failed,
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)
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# Load specified agents
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teammate_configs = []
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if req.agent_ids:
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for aid in req.agent_ids:
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agent = db.query(Agent).filter(Agent.id == aid).first()
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if agent:
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wc = agent.workflow_config or {}
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agent_node_cfg = {}
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for node in wc.get("nodes", []):
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if node.get("type") in ("agent", "llm", "template"):
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agent_node_cfg = node.get("data") or {}
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break
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teammate_configs.append(AgentConfig(
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name=agent.name,
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system_prompt=agent_node_cfg.get("system_prompt") or agent.description or "你是一个有用的AI助手。",
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llm=AgentLLMConfig(
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model=agent_node_cfg.get("model", req.teammate_model or "deepseek-v4-flash"),
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provider=agent_node_cfg.get("provider", "deepseek"),
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temperature=float(agent_node_cfg.get("temperature", 0.7)),
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max_iterations=int(agent_node_cfg.get("max_iterations", 10)),
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),
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tools=AgentToolConfig(include_tools=agent_node_cfg.get("tools", [])),
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user_id=uid,
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))
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leader_config = AgentConfig(
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name="SwarmLeader",
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system_prompt="你是一个AI任务协调者。",
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llm=AgentLLMConfig(model=config.leader_model, temperature=0.3, max_iterations=10),
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user_id=uid,
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)
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swarm = SwarmRuntime(config=config, leader_config=leader_config,
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teammate_configs=teammate_configs)
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yield f"data: {_json.dumps({'type': 'swarm_start', 'mode': req.mode, 'max_teammates': req.max_teammates}, ensure_ascii=False)}\n\n"
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result = await swarm.run(req.message)
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yield f"data: {_json.dumps({'type': 'swarm_done', 'success': result.success, 'final_answer': result.final_answer, 'total_duration_ms': result.total_duration_ms, 'total_iterations': result.total_iterations, 'total_tool_calls': result.total_tool_calls}, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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_stream(),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"},
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)
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