mirror of
https://github.com/ThearchyHelios/AgentLab.git
synced 2026-11-15 14:21:58 +00:00
ThearchyHelios
587bf7e27c
起因是一次探索运行：Copilot 改节点时把工具绑定整份抹掉，agent 没有工具只能写「假设调用」， 校验节点的自动修复编出 total_count=0 让 schema 通过，协作成员把工具调用标记当文字输出了四轮， 轮数用完后节点把原话当成功结果交出去——运行标成已完成，数据库一次都没查。 平台可信度（先写失败测试） - Copilot 修改节点改为合并：没写的字段保留，null 才删除；协作成员按名字合并。前后端同一套规则， 工具集合变小时自查报警、改图回执列出变化 - 新校验规则：提示词点名了某个工具而节点没有绑定它，判错误，Copilot 自查会交回去修 - 校验修复只许改格式：冒出原文没有的值就判修复无效；修复这次模型调用计入用量 - 识别模型把工具调用写成文字的情况（DSML、<tool_call> 等标记）：纠正一次，仍然如此就判失败并说明原因 - 协作团队用完轮数不再按成功交付：默认失败，可选降档，点出一次都没派到的成员 - 审批恢复后，同一节点里已经执行过的工具调用复用结果，不再重复执行 后端 - 查询时限交给数据库执行（Oracle call_timeout、PG statement_timeout、MySQL max_execution_time、 SQLite progress handler），可按数据源配置；超时报错与实际耗时一致 - 等审批的运行可以真正取消；会话列表带最后一轮状态；测连接结果持久化；探查结构可只看不存； 自定义工具可草稿试跑、写坏的参数定义保存时拒绝；MCP 服务改动后作废旧工具缓存； 检索命中带贡献分解；重建索引在后台跑并报进度；报错统一收进 core/errors 前端 - 公共层：管理页共用件、失败说明（lib/explain）、健康状态进公共库；目录按表各自加载并有超时， 一张表卡住不再拖垮整站；上传显示真实字节进度；离线横幅只播报不变的句子 - 画布：卡片只说游标所在的那一次执行；等审批时可放弃运行；团队收尾判定与未完成说清楚； 回放用量取那一刻的值 - 编排页：Copilot 合并语义与工具变化回执；「用完轮数时」选项；正式运行期间锁住所有改图入口 - 回答流 / 问数据：新失败类型说人话并给下一步；画布与右栏互相定位；数据源范围、回收站、重新核对 - 记录页：「航迹」页签复用画布时间轴，可拖动回放、列出关键时刻；列表行带执行 / 等人 / 失败缩略条 - 外壳：统一的离开前确认（data router），导航、⌘K、快捷键、后退都会先问 验证（沙箱）：tsc 干净，pytest 838 passed（两遍），14 个检查脚本中 13 个全绿； check-ui 余 1 项旧断言（按旧日期格式找运行行），3c 更新。
675 lines
35 KiB
Python
675 lines
35 KiB
Python
from __future__ import annotations
|
||
|
||
import asyncio
|
||
import json
|
||
import time
|
||
from typing import Any
|
||
|
||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
|
||
from langgraph.errors import GraphRecursionError
|
||
from langgraph.func import task
|
||
|
||
from app.core.config import settings
|
||
from app.core.errors import describe_exception, raw_detail
|
||
from app.core.events import EventType
|
||
from app.db.base import SessionLocal
|
||
from app.db.models import Workflow
|
||
from app.engine.context import NodeContext, NodeError
|
||
from app.engine.state import GraphState, message_text, template_context
|
||
from app.engine.toolcalls import (
|
||
TOOL_MARKUP_NUDGE,
|
||
ToolTimeout,
|
||
leaked_markup,
|
||
limit_fields,
|
||
limit_of,
|
||
markup_warning,
|
||
run_bounded,
|
||
)
|
||
from app.providers import catalog
|
||
from app.providers.factory import ModelSpec, bind_tools_safely, get_chat_model
|
||
from app.tools.registry import (
|
||
ToolArgsError,
|
||
ToolContext,
|
||
args_model_of,
|
||
build_tools,
|
||
call_is_dangerous,
|
||
prepare_args,
|
||
)
|
||
|
||
_MAX_DEPTH = 3
|
||
|
||
#: 调度者在 llm.end 里的 agent 名。成员用各自的名字
|
||
COORDINATOR = "调度者"
|
||
|
||
#: 成员把工具调用写成文字、纠正之后还是这样时，交给调度者的那句话
|
||
MEMBER_MARKUP_FAILED = ("模型输出了工具调用的原始标记，但没有真正调用工具，这一步什么都没查到"
|
||
"（常见原因：这个成员没有绑定工具，或者模型、服务不支持工具调用）")
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# Supervisor：一个调度者 + 若干专家
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
async def run_supervisor(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
|
||
"""多 agent 协作。
|
||
|
||
调度者每轮只做一件事：看当前进展，决定交给哪个专家、并给它一句具体指令，
|
||
或者宣布收工。专家各自带自己的 system prompt 和工具集。
|
||
把整个协作塞进一个节点，是为了让画布保持可读 —— 展开细节看运行时间线就够了。
|
||
"""
|
||
agents = ctx.cfg("agents", []) or []
|
||
if not agents:
|
||
raise NodeError(ctx.node.id, "supervisor 节点至少要配一个 agent")
|
||
|
||
names = [a.get("name") or f"agent{i}" for i, a in enumerate(agents)]
|
||
agent_map = {n: a for n, a in zip(names, agents)}
|
||
max_rounds = min(int(ctx.cfg("max_rounds", 6) or 6), settings.max_agent_steps)
|
||
#: 轮数用完、调度者始终没判定完成时怎么办。fail：判失败；degrade：照样交付但标明降档。
|
||
#: 缺省 fail——成员的原话不是结论，把它当成功交出去比失败更糟
|
||
on_exhausted = "degrade" if ctx.cfg("on_exhausted") == "degrade" else "fail"
|
||
|
||
goal = ctx.render_str(ctx.cfg("goal", "{{ last_message }}"), state)
|
||
if not goal.strip():
|
||
goal = json.dumps(template_context(state).get("input"), ensure_ascii=False)
|
||
|
||
async with SessionLocal() as session:
|
||
supervisor_model, sup_model_id = await get_chat_model(
|
||
session,
|
||
ModelSpec(
|
||
provider=ctx.cfg("provider"),
|
||
model=ctx.cfg("model"),
|
||
max_tokens=int(ctx.cfg("max_tokens", 2048) or 2048),
|
||
),
|
||
)
|
||
|
||
roster = "\n".join(
|
||
f"- {n}：{agent_map[n].get('description') or agent_map[n].get('system', '')[:100]}"
|
||
for n in names
|
||
)
|
||
# 一轮可以派给多个专家。协议用 assignments 数组而不是单个 next：
|
||
# 后者把"一次只能派一个"编进了协议本身，模型再想并行也表达不出来。
|
||
#
|
||
# 但并行只在任务**互不依赖**时才成立——并发的几个专家看到的是同一份进展
|
||
# 快照，谁也看不见谁。派错了不会报错，只会让两个人基于同样的旧信息
|
||
# 重复劳动，而这件事在结果里很难看出来。所以判断责任明确压在调度者身上，
|
||
# 提示词里把反例写出来。
|
||
route_schema = {
|
||
"title": "route", # langchain 靠 title 识别 JSON Schema dict
|
||
"type": "object",
|
||
"properties": {
|
||
"assignments": {
|
||
"type": "array",
|
||
"description": "这一轮要派出去的任务。互不依赖时可以给多个，它们会同时执行",
|
||
"items": {
|
||
"type": "object",
|
||
"properties": {
|
||
"agent": {"type": "string", "enum": names},
|
||
"instruction": {"type": "string"},
|
||
},
|
||
"required": ["agent", "instruction"],
|
||
},
|
||
},
|
||
"done": {"type": "boolean", "description": "目标已达成，结束协作"},
|
||
"reason": {"type": "string"},
|
||
},
|
||
"required": ["assignments"],
|
||
}
|
||
|
||
#: 一轮最多同时派几个。再多的话调度者多半只是在把任务拆碎，
|
||
#: 而每多一路就多一份"基于陈旧进展"的风险
|
||
max_parallel = max(1, min(int(ctx.cfg("max_parallel", 3) or 3), len(names)))
|
||
#: dangerous：需要人工确认的调用不执行（成员停不下来等人）；never：全部放行
|
||
approval = ctx.approval_mode()
|
||
|
||
transcript: list[dict[str, Any]] = []
|
||
usage_total = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0}
|
||
|
||
def _usage(msg: Any, model_id: str) -> dict[str, Any]:
|
||
meta = getattr(msg, "usage_metadata", None) or {}
|
||
i, o = int(meta.get("input_tokens") or 0), int(meta.get("output_tokens") or 0)
|
||
return {"input_tokens": i, "output_tokens": o,
|
||
"cost_usd": round(catalog.estimate_cost(model_id, i, o), 6)}
|
||
|
||
def _accumulate(one: dict[str, Any], into: dict[str, Any] | None = None) -> None:
|
||
into = usage_total if into is None else into
|
||
into["input_tokens"] += one["input_tokens"]
|
||
into["output_tokens"] += one["output_tokens"]
|
||
into["calls"] += one.get("calls", 1)
|
||
into["cost_usd"] = round(into["cost_usd"] + one["cost_usd"], 6)
|
||
|
||
def _report(who: str, model_id: str, one: dict[str, Any], t0: float) -> None:
|
||
# 每次模型调用各发一条 llm.end，界面上的用量才能实时涨，而且分得清是谁花的。
|
||
# 它只是把 usage_total 这笔账逐次摊开：run.finished 的 usage 仍以累计为准
|
||
ctx.emit(EventType.LLM_END, agent=who, model=model_id,
|
||
duration_ms=int((time.perf_counter() - t0) * 1000), **one)
|
||
|
||
async def _decide(prompt: str, *, closing: bool = False) -> tuple[dict[str, Any], dict[str, Any]]:
|
||
"""问调度者一次。返回 (决定, 这次调用的用量)。"""
|
||
spent = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0}
|
||
try:
|
||
# include_raw：结构化结果之外把原始消息也要回来，用量在那条消息上。
|
||
# 以前只拿解析结果，调度者的 token 一笔都没记进 usage
|
||
out = await supervisor_model.with_structured_output(
|
||
route_schema, include_raw=True).ainvoke(prompt)
|
||
if isinstance(out, dict) and "raw" in out and "parsed" in out:
|
||
spent = _usage(out["raw"], sup_model_id)
|
||
value = out["parsed"]
|
||
if value is None:
|
||
value = json.loads(message_text(out["raw"]))
|
||
else:
|
||
value = out
|
||
if not isinstance(value, dict):
|
||
value = json.loads(message_text(value))
|
||
except Exception: # noqa: BLE001
|
||
# 不支持结构化输出的模型退回文本：从回复里认出一个成员名，按单人派。
|
||
# 收尾判定时同理：回复里还点着哪个成员，就是还没完
|
||
reply = await supervisor_model.ainvoke(prompt)
|
||
spent = _usage(reply, sup_model_id)
|
||
raw = message_text(reply)
|
||
picked = next((n for n in names if n in raw), None)
|
||
if closing:
|
||
value = {"assignments": [], "done": picked is None, "reason": raw[:200]}
|
||
else:
|
||
value = ({"assignments": [{"agent": picked, "instruction": raw[:500]}]}
|
||
if picked else {"assignments": [], "done": True, "reason": raw[:200]})
|
||
return (value if isinstance(value, dict) else {}), spent
|
||
|
||
def _batch_of(decision: dict[str, Any]) -> list[tuple[str, str]]:
|
||
# 只留认得出的成员，并按上限截断。模型偶尔会重复派同一个人或者编一个
|
||
# 不存在的名字——这两种在并发里都是纯浪费
|
||
batch: list[tuple[str, str]] = []
|
||
seen: set[str] = set()
|
||
for item in decision.get("assignments") or []:
|
||
name = str((item or {}).get("agent") or "")
|
||
if name in agent_map and name not in seen:
|
||
seen.add(name)
|
||
batch.append((name, str(item.get("instruction") or goal)))
|
||
if len(batch) >= max_parallel:
|
||
break
|
||
return batch
|
||
|
||
def _finished(round_no: int, reason: str, **extra: Any) -> None:
|
||
# agents / done 是结构化字段，界面照着它渲染。以前只有一句中文
|
||
# 消息串，解码层得拿正则去拆「调度 → X（理由）」——文案一改就散架
|
||
ctx.emit(EventType.LOG, level="info", round=round_no, agents=[], done=True,
|
||
message="调度 → 结束协作" + (f"（{reason}）" if reason else ""), **extra)
|
||
|
||
# 调度者决策前后各一条事件（它常常占掉团队节点大半的时间，以前是黑盒），和派活的
|
||
# 那条日志一起都在 task 里发：「接着跑」一个失败的团队节点时整个节点重放，前几轮
|
||
# 的决定取自 checkpoint，这些事件不该在时间线上再出现一遍
|
||
@task
|
||
async def route(round_no: int, progress: str) -> dict[str, Any]:
|
||
ctx.emit(EventType.AGENT_ROUTE_START, round=round_no)
|
||
prompt = (
|
||
f"你是团队调度者。目标：\n{goal}\n\n"
|
||
f"可用成员：\n{roster}\n\n"
|
||
f"当前进展：\n{progress or '(还没有任何进展)'}\n\n"
|
||
"决定这一轮把什么任务交给谁，每个任务给一句明确的指令。\n\n"
|
||
f"**互不依赖的任务可以放在同一轮**（最多 {max_parallel} 个），它们会同时执行，"
|
||
"总耗时按最慢的那个算。判断依据只有一条：**后一个任务需不需要看到前一个的结果**。\n"
|
||
" 可以同时派：「查 A 产品的资料」和「查 B 产品的资料」——两边互不相干\n"
|
||
" 不能同时派：「查资料」和「根据资料写报告」——后者要等前者\n"
|
||
" 不能同时派：「写初稿」和「校对初稿」——同上\n\n"
|
||
"同时派出去的成员看到的是**同一份进展快照**，谁也看不见谁这一轮干了什么。"
|
||
"拿不准是否独立时就分两轮派，代价只是慢一点；派错了则是两个人基于同样的"
|
||
"旧信息重复劳动，而这在结果里很难看出来。\n\n"
|
||
"目标已经达成时，done 填 true、assignments 给空数组。"
|
||
)
|
||
t0 = time.perf_counter()
|
||
value, spent = await _decide(prompt)
|
||
_report(COORDINATOR, sup_model_id, spent, t0)
|
||
|
||
batch = _batch_of(value)
|
||
finishing = bool(value.get("done")) or not batch
|
||
reason = str(value.get("reason", ""))
|
||
ctx.emit(EventType.AGENT_ROUTE_END, round=round_no,
|
||
duration_ms=int((time.perf_counter() - t0) * 1000),
|
||
agents=[] if finishing else [n for n, _ in batch],
|
||
parallel=0 if finishing else len(batch), done=finishing, reason=reason)
|
||
if finishing:
|
||
_finished(round_no, reason)
|
||
else:
|
||
who = "、".join(n for n, _ in batch)
|
||
ctx.emit(
|
||
EventType.LOG, level="info", round=round_no, parallel=len(batch),
|
||
agents=[n for n, _ in batch], done=False, reason=reason,
|
||
message=(f"调度 → {who}" + (f"（{reason}）" if reason else "")
|
||
+ (f" · {len(batch)} 人同时进行" if len(batch) > 1 else "")),
|
||
)
|
||
return {"decision": value, "usage": spent}
|
||
|
||
# 最后一轮派出去的成员（常常就是定稿的那个）交回来之后，调度者还没看过它的产出。
|
||
# 以前轮数一到就算用完：步数恰好等于最多轮数的正常流水线（查数 → 撰稿，2 轮）也会
|
||
# 被判成没完成。补这一次只判定、不派活的决定，它说完成了才算完成
|
||
@task
|
||
async def verdict(round_no: int, progress: str) -> dict[str, Any]:
|
||
ctx.emit(EventType.AGENT_ROUTE_START, round=round_no, closing=True)
|
||
prompt = (
|
||
f"你是团队调度者。目标：\n{goal}\n\n"
|
||
f"当前进展：\n{progress or '(还没有任何进展)'}\n\n"
|
||
f"轮数已经用完（{max_rounds} 轮），不能再派任何任务。只判断一件事：按上面的进展，"
|
||
"目标是不是已经达成、能不能把最后一个成员的产出作为结论交出去。\n"
|
||
"达成了 done 填 true；没达成 done 填 false，并在 reason 里说清还缺什么。"
|
||
"assignments 给空数组。"
|
||
)
|
||
t0 = time.perf_counter()
|
||
value, spent = await _decide(prompt, closing=True)
|
||
_report(COORDINATOR, sup_model_id, spent, t0)
|
||
done = value.get("done") is True
|
||
reason = str(value.get("reason", ""))
|
||
ctx.emit(EventType.AGENT_ROUTE_END, round=round_no,
|
||
duration_ms=int((time.perf_counter() - t0) * 1000),
|
||
agents=[], parallel=0, done=done, reason=reason, closing=True)
|
||
if done:
|
||
_finished(round_no, reason, closing=True)
|
||
return {"done": done, "reason": reason, "usage": spent}
|
||
|
||
# 成员的开始、结束两条事件也在 task 里发，理由同 route。失败也在 task 里收住、作为
|
||
# 结果缓存：重放时这一步照旧是失败，不会换一个结果。用量随结果带出来再记账——在
|
||
# task 里直接记进 usage_total 的话，重放取了缓存，这一笔就没了
|
||
@task
|
||
async def work(round_no: int, name: str, instruction: str, progress: str,
|
||
parallel: int = 1) -> dict[str, Any]:
|
||
ctx.emit(EventType.AGENT_STEP_START, agent=name, instruction=instruction[:300],
|
||
round=round_no, parallel=parallel)
|
||
tally = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0}
|
||
t0 = time.perf_counter()
|
||
try:
|
||
out: dict[str, Any] = await _member(name, instruction, progress, tally)
|
||
except asyncio.CancelledError:
|
||
raise
|
||
except BaseException as exc: # noqa: BLE001 - 一个专家挂了不该拖垮整轮
|
||
# 把失败当成它的产出交回调度者，让它决定改派还是收工
|
||
why = describe_exception(exc)
|
||
text = f"（{name} 执行失败：{why}）"
|
||
out = {"text": text, "tool_calls": [], "failed": True, "error": why}
|
||
ctx.emit(EventType.LOG, level="warn", round=round_no,
|
||
message=f"{name} 这一轮失败了：{text}")
|
||
each_ms = int((time.perf_counter() - t0) * 1000)
|
||
# failed / error 让矩阵把这一格画成失败，而不是一个"完成"的格子里写着失败原因
|
||
failure = {"failed": True, "error": out.get("error") or ""} if out.get("failed") else {}
|
||
ctx.emit(EventType.AGENT_STEP_END, agent=name, duration_ms=each_ms,
|
||
round=round_no, parallel=parallel, preview=out["text"][:500], **failure)
|
||
return {**out, "duration_ms": each_ms, "usage": tally}
|
||
|
||
async def _member(name: str, instruction: str, progress: str,
|
||
tally: dict[str, Any]) -> dict[str, Any]:
|
||
cfg = agent_map[name]
|
||
async with SessionLocal() as session:
|
||
model, model_id = await get_chat_model(
|
||
session,
|
||
ModelSpec(
|
||
provider=cfg.get("provider") or ctx.cfg("provider"),
|
||
model=cfg.get("model") or ctx.cfg("model"),
|
||
max_tokens=int(cfg.get("max_tokens") or 4096),
|
||
),
|
||
)
|
||
tool_ctx = ToolContext(
|
||
run_id=ctx.run.run_id,
|
||
node_id=f"{ctx.node.id}:{name}",
|
||
sandbox_session=ctx.run.thread_id,
|
||
memory_scope=ctx.run.memory_scope,
|
||
collection=ctx.run.collection,
|
||
)
|
||
tools = await build_tools(cfg.get("tools", []) or [], tool_ctx, session=session)
|
||
|
||
bound = bind_tools_safely(model, tools, parallel=False)
|
||
tool_map = {t.name: t for t in tools}
|
||
messages: list[Any] = [
|
||
SystemMessage(content=cfg.get("system") or f"你是 {name}。"),
|
||
HumanMessage(content=f"团队目标：{goal}\n\n已有进展：\n{progress or '(无)'}\n\n你的任务：{instruction}"),
|
||
]
|
||
calls: list[dict[str, Any]] = []
|
||
# 和 agent 节点同一个硬顶：成员上配多少步都过不去 max_agent_steps
|
||
max_steps = min(int(cfg.get("max_steps") or 4), settings.max_agent_steps)
|
||
nudged = False
|
||
for _ in range(max_steps):
|
||
t0 = time.perf_counter()
|
||
reply = await bound.ainvoke(messages)
|
||
spent = _usage(reply, model_id)
|
||
_accumulate(spent, tally)
|
||
_report(name, model_id, spent, t0)
|
||
messages.append(reply)
|
||
tool_calls = getattr(reply, "tool_calls", None) or []
|
||
if not tool_calls:
|
||
text = message_text(reply)
|
||
snippet = leaked_markup(text)
|
||
if not snippet:
|
||
return {"text": text, "tool_calls": calls}
|
||
# 把工具调用写成了文字：这一步什么都没查到。纠正一次，还这样就记为失败，
|
||
# 原因交给调度者——以前这段标记会被当成这个成员的结论交上去
|
||
if nudged:
|
||
return {"text": f"（{name} 这一步失败：{MEMBER_MARKUP_FAILED}）",
|
||
"tool_calls": calls, "failed": True, "error": MEMBER_MARKUP_FAILED}
|
||
nudged = True
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_markup_leak",
|
||
message=f"{markup_warning(snippet, name)}，已提醒它重试一次")
|
||
messages.append(HumanMessage(content=TOOL_MARKUP_NUDGE))
|
||
continue
|
||
for call in tool_calls:
|
||
tname = call.get("name", "")
|
||
targs = call.get("args", {}) or {}
|
||
cid = call.get("id") or f"{name}-{tname}"
|
||
limit = None
|
||
failure: dict[str, Any] = {}
|
||
started = time.perf_counter()
|
||
if tname not in tool_map:
|
||
ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid)
|
||
content = f"错误：没有名为 {tname} 的工具"
|
||
else:
|
||
schema = args_model_of(tool_map[tname])
|
||
fix_note = None
|
||
args_error = None
|
||
if schema is not None:
|
||
try:
|
||
targs, fix_note = prepare_args(schema, targs)
|
||
except ToolArgsError as e:
|
||
args_error = str(e)
|
||
# 纠正后的参数才是真正要执行的那份，tool.start 要发它
|
||
limit = limit_of(tool_map[tname], tname, targs)
|
||
ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid,
|
||
**limit_fields(limit))
|
||
if fix_note:
|
||
ctx.emit(EventType.LOG, level="warn",
|
||
message=f"工具 {tname}：{fix_note}", code="tool_args_fixed")
|
||
if args_error:
|
||
# 参数就不对，没必要真调一次。把"它接受什么"喂回去让它改
|
||
content = args_error
|
||
elif approval != "never" and call_is_dangerous(tool_map[tname], tname, targs):
|
||
# 专家们并行跑在同一个节点里，停不下来等人：审批恢复时节点整个
|
||
# 重放，几个人的 interrupt 谁先谁后对不上号。以前的做法是不审，
|
||
# shell_exec / file_write / 可写库上的 DELETE 照跑不误——
|
||
# agent 节点守着的那道门，在这里是敞开的。现在是不跑，并说清原因
|
||
content = (
|
||
f"没有执行：{tname} 这次调用需要人工确认，而协作团队里的成员"
|
||
"不能停下来等人。换一种不需要它的做法；实在需要，交给团队外"
|
||
"的 agent 节点去做（那里可以逐次审批）。"
|
||
)
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_needs_approval",
|
||
message=f"{name} 想调用 {tname}，需要人工确认，协作节点里不执行")
|
||
else:
|
||
try:
|
||
raw = await run_bounded(tool_map[tname].ainvoke(targs), limit, tname)
|
||
content = raw if isinstance(raw, str) else json.dumps(raw, ensure_ascii=False, default=str)
|
||
except ToolTimeout as e:
|
||
content = str(e)
|
||
failure = {"error": content, "timed_out": True}
|
||
except Exception as e: # noqa: BLE001
|
||
content = f"工具失败：{describe_exception(e)}"
|
||
failure = {"error": content, "detail": raw_detail(e)}
|
||
ctx.emit(EventType.TOOL_ERROR if failure else EventType.TOOL_END, tool=tname,
|
||
agent=name, call_id=cid, preview=content[:1500],
|
||
duration_ms=int((time.perf_counter() - started) * 1000), **failure)
|
||
calls.append({"tool": tname, "args": targs, "result": content[:2000]})
|
||
messages.append(ToolMessage(content=content, tool_call_id=cid))
|
||
|
||
# 步数用完了：最后一步一定是要了工具的，模型从没拿到过"说结论"的那一轮。
|
||
# messages[-1] 是一条 ToolMessage——以前它就被当成这个成员的产出交给调度者，
|
||
# 工具的原始返回冒充了结论。agent 节点修过同一个问题，这里照做：不给工具
|
||
# （用没绑工具的 model，结构上就发不出调用）补一轮收尾。
|
||
messages.append(HumanMessage(content=(
|
||
f"步数预算用完了（{max_steps} 步），现在起不能再调用任何工具。基于已经查到的"
|
||
"信息给出你这部分的结论，说清哪些没查到、结论因此有什么局限。不要编造数据。"
|
||
)))
|
||
try:
|
||
t0 = time.perf_counter()
|
||
settled = await model.ainvoke(messages)
|
||
spent = _usage(settled, model_id)
|
||
_accumulate(spent, tally)
|
||
_report(name, model_id, spent, t0)
|
||
text = message_text(settled).strip()
|
||
except Exception as e: # noqa: BLE001 - 收尾失败不能把已有的过程一起赔进去
|
||
ctx.emit(EventType.LOG, level="warn", code="settle_failed",
|
||
message=f"{name} 的收尾轮没跑成：{describe_exception(e)}")
|
||
text = next((t for m in reversed(messages)
|
||
if isinstance(m, AIMessage) and (t := message_text(m).strip())
|
||
and not leaked_markup(t)), "")
|
||
if snippet := leaked_markup(text):
|
||
if not calls:
|
||
return {"text": f"（{name} 这一步失败：{MEMBER_MARKUP_FAILED}）",
|
||
"tool_calls": calls, "failed": True, "error": MEMBER_MARKUP_FAILED}
|
||
# 真调过工具，只是收尾轮还想接着查：取它之前说过的话，和 agent 节点一样
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_markup_leak",
|
||
message=f"{markup_warning(snippet, name)}：步数用完后的收尾轮仍想调用工具")
|
||
text = next((t for m in reversed(messages)
|
||
if isinstance(m, AIMessage) and (t := message_text(m).strip())
|
||
and not leaked_markup(t)), "")
|
||
ctx.emit(EventType.LOG, level="warn", code="step_limit_settled",
|
||
message=f"{name} 用满了 {max_steps} 步，结论基于已经查到的部分")
|
||
return {"text": text or f"（{name} 用满了 {max_steps} 步，没有给出结论）", "tool_calls": calls}
|
||
|
||
progress_lines: list[str] = []
|
||
final_text = ""
|
||
rounds_meta: list[dict[str, Any]] = []
|
||
#: 调度者最后一轮给的理由，以及派过活的成员。轮数用完时报错要说清卡在哪、谁从没上过场
|
||
last_reason = ""
|
||
dispatched: set[str] = set()
|
||
finished = False
|
||
for round_no in range(max_rounds):
|
||
progress = "\n\n".join(progress_lines)
|
||
routed = await route(round_no, progress)
|
||
decision = routed.get("decision", routed) if isinstance(routed, dict) else {}
|
||
decision = decision if isinstance(decision, dict) else {}
|
||
_accumulate(routed.get("usage") or _usage(None, sup_model_id))
|
||
reason = str(decision.get("reason", ""))
|
||
last_reason = reason or last_reason
|
||
|
||
batch = _batch_of(decision)
|
||
if bool(decision.get("done")) or not batch:
|
||
final_text = progress_lines[-1].split("】", 1)[-1] if progress_lines else ""
|
||
finished = True
|
||
break
|
||
|
||
dispatched.update(n for n, _ in batch)
|
||
|
||
# 并发执行。它们拿到的是**同一份** progress 快照——这正是"任务必须
|
||
# 互不依赖"的技术含义，也是上面提示词里那几条反例的由来。
|
||
#
|
||
# 每个人单独计时，而不是事后拿整轮墙钟去摊：界面要显示"并行省了多少
|
||
# 时间"，那个数必须是量出来的。按 N×墙钟 推算等于假设每个人都跑满了
|
||
# 最慢那条，而实际上快的那个可能只花了三分之一。
|
||
#
|
||
# 结束事件也在各自的 task 里一完成就发，不等整轮 gather：以前先做完的成员要
|
||
# 一直显示"进行中"，直到最慢的那个做完——矩阵答不了"谁还在跑"
|
||
async def _one(name: str, instruction: str, parallel: int) -> dict[str, Any]:
|
||
return await work(round_no, name, instruction, progress, parallel)
|
||
|
||
started = time.perf_counter()
|
||
results = await asyncio.gather(*(_one(n, i, len(batch)) for n, i in batch))
|
||
wall_ms = int((time.perf_counter() - started) * 1000)
|
||
|
||
# 进展仍按派活顺序拼：它是下一轮调度者读的输入，语义不随谁先回来而变
|
||
sum_ms = 0
|
||
for (name, instruction), result in zip(batch, results):
|
||
outcome = dict(result)
|
||
spent = outcome.pop("usage", None)
|
||
if spent:
|
||
_accumulate(spent)
|
||
each_ms = int(outcome.pop("duration_ms", 0) or 0)
|
||
sum_ms += each_ms
|
||
progress_lines.append(f"【{name}】{outcome['text']}")
|
||
transcript.append(
|
||
{"round": round_no, "agent": name, "instruction": instruction, **outcome,
|
||
"duration_ms": each_ms}
|
||
)
|
||
final_text = outcome["text"]
|
||
|
||
rounds_meta.append({
|
||
"round": round_no, "agents": [n for n, _ in batch],
|
||
"parallel": len(batch), "wall_ms": wall_ms, "sum_ms": sum_ms,
|
||
})
|
||
|
||
if not finished and progress_lines:
|
||
judged = await verdict(max_rounds, "\n\n".join(progress_lines))
|
||
_accumulate(judged.get("usage") or _usage(None, sup_model_id))
|
||
last_reason = str(judged.get("reason") or "") or last_reason
|
||
if judged.get("done"):
|
||
final_text = progress_lines[-1].split("】", 1)[-1]
|
||
finished = True
|
||
|
||
exhausted: dict[str, Any] = {}
|
||
if not finished:
|
||
# 轮数用完，调度者一次都没说「完成」。这时手上只有最后一个成员的原话——
|
||
# 以前它就被当作团队的结论交了出去：真实运行里是一段没执行的工具调用标记，
|
||
# 而负责定稿的成员一次都没被派到
|
||
never = [n for n in names if n not in dispatched]
|
||
summary = (f"协作团队用完 {max_rounds} 轮仍未完成："
|
||
f"{last_reason or '调度者没有给出理由'}")
|
||
if never:
|
||
summary += f"。一次都没被派到的成员：{'、'.join(never)}"
|
||
if on_exhausted == "fail":
|
||
raise NodeError(ctx.node.id, f"{summary}。先看成员有没有绑定要用的工具，再调大「最多"
|
||
"轮数」；也可以把「用完轮数时」改成降档交付")
|
||
exhausted = {"exhausted": True, "exhausted_reason": last_reason,
|
||
"never_dispatched": never}
|
||
ctx.emit(EventType.LOG, level="warn", code="team_exhausted",
|
||
message=f"{summary}。按降档交付：成果是成员最后的原话，不是调度者认可的结论")
|
||
|
||
usage_total["total_tokens"] = usage_total["input_tokens"] + usage_total["output_tokens"]
|
||
# 并行省下的时间：各轮"串行本该花的"减去"实际花的"。只有并发过才有差值
|
||
saved_ms = sum(r["sum_ms"] - r["wall_ms"] for r in rounds_meta)
|
||
result = {
|
||
"text": final_text,
|
||
"rounds": len(rounds_meta),
|
||
"steps": len(transcript),
|
||
"transcript": transcript,
|
||
"agents": names,
|
||
"schedule": rounds_meta,
|
||
"parallel_saved_ms": saved_ms,
|
||
**exhausted,
|
||
}
|
||
updates: dict[str, Any] = {
|
||
"nodes": {ctx.node.id: result},
|
||
"usage": usage_total,
|
||
}
|
||
if ctx.cfg("emit_message", True):
|
||
updates["messages"] = [AIMessage(content=final_text or "(空)")]
|
||
var_name = ctx.cfg("assign_to", "")
|
||
if var_name:
|
||
updates["vars"] = {var_name: final_text}
|
||
return updates
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 子图：把另一张工作流当成一个节点
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
async def run_subgraph(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
|
||
"""嵌套执行另一个工作流。
|
||
|
||
子图共享同一个 run 的事件流（节点 id 会加前缀），所以在时间线上能看到它内部
|
||
每一步；但它有独立的状态，不会污染父图的变量池。
|
||
"""
|
||
from app.engine.compiler import compile_graph # 延迟导入，避免循环依赖
|
||
from app.engine.schema import GraphSpec, loop_steps
|
||
|
||
if ctx.run.depth >= _MAX_DEPTH:
|
||
raise NodeError(ctx.node.id, f"子工作流嵌套超过 {_MAX_DEPTH} 层，已阻止。"
|
||
"多半是几张工作流互相引用了，检查「子工作流」节点选的是哪一张")
|
||
|
||
workflow_id = ctx.cfg("workflow_id")
|
||
if not workflow_id:
|
||
raise NodeError(ctx.node.id, "「子工作流」节点还没选要嵌套的工作流")
|
||
|
||
# 版本钉死：这就是"方法卡"的机制核心。钉了 workflow_version 就永远
|
||
# 执行那个不可变快照，上游改了方法卡也不会让这里的口径悄悄漂移；
|
||
# 没钉则跟随最新（画布试跑方便，但治理 lint 会在受管模板里拦下它）。
|
||
pinned = ctx.cfg("workflow_version")
|
||
async with SessionLocal() as session:
|
||
workflow = await session.get(Workflow, workflow_id)
|
||
if not workflow:
|
||
raise NodeError(ctx.node.id, f"「子工作流」引用的工作流（{workflow_id}）已经不在了，"
|
||
"可能被删除了。在节点里重新选一张")
|
||
if pinned:
|
||
from sqlalchemy import select
|
||
|
||
from app.db.models import WorkflowVersion
|
||
|
||
snapshot = (
|
||
await session.execute(
|
||
select(WorkflowVersion).where(
|
||
WorkflowVersion.workflow_id == workflow_id,
|
||
WorkflowVersion.version == int(pinned),
|
||
)
|
||
)
|
||
).scalar_one_or_none()
|
||
if not snapshot:
|
||
raise NodeError(
|
||
ctx.node.id, f"工作流「{workflow.name}」没有 v{pinned} 这个版本"
|
||
)
|
||
sub_graph = snapshot.graph
|
||
used_version = int(pinned)
|
||
else:
|
||
sub_graph = workflow.graph
|
||
used_version = workflow.version
|
||
|
||
sub_input = ctx.render(ctx.cfg("input", {}) or {}, state)
|
||
if not isinstance(sub_input, dict):
|
||
sub_input = {"input": sub_input}
|
||
|
||
sub_spec = GraphSpec.model_validate(sub_graph)
|
||
sub_run = type(ctx.run)(
|
||
run_id=ctx.run.run_id,
|
||
thread_id=f"{ctx.run.thread_id}:{ctx.node.id}",
|
||
spec=sub_spec,
|
||
workflow_id=workflow.id,
|
||
depth=ctx.run.depth + 1,
|
||
memory_scope=ctx.run.memory_scope,
|
||
collection=ctx.run.collection,
|
||
approval_default=ctx.run.approval_default,
|
||
# 恢复重放的标记和签批人只属于父图的节点，不能漏进子图（节点 id 可能重名）
|
||
extra={**{k: v for k, v in ctx.run.extra.items() if k not in ("resumed", "actors")},
|
||
"node_prefix": f"{ctx.node.id}/"},
|
||
)
|
||
|
||
ctx.emit(EventType.LOG, level="info",
|
||
message=f"进入子工作流「{workflow.name}」（{len(sub_spec.nodes)} 个节点）")
|
||
|
||
compiled = compile_graph(sub_spec, sub_run)
|
||
# 子图不带 checkpointer：它的断点由父图的 checkpoint 统一承载
|
||
app = compiled.compile()
|
||
limit = settings.max_graph_steps + loop_steps(sub_spec)
|
||
try:
|
||
result_state = await app.ainvoke(
|
||
{
|
||
"input": sub_input,
|
||
"vars": sub_input,
|
||
"nodes": {},
|
||
"output": {},
|
||
"messages": [],
|
||
"usage": {},
|
||
"trail": [],
|
||
"loops": {},
|
||
},
|
||
{"recursion_limit": limit},
|
||
)
|
||
except GraphRecursionError as e:
|
||
# 不接住的话，外层包装成 NodeError 时带的是 LangGraph 的英文原文
|
||
raise NodeError(
|
||
ctx.node.id,
|
||
f"子图「{workflow.name}」走满了 {limit} 步还没跑完，已中止。多半是子图里有个环"
|
||
"在空转：分支连回了上游、却没有 loop 节点给它定轮数上限",
|
||
) from e
|
||
|
||
output = result_state.get("output") or {}
|
||
result = {
|
||
"output": output,
|
||
"workflow": workflow.name,
|
||
# 溯源信息：这次到底跑的是哪个版本、是不是钉死的
|
||
"workflow_version": used_version,
|
||
"pinned": bool(pinned),
|
||
"text": json.dumps(output, ensure_ascii=False) if output else "",
|
||
}
|
||
ctx.emit(EventType.LOG, level="info", message=f"子工作流「{workflow.name}」完成")
|
||
|
||
updates: dict[str, Any] = {
|
||
"nodes": {ctx.node.id: result},
|
||
"usage": result_state.get("usage") or {},
|
||
}
|
||
var_name = ctx.cfg("assign_to", "")
|
||
if var_name:
|
||
updates["vars"] = {var_name: output}
|
||
return updates
|