steven_li
520a21a027
添加 DEFAULT_TEAM_NODE_MAX_TOOL_ITERATIONS 配置项以控制团队节点的最大工具迭代次数， 并修改 LocalAgentRunner 中的逻辑来使用此默认值当 envelope 中未指定时。 fix(runtime): 修复团队节点运行成功判断逻辑 更新运行成功判断条件，将 finish_reason 为 "max_tool_iterations_finalized" 的情况 视为运行失败，并添加对原始工具调用输出的检测，避免将其误判为成功完成。 feat(mcp): 添加团队工作流MCP工具类别支持 增加新的本地MCP工具类别 "team_workflow" 及其对应的工具创建功能， 为团队工作流提供本地工具支持。 refactor(engine): 调整AgentLoop最大工具迭代次数设置 将 AgentProfile 中的默认 max_tool_iterations 从 30 增加到 100， 同时移除 TaskExecutionPlanner 构造函数中的重复参数传递。 perf(mcp): 优化MCP连接管理避免重复连接 添加 mcp_connected 标志来跟踪MCP连接状态，确保 connect_all 只执行一次， 提高性能并避免不必要的重复连接。 refactor(skills): 移除技能团队模板相关功能 移除与技能团队模板相关的代码，包括解析、存储和处理逻辑， 简化技能记录结构和加载流程。 feat(process): 增强会话过程投影器功能 添加技能激活快照事件处理，改进团队运行完成消息显示， 并增强技能激活事件的时间戳记录功能。 refactor(tasks): 简化任务尝试编排器团队执行逻辑 移除团队执行相关代码，将所有任务统一按单步执行处理， 简化任务编排器的复杂度并提升执行效率。 fix(evidence): 修复节点证据评估中需求验证逻辑 更新节点证据评估逻辑，跳过自然语言证据需求的确定性验证， 只执行机器可读的需求验证，避免因自然语言需求导致的节点失败。
317 lines
14 KiB
Python
317 lines
14 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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from dataclasses import asdict
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from pathlib import Path
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from typing import Any
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from beaver.engine import AgentLoop, EngineLoader
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from beaver.engine.context import SkillContext
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from beaver.engine.providers.base import LLMProvider, LLMResponse, ToolCallRequest
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from beaver.engine.providers.factory import ProviderBundle, build_provider_runtime
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from beaver.services.team_service import TeamService
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from beaver.skills.catalog.loader import SkillsLoader
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from beaver.skills.catalog.utils import strip_frontmatter
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from beaver.skills.drafts import DraftService
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from beaver.skills.specs import SkillSpecStore
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from beaver.tasks.attempt_orchestrator import TaskAttemptOrchestrator
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from beaver.tasks.models import TaskRecord
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from beaver.tasks.planner import TaskExecutionPlanner
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from beaver.tasks.skill_resolver import TaskSkillResolver
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WORKSPACE = Path("/root/.beaver/workspace")
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SKILL_NAME = "mgm-galaxy-financial-chart-report-safe"
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def _text_from_messages(messages: list[dict[str, Any]]) -> str:
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return "\n".join(str(message.get("content") or "") for message in messages)
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def _tool_names(tools: list[dict[str, Any]] | None) -> list[str]:
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names: list[str] = []
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for tool in tools or []:
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if "function" in tool:
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names.append(str(tool["function"].get("name") or ""))
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else:
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names.append(str(tool.get("name") or ""))
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return [name for name in names if name]
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class DemoProvider(LLMProvider):
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def __init__(self, *, collect_uses_tool: bool) -> None:
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super().__init__()
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self.collect_uses_tool = collect_uses_tool
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self.calls: list[dict[str, Any]] = []
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async def chat(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]] | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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temperature: float = 0.0,
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thinking_enabled: bool | None = None,
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) -> LLMResponse:
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text = _text_from_messages(messages)
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names = _tool_names(tools)
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self.calls.append(
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{
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"tool_names": names,
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"has_tool_result": any(message.get("role") == "tool" for message in messages),
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"text_preview": text[:300],
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}
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)
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if "You choose whether an internal Beaver Task attempt" in text:
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return LLMResponse(
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content=json.dumps(_planner_json(), ensure_ascii=False),
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provider_name="demo",
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model="demo-model",
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)
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if "You select Beaver skills for a single run" in text:
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return LLMResponse(content="[]", provider_name="demo", model="demo-model")
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if "team:generate_chart_report" in text:
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return LLMResponse(
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content=(
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"# MGM China vs Galaxy Entertainment Demo Report\n\n"
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"| Company | Metric | Value | Source |\n"
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"|---|---:|---:|---|\n"
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"| MGM China | Revenue | demo value | upstream source |\n"
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"| Galaxy Entertainment | Revenue | demo value | upstream source |\n\n"
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"Chart-ready data is provided as Markdown. No image or saved chart file was generated."
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),
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provider_name="demo",
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model="demo-model",
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)
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if "team:validate_metrics" in text:
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return LLMResponse(
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content="Validation complete: periods and units are labeled; no generated chart artifact is claimed.",
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provider_name="demo",
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model="demo-model",
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)
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if "team:extract_financial_metrics" in text:
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return LLMResponse(
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content=(
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"Extracted demo metric table: MGM China revenue: source-backed placeholder; "
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"Galaxy Entertainment revenue: source-backed placeholder. Currency, period, and source URL fields are labeled."
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),
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provider_name="demo",
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model="demo-model",
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)
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if "team:collect_official_sources" in text:
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if self.collect_uses_tool and "web_fetch" in names and not any(message.get("role") == "tool" for message in messages):
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return LLMResponse(
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content=None,
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tool_calls=[
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ToolCallRequest(
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id="call_collect_fetch",
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name="web_fetch",
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arguments={
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"url": "https://www.bing.com/search?q=MGM+China+Galaxy+Entertainment+annual+report",
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"max_chars": 1000,
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},
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)
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],
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finish_reason="tool_calls",
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provider_name="demo",
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model="demo-model",
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)
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return LLMResponse(
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content=(
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"Collected official-source candidates for MGM China Holdings and Galaxy Entertainment. "
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"Demo evidence includes a successful web_fetch tool result with URL captured by Beaver."
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),
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provider_name="demo",
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model="demo-model",
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)
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return LLMResponse(content="Demo final synthesis.", provider_name="demo", model="demo-model")
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def get_default_model(self) -> str:
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return "demo-model"
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def _planner_json() -> dict[str, Any]:
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return {
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"mode": "team",
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"reason": "finance comparison benefits from staged source collection, extraction, validation, and reporting",
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"strategy": "dag",
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"nodes": [
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{
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"node_id": "collect_official_sources",
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"task": "Collect official MGM China Holdings and Galaxy Entertainment financial disclosure sources for the requested period. Prefer annual reports, interim reports, results announcements, investor relations pages, and exchange filings. Return source URLs with short notes about period coverage.",
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"use_skill": "web-operation",
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"skill_query": "official financial disclosure web research",
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"depends_on": [],
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"requested_tools": ["web_search", "web_fetch"],
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"required_evidence": ["tool_result", "url"],
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"evidence_contract": {"version": 1, "entities": ["MGM China Holdings", "Galaxy Entertainment Group"]},
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"required_for_completion": True,
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"block_downstream_on_partial": True,
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"max_tool_iterations": 2,
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},
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{
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"node_id": "extract_financial_metrics",
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"task": "Extract comparable financial metrics for MGM China Holdings and Galaxy Entertainment from the collected official sources. Include revenue or net revenue, adjusted EBITDA where available, net profit/loss where available, period, currency, unit, and source URL for each metric.",
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"use_skill": "web-operation",
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"skill_query": "financial metric extraction from official disclosures",
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"depends_on": ["collect_official_sources"],
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"requested_tools": ["web_fetch"],
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"required_evidence": ["output"],
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"evidence_contract": {"version": 1, "metrics": ["revenue", "adjusted_ebitda", "net_profit_or_loss"]},
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"required_for_completion": True,
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"block_downstream_on_partial": True,
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"max_tool_iterations": 1,
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},
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{
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"node_id": "validate_metrics",
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"task": "Validate extracted metrics for source consistency, period alignment, currency/unit consistency, and obvious transcription errors. Produce a concise validation note and list any evidence gaps.",
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"use_skill": "utility-tools",
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"skill_query": "finance metric validation",
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"depends_on": ["extract_financial_metrics"],
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"requested_tools": [],
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"required_evidence": ["output"],
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"evidence_contract": {"version": 1, "checks": ["source_consistency", "period_alignment"]},
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"required_for_completion": True,
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"block_downstream_on_partial": True,
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"max_tool_iterations": 0,
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},
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{
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"node_id": "generate_chart_report",
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"task": "Generate the final Markdown comparison report. Include an executive summary, source-backed comparison table, chart-ready data table, optional Mermaid or text bar chart section, and caveats. Do not claim that a chart image, chart file, or saved artifact was generated.",
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"use_skill": "utility-tools",
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"skill_query": "financial markdown report with chart-ready data",
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"depends_on": ["validate_metrics"],
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"requested_tools": [],
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"required_evidence": ["output"],
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"evidence_contract": {"version": 1, "outputs": ["comparison_table", "chart_ready_data", "markdown_report"]},
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"required_for_completion": True,
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"block_downstream_on_partial": False,
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"max_tool_iterations": 0,
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},
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],
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"adaptation": {"template_used": True},
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"final_synthesis_instruction": "Synthesize node outputs into a concise Markdown finance report.",
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}
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def _task() -> TaskRecord:
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return TaskRecord(
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task_id="demo-task-mgm-galaxy",
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session_id="web:demo-mgm-galaxy-harness",
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description="Compare MGM China and Galaxy Entertainment using official public financial disclosures.",
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goal="Compare MGM China and Galaxy Entertainment using official public financial disclosures.",
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constraints=[],
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priority=0,
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status="open",
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creator="demo",
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created_at="demo",
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updated_at="demo",
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)
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def _finance_skill_context(loader: SkillsLoader) -> SkillContext:
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record = loader.get_skill_record(SKILL_NAME)
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raw = loader.load_published_skill(SKILL_NAME)
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if record is None or raw is None:
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raise RuntimeError(f"missing published skill: {SKILL_NAME}")
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return SkillContext(
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name=record.name,
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version=record.version,
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content=strip_frontmatter(raw).strip(),
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content_hash=record.content_hash or "",
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activation_reason="demo_exact_skill",
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tool_hints=list(record.tool_hints),
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team_template=record.team_template,
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team_template_warnings=list(record.team_template_warnings),
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)
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async def _run_case(*, collect_uses_tool: bool) -> dict[str, Any]:
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loader = SkillsLoader(WORKSPACE)
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store = SkillSpecStore(WORKSPACE)
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runtime = build_provider_runtime(model="demo-model", provider_name="custom", api_key="demo", api_base="http://demo.invalid/v1")
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provider = DemoProvider(collect_uses_tool=collect_uses_tool)
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bundle = ProviderBundle(main_runtime=runtime, main_provider=provider)
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engine_loader = EngineLoader(workspace=WORKSPACE)
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loop = AgentLoop(loader=engine_loader)
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loaded = loop.boot()
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resolver = TaskSkillResolver(skills_loader=loader, draft_service=DraftService(store))
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planner = TaskExecutionPlanner(task_skill_resolver=resolver, tool_registry=loaded.tool_registry)
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task = _task()
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skill_context = _finance_skill_context(loader)
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plan = await planner.plan(
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task=task,
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user_message=task.description,
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attempt_index=1,
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provider_bundle=bundle,
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activated_skills=[skill_context],
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timeout_seconds=5.0,
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)
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team_result = None
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if plan.is_team:
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team_result = await TeamService(loop).run_team(
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plan.graph,
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parent_task_id=None,
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parent_session_id=task.session_id,
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provider_bundle_factory=lambda node: bundle,
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inherited_pinned_skill_contexts=[skill_context],
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)
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context, prefix, metadata = TaskAttemptOrchestrator._team_synthesis_outcome(plan, team_result, prompt_locale="en")
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return {
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"case": "complete" if collect_uses_tool else "incomplete",
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"plan_mode": plan.mode,
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"plan_reason": plan.reason,
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"planner_adaptation": plan.planner_adaptation,
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"node_ids": [node.node_id for node in plan.graph.nodes] if plan.graph else [],
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"node_tool_scopes": {node.node_id: node.allowed_tool_names for node in plan.graph.nodes} if plan.graph else {},
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"node_skill_bindings": [
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{
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"node_id": node.node_id,
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"pinned_skill_names": node.inherited_pinned_skills,
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"pinned_skill_contexts": [skill.name for skill in node.inherited_pinned_skill_contexts],
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"role": node.agent.role,
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"sub_agent_kind": node.agent.metadata.get("sub_agent_kind"),
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"exact_binding_used": node.agent.metadata.get("exact_binding_used"),
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}
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for node in (plan.graph.nodes if plan.graph else [])
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],
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"team_success": team_result.success if team_result else None,
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"team_summary": team_result.summary if team_result else None,
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"team_run_ids": team_result.run_ids if team_result else [],
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"node_results": [
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{
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"node_id": result.node_id,
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"success": result.success,
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"completion_status": result.completion_status,
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"finish_reason": result.finish_reason,
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"evidence_gaps": result.evidence_gaps,
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"output_preview": result.output_text[:180],
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}
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for result in (team_result.node_results if team_result else [])
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],
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"synthesis_metadata": metadata,
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"incomplete_prefix_present": bool(prefix),
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"outcome_context_preview": context[:600],
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"provider_calls": provider.calls,
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}
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async def main() -> None:
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results = [
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await _run_case(collect_uses_tool=True),
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await _run_case(collect_uses_tool=False),
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]
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print(json.dumps(results, ensure_ascii=False, indent=2, default=str))
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if __name__ == "__main__":
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asyncio.run(main())
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