{
  "canonical_name": "microsoft/agent-lightning",
  "compilation_id": "pack_f3faf6973a8845ff866e0cfbf7b92e4f",
  "created_at": "2026-05-21T08:41:00.734158+00:00",
  "created_by": "project-pack-compiler",
  "feedback": {
    "carrier_selection_notes": [
      "viable_asset_types=skill, recipe, host_instruction, eval, preflight",
      "recommended_asset_types=skill, recipe, host_instruction, eval, preflight"
    ],
    "evidence_delta": {
      "confirmed_claims": [
        "identity_anchor_present",
        "capability_and_host_targets_present",
        "install_path_declared_or_better"
      ],
      "missing_required_fields": [],
      "must_verify_forwarded": [
        "Run or inspect `pip install agentlightning` in an isolated environment.",
        "Confirm the project exposes the claimed capability to at least one target host."
      ],
      "quickstart_execution_scope": "allowlisted_sandbox_smoke",
      "sandbox_command": "pip install agentlightning",
      "sandbox_container_image": "python:3.12-slim",
      "sandbox_execution_backend": "docker",
      "sandbox_planner_decision": "llm_execute_isolated_install",
      "sandbox_validation_id": "sbx_eeadcb5857144158b92e94d8ca569d59"
    },
    "feedback_event_type": "project_pack_compilation_feedback",
    "learning_candidate_reasons": [],
    "template_gaps": []
  },
  "identity": {
    "canonical_id": "project_881090af417e3985d27876dc0763be86",
    "canonical_name": "microsoft/agent-lightning",
    "homepage_url": null,
    "license": "unknown",
    "repo_url": "https://github.com/microsoft/agent-lightning",
    "slug": "agent-lightning",
    "source_packet_id": "phit_120f65fdc5bf4dea9a3c4d2f7f4b74c3",
    "source_validation_id": "dval_7811550190c749da836bb4a10c48be53"
  },
  "merchandising": {
    "best_for": "需要软件开发与交付能力，并使用 chatgpt的用户",
    "github_forks": null,
    "github_stars": null,
    "one_liner_en": "<p align=\"center\">",
    "one_liner_zh": "<p align=\"center\">",
    "primary_category": {
      "category_id": "software-development",
      "confidence": "medium",
      "name_en": "Software Development",
      "name_zh": "软件开发与交付",
      "reason": "matched_keywords:git"
    },
    "target_user": "使用 chatgpt 等宿主 AI 的用户",
    "title_en": "agent-lightning",
    "title_zh": "agent-lightning 能力包",
    "visible_tags": [
      {
        "label_en": "Knowledge Retrieval",
        "label_zh": "知识检索",
        "source": "repo_evidence_project_characteristics",
        "tag_id": "product_domain-knowledge-retrieval",
        "type": "product_domain"
      },
      {
        "label_en": "Knowledge Base Q&A",
        "label_zh": "知识库问答",
        "source": "repo_evidence_project_characteristics",
        "tag_id": "user_job-knowledge-base-q-a",
        "type": "user_job"
      },
      {
        "label_en": "Multi-agent Collaboration",
        "label_zh": "多 Agent 协作",
        "source": "repo_evidence_project_characteristics",
        "tag_id": "core_capability-multi-agent-collaboration",
        "type": "core_capability"
      },
      {
        "label_en": "Multi-role Workflow",
        "label_zh": "多角色协作流程",
        "source": "repo_evidence_project_characteristics",
        "tag_id": "workflow_pattern-multi-role-workflow",
        "type": "workflow_pattern"
      },
      {
        "label_en": "Evaluation Suite",
        "label_zh": "评测体系",
        "source": "repo_evidence_project_characteristics",
        "tag_id": "selection_signal-evaluation-suite",
        "type": "selection_signal"
      }
    ]
  },
  "packet_id": "phit_120f65fdc5bf4dea9a3c4d2f7f4b74c3",
  "page_model": {
    "artifacts": {
      "artifact_slug": "agent-lightning",
      "files": [
        "PROJECT_PACK.json",
        "QUICK_START.md",
        "PROMPT_PREVIEW.md",
        "HUMAN_MANUAL.md",
        "AI_CONTEXT_PACK.md",
        "BOUNDARY_RISK_CARD.md",
        "PITFALL_LOG.md",
        "REPO_INSPECTION.json",
        "REPO_INSPECTION.md",
        "CAPABILITY_CONTRACT.json",
        "EVIDENCE_INDEX.json",
        "CLAIM_GRAPH.json"
      ],
      "required_files": [
        "PROJECT_PACK.json",
        "QUICK_START.md",
        "PROMPT_PREVIEW.md",
        "HUMAN_MANUAL.md",
        "AI_CONTEXT_PACK.md",
        "BOUNDARY_RISK_CARD.md",
        "PITFALL_LOG.md",
        "REPO_INSPECTION.json"
      ]
    },
    "detail": {
      "capability_source": "Project Hit Packet + DownstreamValidationResult",
      "commands": [
        {
          "command": "pip install agentlightning",
          "label": "Python / pip · 官方安装入口",
          "source": "https://github.com/microsoft/agent-lightning#readme",
          "verified": true
        }
      ],
      "display_tags": [
        "知识检索",
        "知识库问答",
        "多 Agent 协作",
        "多角色协作流程",
        "评测体系"
      ],
      "eyebrow": "软件开发与交付",
      "glance": [
        {
          "body": "判断自己是不是目标用户。",
          "label": "最适合谁",
          "value": "需要软件开发与交付能力，并使用 chatgpt的用户"
        },
        {
          "body": "先理解能力边界，再决定是否继续。",
          "label": "核心价值",
          "value": "<p align=\"center\">"
        },
        {
          "body": "未完成验证前保持审慎。",
          "label": "继续前",
          "value": "publish to Doramagic.ai project surfaces"
        }
      ],
      "guardrail_source": "Boundary & Risk Card",
      "guardrails": [
        {
          "body": "Prompt Preview 只展示流程，不证明项目已安装或运行。",
          "label": "Check 1",
          "value": "不要把试用当真实运行"
        },
        {
          "body": "chatgpt",
          "label": "Check 2",
          "value": "确认宿主兼容"
        },
        {
          "body": "publish to Doramagic.ai project surfaces",
          "label": "Check 3",
          "value": "先隔离验证"
        }
      ],
      "mode": "skill, recipe, host_instruction, eval, preflight",
      "pitfall_log": {
        "items": [
          {
            "body": "README/documentation is current enough for a first validation pass.",
            "category": "能力坑",
            "evidence": [
              "capability.assumptions | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | README/documentation is current enough for a first validation pass."
            ],
            "severity": "medium",
            "suggested_check": "将假设转成下游验证清单。",
            "title": "能力判断依赖假设",
            "user_impact": "假设不成立时，用户拿不到承诺的能力。"
          },
          {
            "body": "未记录 last_activity_observed。",
            "category": "维护坑",
            "evidence": [
              "evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | last_activity_observed missing"
            ],
            "severity": "medium",
            "suggested_check": "补 GitHub 最近 commit、release、issue/PR 响应信号。",
            "title": "维护活跃度未知",
            "user_impact": "新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。"
          },
          {
            "body": "no_demo",
            "category": "安全/权限坑",
            "evidence": [
              "downstream_validation.risk_items | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium"
            ],
            "severity": "medium",
            "suggested_check": "进入安全/权限治理复核队列。",
            "title": "下游验证发现风险项",
            "user_impact": "下游已经要求复核，不能在页面中弱化。"
          },
          {
            "body": "No sandbox install has been executed yet; downstream must verify before user use.",
            "category": "安全/权限坑",
            "evidence": [
              "risks.safety_notes | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | No sandbox install has been executed yet; downstream must verify before user use."
            ],
            "severity": "medium",
            "suggested_check": "转成明确权限清单和安全审查提示。",
            "title": "存在安全注意事项",
            "user_impact": "用户安装前需要知道权限边界和敏感操作。"
          },
          {
            "body": "no_demo",
            "category": "安全/权限坑",
            "evidence": [
              "risks.scoring_risks | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium"
            ],
            "severity": "medium",
            "suggested_check": "把风险写入边界卡，并确认是否需要人工复核。",
            "title": "存在评分风险",
            "user_impact": "风险会影响是否适合普通用户安装。"
          },
          {
            "body": "issue_or_pr_quality=unknown。",
            "category": "维护坑",
            "evidence": [
              "evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | issue_or_pr_quality=unknown"
            ],
            "severity": "low",
            "suggested_check": "抽样最近 issue/PR，判断是否长期无人处理。",
            "title": "issue/PR 响应质量未知",
            "user_impact": "用户无法判断遇到问题后是否有人维护。"
          },
          {
            "body": "release_recency=unknown。",
            "category": "维护坑",
            "evidence": [
              "evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | release_recency=unknown"
            ],
            "severity": "low",
            "suggested_check": "确认最近 release/tag 和 README 安装命令是否一致。",
            "title": "发布节奏不明确",
            "user_impact": "安装命令和文档可能落后于代码，用户踩坑概率升高。"
          }
        ],
        "source": "ProjectPitfallLog + ProjectHitPacket + validation + community signals",
        "summary": "发现 7 个潜在踩坑项，其中 0 个为 high/blocking；最高优先级：能力坑 - 能力判断依赖假设。",
        "title": "踩坑日志"
      },
      "snapshot": {
        "contributors": null,
        "forks": null,
        "license": "unknown",
        "note": "站点快照，非实时质量证明；用于开工前背景判断。",
        "stars": null
      },
      "source_url": "https://github.com/microsoft/agent-lightning",
      "steps": [
        {
          "body": "不安装项目，先体验能力节奏。",
          "code": "preview",
          "title": "先试 Prompt"
        },
        {
          "body": "理解输入、输出、失败模式和边界。",
          "code": "manual",
          "title": "读说明书"
        },
        {
          "body": "把上下文交给宿主 AI 继续工作。",
          "code": "context",
          "title": "带给 AI"
        },
        {
          "body": "进入主力环境前先完成安装入口与风险边界验证。",
          "code": "verify",
          "title": "沙箱验证"
        }
      ],
      "subtitle": "<p align=\"center\">",
      "title": "agent-lightning 能力包",
      "trial_prompt": "# agent-lightning - Prompt Preview\n\n> 复制下面这段 Prompt 到你常用的 AI，先试一次，不需要安装。\n> 它的目标是让你直接体验这个项目的服务方式，而不是阅读项目介绍。\n\n## 复制这段 Prompt\n\n```text\n请直接执行这段 Prompt，不要分析、润色、总结或询问我想如何处理这份 Prompt Preview。\n\n你现在扮演 agent-lightning 的“安装前体验版”。\n这不是项目介绍、不是评价报告、不是 README 总结。你的任务是让我用最小成本体验它的核心服务。\n\n我的试用任务：我想用它完成一个真实的软件开发与交付任务。\n我常用的宿主 AI：chatgpt\n\n【体验目标】\n围绕我的真实任务，现场演示这个项目如何把输入转成 示例引导, 判断线索。重点是让我感受到工作方式，而不是给我项目背景。\n\n【业务流约束】\n- 你必须像一个正在提供服务的项目能力包，而不是像一个讲解员。\n- 每一轮只推进一个步骤；提出问题后必须停下来等我回答。\n- 每一步都必须让我感受到一个具体服务动作：澄清、整理、规划、检查、判断或收尾。\n- 每一步都要说明：当前目标、你需要我提供什么、我回答后你会产出什么。\n- 不要安装、不要运行命令、不要写代码、不要声称测试通过、不要声称已经修改文件。\n- 需要真实安装或宿主加载后才能验证的内容，必须明确说“这一步需要安装后验证”。\n- 如果我说“用示例继续”，你可以用虚构示例推进，但仍然不能声称真实执行。\n\n【可体验服务能力】\n- 安装前能力预览: <p align=\"center\"> 输入：用户任务, 当前 AI 对话上下文；输出：示例引导, 判断线索。\n\n【必须安装后才可验证的能力】\n- 命令行启动或安装流程: 项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 输入：终端环境, 包管理器, 项目依赖；输出：安装结果, 列表/更新/运行结果。\n\n【核心服务流】\n请严格按这个顺序带我体验。不要一次性输出完整流程：\n1. overview：仓库概览。围绕“仓库概览”模拟一次用户任务，不展示安装或运行结果。\n2. entrypoints：入口与运行边界。围绕“入口与运行边界”模拟一次用户任务，不展示安装或运行结果。\n3. architecture：架构证据地图。围绕“架构证据地图”模拟一次用户任务，不展示安装或运行结果。\n4. operations：运维与验证边界。围绕“运维与验证边界”模拟一次用户任务，不展示安装或运行结果。\n\n【核心能力体验剧本】\n每一步都必须按“输入 -> 服务动作 -> 中间产物”执行。不要只说流程名：\n1. overview\n输入：用户提供的“仓库概览”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n2. entrypoints\n输入：用户提供的“入口与运行边界”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n3. architecture\n输入：用户提供的“架构证据地图”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n4. operations\n输入：用户提供的“运维与验证边界”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n【项目服务规则】\n这些规则决定你如何服务用户。不要解释规则本身，而要在每一步执行时遵守：\n- 先确认用户任务、输入材料和成功标准，再模拟项目能力。\n- 每一步都必须形成可检查的小产物，并等待用户确认后再继续。\n- 凡是需要安装、调用工具或访问外部服务的能力，都必须标记为安装后验证。\n\n【每一步的服务约束】\n- Step 1 / overview：Step 1 必须围绕“仓库概览”形成一个小中间产物，并等待用户确认。\n- Step 2 / entrypoints：Step 2 必须围绕“入口与运行边界”形成一个小中间产物，并等待用户确认。\n- Step 3 / architecture：Step 3 必须围绕“架构证据地图”形成一个小中间产物，并等待用户确认。\n- Step 4 / operations：Step 4 必须围绕“运维与验证边界”形成一个小中间产物，并等待用户确认。\n\n【边界与风险】\n- 不要声称已经安装、运行、调用 API、读写本地文件或完成真实任务。\n- 安装前预览只能展示工作方式，不能证明兼容性、性能或输出质量。\n- 涉及安装、插件加载、工具调用或外部服务的能力必须安装后验证。\n\n【可追溯依据】\n这些路径只用于你内部校验或在我追问“依据是什么”时简要引用。不要在首次回复主动展开：\n- https://github.com/microsoft/agent-lightning#readme\n- README.md\n- pyproject.toml\n- contrib/README.md\n- dashboard/README.md\n- dashboard/package.json\n- examples/README.md\n- examples/apo/README.md\n- examples/azure/README.md\n- examples/calc_x/README.md\n- examples/chartqa/README.md\n- examples/claude_code/README.md\n\n【首次问题规则】\n- 首次三问必须先确认用户目标、成功标准和边界，不要提前进入工具、安装或实现细节。\n- 如果后续需要技术条件、文件路径或运行环境，必须等用户确认目标后再追问。\n\n首次回复必须只输出下面 4 个部分：\n1. 体验开始：用 1 句话说明你将带我体验 agent-lightning 的核心服务。\n2. 当前步骤：明确进入 Step 1，并说明这一步要解决什么。\n3. 你会如何服务我：说明你会先改变我完成任务的哪个动作。\n4. 只问我 3 个问题，然后停下等待回答。\n\n首次回复禁止输出：后续完整流程、证据清单、安装命令、项目评价、营销文案、已经安装或运行的说法。\n\nStep 1 / brainstorming 的二轮协议：\n- 我回答首次三问后，你仍然停留在 Step 1 / brainstorming，不要进入 Step 2。\n- 第二次回复必须产出 6 个部分：澄清后的任务定义、成功标准、边界条件、\n  2-3 个可选方案、每个方案的权衡、推荐方案。\n- 第二次回复最后必须问我是否确认推荐方案；只有我明确确认后，才能进入下一步。\n- 第二次回复禁止输出 git worktree、代码计划、测试文件、命令或真实执行结果。\n\n后续对话规则：\n- 我回答后，你先完成当前步骤的中间产物并等待确认；只有我确认后，才能进入下一步。\n- 每一步都要生成一个小的中间产物，例如澄清后的目标、计划草案、测试意图、验证清单或继续/停止判断。\n- 所有演示都写成“我会建议/我会引导/这一步会形成”，不要写成已经真实执行。\n- 不要声称已经测试通过、文件已修改、命令已运行或结果已产生。\n- 如果某个能力必须安装后验证，请直接说“这一步需要安装后验证”。\n- 如果证据不足，请明确说“证据不足”，不要补事实。\n```\n",
      "voices": [
        {
          "body": "来源平台：reddit。reddit: We discovered an approach to train any AI agent with RL, with (almost ..（https://www.reddit.com/r/LocalLLaMA/comments/1m9m670/we_discovered_an_approach_to_train_any_ai_agent/）。这些是项目级外部声音，不作为单独质量证明。",
          "items": [
            {
              "kind": "searxng_indexed",
              "source": "reddit",
              "title": "We discovered an approach to train any AI agent with RL, with (almost ..",
              "url": "https://www.reddit.com/r/LocalLLaMA/comments/1m9m670/we_discovered_an_approach_to_train_any_ai_agent/"
            }
          ],
          "status": "已收录 1 条来源",
          "title": "社区讨论"
        }
      ]
    },
    "homepage_card": {
      "category": "软件开发与交付",
      "desc": "<p align=\"center\">",
      "effort": "安装已验证",
      "forks": null,
      "icon": "code",
      "name": "agent-lightning 能力包",
      "risk": "需复核",
      "slug": "agent-lightning",
      "stars": null,
      "tags": [
        "知识检索",
        "知识库问答",
        "多 Agent 协作",
        "多角色协作流程",
        "评测体系"
      ],
      "thumb": "gray",
      "type": "Skill Pack"
    },
    "manual": {
      "markdown": "# https://github.com/microsoft/agent-lightning 项目说明书\n\n生成时间: 2026-05-21 08:39:26 UTC\n\n## 目录\n\n- [仓库概览](#overview)\n- [入口与运行边界](#entrypoints)\n- [架构证据地图](#architecture)\n- [运维与验证边界](#operations)\n\n<a id='overview'></a>\n\n## 仓库概览\n\n### 相关页面\n\n相关主题：[入口与运行边界](#entrypoints), [架构证据地图](#architecture), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 仓库概览\n\n本页在 Human Wiki provider 不可用时基于仓库证据生成，只使用 README、文件树和已选源码文件，不把模板描述冒充项目事实。\n\n## README 证据\n\n<p align=\"center\">\n  <img src=\"docs/assets/readme-banner.svg\" alt=\"Agent-lightning-banner\" style=\"width:600px\"/>\n</p>\n\n# Agent Lightning⚡\n\n[![Unit Tests](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml)\n[![Documentation](https://img.shields.io/badge/GitHub%20Pages-Documentation-blue)](https://microsoft.github.io/agent-lightning/)\n[![PyPI version](https://badge.fury.io/py/agentlightning.svg)](https://badge.fury.io/py/agentlightning)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/microsoft/agent-lightning)\n[![Discord](https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white)](https://discord.gg/RYk7CdvDR7)\n\n**The absolute trainer to light up AI agents.**\n\nJoin our [Discord community](https://discord.gg/RYk7CdvDR7) to connect with other users and contributors.\n\n## ⚡ Core Features\n\n- Turn your agent into an optimizable beast with **ZERO CODE CHANGE** (almost)! 💤\n- Build with **ANY** agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖\n- **Selectively** optimize one or more agents in a multi-agent system. 🎯\n- Embraces **Algorithms** like Reinforcement Learning, Automatic Prompt Optimization, Supervised Fine-tuning and more. 🤗\n\nRead more on our [documentation website](https://microsoft.github.io/agent-lightning/).\n\n<p align=\"center\">\n  <img src=\"docs/assets/readme-diff.svg\" alt=\"Agent-Lightning Core Quickstart\" style=\"width:100%\"/>\n</p>\n\n## ⚡ Installation\n\n```bash\npip install agentlightning\n```\n\nFor the latest nightly build (cutting-edge features), you can install from Test PyPI:\n\n```bash\npip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre agentlightning\n```\n\nPlease refer to our [installation guide](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/) for more details.\n\nTo start using Agent-lightning, check out our [documentation](https://microsoft.github.io/agent-lightning/) and [examples](./examples).\n\n## ⚡ Articles\n\n- 12/17/2025 [Adopting the Trajectory Level Aggregation for Faster Training](https://agent-lightning.github.io/posts/trajectory_\n\n[excerpt truncated]\n\n## 已选源码清单\n\n- `README.md`\n- `pyproject.toml`\n- `contrib/README.md`\n- `dashboard/README.md`\n- `dashboard/package.json`\n- `examples/README.md`\n- `examples/apo/README.md`\n- `examples/azure/README.md`\n- `examples/calc_x/README.md`\n- `examples/chartqa/README.md`\n- `examples/claude_code/README.md`\n- `examples/minimal/README.md`\n\n| 文件 | 证据角色 | 大小 |\n|---|---|---|\n| `README.md` | README/产品与使用证据 | 9958 bytes |\n| `pyproject.toml` | 包与运行时元数据 | 8738 bytes |\n| `contrib/README.md` | 文档证据 | 2011 bytes |\n| `dashboard/README.md` | 文档证据 | 790 bytes |\n| `dashboard/package.json` | 包与运行时元数据 | 2377 bytes |\n| `examples/README.md` | 文档证据 | 6244 bytes |\n| `examples/apo/README.md` | 文档证据 | 3914 bytes |\n| `examples/azure/README.md` | 文档证据 | 9661 bytes |\n| `examples/calc_x/README.md` | 文档证据 | 3563 bytes |\n| `examples/chartqa/README.md` | 文档证据 | 4077 bytes |\n| `examples/claude_code/README.md` | 文档证据 | 5821 bytes |\n| `examples/minimal/README.md` | 文档证据 | 1786 bytes |\n\n资料来源：`[README.md:1-120]()`\n\n---\n\n<a id='entrypoints'></a>\n\n## 入口与运行边界\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [架构证据地图](#architecture), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 入口与运行边界\n\n下面文件是安装、启动、配置或宿主集成的高信号候选。这里给出证据地图，不推断未经执行验证的 API 契约。\n\n| 文件 | 证据角色 | 大小 |\n|---|---|---|\n| `README.md` | README/产品与使用证据 | 9958 bytes |\n| `pyproject.toml` | 包与运行时元数据 | 8738 bytes |\n| `contrib/README.md` | 文档证据 | 2011 bytes |\n| `dashboard/README.md` | 文档证据 | 790 bytes |\n| `dashboard/package.json` | 包与运行时元数据 | 2377 bytes |\n| `examples/README.md` | 文档证据 | 6244 bytes |\n| `examples/apo/README.md` | 文档证据 | 3914 bytes |\n| `examples/azure/README.md` | 文档证据 | 9661 bytes |\n| `examples/calc_x/README.md` | 文档证据 | 3563 bytes |\n| `examples/chartqa/README.md` | 文档证据 | 4077 bytes |\n| `examples/claude_code/README.md` | 文档证据 | 5821 bytes |\n| `examples/minimal/README.md` | 文档证据 | 1786 bytes |\n\n资料来源：`[README.md:1-120](https://github.com/microsoft/agent-lightning/blob/main/README.md)`\n\n---\n\n<a id='architecture'></a>\n\n## 架构证据地图\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [入口与运行边界](#entrypoints), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 架构证据地图\n\n本节只根据仓库路径组织可能的架构区域；需要运行验证的行为不会在这里断言。\n\n- `.`: `README.md`, `pyproject.toml`\n- `contrib`: `contrib/README.md`\n- `dashboard`: `dashboard/README.md`, `dashboard/package.json`\n- `examples`: `examples/README.md`, `examples/apo/README.md`, `examples/azure/README.md`, `examples/calc_x/README.md`, `examples/chartqa/README.md`, `examples/claude_code/README.md`\n\n资料来源：`[pyproject.toml:1-120](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)`\n\n---\n\n<a id='operations'></a>\n\n## 运维与验证边界\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [入口与运行边界](#entrypoints), [架构证据地图](#architecture)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 运维与验证边界\n\n运维建议仅来自仓库中真实存在的文件。把该项目用于 agent 工作流前，仍需在 sandbox 中验证安装、quickstart 和运行行为。\n\n- Documentation signal: `README.md`\n- Runtime/package signal: `pyproject.toml`\n- Documentation signal: `contrib/README.md`\n- Documentation signal: `dashboard/README.md`\n- Runtime/package signal: `dashboard/package.json`\n- Documentation signal: `examples/README.md`\n- Documentation signal: `examples/apo/README.md`\n- Documentation signal: `examples/azure/README.md`\n- Documentation signal: `examples/calc_x/README.md`\n- Documentation signal: `examples/chartqa/README.md`\n\n资料来源：`[contrib/README.md:1-120](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)`\n\n---\n\n---\n\n## Doramagic 踩坑日志\n\n项目：microsoft/agent-lightning\n\n摘要：发现 7 个潜在踩坑项，其中 0 个为 high/blocking；最高优先级：能力坑 - 能力判断依赖假设。\n\n## 1. 能力坑 · 能力判断依赖假设\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：README/documentation is current enough for a first validation pass.\n- 对用户的影响：假设不成立时，用户拿不到承诺的能力。\n- 建议检查：将假设转成下游验证清单。\n- 防护动作: 假设必须转成验证项；没有验证结果前不能写成事实。\n- 证据：capability.assumptions | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | README/documentation is current enough for a first validation pass.\n\n## 2. 维护坑 · 维护活跃度未知\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：未记录 last_activity_observed。\n- 对用户的影响：新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。\n- 建议检查：补 GitHub 最近 commit、release、issue/PR 响应信号。\n- 防护动作: 维护活跃度未知时，推荐强度不能标为高信任。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | last_activity_observed missing\n\n## 3. 安全/权限坑 · 下游验证发现风险项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：下游已经要求复核，不能在页面中弱化。\n- 建议检查：进入安全/权限治理复核队列。\n- 防护动作: 下游风险存在时必须保持 review/recommendation 降级。\n- 证据：downstream_validation.risk_items | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 4. 安全/权限坑 · 存在安全注意事项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：No sandbox install has been executed yet; downstream must verify before user use.\n- 对用户的影响：用户安装前需要知道权限边界和敏感操作。\n- 建议检查：转成明确权限清单和安全审查提示。\n- 防护动作: 安全注意事项必须面向用户前置展示。\n- 证据：risks.safety_notes | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | No sandbox install has been executed yet; downstream must verify before user use.\n\n## 5. 安全/权限坑 · 存在评分风险\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：风险会影响是否适合普通用户安装。\n- 建议检查：把风险写入边界卡，并确认是否需要人工复核。\n- 防护动作: 评分风险必须进入边界卡，不能只作为内部分数。\n- 证据：risks.scoring_risks | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 6. 维护坑 · issue/PR 响应质量未知\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：issue_or_pr_quality=unknown。\n- 对用户的影响：用户无法判断遇到问题后是否有人维护。\n- 建议检查：抽样最近 issue/PR，判断是否长期无人处理。\n- 防护动作: issue/PR 响应未知时，必须提示维护风险。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | issue_or_pr_quality=unknown\n\n## 7. 维护坑 · 发布节奏不明确\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：release_recency=unknown。\n- 对用户的影响：安装命令和文档可能落后于代码，用户踩坑概率升高。\n- 建议检查：确认最近 release/tag 和 README 安装命令是否一致。\n- 防护动作: 发布节奏未知或过期时，安装说明必须标注可能漂移。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | release_recency=unknown\n\n<!-- canonical_name: microsoft/agent-lightning; human_manual_source: deepwiki_human_wiki -->\n",
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      "asset_id": "ai_context_pack",
      "filename": "AI_CONTEXT_PACK.md",
      "markdown": "# @example/webshop-training - Doramagic AI Context Pack\n\n> 定位：安装前体验与判断资产。它帮助宿主 AI 有一个好的开始，但不代表已经安装、执行或验证目标项目。\n\n## 充分原则\n\n- **充分原则，不是压缩原则**：AI Context Pack 应该充分到让宿主 AI 在开工前理解项目价值、能力边界、使用入口、风险和证据来源；它可以分层组织，但不以最短摘要为目标。\n- **压缩策略**：只压缩噪声和重复内容，不压缩会影响判断和开工质量的上下文。\n\n## 给宿主 AI 的使用方式\n\n你正在读取 Doramagic 为 @example/webshop-training 编译的 AI Context Pack。请把它当作开工前上下文：帮助用户理解适合谁、能做什么、如何开始、哪些必须安装后验证、风险在哪里。不要声称你已经安装、运行或执行了目标项目。\n\n## Claim 消费规则\n\n- **事实来源**：Repo Evidence + Claim/Evidence Graph；Human Wiki 只提供显著性、术语和叙事结构。\n- **事实最低状态**：`supported`\n- `supported`：可以作为项目事实使用，但回答中必须引用 claim_id 和证据路径。\n- `weak`：只能作为低置信度线索，必须要求用户继续核实。\n- `inferred`：只能用于风险提示或待确认问题，不能包装成项目事实。\n- `unverified`：不得作为事实使用，应明确说证据不足。\n- `contradicted`：必须展示冲突来源，不得替用户强行选择一个版本。\n\n## 它最适合谁\n\n- **AI 研究者或研究型 Agent 构建者**：README 明确围绕研究、实验或论文工作流展开。 证据：`README.md` Claim：`clm_0002` supported 0.86\n- **正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**：README 或插件配置提到多个宿主 AI。 证据：`README.md` Claim：`clm_0003` supported 0.86\n\n## 它能做什么\n\n- **命令行启动或安装流程**（需要安装后验证）：项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 证据：`README.md` Claim：`clm_0001` supported 0.86\n\n## 怎么开始\n\n- `pip install agentlightning` 证据：`README.md` Claim：`clm_0004` supported 0.86\n- `pip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre agentlightning` 证据：`README.md` Claim：`clm_0005` supported 0.86\n\n## 继续前判断卡\n\n- **当前建议**：先做角色匹配试用\n- **为什么**：这个项目更像角色库，核心风险是选错角色或把角色文案当执行能力；先用 Prompt Preview 试角色匹配，再决定是否沙盒导入。\n\n### 30 秒判断\n\n- **现在怎么做**：先做角色匹配试用\n- **最小安全下一步**：先用 Prompt Preview 试角色匹配；满意后再隔离导入\n- **先别相信**：角色质量和任务匹配不能直接相信。\n- **继续会触碰**：角色选择偏差、命令执行、宿主 AI 配置\n\n### 现在可以相信\n\n- **适合人群线索：AI 研究者或研究型 Agent 构建者**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0002` supported 0.86\n- **适合人群线索：正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0003` supported 0.86\n- **能力存在：命令行启动或安装流程**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`README.md` Claim：`clm_0001` supported 0.86\n- **存在 Quick Start / 安装命令线索**（supported）：可以相信项目文档出现过启动或安装入口；不要因此直接在主力环境运行。 证据：`README.md` Claim：`clm_0004` supported 0.86\n\n### 现在还不能相信\n\n- **角色质量和任务匹配不能直接相信。**（unverified）：角色库证明有很多角色，不证明每个角色都适合你的具体任务，也不证明角色能产生高质量结果。\n- **不能把角色文案当成真实执行能力。**（unverified）：安装前只能判断角色描述和任务画像是否匹配，不能证明它能在宿主 AI 里完成任务。\n- **真实输出质量不能在安装前相信。**（unverified）：Prompt Preview 只能展示引导方式，不能证明真实项目中的结果质量。\n- **宿主 AI 版本兼容性不能在安装前相信。**（unverified）：Claude、Cursor、Codex、Gemini 等宿主加载规则和版本差异必须在真实环境验证。\n- **不会污染现有宿主 AI 行为，不能直接相信。**（inferred）：Skill、plugin、AGENTS/CLAUDE/GEMINI 指令可能改变宿主 AI 的默认行为。 证据：`AGENTS.md`\n- **可安全回滚不能默认相信。**（unverified）：除非项目明确提供卸载和恢复说明，否则必须先在隔离环境验证。\n- **真实安装后是否与用户当前宿主 AI 版本兼容？**（unverified）：兼容性只能通过实际宿主环境验证。\n- **项目输出质量是否满足用户具体任务？**（unverified）：安装前预览只能展示流程和边界，不能替代真实评测。\n\n### 继续会触碰什么\n\n- **角色选择偏差**：用户对任务应该由哪个专家角色处理的判断。 原因：选错角色会让 AI 从错误专业视角回答，浪费时间或误导决策。\n- **命令执行**：包管理器、网络下载、本地插件目录、项目配置或用户主目录。 原因：运行第一条命令就可能产生环境改动；必须先判断是否值得跑。 证据：`README.md`\n- **宿主 AI 配置**：Claude/Codex/Cursor/Gemini/OpenCode 等宿主的 plugin、Skill 或规则加载配置。 原因：宿主配置会改变 AI 后续工作方式，可能和用户已有规则冲突。 证据：`AGENTS.md`\n- **本地环境或项目文件**：安装结果、插件缓存、项目配置或本地依赖目录。 原因：安装前无法证明写入范围和回滚方式，需要隔离验证。 证据：`README.md`\n- **宿主 AI 上下文**：AI Context Pack、Prompt Preview、Skill 路由、风险规则和项目事实。 原因：导入上下文会影响宿主 AI 后续判断，必须避免把未验证项包装成事实。\n\n### 最小安全下一步\n\n- **先跑 Prompt Preview**：先用交互式试用验证任务画像和角色匹配，不要先导入整套角色库。（适用：任何项目都适用，尤其是输出质量未知时。）\n- **只在隔离目录或测试账号试装**：避免安装命令污染主力宿主 AI、真实项目或用户主目录。（适用：存在命令执行、插件配置或本地写入线索时。）\n- **先备份宿主 AI 配置**：Skill、plugin、规则文件可能改变 Claude/Cursor/Codex 的默认行为。（适用：存在插件 manifest、Skill 或宿主规则入口时。）\n- **安装后只验证一个最小任务**：先验证加载、兼容、输出质量和回滚，再决定是否深用。（适用：准备从试用进入真实工作流时。）\n\n### 退出方式\n\n- **保留安装前状态**：记录原始宿主配置和项目状态，后续才能判断是否可恢复。\n- **准备移除宿主 plugin / Skill / 规则入口**：如果试装后行为异常，可以把宿主 AI 恢复到试装前状态。\n- **保留原始角色选择记录**：如果输出偏题，可以回到任务画像阶段重新选择角色，而不是继续沿着错误角色推进。\n- **记录安装命令和写入路径**：没有明确卸载说明时，至少要知道哪些目录或配置需要手动清理。\n- **如果没有回滚路径，不进入主力环境**：不可回滚是继续前阻断项，不应靠信任或运气继续。\n\n## 哪些只能预览\n\n- 解释项目适合谁和能做什么\n- 基于项目文档演示典型对话流程\n- 帮助用户判断是否值得安装或继续研究\n\n## 哪些必须安装后验证\n\n- 真实安装 Skill、插件或 CLI\n- 执行脚本、修改本地文件或访问外部服务\n- 验证真实输出质量、性能和兼容性\n\n## 边界与风险判断卡\n\n- **把安装前预览误认为真实运行**：用户可能高估项目已经完成的配置、权限和兼容性验证。 处理方式：明确区分 prompt_preview_can_do 与 runtime_required。 Claim：`clm_0006` inferred 0.45\n- **命令执行会修改本地环境**：安装命令可能写入用户主目录、宿主插件目录或项目配置。 处理方式：先在隔离环境或测试账号中运行。 证据：`README.md` Claim：`clm_0007` supported 0.86\n- **待确认**：真实安装后是否与用户当前宿主 AI 版本兼容？。原因：兼容性只能通过实际宿主环境验证。\n- **待确认**：项目输出质量是否满足用户具体任务？。原因：安装前预览只能展示流程和边界，不能替代真实评测。\n- **待确认**：安装命令是否需要网络、权限或全局写入？。原因：这影响企业环境和个人环境的安装风险。\n\n## 开工前工作上下文\n\n### 加载顺序\n\n- 先读取 how_to_use.host_ai_instruction，建立安装前判断资产的边界。\n- 读取 claim_graph_summary，确认事实来自 Claim/Evidence Graph，而不是 Human Wiki 叙事。\n- 再读取 intended_users、capabilities 和 quick_start_candidates，判断用户是否匹配。\n- 需要执行具体任务时，优先查 role_skill_index，再查 evidence_index。\n- 遇到真实安装、文件修改、网络访问、性能或兼容性问题时，转入 risk_card 和 boundaries.runtime_required。\n\n### 任务路由\n\n- **命令行启动或安装流程**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`README.md` Claim：`clm_0001` supported 0.86\n\n### 上下文规模\n\n- 文件总数：569\n- 重要文件覆盖：40/569\n- 证据索引条目：80\n- 角色 / Skill 条目：54\n\n### 证据不足时的处理\n\n- **missing_evidence**：说明证据不足，要求用户提供目标文件、README 段落或安装后验证记录；不要补全事实。\n- **out_of_scope_request**：说明该任务超出当前 AI Context Pack 证据范围，并建议用户先查看 Human Manual 或真实安装后验证。\n- **runtime_request**：给出安装前检查清单和命令来源，但不要替用户执行命令或声称已执行。\n- **source_conflict**：同时展示冲突来源，标记为待核实，不要强行选择一个版本。\n\n## Prompt Recipes\n\n### 适配判断\n\n- 目标：判断这个项目是否适合用户当前任务。\n- 预期输出：适配结论、关键理由、证据引用、安装前可预览内容、必须安装后验证内容、下一步建议。\n\n```text\n请基于 @example/webshop-training 的 AI Context Pack，先问我 3 个必要问题，然后判断它是否适合我的任务。回答必须包含：适合谁、能做什么、不能做什么、是否值得安装、证据来自哪里。所有项目事实必须引用 evidence_refs、source_paths 或 claim_id。\n```\n\n### 安装前体验\n\n- 目标：让用户在安装前感受核心工作流，同时避免把预览包装成真实能力或营销承诺。\n- 预期输出：一段带边界标签的体验剧本、安装后验证清单和谨慎建议；不含真实运行承诺或强营销表述。\n\n```text\n请把 @example/webshop-training 当作安装前体验资产，而不是已安装工具或真实运行环境。\n\n请严格输出四段：\n1. 先问我 3 个必要问题。\n2. 给出一段“体验剧本”：用 [安装前可预览]、[必须安装后验证]、[证据不足] 三种标签展示它可能如何引导工作流。\n3. 给出安装后验证清单：列出哪些能力只有真实安装、真实宿主加载、真实项目运行后才能确认。\n4. 给出谨慎建议：只能说“值得继续研究/试装”“先补充信息后再判断”或“不建议继续”，不得替项目背书。\n\n硬性边界：\n- 不要声称已经安装、运行、执行测试、修改文件或产生真实结果。\n- 不要写“自动适配”“确保通过”“完美适配”“强烈建议安装”等承诺性表达。\n- 如果描述安装后的工作方式，必须使用“如果安装成功且宿主正确加载 Skill，它可能会……”这种条件句。\n- 体验剧本只能写成“示例台词/假设流程”：使用“可能会询问/可能会建议/可能会展示”，不要写“已写入、已生成、已通过、正在运行、正在生成”。\n- Prompt Preview 不负责给安装命令；如用户准备试装，只能提示先阅读 Quick Start 和 Risk Card，并在隔离环境验证。\n- 所有项目事实必须来自 supported claim、evidence_refs 或 source_paths；inferred/unverified 只能作风险或待确认项。\n\n```\n\n### 角色 / Skill 选择\n\n- 目标：从项目里的角色或 Skill 中挑选最匹配的资产。\n- 预期输出：候选角色或 Skill 列表，每项包含适用场景、证据路径、风险边界和是否需要安装后验证。\n\n```text\n请读取 role_skill_index，根据我的目标任务推荐 3-5 个最相关的角色或 Skill。每个推荐都要说明适用场景、可能输出、风险边界和 evidence_refs。\n```\n\n### 风险预检\n\n- 目标：安装或引入前识别环境、权限、规则冲突和质量风险。\n- 预期输出：环境、权限、依赖、许可、宿主冲突、质量风险和未知项的检查清单。\n\n```text\n请基于 risk_card、boundaries 和 quick_start_candidates，给我一份安装前风险预检清单。不要替我执行命令，只说明我应该检查什么、为什么检查、失败会有什么影响。\n```\n\n### 宿主 AI 开工指令\n\n- 目标：把项目上下文转成一次对话开始前的宿主 AI 指令。\n- 预期输出：一段边界明确、证据引用明确、适合复制给宿主 AI 的开工前指令。\n\n```text\n请基于 @example/webshop-training 的 AI Context Pack，生成一段我可以粘贴给宿主 AI 的开工前指令。这段指令必须遵守 not_runtime=true，不能声称项目已经安装、运行或产生真实结果。\n```\n\n\n## 角色 / Skill 索引\n\n- 共索引 54 个角色 / Skill / 项目文档条目。\n\n- **Contributing Guide**（project_doc）：Agent Lightning gets better every time someone files a clear bug, polishes docs, improves tests, or lands a new feature. This guide collects the expectations, checklists, and tips that help you go from “I have an idea” to “my pull request just merged.” 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/community/contributing.md`\n- **Repository Guidelines**（project_doc）：Architecture Overview Agent Lightning runs through a continuous loop: runners and tracers emit spans, LightningStore agentlightning/store/ keeps them synchronized, and algorithms in agentlightning/algorithm/ consume those traces to improve behavior. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`AGENTS.md`\n- **Agent Lightning⚡**（project_doc）：! Unit Tests https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml ! Documentation https://img.shields.io/badge/GitHub%20Pages-Documentation-blue https://microsoft.github.io/agent-lightning/ ! PyPI version https://badge.fury.io/py/agentlightning.svg https://badge.fury.io/py/agentlightning ! License https:/… 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`README.md`\n- **Contrib Area**（project_doc）：This tree hosts experimental integrations, third-party recipes, and curated recipes that are not ready for the main agentlightning/ , examples/ , or docs/ trees. Treat it as an incubator: keep contributions self-contained, clearly owned, and reproducible so downstream users can vendor them without guesswork. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`contrib/README.md`\n- **Agent-lightning Dashboard**（project_doc）：This is the dashboard for Agent-lightning. It is a web application that allows you to inspect your Agent-lightning store and debug running experiments. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`dashboard/README.md`\n- **⚡ Examples Catalog**（project_doc）：This catalog highlights the examples shipped with Agent-lightning. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/README.md`\n- **Agent-OS Integration for Agent-Lightning**（project_doc）：Agent-OS Integration for Agent-Lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`contrib/recipes/agentos/README.md`\n- **Example of AGL Environments**（project_doc）：This example implements agents across various environments within Agent Lightning. The example is designed to run on a single node with 8 GPUs, each having at least 40 GB of memory. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`contrib/recipes/envs/README.md`\n- **Search-R1 Example**（project_doc）：This example implements Search R1 within Agent Lightning. It also serves as a demonstration of a framework-free agent training pipeline , showing how to run end-to-end RL training without relying on specialized frameworks. It's tested and compatible with Agent-lightning v0.2.x . 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`contrib/recipes/search_r1/README.md`\n- **WebShop Example**（project_doc）：This example demonstrates how to train a Vercel AI SDK agent on the WebShop benchmark using Agent Lightning with reinforcement learning VERL/GRPO . The training pipeline uses a headless TypeScript runner that executes agent rollouts and reports traces to the Agent Lightning coordinator. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`contrib/recipes/webshop/README.md`\n- **APO Example**（project_doc）：! apo CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-apo.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-apo.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/apo/README.md`\n- **Supervised Fine-tuning with Azure OpenAI**（project_doc）：Supervised Fine-tuning with Azure OpenAI 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/azure/README.md`\n- **Calc-X Example**（project_doc）：! calc x CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-calc-x.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-calc-x.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/calc_x/README.md`\n- **ChartQA Example**（project_doc）：! chartqa workflow status https://github.com/microsoft/agent-lightning/actions/workflows/badge-chartqa.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-chartqa.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/chartqa/README.md`\n- **Training Claude Code with Agent-lightning**（project_doc）：Training Claude Code with Agent-lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/claude_code/README.md`\n- **Minimal Component Showcase**（project_doc）：! minimal CI status https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/minimal/README.md`\n- **RAG Agent Example**（project_doc）：! rag workflow status https://github.com/microsoft/agent-lightning/actions/workflows/examples-rag.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-rag.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/rag/README.md`\n- **Spider Example**（project_doc）：! spider CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/spider/README.md`\n- **Tinker + Agent-lightning Integration**（project_doc）：Tinker + Agent-lightning Integration 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/tinker/README.md`\n- **Unsloth SFT Example**（project_doc）：! unsloth CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-unsloth.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-unsloth.yml 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/unsloth/README.md`\n- **Changelog**（project_doc）：Agent-lightning v0.3.0 is a major release that introduces several new features and bug fixes. The release is a collaborative effort between Agent-lightning core teams and the community. Thanks to all the contributors who made this release possible. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/changelog.md`\n- **Agent Lightning**（project_doc）：Agent Lightning is the absolute trainer to light up AI agents. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/index.md`\n- **APO**（project_doc）：You can use the shortcut agl.APO ... to create an APO instance. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/algorithm-zoo/apo.md`\n- **Algorithm Zoo**（project_doc）：AgentLightning includes several popular and frequently requested algorithms in its built-in library, allowing agent developers to use them directly. These algorithms are designed to be compatible with most agent scenarios. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/algorithm-zoo/index.md`\n- **VERL**（project_doc）：You can use the shortcut agl.VERL ... to create a VERL instance. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/algorithm-zoo/verl.md`\n- **Maintainer Guide**（project_doc）：This guide describes the day-to-day responsibilities for Agent Lightning maintainers—how to bump versions, run release ceremonies, interact with CI, and backport fixes safely. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/community/maintainers.md`\n- **The Bird's Eye View of Agent-lightning**（project_doc）：The Bird's Eye View of Agent-lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/deep-dive/birds-eye-view.md`\n- **Serving LLMs under Agent-lightning**（project_doc）：Agent-lightning focuses on data, learning signals, and control flow — not on running model inference. This deep dive explains how to serve a model alongside Agent-lightning so runners can call it reliably, how the LLM Proxy fits into the loop, and why token IDs matter if you care about correctness in training and evaluation. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/deep-dive/serving-llm.md`\n- **Understanding Store**（project_doc）：The LightningStore agentlightning.LightningStore is the central coordination point for Agent-lightning. It holds the task queue, rollouts, attempts, spans, and versioned resources, and exposes a small API both Runners and Algorithms use to communicate. This document explains what's in the store, how statuses transition, how spans are recorded, and the concurrency model threads & processes . 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/deep-dive/store.md`\n- **Examples Catalog**（project_doc）：We welcome contributions to the examples catalog! Please refer to the Contributing ../community/contributing.md guide for more details. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/how-to/examples-catalog.md`\n- **Train the First Agent with Agent-lightning**（project_doc）：Train the First Agent with Agent-lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/how-to/train-first-agent.md`\n- **Train SQL Agent with Agent-lightning and VERL**（project_doc）：Train SQL Agent with Agent-lightning and VERL 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/how-to/train-sql-agent.md`\n- **Fine-tune with Unsloth SFT**（project_doc）：Please make sure you have read Write the First Algorithm ./write-first-algorithm.md . Although that recipe is based on a simple prompt tuning algorithm, it introduces the core concepts of Agent-lightning and you should be familiar with them before proceeding. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/how-to/unsloth-sft.md`\n- **Write the First Algorithm with Agent-lightning**（project_doc）：Write the First Algorithm with Agent-lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/how-to/write-first-algorithm.md`\n- **Agent Developer APIs**（project_doc）：These are convenient helpers for creating agents from functions. First-time users are recommended to use these decorators to create agents. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/agent.md`\n- **Algorithm-side References**（project_doc）：This reference covers APIs that are designed to be used at \"Algorithm Side\". For built-in algorithms, see Algorithm Zoo ../algorithm-zoo/index.md . 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/algorithm.md`\n- **Command Line Interface**（project_doc）：This document is a work in progress and might not be updated with the latest changes. Try to use agl -h to get the latest help message. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/cli.md`\n- **Instrumentation API**（project_doc）：::: agentlightning.instrumentation.instrument all 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/instrumentation.md`\n- **Internal API References**（project_doc）：The following APIs should be used with extra caution because they are very likely to change in the future. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/internal.md`\n- **RESTful API References**（project_doc）：Shown in the following is the RESTful API for Lightning Store. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/restful.md`\n- **Runner-side References**（project_doc）：This reference covers APIs that are designed to be used at \"Runner Side\". 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/runner.md`\n- **Semantic Conventions**（project_doc）： 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/semconv.md`\n- **Store References**（project_doc）：::: agentlightning.LightningStoreCapabilities 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/store.md`\n- **Agent-lightning Trainer**（project_doc）：::: agentlightning.ExecutionStrategy 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/trainer.md`\n- **Type References**（project_doc）：::: agentlightning.RolloutRawResult 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/types.md`\n- **Utility References**（project_doc）：::: agentlightning.utils.id.generate id 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/utilities.md`\n- **Debugging and Troubleshooting**（project_doc）：When you train your own agent with Agent-lightning, most failures surface because the agent logic is brittle or simply incorrect. Debugging becomes easier when you peel back the stack: start by driving the rollout logic on its own, dry-run the trainer loop, and only then bring the full algorithm and runner topology online. The examples/apo/apo debug.py {{ src \"examples/apo/apo debug.py\" }} script demonstrates these… 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/debug.md`\n- **Using Emitters**（project_doc）：While returning a single float for the final reward is sufficient for many algorithm-agent combinations, some advanced scenarios require richer feedback. For instance, an algorithm might learn more effectively if it receives intermediate rewards throughout a multi-step task, or if the agent needs to emit additional spans for debugging or analysis. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/emitter.md`\n- **Installation Guide**（project_doc）：This guide explains how to install Agent-Lightning . You can install it from PyPI the Python Package Index for general use or directly from the source code if you plan to contribute or need fine-grained control over dependencies. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/installation.md`\n- **Scaling out Agent-lightning**（project_doc）：Agent-lightning splits training into an algorithm bundle and a runner bundle that exchange work through the LightningStore agentlightning.LightningStore . This tutorial shows how to increase rollout throughput, place bundles across processes or machines, and keep the algorithm side scalable with external frameworks. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/parallelize.md`\n- **Working with Traces**（project_doc）：Tracing is the secret capability that lets Agent-lightning train almost any agent without rewriting its core logic. The idea was born in observability tooling inside LLMOps workflows and, in Agent-lightning, evolved into a first-class primitive inside the learning loop. Beyond helping you understand what happened inside a rollout, traces provide reward spans and other learning signals that power reinforcement learni… 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/traces.md`\n- **Writing Agents**（project_doc）：This tutorial will focus on the heart of the system: the agent itself, guiding you through the different ways to define an agent's logic in Agent-lightning. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/tutorials/write-agents.md`\n- **Responsible AI Transparency Documentation - Agent Lightning**（project_doc）：Responsible AI Transparency Documentation - Agent Lightning 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`RAI_README.md`\n- **Security**（project_doc）：Microsoft takes the security of our software products and services seriously, which includes all source code repositories in our GitHub organizations. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`SECURITY.md`\n\n## 证据索引\n\n- 共索引 80 条证据。\n\n- **Contributing Guide**（documentation）：Agent Lightning gets better every time someone files a clear bug, polishes docs, improves tests, or lands a new feature. This guide collects the expectations, checklists, and tips that help you go from “I have an idea” to “my pull request just merged.” 证据：`docs/community/contributing.md`\n- **Repository Guidelines**（documentation）：Architecture Overview Agent Lightning runs through a continuous loop: runners and tracers emit spans, LightningStore agentlightning/store/ keeps them synchronized, and algorithms in agentlightning/algorithm/ consume those traces to improve behavior. 证据：`AGENTS.md`\n- **Agent Lightning⚡**（documentation）：! Unit Tests https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml ! Documentation https://img.shields.io/badge/GitHub%20Pages-Documentation-blue https://microsoft.github.io/agent-lightning/ ! PyPI version https://badge.fury.io/py/agentlightning.svg https://badge.fury.io/py/agentlightning ! License https://img.shields.io/badge/license-MIT-blue.svg LICENSE ! Ask DeepWiki https://deepwiki.com/badge.svg https://deepwiki.com/microsoft/agent-lightning ! Discord https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white https://discord.gg/RYk7CdvDR7 证据：`README.md`\n- **Contrib Area**（documentation）：This tree hosts experimental integrations, third-party recipes, and curated recipes that are not ready for the main agentlightning/ , examples/ , or docs/ trees. Treat it as an incubator: keep contributions self-contained, clearly owned, and reproducible so downstream users can vendor them without guesswork. 证据：`contrib/README.md`\n- **Agent-lightning Dashboard**（documentation）：This is the dashboard for Agent-lightning. It is a web application that allows you to inspect your Agent-lightning store and debug running experiments. 证据：`dashboard/README.md`\n- **⚡ Examples Catalog**（documentation）：This catalog highlights the examples shipped with Agent-lightning. 证据：`examples/README.md`\n- **Agent-OS Integration for Agent-Lightning**（documentation）：Agent-OS Integration for Agent-Lightning 证据：`contrib/recipes/agentos/README.md`\n- **Example of AGL Environments**（documentation）：This example implements agents across various environments within Agent Lightning. The example is designed to run on a single node with 8 GPUs, each having at least 40 GB of memory. 证据：`contrib/recipes/envs/README.md`\n- **Search-R1 Example**（documentation）：This example implements Search R1 within Agent Lightning. It also serves as a demonstration of a framework-free agent training pipeline , showing how to run end-to-end RL training without relying on specialized frameworks. It's tested and compatible with Agent-lightning v0.2.x . 证据：`contrib/recipes/search_r1/README.md`\n- **WebShop Example**（documentation）：This example demonstrates how to train a Vercel AI SDK agent on the WebShop benchmark using Agent Lightning with reinforcement learning VERL/GRPO . The training pipeline uses a headless TypeScript runner that executes agent rollouts and reports traces to the Agent Lightning coordinator. 证据：`contrib/recipes/webshop/README.md`\n- **APO Example**（documentation）：! apo CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-apo.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-apo.yml 证据：`examples/apo/README.md`\n- **Supervised Fine-tuning with Azure OpenAI**（documentation）：Supervised Fine-tuning with Azure OpenAI 证据：`examples/azure/README.md`\n- **Calc-X Example**（documentation）：! calc x CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-calc-x.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-calc-x.yml 证据：`examples/calc_x/README.md`\n- **ChartQA Example**（documentation）：! chartqa workflow status https://github.com/microsoft/agent-lightning/actions/workflows/badge-chartqa.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-chartqa.yml 证据：`examples/chartqa/README.md`\n- **Training Claude Code with Agent-lightning**（documentation）：Training Claude Code with Agent-lightning 证据：`examples/claude_code/README.md`\n- **Minimal Component Showcase**（documentation）：! minimal CI status https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml 证据：`examples/minimal/README.md`\n- **RAG Agent Example**（documentation）：! rag workflow status https://github.com/microsoft/agent-lightning/actions/workflows/examples-rag.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-rag.yml 证据：`examples/rag/README.md`\n- **Spider Example**（documentation）：! spider CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml 证据：`examples/spider/README.md`\n- **Tinker + Agent-lightning Integration**（documentation）：Tinker + Agent-lightning Integration 证据：`examples/tinker/README.md`\n- **Unsloth SFT Example**（documentation）：! unsloth CI status https://github.com/microsoft/agent-lightning/actions/workflows/examples-unsloth.yml/badge.svg https://github.com/microsoft/agent-lightning/actions/workflows/examples-unsloth.yml 证据：`examples/unsloth/README.md`\n- **Package**（package_manifest）：{ \"name\": \"agent-lightning-dashboard\", \"type\": \"module\", \"version\": \"0.3.1\", \"scripts\": { \"dev\": \"vite\", \"build\": \"tsc && vite build\", \"preview\": \"vite preview\", \"typecheck\": \"tsc --noEmit\", \"eslint\": \"eslint .\", \"stylelint\": \"stylelint ' / .css'\", \"prettier\": \"prettier --check \\\" / .{ts,tsx,mjs,cjs}\\\"\", \"vitest\": \"vitest run --project unit\", \"vitest-storybook\": \"vitest run --project storybook\", \"storybook\": \"storybook dev -p 6006\", \"build-storybook\": \"storybook build\", \"chromatic\": \"chromatic\" }, \"dependencies\": { \"@mantine/core\": \"8.3.5\", \"@mantine/hooks\": \"8.3.5\", \"@monaco-editor/react\": \"^4.7.0\", \"@reduxjs/toolkit\": \"^2.9.2\", \"@tabler/icons-react\": \"^3.35.0\", \"clsx\": \"^2.1.1\", \"dayjs\":… 证据：`dashboard/package.json`\n- **Package**（package_manifest）：{ \"name\": \"@example/webshop-training\", \"version\": \"0.0.0\", \"private\": true, \"scripts\": { \"build:headless\": \"tsup scripts/headless-runner.ts --format cjs --out-dir dist --clean\", \"headless\": \"node dist/headless-runner.js\" }, \"dependencies\": { \"@ai-sdk/openai\": \"3.0.0-beta.89\", \"@opentelemetry/api\": \"^1.9.0\", \"@opentelemetry/context-async-hooks\": \"^1.30.0\", \"@opentelemetry/exporter-trace-otlp-proto\": \"^0.57.0\", \"@opentelemetry/resources\": \"^1.30.0\", \"@opentelemetry/sdk-trace-base\": \"^1.30.0\", \"@opentelemetry/semantic-conventions\": \"^1.30.0\", \"ai\": \"6.0.0-beta.139\", \"zod\": \"3.25.76\" }, \"devDependencies\": { \"@types/node\": \"20.17.24\", \"tsup\": \"^8.0.0\", \"tsx\": \"^4.19.0\", \"typescript\": \"5.8.3\" } } 证据：`contrib/recipes/webshop/package.json`\n- **License**（source_file）：Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files the \"Software\" , to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 证据：`LICENSE`\n- **Changelog**（documentation）：Agent-lightning v0.3.0 is a major release that introduces several new features and bug fixes. The release is a collaborative effort between Agent-lightning core teams and the community. Thanks to all the contributors who made this release possible. 证据：`docs/changelog.md`\n- **Agent Lightning**（documentation）：Agent Lightning is the absolute trainer to light up AI agents. 证据：`docs/index.md`\n- **APO**（documentation）：You can use the shortcut agl.APO ... to create an APO instance. 证据：`docs/algorithm-zoo/apo.md`\n- **Algorithm Zoo**（documentation）：AgentLightning includes several popular and frequently requested algorithms in its built-in library, allowing agent developers to use them directly. These algorithms are designed to be compatible with most agent scenarios. 证据：`docs/algorithm-zoo/index.md`\n- **VERL**（documentation）：You can use the shortcut agl.VERL ... to create a VERL instance. 证据：`docs/algorithm-zoo/verl.md`\n- **Maintainer Guide**（documentation）：This guide describes the day-to-day responsibilities for Agent Lightning maintainers—how to bump versions, run release ceremonies, interact with CI, and backport fixes safely. 证据：`docs/community/maintainers.md`\n- **The Bird's Eye View of Agent-lightning**（documentation）：The Bird's Eye View of Agent-lightning 证据：`docs/deep-dive/birds-eye-view.md`\n- **Serving LLMs under Agent-lightning**（documentation）：Agent-lightning focuses on data, learning signals, and control flow — not on running model inference. This deep dive explains how to serve a model alongside Agent-lightning so runners can call it reliably, how the LLM Proxy fits into the loop, and why token IDs matter if you care about correctness in training and evaluation. 证据：`docs/deep-dive/serving-llm.md`\n- **Understanding Store**（documentation）：The LightningStore agentlightning.LightningStore is the central coordination point for Agent-lightning. It holds the task queue, rollouts, attempts, spans, and versioned resources, and exposes a small API both Runners and Algorithms use to communicate. This document explains what's in the store, how statuses transition, how spans are recorded, and the concurrency model threads & processes . 证据：`docs/deep-dive/store.md`\n- **Examples Catalog**（documentation）：We welcome contributions to the examples catalog! Please refer to the Contributing ../community/contributing.md guide for more details. 证据：`docs/how-to/examples-catalog.md`\n- **Train the First Agent with Agent-lightning**（documentation）：Train the First Agent with Agent-lightning 证据：`docs/how-to/train-first-agent.md`\n- **Train SQL Agent with Agent-lightning and VERL**（documentation）：Train SQL Agent with Agent-lightning and VERL 证据：`docs/how-to/train-sql-agent.md`\n- **Fine-tune with Unsloth SFT**（documentation）：Please make sure you have read Write the First Algorithm ./write-first-algorithm.md . Although that recipe is based on a simple prompt tuning algorithm, it introduces the core concepts of Agent-lightning and you should be familiar with them before proceeding. 证据：`docs/how-to/unsloth-sft.md`\n- **Write the First Algorithm with Agent-lightning**（documentation）：Write the First Algorithm with Agent-lightning 证据：`docs/how-to/write-first-algorithm.md`\n- **Agent Developer APIs**（documentation）：These are convenient helpers for creating agents from functions. First-time users are recommended to use these decorators to create agents. 证据：`docs/reference/agent.md`\n- **Algorithm-side References**（documentation）：This reference covers APIs that are designed to be used at \"Algorithm Side\". For built-in algorithms, see Algorithm Zoo ../algorithm-zoo/index.md . 证据：`docs/reference/algorithm.md`\n- **Command Line Interface**（documentation）：This document is a work in progress and might not be updated with the latest changes. Try to use agl -h to get the latest help message. 证据：`docs/reference/cli.md`\n- **Instrumentation API**（documentation）：::: agentlightning.instrumentation.instrument all 证据：`docs/reference/instrumentation.md`\n- **Internal API References**（documentation）：The following APIs should be used with extra caution because they are very likely to change in the future. 证据：`docs/reference/internal.md`\n- **RESTful API References**（documentation）：Shown in the following is the RESTful API for Lightning Store. 证据：`docs/reference/restful.md`\n- **Runner-side References**（documentation）：This reference covers APIs that are designed to be used at \"Runner Side\". 证据：`docs/reference/runner.md`\n- **Semantic Conventions**（documentation）：Semantic Conventions ::: agentlightning.semconv 证据：`docs/reference/semconv.md`\n- **Store References**（documentation）：::: agentlightning.LightningStoreCapabilities 证据：`docs/reference/store.md`\n- **Agent-lightning Trainer**（documentation）：::: agentlightning.ExecutionStrategy 证据：`docs/reference/trainer.md`\n- **Type References**（documentation）：::: agentlightning.RolloutRawResult 证据：`docs/reference/types.md`\n- **Utility References**（documentation）：::: agentlightning.utils.id.generate id 证据：`docs/reference/utilities.md`\n- **Debugging and Troubleshooting**（documentation）：When you train your own agent with Agent-lightning, most failures surface because the agent logic is brittle or simply incorrect. Debugging becomes easier when you peel back the stack: start by driving the rollout logic on its own, dry-run the trainer loop, and only then bring the full algorithm and runner topology online. The examples/apo/apo debug.py {{ src \"examples/apo/apo debug.py\" }} script demonstrates these techniques; this guide expands on each approach and helps you decide when to reach for them. 证据：`docs/tutorials/debug.md`\n- **Using Emitters**（documentation）：While returning a single float for the final reward is sufficient for many algorithm-agent combinations, some advanced scenarios require richer feedback. For instance, an algorithm might learn more effectively if it receives intermediate rewards throughout a multi-step task, or if the agent needs to emit additional spans for debugging or analysis. 证据：`docs/tutorials/emitter.md`\n- **Installation Guide**（documentation）：This guide explains how to install Agent-Lightning . You can install it from PyPI the Python Package Index for general use or directly from the source code if you plan to contribute or need fine-grained control over dependencies. 证据：`docs/tutorials/installation.md`\n- **Scaling out Agent-lightning**（documentation）：Agent-lightning splits training into an algorithm bundle and a runner bundle that exchange work through the LightningStore agentlightning.LightningStore . This tutorial shows how to increase rollout throughput, place bundles across processes or machines, and keep the algorithm side scalable with external frameworks. 证据：`docs/tutorials/parallelize.md`\n- **Working with Traces**（documentation）：Tracing is the secret capability that lets Agent-lightning train almost any agent without rewriting its core logic. The idea was born in observability tooling inside LLMOps workflows and, in Agent-lightning, evolved into a first-class primitive inside the learning loop. Beyond helping you understand what happened inside a rollout, traces provide reward spans and other learning signals that power reinforcement learning and fine-tuning algorithms. 证据：`docs/tutorials/traces.md`\n- **Writing Agents**（documentation）：This tutorial will focus on the heart of the system: the agent itself, guiding you through the different ways to define an agent's logic in Agent-lightning. 证据：`docs/tutorials/write-agents.md`\n- **Responsible AI Transparency Documentation - Agent Lightning**（documentation）：Responsible AI Transparency Documentation - Agent Lightning 证据：`RAI_README.md`\n- **Security**（documentation）：Microsoft takes the security of our software products and services seriously, which includes all source code repositories in our GitHub organizations. 证据：`SECURITY.md`\n- **.Stylelintrc**（structured_config）：{ \"extends\": \"stylelint-config-standard-scss\" , \"rules\": { \"custom-property-pattern\": null, \"selector-class-pattern\": null, \"scss/no-duplicate-mixins\": null, \"declaration-empty-line-before\": null, \"declaration-block-no-redundant-longhand-properties\": null, \"alpha-value-notation\": null, \"custom-property-empty-line-before\": null, \"property-no-vendor-prefix\": null, \"color-function-notation\": null, \"length-zero-no-unit\": null, \"selector-not-notation\": null, \"no-descending-specificity\": null, \"comment-empty-line-before\": null, \"scss/at-mixin-pattern\": null, \"scss/at-rule-no-unknown\": null, \"value-keyword-case\": null, \"media-feature-range-notation\": null, \"selector-pseudo-class-no-unknown\": true,… 证据：`dashboard/.stylelintrc.json`\n- **Tsconfig**（structured_config）：{ \"compilerOptions\": { \"types\": \"node\", \"@testing-library/jest-dom\", \"vitest/globals\" , \"target\": \"ESNext\", \"useDefineForClassFields\": true, \"lib\": \"DOM\", \"DOM.Iterable\", \"ESNext\" , \"allowJs\": false, \"skipLibCheck\": true, \"esModuleInterop\": false, \"allowSyntheticDefaultImports\": true, \"strict\": true, \"forceConsistentCasingInFileNames\": true, \"module\": \"ESNext\", \"moduleResolution\": \"Node\", \"resolveJsonModule\": true, \"isolatedModules\": true, \"noEmit\": true, \"jsx\": \"react-jsx\", \"paths\": { \"@/ \": \"./src/ \" , \"@test-utils\": \"./test-utils\" } }, \"include\": \"src\", \"public\", \"test-utils\", \".storybook/main.ts\", \".storybook/preview.tsx\", \".storybook/modes.ts\", \".storybook/constants.ts\", \".storybook/vi… 证据：`dashboard/tsconfig.json`\n- **Pyrightconfig.Fast**（structured_config）：{ \"include\": \"agentlightning\" , \"exclude\": \" /data\", \" /assets\", \"agentlightning/verl\", \"agentlightning/instrumentation\", \"agentlightning/algorithm/apo\", \"agentlightning/algorithm/verl\", \"agentlightning/cli/vllm.py\", \"agentlightning/store/collection/mongo.py\", \"agentlightning/store/mongo.py\", \"agentlightning/tracer/weave.py\", \"contrib/ \" , 证据：`pyrightconfig.fast.json`\n- 其余 20 条证据见 `AI_CONTEXT_PACK.json` 或 `EVIDENCE_INDEX.json`。\n\n## 宿主 AI 必须遵守的规则\n\n- **把本资产当作开工前上下文，而不是运行环境。**：AI Context Pack 只包含证据化项目理解，不包含目标项目的可执行状态。 证据：`docs/community/contributing.md`, `AGENTS.md`, `README.md`\n- **回答用户时区分可预览内容与必须安装后才能验证的内容。**：安装前体验的消费者价值来自降低误装和误判，而不是伪装成真实运行。 证据：`docs/community/contributing.md`, `AGENTS.md`, `README.md`\n\n## 用户开工前应该回答的问题\n\n- 你准备在哪个宿主 AI 或本地环境中使用它？\n- 你只是想先体验工作流，还是准备真实安装？\n- 你最在意的是安装成本、输出质量、还是和现有规则的冲突？\n\n## 验收标准\n\n- 所有能力声明都能回指到 evidence_refs 中的文件路径。\n- AI_CONTEXT_PACK.md 没有把预览包装成真实运行。\n- 用户能在 3 分钟内看懂适合谁、能做什么、如何开始和风险边界。\n\n---\n\n## Doramagic Context Augmentation\n\n下面内容用于强化 Repomix/AI Context Pack 主体。Human Manual 只提供阅读骨架；踩坑日志会被转成宿主 AI 必须遵守的工作约束。\n\n## Human Manual 骨架\n\n使用规则：这里只是项目阅读路线和显著性信号，不是事实权威。具体事实仍必须回到 repo evidence / Claim Graph。\n\n宿主 AI 硬性规则：\n- 不得把页标题、章节顺序、摘要或 importance 当作项目事实证据。\n- 解释 Human Manual 骨架时，必须明确说它只是阅读路线/显著性信号。\n- 能力、安装、兼容性、运行状态和风险判断必须引用 repo evidence、source path 或 Claim Graph。\n\n- **仓库概览**：importance `high`\n  - source_paths: README.md, pyproject.toml, contrib/README.md, dashboard/README.md, dashboard/package.json\n- **入口与运行边界**：importance `high`\n  - source_paths: README.md, pyproject.toml, contrib/README.md, dashboard/README.md, dashboard/package.json\n- **架构证据地图**：importance `high`\n  - source_paths: README.md, pyproject.toml, contrib/README.md, dashboard/README.md, dashboard/package.json\n- **运维与验证边界**：importance `high`\n  - source_paths: README.md, pyproject.toml, contrib/README.md, dashboard/README.md, dashboard/package.json\n\n## Repo Inspection Evidence / 源码检查证据\n\n- repo_clone_verified: true\n- repo_inspection_verified: true\n- repo_commit: `0b40cb724a0ad4f944810f8514884051777bb38b`\n- inspected_files: `pyproject.toml`, `README.md`, `uv.lock`, `docs/index.md`, `docs/changelog.md`, `docs/how-to/examples-catalog.md`, `docs/how-to/train-first-agent.md`, `docs/how-to/write-first-algorithm.md`, `docs/how-to/unsloth-sft.md`, `docs/how-to/train-sql-agent.md`, `docs/reference/algorithm.md`, `docs/reference/cli.md`, `docs/reference/store.md`, `docs/reference/agent.md`, `docs/reference/utilities.md`, `docs/reference/restful.md`, `docs/reference/instrumentation.md`, `docs/reference/internal.md`, `docs/reference/runner.md`, `docs/reference/types.md`\n\n宿主 AI 硬性规则：\n- 没有 repo_clone_verified=true 时，不得声称已经读过源码。\n- 没有 repo_inspection_verified=true 时，不得把 README/docs/package 文件判断写成事实。\n- 没有 quick_start_verified=true 时，不得声称 Quick Start 已跑通。\n\n## Doramagic Pitfall Constraints / 踩坑约束\n\n这些规则来自 Doramagic 发现、验证或编译过程中的项目专属坑点。宿主 AI 必须把它们当作工作约束，而不是普通说明文字。\n\n### Constraint 1: 能力判断依赖假设\n\n- Trigger: README/documentation is current enough for a first validation pass.\n- Host AI rule: 将假设转成下游验证清单。\n- Why it matters: 假设不成立时，用户拿不到承诺的能力。\n- Evidence: capability.assumptions | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | README/documentation is current enough for a first validation pass.\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 2: 维护活跃度未知\n\n- Trigger: 未记录 last_activity_observed。\n- Host AI rule: 补 GitHub 最近 commit、release、issue/PR 响应信号。\n- Why it matters: 新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。\n- Evidence: evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | last_activity_observed missing\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 3: 下游验证发现风险项\n\n- Trigger: no_demo\n- Host AI rule: 进入安全/权限治理复核队列。\n- Why it matters: 下游已经要求复核，不能在页面中弱化。\n- Evidence: downstream_validation.risk_items | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 4: 存在安全注意事项\n\n- Trigger: No sandbox install has been executed yet; downstream must verify before user use.\n- Host AI rule: 转成明确权限清单和安全审查提示。\n- Why it matters: 用户安装前需要知道权限边界和敏感操作。\n- Evidence: risks.safety_notes | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | No sandbox install has been executed yet; downstream must verify before user use.\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 5: 存在评分风险\n\n- Trigger: no_demo\n- Host AI rule: 把风险写入边界卡，并确认是否需要人工复核。\n- Why it matters: 风险会影响是否适合普通用户安装。\n- Evidence: risks.scoring_risks | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 6: issue/PR 响应质量未知\n\n- Trigger: issue_or_pr_quality=unknown。\n- Host AI rule: 抽样最近 issue/PR，判断是否长期无人处理。\n- Why it matters: 用户无法判断遇到问题后是否有人维护。\n- Evidence: evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | issue_or_pr_quality=unknown\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n\n### Constraint 7: 发布节奏不明确\n\n- Trigger: release_recency=unknown。\n- Host AI rule: 确认最近 release/tag 和 README 安装命令是否一致。\n- Why it matters: 安装命令和文档可能落后于代码，用户踩坑概率升高。\n- Evidence: evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | release_recency=unknown\n- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。\n",
      "summary": "给宿主 AI 的上下文和工作边界。",
      "title": "AI Context Pack / 带给我的 AI"
    },
    "boundary_risk_card": {
      "asset_id": "boundary_risk_card",
      "filename": "BOUNDARY_RISK_CARD.md",
      "markdown": "# Boundary & Risk Card / 安装前决策卡\n\n项目：microsoft/agent-lightning\n\n## Doramagic 试用结论\n\n当前结论：可以进入发布前推荐检查；首次使用仍应从最小权限、临时目录和可回滚配置开始。\n\n## 用户现在可以做\n\n- 可以先阅读 Human Manual，理解项目目的和主要工作流。\n- 可以复制 Prompt Preview 做安装前体验；这只验证交互感，不代表真实运行。\n- 可以把官方 Quick Start 命令放到隔离环境中验证，不要直接进主力环境。\n\n## 现在不要做\n\n- 不要把 Prompt Preview 当成项目实际运行结果。\n- 不要把 metadata-only validation 当成沙箱安装验证。\n- 不要把未验证能力写成“已支持、已跑通、可放心安装”。\n- 不要在首次试用时交出生产数据、私人文件、真实密钥或主力配置目录。\n\n## 安装前检查\n\n- 宿主 AI 是否匹配：chatgpt\n- 官方安装入口状态：已发现官方入口\n- 是否在临时目录、临时宿主或容器中验证：必须是\n- 是否能回滚配置改动：必须能\n- 是否需要 API Key、网络访问、读写文件或修改宿主配置：未确认前按高风险处理\n- 是否记录了安装命令、实际输出和失败日志：必须记录\n\n## 当前阻塞项\n\n- review_required: community_discussion_evidence_below_public_threshold\n\n## 项目专属踩坑\n\n- 能力判断依赖假设（medium）：假设不成立时，用户拿不到承诺的能力。 建议检查：将假设转成下游验证清单。\n- 维护活跃度未知（medium）：新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。 建议检查：补 GitHub 最近 commit、release、issue/PR 响应信号。\n- 下游验证发现风险项（medium）：下游已经要求复核，不能在页面中弱化。 建议检查：进入安全/权限治理复核队列。\n- 存在安全注意事项（medium）：用户安装前需要知道权限边界和敏感操作。 建议检查：转成明确权限清单和安全审查提示。\n- 存在评分风险（medium）：风险会影响是否适合普通用户安装。 建议检查：把风险写入边界卡，并确认是否需要人工复核。\n\n## 风险与权限提示\n\n- no_demo: medium\n\n## 证据缺口\n\n- 暂未发现结构化证据缺口。\n",
      "summary": "安装、权限、验证和推荐前风险。",
      "title": "Boundary & Risk Card / 边界与风险卡"
    },
    "human_manual": {
      "asset_id": "human_manual",
      "filename": "HUMAN_MANUAL.md",
      "markdown": "# https://github.com/microsoft/agent-lightning 项目说明书\n\n生成时间: 2026-05-21 08:39:26 UTC\n\n## 目录\n\n- [仓库概览](#overview)\n- [入口与运行边界](#entrypoints)\n- [架构证据地图](#architecture)\n- [运维与验证边界](#operations)\n\n<a id='overview'></a>\n\n## 仓库概览\n\n### 相关页面\n\n相关主题：[入口与运行边界](#entrypoints), [架构证据地图](#architecture), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 仓库概览\n\n本页在 Human Wiki provider 不可用时基于仓库证据生成，只使用 README、文件树和已选源码文件，不把模板描述冒充项目事实。\n\n## README 证据\n\n<p align=\"center\">\n  <img src=\"docs/assets/readme-banner.svg\" alt=\"Agent-lightning-banner\" style=\"width:600px\"/>\n</p>\n\n# Agent Lightning⚡\n\n[![Unit Tests](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml)\n[![Documentation](https://img.shields.io/badge/GitHub%20Pages-Documentation-blue)](https://microsoft.github.io/agent-lightning/)\n[![PyPI version](https://badge.fury.io/py/agentlightning.svg)](https://badge.fury.io/py/agentlightning)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/microsoft/agent-lightning)\n[![Discord](https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white)](https://discord.gg/RYk7CdvDR7)\n\n**The absolute trainer to light up AI agents.**\n\nJoin our [Discord community](https://discord.gg/RYk7CdvDR7) to connect with other users and contributors.\n\n## ⚡ Core Features\n\n- Turn your agent into an optimizable beast with **ZERO CODE CHANGE** (almost)! 💤\n- Build with **ANY** agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖\n- **Selectively** optimize one or more agents in a multi-agent system. 🎯\n- Embraces **Algorithms** like Reinforcement Learning, Automatic Prompt Optimization, Supervised Fine-tuning and more. 🤗\n\nRead more on our [documentation website](https://microsoft.github.io/agent-lightning/).\n\n<p align=\"center\">\n  <img src=\"docs/assets/readme-diff.svg\" alt=\"Agent-Lightning Core Quickstart\" style=\"width:100%\"/>\n</p>\n\n## ⚡ Installation\n\n```bash\npip install agentlightning\n```\n\nFor the latest nightly build (cutting-edge features), you can install from Test PyPI:\n\n```bash\npip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre agentlightning\n```\n\nPlease refer to our [installation guide](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/) for more details.\n\nTo start using Agent-lightning, check out our [documentation](https://microsoft.github.io/agent-lightning/) and [examples](./examples).\n\n## ⚡ Articles\n\n- 12/17/2025 [Adopting the Trajectory Level Aggregation for Faster Training](https://agent-lightning.github.io/posts/trajectory_\n\n[excerpt truncated]\n\n## 已选源码清单\n\n- `README.md`\n- `pyproject.toml`\n- `contrib/README.md`\n- `dashboard/README.md`\n- `dashboard/package.json`\n- `examples/README.md`\n- `examples/apo/README.md`\n- `examples/azure/README.md`\n- `examples/calc_x/README.md`\n- `examples/chartqa/README.md`\n- `examples/claude_code/README.md`\n- `examples/minimal/README.md`\n\n| 文件 | 证据角色 | 大小 |\n|---|---|---|\n| `README.md` | README/产品与使用证据 | 9958 bytes |\n| `pyproject.toml` | 包与运行时元数据 | 8738 bytes |\n| `contrib/README.md` | 文档证据 | 2011 bytes |\n| `dashboard/README.md` | 文档证据 | 790 bytes |\n| `dashboard/package.json` | 包与运行时元数据 | 2377 bytes |\n| `examples/README.md` | 文档证据 | 6244 bytes |\n| `examples/apo/README.md` | 文档证据 | 3914 bytes |\n| `examples/azure/README.md` | 文档证据 | 9661 bytes |\n| `examples/calc_x/README.md` | 文档证据 | 3563 bytes |\n| `examples/chartqa/README.md` | 文档证据 | 4077 bytes |\n| `examples/claude_code/README.md` | 文档证据 | 5821 bytes |\n| `examples/minimal/README.md` | 文档证据 | 1786 bytes |\n\n资料来源：`[README.md:1-120]()`\n\n---\n\n<a id='entrypoints'></a>\n\n## 入口与运行边界\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [架构证据地图](#architecture), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 入口与运行边界\n\n下面文件是安装、启动、配置或宿主集成的高信号候选。这里给出证据地图，不推断未经执行验证的 API 契约。\n\n| 文件 | 证据角色 | 大小 |\n|---|---|---|\n| `README.md` | README/产品与使用证据 | 9958 bytes |\n| `pyproject.toml` | 包与运行时元数据 | 8738 bytes |\n| `contrib/README.md` | 文档证据 | 2011 bytes |\n| `dashboard/README.md` | 文档证据 | 790 bytes |\n| `dashboard/package.json` | 包与运行时元数据 | 2377 bytes |\n| `examples/README.md` | 文档证据 | 6244 bytes |\n| `examples/apo/README.md` | 文档证据 | 3914 bytes |\n| `examples/azure/README.md` | 文档证据 | 9661 bytes |\n| `examples/calc_x/README.md` | 文档证据 | 3563 bytes |\n| `examples/chartqa/README.md` | 文档证据 | 4077 bytes |\n| `examples/claude_code/README.md` | 文档证据 | 5821 bytes |\n| `examples/minimal/README.md` | 文档证据 | 1786 bytes |\n\n资料来源：`[README.md:1-120](https://github.com/microsoft/agent-lightning/blob/main/README.md)`\n\n---\n\n<a id='architecture'></a>\n\n## 架构证据地图\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [入口与运行边界](#entrypoints), [运维与验证边界](#operations)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 架构证据地图\n\n本节只根据仓库路径组织可能的架构区域；需要运行验证的行为不会在这里断言。\n\n- `.`: `README.md`, `pyproject.toml`\n- `contrib`: `contrib/README.md`\n- `dashboard`: `dashboard/README.md`, `dashboard/package.json`\n- `examples`: `examples/README.md`, `examples/apo/README.md`, `examples/azure/README.md`, `examples/calc_x/README.md`, `examples/chartqa/README.md`, `examples/claude_code/README.md`\n\n资料来源：`[pyproject.toml:1-120](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)`\n\n---\n\n<a id='operations'></a>\n\n## 运维与验证边界\n\n### 相关页面\n\n相关主题：[仓库概览](#overview), [入口与运行边界](#entrypoints), [架构证据地图](#architecture)\n\n<details>\n<summary>相关源码文件</summary>\n\n以下源码文件用于生成本页说明：\n\n- [README.md](https://github.com/microsoft/agent-lightning/blob/main/README.md)\n- [pyproject.toml](https://github.com/microsoft/agent-lightning/blob/main/pyproject.toml)\n- [contrib/README.md](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)\n- [dashboard/README.md](https://github.com/microsoft/agent-lightning/blob/main/dashboard/README.md)\n- [dashboard/package.json](https://github.com/microsoft/agent-lightning/blob/main/dashboard/package.json)\n- [examples/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/README.md)\n- [examples/apo/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/apo/README.md)\n- [examples/azure/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/azure/README.md)\n- [examples/calc_x/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/calc_x/README.md)\n- [examples/chartqa/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/chartqa/README.md)\n- [examples/claude_code/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/claude_code/README.md)\n- [examples/minimal/README.md](https://github.com/microsoft/agent-lightning/blob/main/examples/minimal/README.md)\n</details>\n\n# 运维与验证边界\n\n运维建议仅来自仓库中真实存在的文件。把该项目用于 agent 工作流前，仍需在 sandbox 中验证安装、quickstart 和运行行为。\n\n- Documentation signal: `README.md`\n- Runtime/package signal: `pyproject.toml`\n- Documentation signal: `contrib/README.md`\n- Documentation signal: `dashboard/README.md`\n- Runtime/package signal: `dashboard/package.json`\n- Documentation signal: `examples/README.md`\n- Documentation signal: `examples/apo/README.md`\n- Documentation signal: `examples/azure/README.md`\n- Documentation signal: `examples/calc_x/README.md`\n- Documentation signal: `examples/chartqa/README.md`\n\n资料来源：`[contrib/README.md:1-120](https://github.com/microsoft/agent-lightning/blob/main/contrib/README.md)`\n\n---\n\n---\n\n## Doramagic 踩坑日志\n\n项目：microsoft/agent-lightning\n\n摘要：发现 7 个潜在踩坑项，其中 0 个为 high/blocking；最高优先级：能力坑 - 能力判断依赖假设。\n\n## 1. 能力坑 · 能力判断依赖假设\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：README/documentation is current enough for a first validation pass.\n- 对用户的影响：假设不成立时，用户拿不到承诺的能力。\n- 建议检查：将假设转成下游验证清单。\n- 防护动作: 假设必须转成验证项；没有验证结果前不能写成事实。\n- 证据：capability.assumptions | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | README/documentation is current enough for a first validation pass.\n\n## 2. 维护坑 · 维护活跃度未知\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：未记录 last_activity_observed。\n- 对用户的影响：新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。\n- 建议检查：补 GitHub 最近 commit、release、issue/PR 响应信号。\n- 防护动作: 维护活跃度未知时，推荐强度不能标为高信任。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | last_activity_observed missing\n\n## 3. 安全/权限坑 · 下游验证发现风险项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：下游已经要求复核，不能在页面中弱化。\n- 建议检查：进入安全/权限治理复核队列。\n- 防护动作: 下游风险存在时必须保持 review/recommendation 降级。\n- 证据：downstream_validation.risk_items | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 4. 安全/权限坑 · 存在安全注意事项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：No sandbox install has been executed yet; downstream must verify before user use.\n- 对用户的影响：用户安装前需要知道权限边界和敏感操作。\n- 建议检查：转成明确权限清单和安全审查提示。\n- 防护动作: 安全注意事项必须面向用户前置展示。\n- 证据：risks.safety_notes | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | No sandbox install has been executed yet; downstream must verify before user use.\n\n## 5. 安全/权限坑 · 存在评分风险\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：风险会影响是否适合普通用户安装。\n- 建议检查：把风险写入边界卡，并确认是否需要人工复核。\n- 防护动作: 评分风险必须进入边界卡，不能只作为内部分数。\n- 证据：risks.scoring_risks | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 6. 维护坑 · issue/PR 响应质量未知\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：issue_or_pr_quality=unknown。\n- 对用户的影响：用户无法判断遇到问题后是否有人维护。\n- 建议检查：抽样最近 issue/PR，判断是否长期无人处理。\n- 防护动作: issue/PR 响应未知时，必须提示维护风险。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | issue_or_pr_quality=unknown\n\n## 7. 维护坑 · 发布节奏不明确\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：release_recency=unknown。\n- 对用户的影响：安装命令和文档可能落后于代码，用户踩坑概率升高。\n- 建议检查：确认最近 release/tag 和 README 安装命令是否一致。\n- 防护动作: 发布节奏未知或过期时，安装说明必须标注可能漂移。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | release_recency=unknown\n\n<!-- canonical_name: microsoft/agent-lightning; human_manual_source: deepwiki_human_wiki -->\n",
      "summary": "DeepWiki/Human Wiki 完整输出，末尾追加 Discovery Agent 踩坑日志。",
      "title": "Human Manual / 人类版说明书"
    },
    "pitfall_log": {
      "asset_id": "pitfall_log",
      "filename": "PITFALL_LOG.md",
      "markdown": "# Pitfall Log / 踩坑日志\n\n项目：microsoft/agent-lightning\n\n摘要：发现 7 个潜在踩坑项，其中 0 个为 high/blocking；最高优先级：能力坑 - 能力判断依赖假设。\n\n## 1. 能力坑 · 能力判断依赖假设\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：README/documentation is current enough for a first validation pass.\n- 对用户的影响：假设不成立时，用户拿不到承诺的能力。\n- 建议检查：将假设转成下游验证清单。\n- 防护动作: 假设必须转成验证项；没有验证结果前不能写成事实。\n- 证据：capability.assumptions | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | README/documentation is current enough for a first validation pass.\n\n## 2. 维护坑 · 维护活跃度未知\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：未记录 last_activity_observed。\n- 对用户的影响：新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。\n- 建议检查：补 GitHub 最近 commit、release、issue/PR 响应信号。\n- 防护动作: 维护活跃度未知时，推荐强度不能标为高信任。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | last_activity_observed missing\n\n## 3. 安全/权限坑 · 下游验证发现风险项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：下游已经要求复核，不能在页面中弱化。\n- 建议检查：进入安全/权限治理复核队列。\n- 防护动作: 下游风险存在时必须保持 review/recommendation 降级。\n- 证据：downstream_validation.risk_items | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 4. 安全/权限坑 · 存在安全注意事项\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：No sandbox install has been executed yet; downstream must verify before user use.\n- 对用户的影响：用户安装前需要知道权限边界和敏感操作。\n- 建议检查：转成明确权限清单和安全审查提示。\n- 防护动作: 安全注意事项必须面向用户前置展示。\n- 证据：risks.safety_notes | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | No sandbox install has been executed yet; downstream must verify before user use.\n\n## 5. 安全/权限坑 · 存在评分风险\n\n- 严重度：medium\n- 证据强度：source_linked\n- 发现：no_demo\n- 对用户的影响：风险会影响是否适合普通用户安装。\n- 建议检查：把风险写入边界卡，并确认是否需要人工复核。\n- 防护动作: 评分风险必须进入边界卡，不能只作为内部分数。\n- 证据：risks.scoring_risks | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | no_demo; severity=medium\n\n## 6. 维护坑 · issue/PR 响应质量未知\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：issue_or_pr_quality=unknown。\n- 对用户的影响：用户无法判断遇到问题后是否有人维护。\n- 建议检查：抽样最近 issue/PR，判断是否长期无人处理。\n- 防护动作: issue/PR 响应未知时，必须提示维护风险。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | issue_or_pr_quality=unknown\n\n## 7. 维护坑 · 发布节奏不明确\n\n- 严重度：low\n- 证据强度：source_linked\n- 发现：release_recency=unknown。\n- 对用户的影响：安装命令和文档可能落后于代码，用户踩坑概率升高。\n- 建议检查：确认最近 release/tag 和 README 安装命令是否一致。\n- 防护动作: 发布节奏未知或过期时，安装说明必须标注可能漂移。\n- 证据：evidence.maintainer_signals | art_9b504779cfa046a894eeb7c9d3a298c6 | https://github.com/microsoft/agent-lightning#readme | release_recency=unknown\n",
      "summary": "用户实践前最可能遇到的身份、安装、配置、运行和安全坑。",
      "title": "Pitfall Log / 踩坑日志"
    },
    "prompt_preview": {
      "asset_id": "prompt_preview",
      "filename": "PROMPT_PREVIEW.md",
      "markdown": "# agent-lightning - Prompt Preview\n\n> 复制下面这段 Prompt 到你常用的 AI，先试一次，不需要安装。\n> 它的目标是让你直接体验这个项目的服务方式，而不是阅读项目介绍。\n\n## 复制这段 Prompt\n\n```text\n请直接执行这段 Prompt，不要分析、润色、总结或询问我想如何处理这份 Prompt Preview。\n\n你现在扮演 agent-lightning 的“安装前体验版”。\n这不是项目介绍、不是评价报告、不是 README 总结。你的任务是让我用最小成本体验它的核心服务。\n\n我的试用任务：我想用它完成一个真实的软件开发与交付任务。\n我常用的宿主 AI：chatgpt\n\n【体验目标】\n围绕我的真实任务，现场演示这个项目如何把输入转成 示例引导, 判断线索。重点是让我感受到工作方式，而不是给我项目背景。\n\n【业务流约束】\n- 你必须像一个正在提供服务的项目能力包，而不是像一个讲解员。\n- 每一轮只推进一个步骤；提出问题后必须停下来等我回答。\n- 每一步都必须让我感受到一个具体服务动作：澄清、整理、规划、检查、判断或收尾。\n- 每一步都要说明：当前目标、你需要我提供什么、我回答后你会产出什么。\n- 不要安装、不要运行命令、不要写代码、不要声称测试通过、不要声称已经修改文件。\n- 需要真实安装或宿主加载后才能验证的内容，必须明确说“这一步需要安装后验证”。\n- 如果我说“用示例继续”，你可以用虚构示例推进，但仍然不能声称真实执行。\n\n【可体验服务能力】\n- 安装前能力预览: <p align=\"center\"> 输入：用户任务, 当前 AI 对话上下文；输出：示例引导, 判断线索。\n\n【必须安装后才可验证的能力】\n- 命令行启动或安装流程: 项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 输入：终端环境, 包管理器, 项目依赖；输出：安装结果, 列表/更新/运行结果。\n\n【核心服务流】\n请严格按这个顺序带我体验。不要一次性输出完整流程：\n1. overview：仓库概览。围绕“仓库概览”模拟一次用户任务，不展示安装或运行结果。\n2. entrypoints：入口与运行边界。围绕“入口与运行边界”模拟一次用户任务，不展示安装或运行结果。\n3. architecture：架构证据地图。围绕“架构证据地图”模拟一次用户任务，不展示安装或运行结果。\n4. operations：运维与验证边界。围绕“运维与验证边界”模拟一次用户任务，不展示安装或运行结果。\n\n【核心能力体验剧本】\n每一步都必须按“输入 -> 服务动作 -> 中间产物”执行。不要只说流程名：\n1. overview\n输入：用户提供的“仓库概览”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n2. entrypoints\n输入：用户提供的“入口与运行边界”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n3. architecture\n输入：用户提供的“架构证据地图”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n4. operations\n输入：用户提供的“运维与验证边界”相关信息。\n服务动作：模拟项目在这一步的核心判断和整理方式。\n中间产物：一个可检查的小结果。\n\n【项目服务规则】\n这些规则决定你如何服务用户。不要解释规则本身，而要在每一步执行时遵守：\n- 先确认用户任务、输入材料和成功标准，再模拟项目能力。\n- 每一步都必须形成可检查的小产物，并等待用户确认后再继续。\n- 凡是需要安装、调用工具或访问外部服务的能力，都必须标记为安装后验证。\n\n【每一步的服务约束】\n- Step 1 / overview：Step 1 必须围绕“仓库概览”形成一个小中间产物，并等待用户确认。\n- Step 2 / entrypoints：Step 2 必须围绕“入口与运行边界”形成一个小中间产物，并等待用户确认。\n- Step 3 / architecture：Step 3 必须围绕“架构证据地图”形成一个小中间产物，并等待用户确认。\n- Step 4 / operations：Step 4 必须围绕“运维与验证边界”形成一个小中间产物，并等待用户确认。\n\n【边界与风险】\n- 不要声称已经安装、运行、调用 API、读写本地文件或完成真实任务。\n- 安装前预览只能展示工作方式，不能证明兼容性、性能或输出质量。\n- 涉及安装、插件加载、工具调用或外部服务的能力必须安装后验证。\n\n【可追溯依据】\n这些路径只用于你内部校验或在我追问“依据是什么”时简要引用。不要在首次回复主动展开：\n- https://github.com/microsoft/agent-lightning#readme\n- README.md\n- pyproject.toml\n- contrib/README.md\n- dashboard/README.md\n- dashboard/package.json\n- examples/README.md\n- examples/apo/README.md\n- examples/azure/README.md\n- examples/calc_x/README.md\n- examples/chartqa/README.md\n- examples/claude_code/README.md\n\n【首次问题规则】\n- 首次三问必须先确认用户目标、成功标准和边界，不要提前进入工具、安装或实现细节。\n- 如果后续需要技术条件、文件路径或运行环境，必须等用户确认目标后再追问。\n\n首次回复必须只输出下面 4 个部分：\n1. 体验开始：用 1 句话说明你将带我体验 agent-lightning 的核心服务。\n2. 当前步骤：明确进入 Step 1，并说明这一步要解决什么。\n3. 你会如何服务我：说明你会先改变我完成任务的哪个动作。\n4. 只问我 3 个问题，然后停下等待回答。\n\n首次回复禁止输出：后续完整流程、证据清单、安装命令、项目评价、营销文案、已经安装或运行的说法。\n\nStep 1 / brainstorming 的二轮协议：\n- 我回答首次三问后，你仍然停留在 Step 1 / brainstorming，不要进入 Step 2。\n- 第二次回复必须产出 6 个部分：澄清后的任务定义、成功标准、边界条件、\n  2-3 个可选方案、每个方案的权衡、推荐方案。\n- 第二次回复最后必须问我是否确认推荐方案；只有我明确确认后，才能进入下一步。\n- 第二次回复禁止输出 git worktree、代码计划、测试文件、命令或真实执行结果。\n\n后续对话规则：\n- 我回答后，你先完成当前步骤的中间产物并等待确认；只有我确认后，才能进入下一步。\n- 每一步都要生成一个小的中间产物，例如澄清后的目标、计划草案、测试意图、验证清单或继续/停止判断。\n- 所有演示都写成“我会建议/我会引导/这一步会形成”，不要写成已经真实执行。\n- 不要声称已经测试通过、文件已修改、命令已运行或结果已产生。\n- 如果某个能力必须安装后验证，请直接说“这一步需要安装后验证”。\n- 如果证据不足，请明确说“证据不足”，不要补事实。\n```\n",
      "summary": "不安装项目也能感受能力节奏的安全试用 Prompt。",
      "title": "Prompt Preview / 安装前试用 Prompt"
    },
    "quick_start": {
      "asset_id": "quick_start",
      "filename": "QUICK_START.md",
      "markdown": "# Quick Start / 官方入口\n\n项目：microsoft/agent-lightning\n\n## 官方安装入口\n\n### Python / pip · 官方安装入口\n\n```bash\npip install agentlightning\n```\n\n来源：https://github.com/microsoft/agent-lightning#readme\n\n## 来源\n\n- docs: https://github.com/microsoft/agent-lightning#readme\n",
      "summary": "从项目官方 README 或安装文档提取的开工入口。",
      "title": "Quick Start / 官方入口"
    }
  },
  "validation_id": "dval_7811550190c749da836bb4a10c48be53"
}