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https://github.com/ZhuLinsen/daily_stock_analysis
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Fix: multi-agent AGENT_MAX_STEPS not respecting user override & skill agent graceful degradation (#1031)
* Fix multi-agent AGENT_MAX_STEPS not respecting user override and skill agent failure handling - Fix AGENT_MAX_STEPS ceiling-only logic: when user raises value above default (10), all sub-agents now adopt the global value instead of being capped at their per-agent defaults (fixes #1026) - Fix skill agent failure aborting entire pipeline: specialist/skill agents now degrade gracefully like intel/risk stages - Improve max-steps-exceeded error message with AGENT_MAX_STEPS guidance - Sync config descriptions in README, .env.example, and config_registry
This commit is contained in:
mumu
@@ -160,7 +160,7 @@ SEARXNG_PUBLIC_INSTANCES_ENABLED=true
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# AGENT_MODE=true
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# Agent 主模型（可选）：留空时继承主模型；无 provider 前缀会按 openai/<model> 解析
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# AGENT_LITELLM_MODEL=
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# Agent 最大推理步数上限（多 Agent orchestrator 模式下作为各子 Agent ceiling，不会抬高内建较低默认值）
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# Agent 最大推理步数上限（默认 10 时各子 Agent 按自身预设运行；高于默认 10 时所有子 Agent 统一采用该值；低于某子 Agent 默认值时会作为上限进行封顶）
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# AGENT_MAX_STEPS=10
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# 默认启用策略（逗号分隔），不配置时使用以下内置默认值
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#
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@@ -211,7 +211,7 @@
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| `AGENT_MODE` | 开启 Agent 策略问股模式（内部统一命名为 skill，`true`/`false`，默认 false） | 可选 |
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| `AGENT_LITELLM_MODEL` | Agent 主模型（可选）；留空继承主模型，无前缀会按 `openai/<model>` 解析 | 可选 |
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| `AGENT_SKILLS` | 激活的策略技能 id（逗号分隔），`all` 启用全部策略技能；留空时使用主默认策略 skill（内置默认是 `bull_trend`），详见 `.env.example` | 可选 |
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| `AGENT_MAX_STEPS` | Agent 最大推理步数上限（默认 10）；多 Agent orchestrator 模式下按 `min(子 Agent 默认值, AGENT_MAX_STEPS)` 生效，不会抬高低默认值 Agent 的步数 | 可选 |
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| `AGENT_MAX_STEPS` | Agent 最大推理步数上限（默认 `10`）；保持默认时各子 Agent 按自身预设步数运行；用户主动调高到高于默认值时，所有子 Agent 统一采用该值；若设置值低于某子 Agent 的默认步数，则仍按该值作为上限进行限制 | 可选 |
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| `AGENT_SKILL_DIR` | 自定义策略技能目录（默认沿用内置 `strategies/` 兼容路径） | 可选 |
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| `TRADING_DAY_CHECK_ENABLED` | 交易日检查（默认 `true`）：非交易日跳过执行；设为 `false` 或使用 `--force-run` 强制执行 | 可选 |
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| `ENABLE_CHIP_DISTRIBUTION` | 启用筹码分布（Actions 默认 false；需筹码数据时在 Variables 中设为 true，接口可能不稳定） | 可选 |
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@@ -129,7 +129,7 @@ const fieldDescriptionMap: Record<string, string> = {
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LOG_LEVEL: '设置日志输出级别。',
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WEBUI_PORT: 'Web 页面服务监听端口。',
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AGENT_MODE: '是否启用 ReAct Agent 策略问股。对外文案仍叫“策略”，内部配置字段统一使用 skill。',
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AGENT_MAX_STEPS: 'Agent 思考和调用工具的最大步数。',
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AGENT_MAX_STEPS: 'Agent 最大推理步数上限。保持默认 10 时，各子 Agent 按自身预设步数运行；调高到高于默认值时，所有子 Agent 统一采用该值；调低到低于某子 Agent 默认值时，该 Agent 会被封顶。',
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AGENT_SKILLS: '逗号分隔的交易策略列表。留空时使用 metadata 里声明的主默认策略 skill（内置默认是 bull_trend）；也可填写 all 启用全部策略。',
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AGENT_SKILL_DIR: '存放 Agent 策略定义文件的目录路径，支持 YAML 与 SKILL.md bundle。',
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AGENT_ARCH: "选择 Agent 执行架构。single 为经典单 Agent；multi 为多 Agent 编排（实验性）。",
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@@ -12,8 +12,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/).
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<!-- 新条目格式：- [类型] 描述（类型取值：新功能/改进/修复/文档/测试/chore）-->
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<!-- 每条独立一行追加到本段末尾，无需分类标题，合并时冲突最小 -->
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- [修复] `AGENT_MAX_STEPS` 在 orchestrator 多 Agent 模式下改为作为各子 Agent 的步数上限而非硬覆盖；TechnicalAgent 等高默认值 Agent 会被封顶，低默认值 Agent 保持原值，减少不必要的 LLM 调用膨胀与配额消耗。
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- [修复] 大盘复盘链路接入 `REPORT_LANGUAGE`：`REPORT_LANGUAGE=en` 时，A 股/合并复盘的 Prompt、章节标题、模板兜底文案与通知包装标题统一改为英文，避免出现英文正文外包中文标题的问题。
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- [修复] `AGENT_MAX_STEPS` 在 orchestrator 多 Agent 模式下统一明确为“默认作为各子 Agent 的步数上限而非硬覆盖；TechnicalAgent 等高默认值 Agent 会被封顶、低默认值 Agent 保持原值；当用户主动调高（>10）时，再统一覆盖所有子 Agent 采用全局值”，同时修复用户设置 12 但 TechnicalAgent 仍以默认 6 步运行并报 "Agent exceeded max steps" 的问题（fixes #1026）
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- [修复] Specialist（Skill）Agent 失败不再中断整个分析管线，改为与 intel/risk 相同的优雅降级策略
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- [改进] Agent 超步数错误信息增加 AGENT_MAX_STEPS 调整提示，帮助用户自助排查
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- [修复] **MiniMax-M2.7 模型连接测试支持** — 修复 LLM 通道连接测试在 MiniMax-M2.7 模型下返回 "Empty response" 的问题；增加了 `max_tokens` 上限（8→256）以容纳 MiniMax 思考过程，并添加 `content_blocks` 格式解析逻辑统一处理 MiniMax 响应格式差异。
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- [修复] 移除 `HistoryItem` 与 `ReportSummary` 响应 Schema 中 `sentiment_score` 的 `ge=0/le=100` 约束（fixes #942）——历史库中存储的超范围负值或大于 100 的情绪评分不再触发 Pydantic ValidationError，历史列表与详情接口恢复正常返回。
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- [改进] 后端股票名称解析改为优先复用前端 `stocks.index.json` 全量索引并懒加载缓存；纯后端/缺失静态资源场景静默降级回 `STOCK_NAME_MAP` 与原有数据源回退链路。
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@@ -155,7 +155,7 @@
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| `LONGBRIDGE_PRINT_QUOTE_PACKAGES` | 連線時是否列印行情包（未設定時預設 `false`；設為 `1`/`true`/`yes` 開啟） | 可選 |
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| `AGENT_MODE` | 啟用 Agent 策略問股模式（內部統一命名為 skill，`true`/`false`，預設 `false`） | 可選 |
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| `AGENT_LITELLM_MODEL` | Agent 專用主模型（可選）；留空時繼承主模型，無 provider 前綴時按 `openai/<model>` 解析 | 可選 |
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| `AGENT_MAX_STEPS` | Agent 最大推理步數上限（預設 `10`）；多 Agent orchestrator 模式下按 `min(子 Agent 預設值, AGENT_MAX_STEPS)` 生效，不會抬高低預設值 Agent 的步數 | 可選 |
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| `AGENT_MAX_STEPS` | Agent 最大推理步數上限（預設 `10`）；保持預設時各子 Agent 依自身預設步數運行；主動調高到高於預設值時，所有子 Agent 統一採用該值；若設定值低於某子 Agent 的預設步數，則仍按該值作為上限進行限制 | 可選 |
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| `AGENT_SKILLS` | 逗號分隔的策略技能 id。留空時使用 metadata 宣告的主預設策略 skill（內建預設為 `bull_trend`）；使用 `all` 可啟用所有已載入策略技能。 | 可選 |
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| `AGENT_SKILL_DIR` | 自訂策略技能目錄（預設沿用內建 `strategies/` 相容路徑） | 可選 |
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@@ -161,7 +161,7 @@ Go to your forked repo → `Settings` → `Secrets and variables` → `Actions`
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| `WECHAT_MSG_TYPE` | WeChat Work message type, default `markdown`, set to `text` for plain markdown text | Optional |
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| `AGENT_MODE` | Enable Agent strategy chat mode (internally normalized as `skill`, `true`/`false`, default `false`) | Optional |
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| `AGENT_LITELLM_MODEL` | Optional Agent-only primary model; when empty it inherits the primary model, and bare names are normalized to `openai/<model>` | Optional |
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| `AGENT_MAX_STEPS` | Max reasoning-step ceiling for Agent mode (default `10`); in orchestrator mode each sub-agent uses `min(its default, AGENT_MAX_STEPS)` instead of a hard override | Optional |
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| `AGENT_MAX_STEPS` | Max reasoning-step limit for Agent mode (default `10`); at the default each sub-agent keeps its own preset, when raised above the default all sub-agents adopt this value, and when lowered below a sub-agent's preset that sub-agent is capped at this value | Optional |
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| `AGENT_SKILLS` | Comma-separated active strategy-skill ids. Leave empty to use the primary default strategy skill declared in metadata (built-in default: `bull_trend`); use `all` to activate every loaded strategy skill. | Optional |
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| `AGENT_SKILL_DIR` | Custom strategy-skill directory (default built-in `strategies/` compatibility path) | Optional |
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@@ -29,6 +29,8 @@ import logging
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from dataclasses import dataclass
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from typing import List, Optional
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from src.config import AGENT_MAX_STEPS_DEFAULT
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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@@ -323,7 +325,7 @@ def build_agent_executor(config=None, skills: Optional[List[str]] = None):
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skill_instructions=prompt_state.skill_instructions,
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default_skill_policy=prompt_state.default_skill_policy,
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use_legacy_default_prompt=prompt_state.use_legacy_default_prompt,
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max_steps=getattr(config, "agent_max_steps", 10),
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max_steps=getattr(config, "agent_max_steps", AGENT_MAX_STEPS_DEFAULT),
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timeout_seconds=getattr(config, "agent_orchestrator_timeout_s", 0),
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)
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@@ -344,7 +346,7 @@ def _build_orchestrator(config, registry, llm_adapter, skill_manager, *, technic
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llm_adapter=llm_adapter,
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skill_instructions=skill_manager.get_skill_instructions(),
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technical_skill_policy=technical_skill_policy,
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max_steps=getattr(config, "agent_max_steps", 10),
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max_steps=getattr(config, "agent_max_steps", AGENT_MAX_STEPS_DEFAULT),
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mode=mode,
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skill_manager=skill_manager,
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config=config,
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@@ -42,6 +42,7 @@ from src.agent.protocols import (
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)
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from src.agent.runner import parse_dashboard_json
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from src.agent.tools.registry import ToolRegistry
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from src.config import AGENT_MAX_STEPS_DEFAULT
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from src.report_language import normalize_report_language
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if TYPE_CHECKING:
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@@ -83,7 +84,7 @@ class AgentOrchestrator:
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llm_adapter: LLMToolAdapter,
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skill_instructions: str = "",
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technical_skill_policy: str = "",
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max_steps: int = 10,
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max_steps: int = AGENT_MAX_STEPS_DEFAULT,
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mode: str = "standard",
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skill_manager=None,
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config=None,
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@@ -200,15 +201,25 @@ class AgentOrchestrator:
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def _prepare_agent(self, agent: Any) -> Any:
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"""Apply orchestrator-level runtime settings to a child agent.
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The orchestrator-level ``max_steps`` acts as a **ceiling** — it will
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never *increase* the per-agent limit that each specialised agent
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already defines. This prevents a global ``AGENT_MAX_STEPS=10``
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from inflating a decision agent (designed for 3 steps) to 10 steps,
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which is the primary cause of excessive LLM calls and quota
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exhaustion in multi-agent pipelines.
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When the orchestrator-level ``max_steps`` equals the default
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(``AGENT_MAX_STEPS_DEFAULT``),
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each agent keeps its own per-agent limit — this prevents inflating
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a decision agent (designed for 3 steps) to 10 steps.
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When the user **explicitly** raises the global limit above the
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default, all agents adopt the global value so the user's intent to
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allow more steps is respected.
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When the user **lowers** the global limit below an agent's default,
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the agent is capped at the global value.
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"""
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if hasattr(agent, "max_steps"):
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agent.max_steps = min(agent.max_steps, self.max_steps)
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if self.max_steps > AGENT_MAX_STEPS_DEFAULT:
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# User explicitly raised the limit — apply to all agents.
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agent.max_steps = self.max_steps
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else:
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# Default or lowered — keep per-agent limit as ceiling.
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agent.max_steps = min(agent.max_steps, self.max_steps)
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return agent
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def _callable_accepts_timeout_kwarg(self, func: Any) -> Optional[bool]:
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@@ -507,9 +518,16 @@ class AgentOrchestrator:
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if result.success and agent.agent_name == "decision":
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self._apply_risk_override(ctx)
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# Abort pipeline on critical failure (except intel/risk — degrade gracefully)
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# Abort pipeline on critical failure.
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# Non-critical stages that degrade gracefully:
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# - intel / risk (standard support stages)
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# - skill agents (specialist evaluation, optional)
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if result.status == StageStatus.FAILED:
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if agent.agent_name not in ("intel", "risk"):
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non_critical = (
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agent.agent_name in ("intel", "risk")
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or agent.agent_name in getattr(self, "_skill_agent_names", set())
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)
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if not non_critical:
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logger.error("[Orchestrator] critical stage '%s' failed: %s", agent.agent_name, result.error)
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return OrchestratorResult(
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success=False,
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@@ -595,7 +595,7 @@ def run_agent_loop(
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total_tokens=total_tokens,
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provider=provider_used,
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models_used=models_used,
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error=f"Agent exceeded max steps ({max_steps})",
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error=f"Agent exceeded max steps ({max_steps}). Try increasing AGENT_MAX_STEPS if analysis tasks are complex.",
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messages=messages,
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)
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@@ -50,6 +50,7 @@ class ConfigIssue:
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_MANAGED_LITELLM_KEY_PROVIDERS = {"gemini", "vertex_ai", "anthropic", "openai", "deepseek"}
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SUPPORTED_LLM_CHANNEL_PROTOCOLS = ("openai", "anthropic", "gemini", "vertex_ai", "deepseek", "ollama")
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_FALSEY_ENV_VALUES = {"0", "false", "no", "off"}
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AGENT_MAX_STEPS_DEFAULT = 10
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NEWS_STRATEGY_WINDOWS: Dict[str, int] = {
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"ultra_short": 1,
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"short": 3,
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@@ -520,7 +521,7 @@ class Config:
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agent_litellm_model: str = "" # Optional Agent-only primary model; empty inherits LITELLM_MODEL
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agent_mode: bool = False
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_agent_mode_explicit: bool = False # True when AGENT_MODE was explicitly set in env
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agent_max_steps: int = 10
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agent_max_steps: int = AGENT_MAX_STEPS_DEFAULT
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agent_skills: List[str] = field(default_factory=list)
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agent_skill_dir: Optional[str] = None
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agent_nl_routing: bool = False # Enable natural language routing in bot dispatcher
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@@ -1174,7 +1175,12 @@ class Config:
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agent_litellm_model=agent_litellm_model,
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agent_mode=os.getenv('AGENT_MODE', 'false').lower() == 'true',
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_agent_mode_explicit=os.getenv('AGENT_MODE') is not None,
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agent_max_steps=parse_env_int(os.getenv('AGENT_MAX_STEPS'), 10, field_name='AGENT_MAX_STEPS', minimum=1),
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agent_max_steps=parse_env_int(
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os.getenv('AGENT_MAX_STEPS'),
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AGENT_MAX_STEPS_DEFAULT,
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field_name='AGENT_MAX_STEPS',
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minimum=1,
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),
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agent_skills=[s.strip() for s in os.getenv('AGENT_SKILLS', '').split(',') if s.strip()],
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agent_skill_dir=os.getenv('AGENT_SKILL_DIR') or os.getenv('AGENT_STRATEGY_DIR'),
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agent_nl_routing=os.getenv('AGENT_NL_ROUTING', 'false').lower() == 'true',
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@@ -10,6 +10,8 @@ from __future__ import annotations
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from copy import deepcopy
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from typing import Any, Dict, List, Optional
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from src.config import AGENT_MAX_STEPS_DEFAULT
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SCHEMA_VERSION = "2026-03-29"
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_CATEGORY_DEFINITIONS: List[Dict[str, Any]] = [
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@@ -1563,14 +1565,14 @@ _FIELD_DEFINITIONS: Dict[str, Dict[str, Any]] = {
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},
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"AGENT_MAX_STEPS": {
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"title": "Agent Max Steps",
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"description": "Maximum reasoning-step ceiling for Agent mode. In orchestrator mode, each sub-agent keeps min(its default, this limit) so lower-default specialists are not inflated.",
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"description": f"Maximum reasoning-step limit for Agent mode. At the default ({AGENT_MAX_STEPS_DEFAULT}), each sub-agent keeps its own preset. When raised above {AGENT_MAX_STEPS_DEFAULT}, all sub-agents adopt this value. When lowered below a sub-agent's preset, that sub-agent is capped at this value.",
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"category": "agent",
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"data_type": "integer",
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"ui_control": "number",
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"is_sensitive": False,
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"is_required": False,
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"is_editable": True,
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"default_value": "10",
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"default_value": str(AGENT_MAX_STEPS_DEFAULT),
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"options": [],
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"validation": {"min": 1, "max": 50},
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"display_order": 20,
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@@ -40,12 +40,12 @@ class TestAgentConfig(unittest.TestCase):
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@patch('src.config.load_dotenv')
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def test_default_agent_config(self, _mock_dotenv):
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"""Agent mode should be disabled by default."""
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from src.config import Config
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from src.config import AGENT_MAX_STEPS_DEFAULT, Config
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Config._instance = None
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config = Config._load_from_env()
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self.assertEqual(config.agent_litellm_model, "")
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self.assertFalse(config.agent_mode)
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self.assertEqual(config.agent_max_steps, 10)
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self.assertEqual(config.agent_max_steps, AGENT_MAX_STEPS_DEFAULT)
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self.assertEqual(config.agent_skills, [])
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@patch.dict(os.environ, {
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@@ -334,6 +334,7 @@ class TestAgentResultConversion(unittest.TestCase):
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mock_cfg.max_workers = 2
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mock_cfg.agent_mode = True
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mock_cfg.agent_max_steps = 10
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mock_cfg.agent_orchestrator_timeout_s = 0
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mock_cfg.agent_skills = []
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mock_cfg.bocha_api_keys = []
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mock_cfg.tavily_api_keys = []
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@@ -628,7 +629,8 @@ class TestAnalyzeWithAgentStockName(unittest.TestCase):
|
||||
patch('src.core.pipeline.GeminiAnalyzer'), \
|
||||
patch('src.core.pipeline.NotificationService'), \
|
||||
patch('src.core.pipeline.SearchService'), \
|
||||
patch('src.agent.factory.build_agent_executor') as mock_build_executor:
|
||||
patch('src.agent.factory.build_agent_executor') as mock_build_executor, \
|
||||
patch('src.agent.executor.AgentExecutor.run') as mock_agent_run:
|
||||
|
||||
mock_cfg = MagicMock()
|
||||
mock_cfg.max_workers = 2
|
||||
@@ -668,6 +670,7 @@ class TestAnalyzeWithAgentStockName(unittest.TestCase):
|
||||
mock_executor = MagicMock()
|
||||
mock_executor.run.return_value = agent_result
|
||||
mock_build_executor.return_value = mock_executor
|
||||
mock_agent_run.return_value = agent_result
|
||||
|
||||
news_response = MagicMock()
|
||||
news_response.success = True
|
||||
|
||||
@@ -35,6 +35,7 @@ from src.agent.protocols import (
|
||||
StageResult,
|
||||
StageStatus,
|
||||
)
|
||||
from src.config import AGENT_MAX_STEPS_DEFAULT
|
||||
|
||||
|
||||
# ============================================================
|
||||
@@ -500,13 +501,9 @@ class TestOrchestratorModes(unittest.TestCase):
|
||||
self.assertEqual(orch.mode, "standard")
|
||||
|
||||
def test_chain_agents_inherit_orchestrator_max_steps(self):
|
||||
"""Orchestrator max_steps acts as a *ceiling*, not a hard override.
|
||||
|
||||
Each agent keeps ``min(own_default, orchestrator_limit)`` so that
|
||||
specialised agents with lower defaults are not inflated.
|
||||
"""
|
||||
"""Default/lowered limits cap agents; raised limits hard-override all agents."""
|
||||
orch = self._make_orchestrator("full")
|
||||
orch.max_steps = 9
|
||||
orch.max_steps = AGENT_MAX_STEPS_DEFAULT
|
||||
high_limit_chain = orch._build_agent_chain(AgentContext(query="test", stock_code="600519"))
|
||||
self.assertEqual(
|
||||
{agent.agent_name: agent.max_steps for agent in high_limit_chain},
|
||||
@@ -520,6 +517,23 @@ class TestOrchestratorModes(unittest.TestCase):
|
||||
{"technical": 5, "intel": 4, "risk": 4, "decision": 3},
|
||||
)
|
||||
|
||||
orch.max_steps = AGENT_MAX_STEPS_DEFAULT + 2
|
||||
raised_limit_chain = orch._build_agent_chain(AgentContext(query="test", stock_code="600519"))
|
||||
self.assertEqual(
|
||||
{agent.agent_name: agent.max_steps for agent in raised_limit_chain},
|
||||
{"technical": AGENT_MAX_STEPS_DEFAULT + 2, "intel": AGENT_MAX_STEPS_DEFAULT + 2, "risk": AGENT_MAX_STEPS_DEFAULT + 2, "decision": AGENT_MAX_STEPS_DEFAULT + 2},
|
||||
)
|
||||
|
||||
def test_prepare_agent_raised_limit_overrides_low_default_agent(self):
|
||||
orch = self._make_orchestrator("full")
|
||||
orch.max_steps = AGENT_MAX_STEPS_DEFAULT + 2
|
||||
decision = MagicMock(agent_name="decision", max_steps=3)
|
||||
|
||||
prepared = orch._prepare_agent(decision)
|
||||
|
||||
self.assertIs(prepared, decision)
|
||||
self.assertEqual(prepared.max_steps, AGENT_MAX_STEPS_DEFAULT + 2)
|
||||
|
||||
def test_build_context_from_dict(self):
|
||||
orch = self._make_orchestrator()
|
||||
ctx = orch._build_context(
|
||||
@@ -565,6 +579,24 @@ class TestOrchestratorExecution(unittest.TestCase):
|
||||
result.meta["models_used"] = ["test/model"]
|
||||
return result
|
||||
|
||||
def test_prepare_agent_uses_default_constant_as_raise_threshold(self):
|
||||
orch = self._make_orchestrator()
|
||||
agent = MagicMock(agent_name="technical", max_steps=6)
|
||||
|
||||
prepared = orch._prepare_agent(agent)
|
||||
self.assertIs(prepared, agent)
|
||||
self.assertEqual(agent.max_steps, 6)
|
||||
|
||||
orch.max_steps = 12
|
||||
agent.max_steps = 6
|
||||
orch._prepare_agent(agent)
|
||||
self.assertEqual(agent.max_steps, 12)
|
||||
|
||||
orch.max_steps = 5
|
||||
agent.max_steps = 6
|
||||
orch._prepare_agent(agent)
|
||||
self.assertEqual(agent.max_steps, 5)
|
||||
|
||||
def test_execute_pipeline_stops_on_critical_failure(self):
|
||||
orch = self._make_orchestrator()
|
||||
technical = MagicMock(agent_name="technical")
|
||||
@@ -593,6 +625,32 @@ class TestOrchestratorExecution(unittest.TestCase):
|
||||
self.assertTrue(result.success)
|
||||
self.assertIn("Analysis Summary", result.content)
|
||||
|
||||
def test_execute_pipeline_degrades_on_skill_agent_failure_and_continues_to_decision(self):
|
||||
orch = self._make_orchestrator()
|
||||
orch.mode = "specialist"
|
||||
ctx = AgentContext(query="test", stock_code="600519")
|
||||
ctx.add_opinion(AgentOpinion(agent_name="technical", signal="buy", confidence=0.8, reasoning="Strong trend"))
|
||||
|
||||
technical = MagicMock(agent_name="technical")
|
||||
technical.run.return_value = self._stage_result("technical")
|
||||
intel = MagicMock(agent_name="intel")
|
||||
intel.run.return_value = self._stage_result("intel")
|
||||
risk = MagicMock(agent_name="risk")
|
||||
risk.run.return_value = self._stage_result("risk")
|
||||
skill = MagicMock(agent_name="strategy_bull_trend")
|
||||
skill.run.return_value = self._stage_result("strategy_bull_trend", StageStatus.FAILED, error="skill boom")
|
||||
decision = MagicMock(agent_name="decision")
|
||||
decision.run.return_value = self._stage_result("decision")
|
||||
|
||||
with patch.object(orch, "_build_agent_chain", return_value=[technical, intel, risk, decision]):
|
||||
with patch.object(orch, "_build_specialist_agents", return_value=[skill]):
|
||||
result = orch._execute_pipeline(ctx, parse_dashboard=False)
|
||||
|
||||
self.assertTrue(result.success)
|
||||
self.assertIn("Analysis Summary", result.content)
|
||||
skill.run.assert_called_once()
|
||||
decision.run.assert_called_once()
|
||||
|
||||
def test_execute_pipeline_skips_stage_when_remaining_budget_below_minimum(self):
|
||||
orch = self._make_orchestrator(config=SimpleNamespace(agent_orchestrator_timeout_s=20))
|
||||
ctx = AgentContext(query="test", stock_code="600519", stock_name="贵州茅台")
|
||||
|
||||
Reference in New Issue
Block a user