admin/aiagent
1
0
Fork 0
Code Issues 14 Pull Requests Actions Packages Projects Releases Wiki Activity

feat: expose graph orchestration mode, fix pipeline multi-agent, add Feishu tools (Phase 3)

Browse Source 
增强编排 + 飞书深度集成：
- Graph 模式：暴露 orchestrator._graph() 到 run() 方法，workflow_integration 支持 graph nodes/edges
- Pipeline 修复：多 Agent 按步骤轮转分配，不再只用 agents[0]
- 4个飞书操作工具: feishu_create_doc / feishu_create_calendar_event / feishu_search_contacts / feishu_send_approval
- 飞书 @mention→Goal：feishu/ orange WS handler 支持 "目标: xxx" 触发自动创建 Goal

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
renjianbo
2026-05-08 20:08:26 +08:00
parent 926ec6c0a1
commit d0b55f2b16
6 changed files with 490 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
backend/app/agent_runtime/orchestrator.py
View File

@@ -151,8 +151,19 @@ class AgentOrchestrator:
question: str,
agents: List[OrchestratorAgentConfig],
on_llm_call: Optional[Callable[[Dict[str, Any]], Any]] = None,
graph_nodes: Optional[List[Dict[str, Any]]] = None,
graph_edges: Optional[List[Dict[str, Any]]] = None,
) -> OrchestratorResult:
"""执行多 Agent 编排。"""
"""执行多 Agent 编排。
Args:
mode: route / sequential / debate / pipeline / graph
question: 用户问题
agents: Agent 配置列表
on_llm_call: LLM 调用回调
graph_nodes: graph 模式的节点定义（mode=graph 时必填）
graph_edges: graph 模式的边定义（mode=graph 时必填）
"""
mode = mode.lower()
if mode == "route":
return await self._route(question, agents, on_llm_call)
@@ -162,8 +173,12 @@ class AgentOrchestrator:
return await self._debate(question, agents, on_llm_call)
elif mode == "pipeline":
return await self._pipeline(question, agents, on_llm_call)
elif mode == "graph":
if not graph_nodes:
raise ValueError("graph 模式需要提供 graph_nodes 参数")
return await self._graph(question, graph_nodes, graph_edges or [], on_llm_call)
else:
raise ValueError(f"不支持的编排模式: {mode}，可选: route, sequential, debate, pipeline")
raise ValueError(f"不支持的编排模式: {mode}，可选: route, sequential, debate, pipeline, graph")
async def _route(
self, question: str, agents: List[OrchestratorAgentConfig],
@@ -500,11 +515,13 @@ class AgentOrchestrator:
steps=steps,
)
# ── 2. Executor：逐步骤执行 ──
executor_cfg = agents[0] if agents else OrchestratorAgentConfig(
id="executor", name="Executor",
system_prompt="你是一个有用的AI助手。",
)
# ── 2. Executor：逐步骤执行（多 Agent 轮转分配）──
executor_pool = agents if agents else [
OrchestratorAgentConfig(
id="executor", name="Executor",
system_prompt="你是一个有用的AI助手。",
)
]
previous_output = "（尚无前序步骤）"
execution_results = []
@@ -514,6 +531,9 @@ class AgentOrchestrator:
step_desc = step_info.get("description", f"步骤 {step_num}")
step_expect = step_info.get("expected_output", "")
# 按步骤轮转分配 Agent：不同步骤可分配给不同 Agent（按专长匹配）
executor_cfg = executor_pool[(step_num - 1) % len(executor_pool)]
executor_prompt = _EXECUTOR_STEP_PROMPT.format(
original_question=question,
plan_title=plan.get("plan_title", ""),
@@ -555,6 +575,7 @@ class AgentOrchestrator:
execution_results.append({
"step": step_num,
"description": step_desc,
"agent": executor_cfg.name,
"output": step_result.content,
"error": step_result.error if not step_result.success else None,
})
@@ -562,7 +583,7 @@ class AgentOrchestrator:
previous_output = step_result.content if step_result.success else f"（步骤{step_num}执行出错）"
if not step_result.success:
logger.warning(f"Pipeline 步骤{step_num} 执行失败: {step_result.error}")
logger.warning(f"Pipeline 步骤{step_num} ({executor_cfg.name}) 执行失败: {step_result.error}")
# ── 3. Reviewer：审查并交付 ──
plan_steps_text = "\n".join(

48
backend/app/agent_runtime/workflow_integration.py
View File

@@ -192,8 +192,8 @@ async def run_orchestrator_node(
# 2. 解析编排模式
mode = node_data.get("mode", "debate").lower()
if mode not in ("route", "sequential", "debate", "pipeline"):
return {"output": f"错误：不支持的编排模式 '{mode}'，可选: route, sequential, debate, pipeline", "status": "error"}
if mode not in ("route", "sequential", "debate", "pipeline", "graph"):
return {"output": f"错误：不支持的编排模式 '{mode}'，可选: route, sequential, debate, pipeline, graph", "status": "error"}
# 3. 解析 Agent 列表
agent_ids = node_data.get("agents", [])
@@ -266,33 +266,45 @@ async def run_orchestrator_node(
temperature=0.3,
),
)
# graph 模式需要传递节点和边定义
graph_nodes = node_data.get("graph_nodes") if mode == "graph" else None
graph_edges = node_data.get("graph_edges") if mode == "graph" else None
result = await orchestrator.run(
mode=mode,
question=query,
agents=agent_configs,
on_llm_call=on_llm_invocation,
graph_nodes=graph_nodes,
graph_edges=graph_edges,
)
# 6. 返回结构化结果
meta: Dict[str, Any] = {
"mode": result.mode,
"agent_count": len(agent_configs),
"steps": [
{
"agent_id": s.agent_id,
"agent_name": s.agent_name,
"input": s.input[:200] if s.input else "",
"output": s.output[:500] if s.output else "",
"iterations_used": s.iterations_used,
"tool_calls_made": s.tool_calls_made,
"error": s.error,
}
for s in result.steps
],
}
if mode == "graph":
meta["graph_node_count"] = len(graph_nodes) if graph_nodes else 0
meta["graph_edge_count"] = len(graph_edges) if graph_edges else 0
return {
"output": result.final_answer,
"status": "success",
"orchestrator_meta": {
"mode": result.mode,
"agent_count": len(agent_configs),
"steps": [
{
"agent_id": s.agent_id,
"agent_name": s.agent_name,
"input": s.input[:200] if s.input else "",
"output": s.output[:500] if s.output else "",
"iterations_used": s.iterations_used,
"tool_calls_made": s.tool_calls_made,
"error": s.error,
}
for s in result.steps
],
},
"orchestrator_meta": meta,
}
except Exception as e: