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  "chapters": [
    {
      "id": "chapter-1",
      "number": "01",
      "shortTitle": "Agent Loop",
      "title": "模型怎样在动作与反馈之间循环",
      "question": "DSH 的 Agent Loop 是什么样的?",
      "codeGuide": {
        "title": "让模型动作进入可验证的执行边界",
        "description": "先建立一条最小基线：Step 固定当前输入，模型提出工具调用，运行时验证并执行，再把结果交给下一步。",
        "observations": [
          {
            "title": "参数检查和构造器固定执行边界",
            "text": "validate_arguments 只覆盖教程使用的 JSON Schema 子集。构造器保存模型、工具表和步数上限，循环不必在每次请求中重新寻找这些依赖。",
            "lines": [
              27,
              45
            ]
          },
          {
            "title": "一次 Step 固定请求并判断是否停止",
            "text": "run 先写入用户目标，再将系统规则、工具 Schema、当前历史与步骤编号组成请求。模型没有 tool_calls 时，当前 Turn 结束。",
            "lines": [
              47,
              65
            ]
          },
          {
            "title": "工具执行和失败都回到消息历史",
            "text": "未知工具、参数错误和执行异常都会转成工具结果并写回 messages；达到 max_steps 时由 Harness 明确报错。",
            "lines": [
              67,
              79
            ]
          }
        ],
        "fills": [
          {
            "label": "模型协议、工具结构与 Agent 轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                26
              ]
            ]
          },
          {
            "label": "参数检查与 Agent 的固定依赖",
            "kind": "body",
            "ranges": [
              [
                27,
                45
              ]
            ]
          },
          {
            "label": "run：构造请求、接收回复并判断停止",
            "kind": "body",
            "ranges": [
              [
                47,
                66
              ]
            ]
          },
          {
            "label": "工具执行：校验、异常与反馈",
            "kind": "body",
            "ranges": [
              [
                67,
                79
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "先建立可验证的 Python 执行基线",
        "summary": "Python Nano DSH 用 Agent.run() 在一个 Turn 中推进多个 Step：模型返回工具调用，运行时按简化的 Schema 检查参数并执行工具，再把结果写回下一次请求。它保留了工具、历史和记录仍在内存中的限制，供后续章节处理。",
        "harnessRole": "执行基线：将模型动作转成经过校验的工具结果",
        "connection": "第一章先让模型、工具和反馈闭环。第二章控制请求视图，第三章管理可安装能力，第四章把运行事实写入共同记录。",
        "outcomes": [
          "能沿 Agent.run() 说明请求、工具调用、工具结果和下一步的顺序",
          "能解释模型提出动作后，运行时为何还要承担工具查找、参数检查和异常包装",
          "能区分 Step、Turn 与 max_steps，并指出这条基线尚未解决的运行时问题"
        ]
      },
      "lesson": "# 一次工具往返：Agent Loop 的起点\n\n先看一个具体任务：用户要知道北京当前时间。模型理解问题，却没有实时数据；它需要调用 `get_time`，读取返回值，再给出回答。这个过程把模型、工具和运行时放进同一条执行链，是后续 DSH 机制共同依赖的起点。\n\n许多 Agent Harness 都能完成一次工具调用。DSH 的差异会在后面的运行时组织中逐渐显现：工具、提示词、会话和 Agent Loop 可以由插件组装，执行事实也会进入共同日志。Python 教学版先保留最小循环，后续章节再分别补足这些运行时职责。\n\n`agent.py` 声明 `Llm` 模型协议、`Tool` 数据结构、参数检查函数和 `Agent`。读代码前先确认边界：**模型给出下一步建议，运行时准备输入、校验并执行工具，再保存反馈。**\n\n<!-- fill {\"index\":0} -->\n\n## 1.1 一次回答为什么会变成多个 Step\n\n模型第一次看到系统规则、`get_time` 的参数说明、用户问题和本步编号。它无法从这些文字得出当前时间，于是返回一次工具调用。运行时执行查询，把结果加入消息历史；下一次请求带着这条新事实再次发给模型。模型返回不含工具调用的普通文本时，这个 Turn 结束。\n\n**Step 是一次模型请求及其触发的工具执行，Turn 可以包含多个 Step。**`validate_arguments()` 先覆盖教程所需的 JSON Schema 子集，`Agent.__init__()` 再固定模型、工具表和最大步数。循环开始后，每一轮都能从同一组依赖出发。\n\n<!-- fill {\"index\":1} -->\n\n准备完成后，`run()` 才处理用户目标。它把目标写入 `messages`，为每个 Step 组成一份请求，调用模型，并把模型文本写回历史。没有 `tool_calls` 时，当前回复就是最终答案；存在调用时，循环进入工具执行阶段。请求构造、模型回复与停止判断位于同一条控制流中。\n\n<!-- fill {\"index\":2} -->\n\n<!-- evidence {\"id\":\"m01-request-feedback\",\"tab\":\"request\",\"requestStep\":2,\"label\":\"工具结果怎样进入下一次请求\",\"description\":\"比较两份请求：第一份只包含任务和工具定义；第二份在原有内容后追加工具调用与查询结果。\"} -->\n\n## 1.2 工具执行需要一条统一的反馈入口\n\n模型只能给出工具名和参数，无法直接决定执行是否安全或成功。运行时需要查找工具，检查参数是否符合 Schema（参数结构规则），再等待工具函数返回。查询成功、工具名不存在、参数不合法和工具抛出异常，都应回到同一种工具结果消息中。\n\n`run()` 因此将这部分逻辑放进遍历 `tool_calls` 的循环。**所有成功与失败都回到消息历史中的工具结果。**未知工具和参数错误先形成 `error`，正常执行中的异常也会被包装成 `error`；每一种结果都作为 `role: \"tool\"` 的消息写回。下一次请求能够读取同一种反馈形状，并据此改参数或选择其他动作。\n\n<!-- fill {\"index\":3} -->\n\n## 1.3 这条循环在 DSH 中处于什么位置\n\n这个最小实现把工具往返变成可检查的程序流程，却仍将工具列表和消息历史直接保存在 `Agent` 中。任务变长后，请求输入会增长；运行能力变动后，构造器会逐渐拥挤；程序结束后，内存里的历史无法支撑回放。\n\n完整 DSH 保留这条执行边界，同时把输入投影、能力组装和会话记录交给独立运行时机制。理解本章的 Step 和 Turn，才能看清这些机制分别接住了循环的哪一部分。\n",
      "source": {
        "path": "python_harness/agent.py",
        "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n",
        "excerpt": "def validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")",
        "startLine": 27,
        "endLine": 79
      },
      "extraFiles": [],
      "diff": "diff --git a/python_harness/agent.py b/python_harness/agent.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/agent.py\n@@ -0,0 +1,79 @@\n+\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n+\n+from __future__ import annotations\n+\n+from dataclasses import dataclass\n+from typing import Any, Awaitable, Callable, Protocol\n+\n+\n+Json = dict[str, Any] | list[Any] | str | int | float | bool | None\n+\n+\n+class Llm(Protocol):\n+    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n+\n+\n+ToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n+\n+\n+@dataclass(frozen=True)\n+class Tool:\n+    name: str\n+    description: str\n+    parameters: dict[str, Any]\n+    execute: ToolHandler\n+\n+\n+def validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n+    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n+    required = schema.get(\"required\", [])\n+    missing = [name for name in required if name not in arguments]\n+    if missing:\n+        return f\"missing required fields: {', '.join(missing)}\"\n+    properties = schema.get(\"properties\", {})\n+    if schema.get(\"additionalProperties\") is False:\n+        unknown = [name for name in arguments if name not in properties]\n+        if unknown:\n+            return f\"unknown fields: {', '.join(unknown)}\"\n+    return None\n+\n+\n+class Agent:\n+    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n+        self.llm = llm\n+        self.tools = {tool.name: tool for tool in tools}\n+        self.max_steps = max_steps\n+\n+    async def run(self, objective: str) -> str:\n+        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n+        schemas = [\n+            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n+            for tool in self.tools.values()\n+        ]\n+\n+        for step in range(1, self.max_steps + 1):\n+            request = {\n+                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n+                \"tools\": schemas,\n+                \"messages\": messages,\n+                \"dynamic_context\": f\"step={step}\",\n+            }\n+            response = await self.llm.complete(request)\n+            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n+            calls = response.get(\"tool_calls\", [])\n+            if not calls:\n+                return str(response.get(\"content\", \"\"))\n+\n+            for call in calls:\n+                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n+                tool = self.tools.get(name)\n+                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n+                result: Json = {\"error\": error}\n+                if tool is not None and error is None:\n+                    try:\n+                        result = await tool.execute(arguments)\n+                    except Exception as exc:  # Tool errors become model-readable results.\n+                        result = {\"error\": str(exc)}\n+                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n+\n+        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n",
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                "name": "get_time",
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                "inputSchema": {
                  "type": "object",
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                    "city": {
                      "type": "string"
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                    "city"
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                "role": "user",
                "content": "北京现在几点？"
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              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 86,
          "prefix": {
            "sharedParts": 0,
            "sharedApproximateTokens": 0,
            "previousParts": 0,
            "currentParts": 4,
            "firstInvalidation": "First request has no predecessor.",
            "note": "Teaching estimate only; no provider cache was queried."
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                "name": "get_time",
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                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
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                }
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            "messages": [
              {
                "role": "user",
                "content": "北京现在几点？"
              },
              {
                "role": "assistant",
                "content": "我先查询时间。"
              },
              {
                "role": "tool",
                "name": "get_time",
                "content": "{\"city\":\"北京\",\"time\":\"14:30\"}"
              }
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                      "city": {
                        "type": "string"
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              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "北京现在几点？"
              },
              "approximateTokens": 9
            },
            {
              "id": "message-1",
              "kind": "message",
              "stability": "append-only",
              "label": "assistant",
              "value": {
                "role": "assistant",
                "content": "我先查询时间。"
              },
              "approximateTokens": 10
            },
            {
              "id": "message-2",
              "kind": "message",
              "stability": "append-only",
              "label": "tool · get_time",
              "value": {
                "role": "tool",
                "name": "get_time",
                "content": "{\"city\":\"北京\",\"time\":\"14:30\"}"
              },
              "approximateTokens": 21
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 117,
          "prefix": {
            "sharedParts": 3,
            "sharedApproximateTokens": 84,
            "previousParts": 4,
            "currentParts": 6,
            "firstInvalidation": "assistant",
            "note": "Longest identical canonical prefix; approximate tokens, not provider cache usage."
          }
        }
      ],
      "events": [],
      "trace": [],
      "graphs": []
    },
    {
      "id": "chapter-2",
      "number": "02",
      "shortTitle": "上下文与缓存复用",
      "title": "让稳定前缀尽可能长",
      "question": "上下文是怎样组织的，为缓存复用做了什么优化？",
      "codeGuide": {
        "title": "从完整事实生成一次可控的模型视图",
        "description": "上下文模块保留原始消息，为当前请求裁剪过长的工具结果，再把稳定内容、累积历史和本步说明按变化频率排列。",
        "observations": [
          {
            "title": "投影保留原始消息",
            "text": "短结果原样返回；长结果保留头尾并写明省略量。这个函数生成模型视图，同时保留 Session 中的原始内容。",
            "lines": [
              9,
              18
            ]
          },
          {
            "title": "变化更慢的部分排在前面",
            "text": "build_request 先放系统规则和工具，再放投影后的历史，最后放每步变化的 dynamicContext。",
            "lines": [
              21,
              38
            ]
          },
          {
            "title": "前缀比较提供教学估算",
            "text": "字符数用于近似 token 数量；shared_prefix 按固定顺序从开头比较到首个差异，不查询 Provider 的真实缓存。",
            "lines": [
              41,
              51
            ]
          }
        ],
        "fills": [
          {
            "label": "投影与请求比较的函数轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                9
              ]
            ]
          },
          {
            "label": "project_tool_result：在模型视图中裁剪长结果",
            "kind": "body",
            "ranges": [
              [
                10,
                20
              ]
            ]
          },
          {
            "label": "build_request：将稳定内容、历史和本步说明排序",
            "kind": "body",
            "ranges": [
              [
                21,
                40
              ]
            ]
          },
          {
            "label": "估算文本量并定位首个变化",
            "kind": "body",
            "ranges": [
              [
                41,
                51
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "完整事实和当前请求分别承担不同职责",
        "summary": "Python Nano DSH 在构建请求时复制消息，并只裁剪工具结果的模型视图。系统规则与工具在前，历史按顺序追加，本步说明最后加入；shared_prefix 提供四个顶层部件的确定性教学比较。",
        "harnessRole": "输入投影层：决定当前 Step 发给模型的内容",
        "connection": "第一章需要一份请求，本章定义请求如何由完整历史生成。第三章变更可用工具，第四章会为完整事实提供更完整的记录结构。",
        "outcomes": [
          "能区分原始消息与发送给模型的投影消息",
          "能解释 project_tool_result() 为什么只改变当前请求视图",
          "能从 system、tools、messages、dynamicContext 的顺序判断首个变化位置"
        ]
      },
      "lesson": "# 让相同前缀尽可能长\n\n第一章的循环每走一步，都会把更多消息带进下一次请求。短任务里，这个做法足够直观；工具返回很长的文本后，模型输入会很快占满上下文窗口。运行时此时要同时保留两份数据：**完整事实用于审查和恢复，当前模型视图用于本次推理。**\n\nDSH 的上下文设计先解决输入可控的问题，再利用稳定的请求顺序争取前缀缓存复用。**先让输入可控，再尽量延长稳定前缀。**Python 教学版把这件事缩为消息投影与四个顶层请求部件的比较；真实缓存命中仍以模型服务返回的数据为准。\n\n`context.py` 定义投影、请求构造和前缀比较函数。它们给出后续代码共同使用的规则：多长的工具结果需要裁剪、请求部件如何排序、两次请求从哪里开始不同。\n\n<!-- fill {\"index\":0} -->\n\n## 2.1 模型视图可以缩短，完整事实必须保留\n\n让 Agent 回答备忘录内容时，`read_note` 可能返回很长的文本。每个 Step 都携带全文会持续增加输入长度；直接覆盖原结果又会让后续审查失去依据。Python 版因此在构建请求时生成一份投影，只处理模型这次要读取的工具结果，原始 `messages` 列表保持不变。\n\n`project_tool_result()` 只裁剪超出限制的字符串。短结果原样保留；长结果保留开头、结尾和省略字符数。开头帮助模型辨认来源，结尾常含结论、状态或错误信息，省略标记明确说明中间内容没有进入本次视图。\n\n这一段实现将裁剪限制在模型视图中：**当前请求可以变短，调用方持有的完整消息不会被改写。**完整 DSH 会把原始事实写入 Session Log（会话日志），供之后的重建、回放和恢复使用。\n\n<!-- fill {\"index\":1} -->\n\n## 2.2 请求的排列决定变化落在哪里\n\n裁剪控制单条结果的体积，请求部件的顺序决定新增内容会影响哪里。Python 版 `build_request()` 先放系统规则和工具定义，随后按时间放入投影后的消息历史，最后加入每个 Step 都会变化的 `dynamicContext`。相邻请求从开头比较时，变化慢的内容会留下更长的相同前缀。\n\n**变化慢的内容放在前面，新增内容追加到后面。**这个函数不调用模型服务，也不管理缓存；它负责把当前状态排成统一请求，让调用方和比较函数都能依据同一种顺序理解一次模型调用。工具往返增加消息后，新内容自然位于历史尾部。\n\n<!-- fill {\"index\":2} -->\n\n<!-- evidence {\"id\":\"m02-prefix\",\"tab\":\"request\",\"requestStep\":2,\"label\":\"变化从历史末尾开始\",\"description\":\"比较两次请求的稳定开头与首次变化位置，确认新增的工具往返怎样影响前缀。\"} -->\n\n## 2.3 用可重复的比较检查结构是否稳定\n\n页面可以说明两次请求共享多少开头，却无法直接得知模型服务的真实缓存命中。`approximate_tokens()` 用字符数给出教学估算，`shared_prefix()` 则按 `system`、`tools`、`messages`、`dynamicContext` 的固定顺序查找第一个不同部件。它不会查询服务端缓存，也不会改变请求。\n\n这份比较比 TypeScript 版更小：它检查四个顶层部件，而不逐项规范化每一段内容。**它仍然能稳定说明变化先落在哪个请求部件中。**当工具、提示词或模型适配器在运行中改变时，稳定部分也会相应变化；完整 DSH 用更细的请求描述与日志重建继续处理这些情况。\n\n<!-- fill {\"index\":3} -->\n",
      "source": {
        "path": "python_harness/context.py",
        "content": "\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import Any\n\n\ndef project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n",
        "excerpt": "def project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}",
        "startLine": 9,
        "endLine": 51
      },
      "extraFiles": [
        {
          "path": "python_harness/agent.py",
          "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n"
        }
      ],
      "diff": "diff --git a/python_harness/context.py b/python_harness/context.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/context.py\n@@ -0,0 +1,51 @@\n+\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n+\n+from __future__ import annotations\n+\n+import json\n+from typing import Any\n+\n+\n+def project_tool_result(value: Any, *, limit: int = 520) -> Any:\n+    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n+    if not isinstance(value, str) or len(value) <= limit:\n+        return value\n+\n+    omitted = len(value) - limit\n+    marker = f\"\\n… omitted {omitted} characters …\\n\"\n+    head_size = limit // 2\n+    tail_size = limit - head_size\n+    return value[:head_size] + marker + value[-tail_size:]\n+\n+\n+def build_request(\n+    system: str,\n+    tools: list[dict[str, Any]],\n+    messages: list[dict[str, Any]],\n+    dynamic_context: str,\n+) -> dict[str, Any]:\n+    projected = []\n+    for message in messages:\n+        copy = dict(message)\n+        if copy.get(\"role\") == \"tool\":\n+            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n+        projected.append(copy)\n+    return {\n+        \"system\": system,\n+        \"tools\": tools,\n+        \"messages\": projected,\n+        \"dynamicContext\": dynamic_context,\n+    }\n+\n+\n+def approximate_tokens(value: Any) -> int:\n+    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n+    return max(1, (len(text) + 3) // 4)\n+\n+\n+def shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n+    if left is None:\n+        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n+    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n+    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n+    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n",
      "diffStats": {
        "filesChanged": 1,
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      "requests": [
        {
          "step": 1,
          "request": {
            "system": "先读取事实，再给出简短回答。",
            "tools": [
              {
                "name": "read_note",
                "description": "读取一条命名备忘录。",
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                    }
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                }
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            "messages": [
              {
                "role": "user",
                "content": "备忘录里写了什么？"
              }
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            "dynamicContext": "step=1"
          },
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            {
              "id": "system",
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              "value": "先读取事实，再给出简短回答。",
              "approximateTokens": 4
            },
            {
              "id": "tools",
              "kind": "tools",
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              },
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            },
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              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=1",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 59,
          "prefix": {
            "sharedParts": 0,
            "sharedApproximateTokens": 0,
            "previousParts": 0,
            "currentParts": 4,
            "firstInvalidation": "First request has no predecessor.",
            "note": "Teaching estimate only; no provider cache was queried."
          }
        },
        {
          "step": 2,
          "request": {
            "system": "先读取事实，再给出简短回答。",
            "tools": [
              {
                "name": "read_note",
                "description": "读取一条命名备忘录。",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "name": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "name"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "user",
                "content": "备忘录里写了什么？"
              },
              {
                "role": "assistant",
                "content": "我先读取备忘录。"
              },
              {
                "role": "tool",
                "name": "read_note",
                "content": "备忘录 today · 会议准备记录\n背景材料：项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨\n… omitted 93 characters …\n题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。\n最终结论：下午三点开会。"
              }
            ],
            "dynamicContext": "step=2"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "先读取事实，再给出简短回答。",
              "approximateTokens": 4
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "read_note",
                  "description": "读取一条命名备忘录。",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "name": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "name"
                    ],
                    "additionalProperties": false
                  }
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              "approximateTokens": 43
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              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "备忘录里写了什么？"
              },
              "approximateTokens": 10
            },
            {
              "id": "message-1",
              "kind": "message",
              "stability": "append-only",
              "label": "assistant",
              "value": {
                "role": "assistant",
                "content": "我先读取备忘录。"
              },
              "approximateTokens": 11
            },
            {
              "id": "message-2",
              "kind": "message",
              "stability": "append-only",
              "label": "tool · read_note",
              "value": {
                "role": "tool",
                "name": "read_note",
                "content": "备忘录 today · 会议准备记录\n背景材料：项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨\n… omitted 93 characters …\n题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。项目状态、待确认问题与讨论过程。\n最终结论：下午三点开会。"
              },
              "approximateTokens": 150
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 220,
          "prefix": {
            "sharedParts": 3,
            "sharedApproximateTokens": 57,
            "previousParts": 4,
            "currentParts": 6,
            "firstInvalidation": "assistant",
            "note": "Longest identical canonical prefix; approximate tokens, not provider cache usage."
          }
        }
      ],
      "events": [],
      "trace": [],
      "graphs": []
    },
    {
      "id": "chapter-3",
      "number": "03",
      "shortTitle": "一切皆插件",
      "title": "用插件统一运行能力",
      "question": "如何实现“一切皆插件”？",
      "codeGuide": {
        "title": "让能力拥有来源、依赖和退出路径",
        "description": "Nano DSH 用 Context 安装插件。插件提供服务、工具、提示词和监听器时，Context 记录来源，并将相应清理函数加入同一条 effect 栈。",
        "observations": [
          {
            "title": "Context 保存 owner 与 effect",
            "text": "运行时 Registry（注册表）保存 Contribution（能力贡献），安装中的插件名决定 owner，每个插件都有独立的清理函数列表。",
            "lines": [
              25,
              38
            ]
          },
          {
            "title": "挂载成功后生效，失败后回滚",
            "text": "setup 抛错时逆序撤销已登记内容；成功后返回幂等的 unmount，主动卸载使用同一组清理函数。",
            "lines": [
              39,
              65
            ]
          },
          {
            "title": "注册内容同时登记清理函数",
            "text": "Tool 与 Prompt 只能在安装期间进入 Registry，并立刻绑定删除该 owner 贡献的 cleanup；inspect 再生成当前运行时视图。",
            "lines": [
              67,
              89
            ]
          }
        ],
        "fills": [
          {
            "label": "插件协议、贡献记录与 Context 轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                25
              ]
            ]
          },
          {
            "label": "安装状态、effect 栈与可逆挂载",
            "kind": "body",
            "ranges": [
              [
                26,
                66
              ]
            ]
          },
          {
            "label": "登记工具和提示词，同时绑定清理动作",
            "kind": "body",
            "ranges": [
              [
                67,
                78
              ]
            ]
          },
          {
            "label": "检查当前能力并守住安装期边界",
            "kind": "body",
            "ranges": [
              [
                79,
                89
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Python 版保留能力来源和可逆生命周期",
        "summary": "完整 DSH 用 Cordis 插件树组织模型、工具、会话和 Agent Loop。Python 教学版把这一点缩为 owner 和 effect：它具体登记 Tool 与 Prompt 贡献，并在安装失败或主动卸载时逆序清理。services 与 listeners 容器为扩展预留，当前示例没有实现 Service 注入和依赖图。",
        "harnessRole": "运行时组装层：维护当前可用工具和提示词的来源与生命周期",
        "connection": "第一章消费工具表，第二章把工具放进请求，本章说明能力怎样进入和离开 Context。第四章会从消息和事件的角度保存运行事实。",
        "outcomes": [
          "能区分 Tool 这个模型动作与 Plugin 这个能力安装单元",
          "能沿 Context.mount()、effect() 和返回的 unmount 说明安装、失败回滚和卸载",
          "能用 inspect() 的 owner 记录解释当前工具和提示词的来源"
        ]
      },
      "lesson": "# 一切运行能力都由插件提供\n\n第一章的 `Agent` 直接拿到工具，第二章把工具排进请求。这样的起点便于理解循环，能力一多，修改会集中到少数构造器和全局状态里：新增工具、替换模型适配器、改变工作区设置，都可能碰到同一片核心代码。\n\n完整 DSH 用 Cordis 组织这类变化。Cordis 是 DSH 使用的 TypeScript 插件运行时：**模型适配器、工具注册表、会话日志和 Agent Loop 都作为插件加入同一棵运行时树。**Python 教学版保留共同的生命周期约束：**每项已登记能力都有 owner（来源插件），安装后可以沿清理路径撤销。**它具体实现 Tool 和 Prompt 的登记，`services` 与 `listeners` 容器则为后续扩展预留。\n\n`runtime.py` 先定义插件协议、带 owner 的 `Contribution`，以及 `Context` 持有的运行时状态。当前 Python 示例还没有 `provide()`、`use()` 或依赖关系 API，因此这一章将重点放在能力的来源、安装和完整退出。\n\n<!-- fill {\"index\":0} -->\n\n## 3.1 安装一项能力时，先确定它的生命周期\n\nPlugin 是一次安装和卸载的单位。它拿到 `Context` 后，可以在 `setup()` 中登记 Tool 或 Prompt。`Context` 为每个贡献保存值和 owner，并为当前正在安装的插件建立一条独立的 effect 栈。这样，运行时至少能回答当前有哪些工具和提示词，以及它们属于哪次安装。\n\n安装时最容易遗漏的是中途失败的状态。一个插件可能已经登记工具，又在贡献提示词时抛出错误；如果此前的贡献留在 Context 中，之后的状态就无法可信。`mount()` 因此先创建 effect 列表，执行 `setup()`，并将清理动作收集到其中。**安装失败和主动卸载都沿同一条逆序清理路径处理。**\n\n<!-- fill {\"index\":1} -->\n\n## 3.2 注册能力时，同时记录它如何退出\n\n`effect()` 是这套生命周期的公共入口：它只在插件安装期间可调用，并把一次清理函数放入当前插件的列表。`register_tool()` 将工具与 owner 写进表中，再登记删除该表项的动作；`contribute_prompt()` 同样保存提示词及其删除动作。**注册一项能力时，也同时登记它的清理动作和来源。**\n\n这里的顺序有实际用途。主动卸载时，后注册的清理动作先运行，相关贡献会依次退出；`mount()` 中的 `disposed` 标记还保证返回的卸载函数只生效一次。Python 版没有实现完整 DSH 的 Service 注入与级联依赖，却保留了它们共同依赖的可逆安装边界。\n\n<!-- fill {\"index\":2} -->\n\n## 3.3 同一条账目覆盖当前示例中的工具和提示词\n\n工具与提示词参数不同，运行时对它们采用相同的登记方式：写入带 owner 的容器，再把删除动作交给 `effect()`。`inspect()` 从这些容器生成当前插件、工具和提示词的快照；`_require_owner()` 则阻止在安装期之外偷偷修改运行时状态。\n\n源码的这一段将贡献登记、状态读取和回滚辅助集中在 `Context` 中。**每个插件都沿同一套规则接入当前 Python 运行时。**后续需要加入 Service、监听器和依赖关系时，也应把它们接到这条 owner 与 effect 生命周期上。\n\n<!-- fill {\"index\":3} -->\n\n## 3.4 这正是 DSH 与常见 Harness 拉开距离的地方\n\n工具循环和上下文投影在许多 Harness 中都很常见。**DSH 进一步把组成这些机制的能力也放进插件树。**配置描述当前组合，插件生命周期管理贡献的进入和退出，运行时检查工具可以追溯来源和依赖。\n\nPython 教学版只展示其中较小的 Tool 与 Prompt 生命周期，完整 DSH 还包含依赖注入、服务就绪等待、配置层叠和隔离作用域。这个差异是实现范围的差异，不改变核心关系：能力应通过可检查的安装进入运行时，也应通过同一生命周期离开运行时。\n",
      "source": {
        "path": "python_harness/runtime.py",
        "content": "\"\"\"Chapter 3: a tiny plugin runtime with ownership and rollback.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\nfrom dataclasses import dataclass\nfrom typing import Any, Protocol\n\n\nCleanup = Callable[[], None]\n\n\nclass Plugin(Protocol):\n    name: str\n\n    def setup(self, context: \"Context\") -> None: ...\n\n\n@dataclass\nclass Contribution:\n    value: Any\n    owner: str\n\n\nclass Context:\n    def __init__(self) -> None:\n        self.services: dict[str, Contribution] = {}\n        self.tools: dict[str, Contribution] = {}\n        self.prompts: list[Contribution] = []\n        self.listeners: dict[str, list[Contribution]] = {}\n        self._installing: str | None = None\n        self._effects: dict[str, list[Cleanup]] = {}\n\n    def effect(self, cleanup: Cleanup) -> None:\n        if self._installing is None:\n            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n        self._effects[self._installing].append(cleanup)\n\n    def mount(self, plugin: Plugin) -> Cleanup:\n        if plugin.name in self._effects:\n            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n        self._effects[plugin.name] = []\n        self._installing = plugin.name\n        try:\n            plugin.setup(self)\n        except Exception:\n            self._rollback(plugin.name)\n            raise\n        finally:\n            self._installing = None\n\n        disposed = False\n\n        def unmount() -> None:\n            nonlocal disposed\n            if disposed:\n                return\n            disposed = True\n            self._rollback(plugin.name)\n\n        return unmount\n\n    def _rollback(self, owner: str) -> None:\n        for cleanup in reversed(self._effects.pop(owner, [])):\n            cleanup()\n\n    def register_tool(self, name: str, tool: Any) -> None:\n        owner = self._require_owner()\n        if name in self.tools:\n            raise ValueError(f\"tool already registered: {name}\")\n        self.tools[name] = Contribution(tool, owner)\n        self.effect(lambda: self.tools.pop(name, None))\n\n    def contribute_prompt(self, text: str) -> None:\n        contribution = Contribution(text, self._require_owner())\n        self.prompts.append(contribution)\n        self.effect(lambda: self.prompts.remove(contribution))\n\n    def inspect(self) -> dict[str, Any]:\n        return {\n            \"plugins\": list(self._effects),\n            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n        }\n\n    def _require_owner(self) -> str:\n        if self._installing is None:\n            raise RuntimeError(\"no plugin is being installed\")\n        return self._installing\n",
        "excerpt": "class Context:\n    def __init__(self) -> None:\n        self.services: dict[str, Contribution] = {}\n        self.tools: dict[str, Contribution] = {}\n        self.prompts: list[Contribution] = []\n        self.listeners: dict[str, list[Contribution]] = {}\n        self._installing: str | None = None\n        self._effects: dict[str, list[Cleanup]] = {}\n\n    def effect(self, cleanup: Cleanup) -> None:\n        if self._installing is None:\n            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n        self._effects[self._installing].append(cleanup)\n\n    def mount(self, plugin: Plugin) -> Cleanup:\n        if plugin.name in self._effects:\n            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n        self._effects[plugin.name] = []\n        self._installing = plugin.name\n        try:\n            plugin.setup(self)\n        except Exception:\n            self._rollback(plugin.name)\n            raise\n        finally:\n            self._installing = None\n\n        disposed = False\n\n        def unmount() -> None:\n            nonlocal disposed\n            if disposed:\n                return\n            disposed = True\n            self._rollback(plugin.name)\n\n        return unmount\n\n    def _rollback(self, owner: str) -> None:\n        for cleanup in reversed(self._effects.pop(owner, [])):\n            cleanup()\n\n    def register_tool(self, name: str, tool: Any) -> None:\n        owner = self._require_owner()\n        if name in self.tools:\n            raise ValueError(f\"tool already registered: {name}\")\n        self.tools[name] = Contribution(tool, owner)\n        self.effect(lambda: self.tools.pop(name, None))\n\n    def contribute_prompt(self, text: str) -> None:\n        contribution = Contribution(text, self._require_owner())\n        self.prompts.append(contribution)\n        self.effect(lambda: self.prompts.remove(contribution))\n\n    def inspect(self) -> dict[str, Any]:\n        return {\n            \"plugins\": list(self._effects),\n            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n        }\n\n    def _require_owner(self) -> str:\n        if self._installing is None:\n            raise RuntimeError(\"no plugin is being installed\")\n        return self._installing",
        "startLine": 25,
        "endLine": 89
      },
      "extraFiles": [
        {
          "path": "python_harness/agent.py",
          "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n"
        },
        {
          "path": "python_harness/context.py",
          "content": "\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import Any\n\n\ndef project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n"
        }
      ],
      "diff": "diff --git a/python_harness/runtime.py b/python_harness/runtime.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/runtime.py\n@@ -0,0 +1,89 @@\n+\"\"\"Chapter 3: a tiny plugin runtime with ownership and rollback.\"\"\"\n+\n+from __future__ import annotations\n+\n+from collections.abc import Callable\n+from dataclasses import dataclass\n+from typing import Any, Protocol\n+\n+\n+Cleanup = Callable[[], None]\n+\n+\n+class Plugin(Protocol):\n+    name: str\n+\n+    def setup(self, context: \"Context\") -> None: ...\n+\n+\n+@dataclass\n+class Contribution:\n+    value: Any\n+    owner: str\n+\n+\n+class Context:\n+    def __init__(self) -> None:\n+        self.services: dict[str, Contribution] = {}\n+        self.tools: dict[str, Contribution] = {}\n+        self.prompts: list[Contribution] = []\n+        self.listeners: dict[str, list[Contribution]] = {}\n+        self._installing: str | None = None\n+        self._effects: dict[str, list[Cleanup]] = {}\n+\n+    def effect(self, cleanup: Cleanup) -> None:\n+        if self._installing is None:\n+            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n+        self._effects[self._installing].append(cleanup)\n+\n+    def mount(self, plugin: Plugin) -> Cleanup:\n+        if plugin.name in self._effects:\n+            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n+        self._effects[plugin.name] = []\n+        self._installing = plugin.name\n+        try:\n+            plugin.setup(self)\n+        except Exception:\n+            self._rollback(plugin.name)\n+            raise\n+        finally:\n+            self._installing = None\n+\n+        disposed = False\n+\n+        def unmount() -> None:\n+            nonlocal disposed\n+            if disposed:\n+                return\n+            disposed = True\n+            self._rollback(plugin.name)\n+\n+        return unmount\n+\n+    def _rollback(self, owner: str) -> None:\n+        for cleanup in reversed(self._effects.pop(owner, [])):\n+            cleanup()\n+\n+    def register_tool(self, name: str, tool: Any) -> None:\n+        owner = self._require_owner()\n+        if name in self.tools:\n+            raise ValueError(f\"tool already registered: {name}\")\n+        self.tools[name] = Contribution(tool, owner)\n+        self.effect(lambda: self.tools.pop(name, None))\n+\n+    def contribute_prompt(self, text: str) -> None:\n+        contribution = Contribution(text, self._require_owner())\n+        self.prompts.append(contribution)\n+        self.effect(lambda: self.prompts.remove(contribution))\n+\n+    def inspect(self) -> dict[str, Any]:\n+        return {\n+            \"plugins\": list(self._effects),\n+            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n+            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n+        }\n+\n+    def _require_owner(self) -> str:\n+        if self._installing is None:\n+            raise RuntimeError(\"no plugin is being installed\")\n+        return self._installing\n",
      "diffStats": {
        "filesChanged": 1,
        "additions": 89,
        "deletions": 0,
        "files": [
          {
            "path": "python_harness/runtime.py",
            "additions": 89,
            "deletions": 0
          }
        ]
      },
      "requests": [],
      "events": [],
      "trace": [],
      "graphs": []
    },
    {
      "id": "chapter-4",
      "number": "04",
      "shortTitle": "让运行有迹可循",
      "title": "从日志重建执行过程",
      "question": "DSH 怎么记录和保存 Agent 执行过程?",
      "codeGuide": {
        "title": "一条事件日志怎样支撑请求和回放",
        "description": "Nano DSH 先按编号追加 SessionEvent，再用目标 Step 的请求头、上下文检查点和此前事件重建模型输入与执行过程。",
        "observations": [
          {
            "title": "Event 以递增编号追加",
            "text": "append 使用当前长度产生稳定 ID（标识符）；request_step_ids 从 header 事件读取能够重建的模型步骤。",
            "lines": [
              16,
              26
            ]
          },
          {
            "title": "请求头确定重建范围",
            "text": "step_id 先定位目标 header；该事件之前的内容属于本步历史，最近的 checkpoint 决定从哪里继续。",
            "lines": [
              28,
              54
            ]
          },
          {
            "title": "Request 与 Trace 使用同一份日志",
            "text": "消息投影读取可识别事件，trace 格式化全部 Event。两种视图均从 Session Log 生成。",
            "lines": [
              56,
              68
            ]
          }
        ],
        "fills": [
          {
            "label": "事件结构与 SessionLog 的基本轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                16
              ]
            ]
          },
          {
            "label": "追加事实并建立可重建 Step 的索引",
            "kind": "body",
            "ranges": [
              [
                17,
                27
              ]
            ]
          },
          {
            "label": "按请求头和检查点重建当前请求",
            "kind": "body",
            "ranges": [
              [
                28,
                55
              ]
            ]
          },
          {
            "label": "从事件恢复消息",
            "kind": "body",
            "ranges": [
              [
                56,
                66
              ]
            ]
          },
          {
            "label": "从同一份事件流生成 Trace",
            "kind": "body",
            "ranges": [
              [
                67,
                68
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Python 版从一条日志派生请求和时间线",
        "summary": "Python Nano DSH 将 Event 追加到 SessionLog，并用 request/header、检查点和此前事件重建模型消息。trace() 从同一序列生成展示用时间线。这个最小版本由调用方显式追加事件，没有将日志自动接入插件运行时或持久化。",
        "harnessRole": "记录层：为请求重建与执行查看提供共同输入",
        "connection": "第一章产生模型和工具往返，第二章说明请求视图，第三章管理能力生命周期。本章为可记录的事实建立简单的事件序列；后两章可在完整 DSH 中继续扩展它。",
        "outcomes": [
          "能说明递增事件编号怎样保留发生顺序",
          "能解释 request/header 和 context/checkpoint 怎样确定重建范围",
          "能区分 SessionLog、build_request() 的请求和 trace() 的时间线"
        ]
      },
      "lesson": "# 模型可见即已记录\n\n前三章已经产生多种状态：用户消息和工具结果构成模型历史，插件改变可用工具和提示词，页面还要展示执行过程。若这些信息分别停留在消息数组、调试输出和界面状态中，同一次运行很容易出现几份互相对不上的版本。\n\n**完整 DSH 先把运行事实写入一条仅追加的 Session Log（会话日志），再从这条日志派生模型历史、回放、界面和持久化数据。**这个顺序建立了一条可检查的约束：**模型能够看到的内容，应当能从记录中重建。**Python 教学版先实现这条关系的最小骨架：事件按顺序追加，请求和 Trace（执行时间线）从同一组事件读取。\n\n`session.py` 定义 `Event` 和 `SessionLog`。每个 Event 只有递增编号、类型和数据，调用方明确决定何时追加用户消息、请求头或工具结果。这份简化让事件顺序和请求重建的关系保持可见。\n\n<!-- fill {\"index\":0} -->\n\n## 4.1 先按发生顺序保存事实\n\n一次工具往返会经过用户消息、请求头、模型回复、外层执行工具和工具结果。这个 Python 最小版由调用方追加用户消息、请求头、模型回复和工具结果，并未自动建立独立的 Tool Call 事件。`SessionLog.append()` 使用当前长度生成递增编号，再把新 Event 追加到末尾。编号让页面能指向确定事件，**追加顺序保留了因果关系：工具结果位于外层执行之后、下一次请求之前。**\n\n`request_step_ids()` 从 `request/header` 事件中取出可以重建的 Step 标识。它没有复制历史，也不猜测事件的语义；索引仍指向同一份追加记录。编号和索引放在一个小类中，正是在为后面的重建函数准备稳定输入。\n\n<!-- fill {\"index\":1} -->\n\n<!-- evidence {\"id\":\"m04-events\",\"tab\":\"events\",\"eventType\":\"tool/result\",\"eventOccurrence\":1,\"label\":\"一次工具往返的共同记录\",\"description\":\"沿事件编号阅读用户消息、请求头、模型回复、工具结果与下一次请求，确认它们的先后关系。\"} -->\n\n## 4.2 日志必须覆盖模型可见的事实\n\n完整 DSH 中，插件挂载和卸载会改变工具列表与提示词；这些变化也需要进入同一份记录，之后才能解释某个 Step 当时为什么看到了某个工具。TypeScript 版的 `sessionPlugin()` 会监听运行时变化并追加事件。\n\nPython 教学版在这里保留了更明确的边界：`SessionLog` 不会自动挂接 `Context`，调用方负责在发生事实时调用 `append()`。**这段代码演示共同事件来源的结构，尚未自动记录插件变化或执行结束原因。**将来补上运行时监听器时，新增事件仍应写入同一条日志，而不应另建一份历史。\n\n<!-- fill {\"index\":2} -->\n\n## 4.3 请求头给重建过程划出当时的边界\n\n消息历史本身无法描述一份完整请求。系统规则、工具定义、本步说明都可能随时间变化。`build_request()` 先按 `step_id` 找到目标 `request/header`，只读取这条头部之前的事件。**请求头保存一个 Step 当时的输入边界。**\n\n随后代码寻找最近的 `context/checkpoint`。检查点存在时，摘要作为系统消息进入模型视图，检查点之后的事件继续逐条转换为消息。`_message_from()` 只处理用户、助手和工具结果事件，并为工具消息保留工具名称。Python 最小版不记录独立的 Tool Call，也没有在这里再次套用第二章的裁剪；它展示的是从事件顺序恢复当前请求的基本方法。\n\n<!-- fill {\"index\":3} -->\n\n## 4.4 Trace 和请求继续复用同一份事实\n\n`trace()` 遍历同一批 Event，为每一项生成带 `event_id`、类型和数据的展示对象。**请求重建和 Trace 共享同一份事件事实。**它们面向不同用途，却没有维护另一份执行历史。\n\n<!-- fill {\"index\":4} -->\n\n完整 DSH 会将 SessionEvent 写入 JSON Lines（JSONL，每行一条 JSON 记录）或 SQLite，并将 Session Log 接入插件运行时。Python 教学版目前保存在内存中，再由教程生成器序列化样本；两者共同强调的是请求与展示要从同一事件流获得依据。\n",
      "source": {
        "path": "python_harness/session.py",
        "content": "\"\"\"Chapter 4: one append-only history for requests and traces.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any\n\n\n@dataclass(frozen=True)\nclass Event:\n    id: int\n    type: str\n    data: dict[str, Any]\n\n\nclass SessionLog:\n    def __init__(self) -> None:\n        self.events: list[Event] = []\n\n    def append(self, event_type: str, **data: Any) -> Event:\n        event = Event(len(self.events) + 1, event_type, data)\n        self.events.append(event)\n        return event\n\n    def request_step_ids(self) -> list[str]:\n        return [event.data[\"step_id\"] for event in self.events if event.type == \"request/header\"]\n\n    def build_request(self, step_id: str) -> dict[str, Any]:\n        header_index = next(\n            index\n            for index, event in enumerate(self.events)\n            if event.type == \"request/header\" and event.data[\"step_id\"] == step_id\n        )\n        header = self.events[header_index].data\n        history = self.events[:header_index]\n        checkpoint_index = max(\n            (index for index, event in enumerate(history) if event.type == \"context/checkpoint\"),\n            default=-1,\n        )\n        messages: list[dict[str, Any]] = []\n        if checkpoint_index >= 0:\n            checkpoint = history[checkpoint_index]\n            messages.append({\"role\": \"system\", \"content\": checkpoint.data[\"summary\"]})\n            history = history[checkpoint_index + 1 :]\n        for event in history:\n            message = self._message_from(event)\n            if message is not None:\n                messages.append(message)\n        return {\n            \"system\": header[\"system\"],\n            \"tools\": header[\"tools\"],\n            \"messages\": messages,\n            \"dynamicContext\": header[\"dynamic_context\"],\n        }\n\n    @staticmethod\n    def _message_from(event: Event) -> dict[str, Any] | None:\n        roles = {\"user/message\": \"user\", \"assistant/message\": \"assistant\", \"tool/result\": \"tool\"}\n        role = roles.get(event.type)\n        if role is None:\n            return None\n        message = {\"role\": role, \"content\": event.data.get(\"content\", \"\")}\n        if role == \"tool\":\n            message[\"name\"] = event.data[\"name\"]\n        return message\n\n    def trace(self) -> list[dict[str, Any]]:\n        return [{\"event_id\": event.id, \"type\": event.type, **event.data} for event in self.events]\n",
        "excerpt": "class SessionLog:\n    def __init__(self) -> None:\n        self.events: list[Event] = []\n\n    def append(self, event_type: str, **data: Any) -> Event:\n        event = Event(len(self.events) + 1, event_type, data)\n        self.events.append(event)\n        return event\n\n    def request_step_ids(self) -> list[str]:\n        return [event.data[\"step_id\"] for event in self.events if event.type == \"request/header\"]\n\n    def build_request(self, step_id: str) -> dict[str, Any]:\n        header_index = next(\n            index\n            for index, event in enumerate(self.events)\n            if event.type == \"request/header\" and event.data[\"step_id\"] == step_id\n        )\n        header = self.events[header_index].data\n        history = self.events[:header_index]\n        checkpoint_index = max(\n            (index for index, event in enumerate(history) if event.type == \"context/checkpoint\"),\n            default=-1,\n        )\n        messages: list[dict[str, Any]] = []\n        if checkpoint_index >= 0:\n            checkpoint = history[checkpoint_index]\n            messages.append({\"role\": \"system\", \"content\": checkpoint.data[\"summary\"]})\n            history = history[checkpoint_index + 1 :]\n        for event in history:\n            message = self._message_from(event)\n            if message is not None:\n                messages.append(message)\n        return {\n            \"system\": header[\"system\"],\n            \"tools\": header[\"tools\"],\n            \"messages\": messages,\n            \"dynamicContext\": header[\"dynamic_context\"],\n        }\n\n    @staticmethod\n    def _message_from(event: Event) -> dict[str, Any] | None:\n        roles = {\"user/message\": \"user\", \"assistant/message\": \"assistant\", \"tool/result\": \"tool\"}\n        role = roles.get(event.type)\n        if role is None:\n            return None\n        message = {\"role\": role, \"content\": event.data.get(\"content\", \"\")}\n        if role == \"tool\":\n            message[\"name\"] = event.data[\"name\"]\n        return message\n\n    def trace(self) -> list[dict[str, Any]]:\n        return [{\"event_id\": event.id, \"type\": event.type, **event.data} for event in self.events]",
        "startLine": 16,
        "endLine": 68
      },
      "extraFiles": [
        {
          "path": "python_harness/agent.py",
          "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n"
        },
        {
          "path": "python_harness/context.py",
          "content": "\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import Any\n\n\ndef project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n"
        },
        {
          "path": "python_harness/runtime.py",
          "content": "\"\"\"Chapter 3: a tiny plugin runtime with ownership and rollback.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\nfrom dataclasses import dataclass\nfrom typing import Any, Protocol\n\n\nCleanup = Callable[[], None]\n\n\nclass Plugin(Protocol):\n    name: str\n\n    def setup(self, context: \"Context\") -> None: ...\n\n\n@dataclass\nclass Contribution:\n    value: Any\n    owner: str\n\n\nclass Context:\n    def __init__(self) -> None:\n        self.services: dict[str, Contribution] = {}\n        self.tools: dict[str, Contribution] = {}\n        self.prompts: list[Contribution] = []\n        self.listeners: dict[str, list[Contribution]] = {}\n        self._installing: str | None = None\n        self._effects: dict[str, list[Cleanup]] = {}\n\n    def effect(self, cleanup: Cleanup) -> None:\n        if self._installing is None:\n            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n        self._effects[self._installing].append(cleanup)\n\n    def mount(self, plugin: Plugin) -> Cleanup:\n        if plugin.name in self._effects:\n            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n        self._effects[plugin.name] = []\n        self._installing = plugin.name\n        try:\n            plugin.setup(self)\n        except Exception:\n            self._rollback(plugin.name)\n            raise\n        finally:\n            self._installing = None\n\n        disposed = False\n\n        def unmount() -> None:\n            nonlocal disposed\n            if disposed:\n                return\n            disposed = True\n            self._rollback(plugin.name)\n\n        return unmount\n\n    def _rollback(self, owner: str) -> None:\n        for cleanup in reversed(self._effects.pop(owner, [])):\n            cleanup()\n\n    def register_tool(self, name: str, tool: Any) -> None:\n        owner = self._require_owner()\n        if name in self.tools:\n            raise ValueError(f\"tool already registered: {name}\")\n        self.tools[name] = Contribution(tool, owner)\n        self.effect(lambda: self.tools.pop(name, None))\n\n    def contribute_prompt(self, text: str) -> None:\n        contribution = Contribution(text, self._require_owner())\n        self.prompts.append(contribution)\n        self.effect(lambda: self.prompts.remove(contribution))\n\n    def inspect(self) -> dict[str, Any]:\n        return {\n            \"plugins\": list(self._effects),\n            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n        }\n\n    def _require_owner(self) -> str:\n        if self._installing is None:\n            raise RuntimeError(\"no plugin is being installed\")\n        return self._installing\n"
        }
      ],
      "diff": "diff --git a/python_harness/session.py b/python_harness/session.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/session.py\n@@ -0,0 +1,68 @@\n+\"\"\"Chapter 4: one append-only history for requests and traces.\"\"\"\n+\n+from __future__ import annotations\n+\n+from dataclasses import dataclass\n+from typing import Any\n+\n+\n+@dataclass(frozen=True)\n+class Event:\n+    id: int\n+    type: str\n+    data: dict[str, Any]\n+\n+\n+class SessionLog:\n+    def __init__(self) -> None:\n+        self.events: list[Event] = []\n+\n+    def append(self, event_type: str, **data: Any) -> Event:\n+        event = Event(len(self.events) + 1, event_type, data)\n+        self.events.append(event)\n+        return event\n+\n+    def request_step_ids(self) -> list[str]:\n+        return [event.data[\"step_id\"] for event in self.events if event.type == \"request/header\"]\n+\n+    def build_request(self, step_id: str) -> dict[str, Any]:\n+        header_index = next(\n+            index\n+            for index, event in enumerate(self.events)\n+            if event.type == \"request/header\" and event.data[\"step_id\"] == step_id\n+        )\n+        header = self.events[header_index].data\n+        history = self.events[:header_index]\n+        checkpoint_index = max(\n+            (index for index, event in enumerate(history) if event.type == \"context/checkpoint\"),\n+            default=-1,\n+        )\n+        messages: list[dict[str, Any]] = []\n+        if checkpoint_index >= 0:\n+            checkpoint = history[checkpoint_index]\n+            messages.append({\"role\": \"system\", \"content\": checkpoint.data[\"summary\"]})\n+            history = history[checkpoint_index + 1 :]\n+        for event in history:\n+            message = self._message_from(event)\n+            if message is not None:\n+                messages.append(message)\n+        return {\n+            \"system\": header[\"system\"],\n+            \"tools\": header[\"tools\"],\n+            \"messages\": messages,\n+            \"dynamicContext\": header[\"dynamic_context\"],\n+        }\n+\n+    @staticmethod\n+    def _message_from(event: Event) -> dict[str, Any] | None:\n+        roles = {\"user/message\": \"user\", \"assistant/message\": \"assistant\", \"tool/result\": \"tool\"}\n+        role = roles.get(event.type)\n+        if role is None:\n+            return None\n+        message = {\"role\": role, \"content\": event.data.get(\"content\", \"\")}\n+        if role == \"tool\":\n+            message[\"name\"] = event.data[\"name\"]\n+        return message\n+\n+    def trace(self) -> list[dict[str, Any]]:\n+        return [{\"event_id\": event.id, \"type\": event.type, **event.data} for event in self.events]\n",
      "diffStats": {
        "filesChanged": 1,
        "additions": 68,
        "deletions": 0,
        "files": [
          {
            "path": "python_harness/session.py",
            "additions": 68,
            "deletions": 0
          }
        ]
      },
      "requests": [
        {
          "step": 1,
          "stepId": "step-1",
          "request": {
            "system": "需要事实时使用工具。",
            "tools": [
              {
                "name": "get_time",
                "description": "查询指定城市的固定时间。",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "user",
                "content": "北京现在几点？"
              }
            ],
            "dynamicContext": "step=1"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "需要事实时使用工具。",
              "approximateTokens": 3
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "get_time",
                  "description": "查询指定城市的固定时间。",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "city": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "city"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 43
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "北京现在几点？"
              },
              "approximateTokens": 9
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=1",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 57,
          "prefix": {
            "sharedParts": 0,
            "sharedApproximateTokens": 0,
            "previousParts": 0,
            "currentParts": 4,
            "firstInvalidation": "First request has no predecessor.",
            "note": "Teaching estimate only; no provider cache was queried."
          }
        },
        {
          "step": 2,
          "stepId": "step-2",
          "request": {
            "system": "需要事实时使用工具。",
            "tools": [
              {
                "name": "get_time",
                "description": "查询指定城市的固定时间。",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "system",
                "content": "已取得北京时间：14:30。"
              }
            ],
            "dynamicContext": "step=2"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "需要事实时使用工具。",
              "approximateTokens": 3
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "get_time",
                  "description": "查询指定城市的固定时间。",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "city": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "city"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 43
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "system",
              "value": {
                "role": "system",
                "content": "已取得北京时间：14:30。"
              },
              "approximateTokens": 11
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 59,
          "prefix": {
            "sharedParts": 2,
            "sharedApproximateTokens": 46,
            "previousParts": 4,
            "currentParts": 4,
            "firstInvalidation": "system",
            "note": "Longest identical canonical prefix; approximate tokens, not provider cache usage."
          }
        }
      ],
      "events": [
        {
          "id": 1,
          "type": "user/message",
          "data": {
            "content": "北京现在几点？"
          }
        },
        {
          "id": 2,
          "type": "request/header",
          "data": {
            "step_id": "step-1",
            "system": "需要事实时使用工具。",
            "tools": [
              {
                "name": "get_time",
                "description": "查询指定城市的固定时间。",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "dynamic_context": "step=1"
          }
        },
        {
          "id": 3,
          "type": "assistant/message",
          "data": {
            "content": "我先查询时间。"
          }
        },
        {
          "id": 4,
          "type": "tool/result",
          "data": {
            "name": "get_time",
            "content": "{\"time\":\"14:30\"}"
          }
        },
        {
          "id": 5,
          "type": "context/checkpoint",
          "data": {
            "summary": "已取得北京时间：14:30。"
          }
        },
        {
          "id": 6,
          "type": "request/header",
          "data": {
            "step_id": "step-2",
            "system": "需要事实时使用工具。",
            "tools": [
              {
                "name": "get_time",
                "description": "查询指定城市的固定时间。",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "dynamic_context": "step=2"
          }
        }
      ],
      "trace": [
        {
          "eventId": 1,
          "type": "user/message",
          "title": "user/message",
          "detail": "北京现在几点？"
        },
        {
          "eventId": 2,
          "type": "request/header",
          "title": "step-1 → local/python-harness",
          "detail": "1 tools · dynamic=step=1"
        },
        {
          "eventId": 3,
          "type": "assistant/message",
          "title": "assistant/message",
          "detail": "我先查询时间。"
        },
        {
          "eventId": 4,
          "type": "tool/result",
          "title": "result get_time",
          "detail": "{\"time\":\"14:30\"}"
        },
        {
          "eventId": 5,
          "type": "context/checkpoint",
          "title": "context/checkpoint",
          "detail": "已取得北京时间：14:30。"
        },
        {
          "eventId": 6,
          "type": "request/header",
          "title": "step-2 → local/python-harness",
          "detail": "1 tools · dynamic=step=2"
        }
      ],
      "graphs": []
    },
    {
      "id": "chapter-5",
      "number": "05",
      "shortTitle": "运行时自进化",
      "title": "编写并运行新的 Cordis 插件",
      "question": "DSH 是如何持续自进化的?",
      "codeGuide": {
        "title": "能力缺口怎样经过检查、挂载和释放被关闭",
        "description": "Runtime Tools 先让 Agent 检查当前 Context，再登记和运行新的 Cordis 插件；动态插件沿用普通 Context 挂载与卸载流程贡献工具和提示词。",
        "observations": [
          {
            "title": "定义表保存代码和运行状态",
            "text": "setup 建立定义表和自增编号，注册卸载时统一清理的 effect；cordis_inspect 返回当前运行时视图。",
            "lines": [
              29,
              40
            ]
          },
          {
            "title": "定义和运行是两个动作",
            "text": "cordis_define 编译校验并保存 Agent 提交的 Python 插件代码。cordis_run 根据 plugin_id 取回代码，执行得到 Plugin，再通过 context.mount() 挂载。",
            "lines": [
              42,
              71
            ]
          },
          {
            "title": "停止与删除复用卸载函数",
            "text": "cordis_stop 执行卸载函数但保留定义，cordis_undefine 停止插件并删除定义；插件贡献随 Context 生命周期一起撤销。",
            "lines": [
              73,
              94
            ]
          }
        ],
        "fills": [
          {
            "label": "工具包装与 Runtime Tools 插件轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                28
              ],
              [
                118,
                120
              ]
            ]
          },
          {
            "label": "定义表、清理动作与检查接口",
            "kind": "body",
            "ranges": [
              [
                29,
                41
              ]
            ]
          },
          {
            "label": "定义并挂载动态 Python 插件",
            "kind": "body",
            "ranges": [
              [
                42,
                72
              ]
            ]
          },
          {
            "label": "停止、删除、注册工具并加载插件代码",
            "kind": "body",
            "ranges": [
              [
                73,
                140
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "面对能力缺口，先检查、挂载、验证、释放",
        "summary": "Python Nano DSH 将 cordis_inspect、cordis_define、cordis_run、cordis_stop 和 cordis_undefine 暴露为普通工具。调用方先确认缺口，登记并挂载一个 Python 插件，从 Context 取出新工具验证结果，再停止或删除定义。动态代码通过内置 compile() 和 exec() 加载，仅用于可信教学样本。",
        "harnessRole": "能力演化层：在任务中补齐并释放当前运行时能力",
        "connection": "这条路径使用前几章的基础：Runtime Tools 提供能力变更接口，Context 管理挂载和卸载。调用方需要把 context.tools 回填进 Agent，后续请求才会看到新工具；Python 最小实现也不会自动将定义和挂载写入 SessionLog。",
        "outcomes": [
          "能按检查、定义、挂载、验证、释放说明一次能力变更",
          "能说明代码已定义、插件已运行和新工具已验证之间的差别",
          "能指出动态代码执行需要可信来源与明确安全边界"
        ]
      },
      "lesson": "# Agent 自己装一个工具\n\n第四章的日志能够说明当前发生了什么，却不会自动增加能力。假设任务需要统计一段文本中的单词数量，当前 Context 中没有 `word_count` 工具。继续猜测工具名或反复调用无关工具，只会消耗 Step，也不会缩小能力缺口。\n\nDSH 将这类需要处理成受控的运行时变更：**先查看当前能力和接口，再挂载临时插件，调用新增工具验证结果，任务结束后释放插件。**Python 教学版保留同一条变更路径，动态代码以 Python 的 `compile()` 和 `exec()` 加载；它只适合可信教学样本，代码本身不提供安全沙箱。\n\n`runtime_tools.py` 先定义统一的工具包装和 `runtime_tools_plugin()` 的入口。下面的实现围绕一张定义表展开，因此读者可以分清插件代码已被登记、插件正在运行、插件已经释放这三种状态。\n\n<!-- fill {\"index\":0} -->\n\n## 5.1 先确认缺少什么，再决定补什么\n\n`cordis_inspect` 返回当前 `Context` 中已挂载插件、工具和提示词的快照。**先确认缺少什么，再决定补什么。**调用方根据这份结果确认 `word_count` 缺失，再决定新插件需要提供怎样的工具。能力图中的基线也很重要：它提供变更前的可比较状态，避免把已有能力误判为缺口。\n\nRuntime Tools 自己也要维护生命周期。它用定义表保存动态插件，并在退出时逆序停止仍在运行的插件、清空定义。与此同时，它注册检查接口和一段提示词，说明何时应先检查、再定义和运行插件。这些准备让一次临时扩展拥有明确入口与收尾。\n\n<!-- fill {\"index\":1} -->\n\n<!-- evidence {\"id\":\"m05-before\",\"tab\":\"graph\",\"requestStep\":1,\"label\":\"变更前先确认能力缺口\",\"description\":\"查看当前 Context：其中没有 word-count 插件，也没有 word_count 工具。\"} -->\n\n## 5.2 定义代码和挂载能力是两个时刻\n\n检查确认缺口后，调用方通过 `cordis_define` 提交插件名称、用途和 Python 代码。代码需要定义 `plugin_factory()`，并返回带 `name` 和 `setup(context)` 的插件对象。**定义代码和挂载能力是两个时刻。**函数先用 `compile()` 检查代码，再将定义放进表中；此时工具列表没有变化。`cordis_run` 取回定义，执行代码得到 Plugin，并调用第三章的 `context.mount()`，挂载成功后，新工具才进入当前 Context。\n\n区分这两个时刻有实际用途。定义阶段可以报告代码是否可加载，挂载阶段可以报告插件是否真的改变了运行时；失败时也能准确指出发生在哪一步。`instantiate_plugin()` 使用新的命名空间执行代码，再检查工厂函数和返回对象的必要形状。这里没有权限隔离，因此部署时需要用受限执行环境替代这段教学加载方式。\n\n<!-- fill {\"index\":2} -->\n\n## 5.3 结果验证和释放让变更形成闭环\n\n插件挂载成功后，`word_count` 出现在工具表中。**它已经可供外层重新组装后续 Agent 请求。**当前 Python `Agent` 在构造器中保存自己的工具表，并不会自动订阅 `Context`；调用方需要从 `context.tools` 读取新工具，再建立或更新 Agent 的工具定义。已经发出的请求保持原样。\n\n<!-- evidence {\"id\":\"m05-installed\",\"tab\":\"graph\",\"requestStep\":2,\"label\":\"新工具已经进入当前 Context\",\"description\":\"与基线对照：word-count 注册了 word_count。外层更新 Agent 工具表后，后续请求才会携带它的参数规则。\"} -->\n\n外层从 `Context` 取出 `word_count` 并得到 `words: 3`，这才证明新能力已经被挂载并完成一次调用。\n\n<!-- evidence {\"id\":\"m05-verified\",\"tab\":\"events\",\"eventType\":\"tool/result\",\"eventOccurrence\":4,\"label\":\"调用结果验证新增能力\",\"description\":\"查看教学调用序列中的 word_count 结果，确认新增工具已经完成一次真实调用。\"} -->\n\n验证完成后，`cordis_stop` 执行挂载时取得的卸载函数并保留定义，方便稍后再次运行；`cordis_undefine` 则先停止仍在运行的插件，再删除定义。五个 `cordis_*` 工具共享同一个工具包装，辅助函数负责读取参数和实例化插件，让主体集中表达状态转换。Python 最小版没有自动把定义、挂载和释放事件写入 SessionLog；完整 DSH 应当把这些变化补入共同记录。\n\n<!-- evidence {\"id\":\"m05-removed\",\"tab\":\"graph\",\"requestStep\":3,\"label\":\"释放后回到基线\",\"description\":\"查看最终能力图，确认 word-count 与 word_count 都已经移除。\"} -->\n\n<!-- fill {\"index\":3} -->\n",
      "source": {
        "path": "python_harness/runtime_tools.py",
        "content": "\"\"\"Chapter 5: define, run, stop, and undefine dynamic plugins.\"\"\"\n\nfrom __future__ import annotations\n\nfrom types import SimpleNamespace\nfrom typing import Any, Awaitable, Callable\n\nfrom .runtime import Context, Plugin\n\nTool = SimpleNamespace\n\n\ndef make_tool(\n    parameters: dict[str, Any],\n    execute: Callable[[dict[str, Any]], Awaitable[Any]],\n) -> Tool:\n    \"\"\"Bundle a JSON-Schema parameters object with an async execute function.\n\n    The agent loop validates arguments against parameters, then awaits\n    execute(arguments), so every registered tool needs this shape.\n    \"\"\"\n    return Tool(parameters=parameters, execute=execute)\n\n\ndef runtime_tools_plugin() -> Plugin:\n    \"\"\"A plugin exposing cordis_* tools for dynamic plugin experiments.\"\"\"\n\n    def setup(context: Context) -> None:\n        definitions: dict[str, dict[str, Any]] = {}\n        next_plugin_id = 1\n\n        def cleanup() -> None:\n            for definition in reversed(list(definitions.values())):\n                dispose = definition.get(\"dispose\")\n                if dispose is not None:\n                    dispose()\n            definitions.clear()\n\n        async def inspect_runtime(arguments: dict[str, Any]) -> dict[str, Any]:\n            return context.inspect()\n\n        async def define(arguments: dict[str, Any]) -> dict[str, Any]:\n            name = arguments[\"name\"]\n            code = arguments[\"code\"]\n            compile(code, f\"<dynamic:{name}>\", \"exec\")\n            nonlocal next_plugin_id\n            plugin_id = f\"dyn-{next_plugin_id}\"\n            next_plugin_id += 1\n            definitions[plugin_id] = {\n                \"plugin_id\": plugin_id,\n                \"name\": name,\n                \"purpose\": arguments[\"purpose\"],\n                \"code\": code,\n            }\n            return {\"ok\": True, \"pluginId\": plugin_id, \"name\": name, \"status\": \"defined\"}\n\n        async def run(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            if definition.get(\"dispose\") is not None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is already running\"}\n            plugin = instantiate_plugin(definition)\n            definition[\"dispose\"] = context.mount(plugin)\n            return {\n                \"ok\": True,\n                \"pluginId\": plugin_id,\n                \"status\": \"running\",\n                \"tools\": list(context.tools),\n            }\n\n        async def stop(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is not running\"}\n            dispose()\n            definition[\"dispose\"] = None\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"stopped\"}\n\n        async def undefine(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is not None:\n                dispose()\n            definitions.pop(plugin_id, None)\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"undefined\"}\n\n        context.effect(cleanup)\n        context.contribute_prompt(\n            \"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\n        )\n        context.register_tool(\"cordis_inspect\", make_tool({\"type\": \"object\", \"properties\": {}, \"additionalProperties\": False}, inspect_runtime))\n        context.register_tool(\"cordis_define\", make_tool(\n            {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"name\": {\"type\": \"string\", \"minLength\": 1},\n                    \"purpose\": {\"type\": \"string\", \"minLength\": 1},\n                    \"code\": {\"type\": \"string\", \"minLength\": 1},\n                },\n                \"required\": [\"name\", \"purpose\", \"code\"],\n                \"additionalProperties\": False,\n            },\n            define,\n        ))\n        context.register_tool(\"cordis_run\", make_tool(plugin_id_schema(), run))\n        context.register_tool(\"cordis_stop\", make_tool(plugin_id_schema(), stop))\n        context.register_tool(\"cordis_undefine\", make_tool(plugin_id_schema(), undefine))\n\n    return SimpleNamespace(name=\"runtime-tools\", setup=setup)\n\n\ndef instantiate_plugin(definition: dict[str, Any]) -> Plugin:\n    \"\"\"Execute the stored code and require a plugin_factory() returning a Plugin.\"\"\"\n    namespace: dict[str, Any] = {}\n    exec(compile(definition[\"code\"], f\"<dynamic:{definition['name']}>\", \"exec\"), namespace)\n    factory = namespace.get(\"plugin_factory\")\n    if not callable(factory):\n        raise RuntimeError(\"dynamic code must define plugin_factory() returning a Plugin\")\n    plugin = factory()\n    if not hasattr(plugin, \"name\") or not callable(getattr(plugin, \"setup\", None)):\n        raise RuntimeError(\"plugin_factory() must return a Plugin with name and setup(context)\")\n    return plugin\n\n\ndef plugin_id_schema() -> dict[str, Any]:\n    return {\n        \"type\": \"object\",\n        \"properties\": {\"pluginId\": {\"type\": \"string\", \"pattern\": r\"^dyn-[1-9][0-9]*$\"}},\n        \"required\": [\"pluginId\"],\n        \"additionalProperties\": False,\n    }\n",
        "excerpt": "def runtime_tools_plugin() -> Plugin:\n    \"\"\"A plugin exposing cordis_* tools for dynamic plugin experiments.\"\"\"\n\n    def setup(context: Context) -> None:\n        definitions: dict[str, dict[str, Any]] = {}\n        next_plugin_id = 1\n\n        def cleanup() -> None:\n            for definition in reversed(list(definitions.values())):\n                dispose = definition.get(\"dispose\")\n                if dispose is not None:\n                    dispose()\n            definitions.clear()\n\n        async def inspect_runtime(arguments: dict[str, Any]) -> dict[str, Any]:\n            return context.inspect()\n\n        async def define(arguments: dict[str, Any]) -> dict[str, Any]:\n            name = arguments[\"name\"]\n            code = arguments[\"code\"]\n            compile(code, f\"<dynamic:{name}>\", \"exec\")\n            nonlocal next_plugin_id\n            plugin_id = f\"dyn-{next_plugin_id}\"\n            next_plugin_id += 1\n            definitions[plugin_id] = {\n                \"plugin_id\": plugin_id,\n                \"name\": name,\n                \"purpose\": arguments[\"purpose\"],\n                \"code\": code,\n            }\n            return {\"ok\": True, \"pluginId\": plugin_id, \"name\": name, \"status\": \"defined\"}\n\n        async def run(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            if definition.get(\"dispose\") is not None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is already running\"}\n            plugin = instantiate_plugin(definition)\n            definition[\"dispose\"] = context.mount(plugin)\n            return {\n                \"ok\": True,\n                \"pluginId\": plugin_id,\n                \"status\": \"running\",\n                \"tools\": list(context.tools),\n            }\n\n        async def stop(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is not running\"}\n            dispose()\n            definition[\"dispose\"] = None\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"stopped\"}\n\n        async def undefine(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is not None:\n                dispose()\n            definitions.pop(plugin_id, None)\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"undefined\"}\n\n        context.effect(cleanup)\n        context.contribute_prompt(\n            \"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\n        )\n        context.register_tool(\"cordis_inspect\", make_tool({\"type\": \"object\", \"properties\": {}, \"additionalProperties\": False}, inspect_runtime))\n        context.register_tool(\"cordis_define\", make_tool(\n            {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"name\": {\"type\": \"string\", \"minLength\": 1},\n                    \"purpose\": {\"type\": \"string\", \"minLength\": 1},\n                    \"code\": {\"type\": \"string\", \"minLength\": 1},\n                },\n                \"required\": [\"name\", \"purpose\", \"code\"],\n                \"additionalProperties\": False,\n            },\n            define,\n        ))\n        context.register_tool(\"cordis_run\", make_tool(plugin_id_schema(), run))\n        context.register_tool(\"cordis_stop\", make_tool(plugin_id_schema(), stop))\n        context.register_tool(\"cordis_undefine\", make_tool(plugin_id_schema(), undefine))\n\n    return SimpleNamespace(name=\"runtime-tools\", setup=setup)\n\n\ndef instantiate_plugin(definition: dict[str, Any]) -> Plugin:\n    \"\"\"Execute the stored code and require a plugin_factory() returning a Plugin.\"\"\"\n    namespace: dict[str, Any] = {}\n    exec(compile(definition[\"code\"], f\"<dynamic:{definition['name']}>\", \"exec\"), namespace)\n    factory = namespace.get(\"plugin_factory\")\n    if not callable(factory):\n        raise RuntimeError(\"dynamic code must define plugin_factory() returning a Plugin\")\n    plugin = factory()\n    if not hasattr(plugin, \"name\") or not callable(getattr(plugin, \"setup\", None)):\n        raise RuntimeError(\"plugin_factory() must return a Plugin with name and setup(context)\")\n    return plugin\n\n\ndef plugin_id_schema() -> dict[str, Any]:\n    return {\n        \"type\": \"object\",\n        \"properties\": {\"pluginId\": {\"type\": \"string\", \"pattern\": r\"^dyn-[1-9][0-9]*$\"}},\n        \"required\": [\"pluginId\"],\n        \"additionalProperties\": False,\n    }",
        "startLine": 25,
        "endLine": 140
      },
      "extraFiles": [
        {
          "path": "python_harness/agent.py",
          "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n"
        },
        {
          "path": "python_harness/context.py",
          "content": "\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import Any\n\n\ndef project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n"
        },
        {
          "path": "python_harness/runtime.py",
          "content": "\"\"\"Chapter 3: a tiny plugin runtime with ownership and rollback.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\nfrom dataclasses import dataclass\nfrom typing import Any, Protocol\n\n\nCleanup = Callable[[], None]\n\n\nclass Plugin(Protocol):\n    name: str\n\n    def setup(self, context: \"Context\") -> None: ...\n\n\n@dataclass\nclass Contribution:\n    value: Any\n    owner: str\n\n\nclass Context:\n    def __init__(self) -> None:\n        self.services: dict[str, Contribution] = {}\n        self.tools: dict[str, Contribution] = {}\n        self.prompts: list[Contribution] = []\n        self.listeners: dict[str, list[Contribution]] = {}\n        self._installing: str | None = None\n        self._effects: dict[str, list[Cleanup]] = {}\n\n    def effect(self, cleanup: Cleanup) -> None:\n        if self._installing is None:\n            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n        self._effects[self._installing].append(cleanup)\n\n    def mount(self, plugin: Plugin) -> Cleanup:\n        if plugin.name in self._effects:\n            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n        self._effects[plugin.name] = []\n        self._installing = plugin.name\n        try:\n            plugin.setup(self)\n        except Exception:\n            self._rollback(plugin.name)\n            raise\n        finally:\n            self._installing = None\n\n        disposed = False\n\n        def unmount() -> None:\n            nonlocal disposed\n            if disposed:\n                return\n            disposed = True\n            self._rollback(plugin.name)\n\n        return unmount\n\n    def _rollback(self, owner: str) -> None:\n        for cleanup in reversed(self._effects.pop(owner, [])):\n            cleanup()\n\n    def register_tool(self, name: str, tool: Any) -> None:\n        owner = self._require_owner()\n        if name in self.tools:\n            raise ValueError(f\"tool already registered: {name}\")\n        self.tools[name] = Contribution(tool, owner)\n        self.effect(lambda: self.tools.pop(name, None))\n\n    def contribute_prompt(self, text: str) -> None:\n        contribution = Contribution(text, self._require_owner())\n        self.prompts.append(contribution)\n        self.effect(lambda: self.prompts.remove(contribution))\n\n    def inspect(self) -> dict[str, Any]:\n        return {\n            \"plugins\": list(self._effects),\n            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n        }\n\n    def _require_owner(self) -> str:\n        if self._installing is None:\n            raise RuntimeError(\"no plugin is being installed\")\n        return self._installing\n"
        },
        {
          "path": "python_harness/session.py",
          "content": "\"\"\"Chapter 4: one append-only history for requests and traces.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any\n\n\n@dataclass(frozen=True)\nclass Event:\n    id: int\n    type: str\n    data: dict[str, Any]\n\n\nclass SessionLog:\n    def __init__(self) -> None:\n        self.events: list[Event] = []\n\n    def append(self, event_type: str, **data: Any) -> Event:\n        event = Event(len(self.events) + 1, event_type, data)\n        self.events.append(event)\n        return event\n\n    def request_step_ids(self) -> list[str]:\n        return [event.data[\"step_id\"] for event in self.events if event.type == \"request/header\"]\n\n    def build_request(self, step_id: str) -> dict[str, Any]:\n        header_index = next(\n            index\n            for index, event in enumerate(self.events)\n            if event.type == \"request/header\" and event.data[\"step_id\"] == step_id\n        )\n        header = self.events[header_index].data\n        history = self.events[:header_index]\n        checkpoint_index = max(\n            (index for index, event in enumerate(history) if event.type == \"context/checkpoint\"),\n            default=-1,\n        )\n        messages: list[dict[str, Any]] = []\n        if checkpoint_index >= 0:\n            checkpoint = history[checkpoint_index]\n            messages.append({\"role\": \"system\", \"content\": checkpoint.data[\"summary\"]})\n            history = history[checkpoint_index + 1 :]\n        for event in history:\n            message = self._message_from(event)\n            if message is not None:\n                messages.append(message)\n        return {\n            \"system\": header[\"system\"],\n            \"tools\": header[\"tools\"],\n            \"messages\": messages,\n            \"dynamicContext\": header[\"dynamic_context\"],\n        }\n\n    @staticmethod\n    def _message_from(event: Event) -> dict[str, Any] | None:\n        roles = {\"user/message\": \"user\", \"assistant/message\": \"assistant\", \"tool/result\": \"tool\"}\n        role = roles.get(event.type)\n        if role is None:\n            return None\n        message = {\"role\": role, \"content\": event.data.get(\"content\", \"\")}\n        if role == \"tool\":\n            message[\"name\"] = event.data[\"name\"]\n        return message\n\n    def trace(self) -> list[dict[str, Any]]:\n        return [{\"event_id\": event.id, \"type\": event.type, **event.data} for event in self.events]\n"
        }
      ],
      "diff": "diff --git a/python_harness/runtime_tools.py b/python_harness/runtime_tools.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/runtime_tools.py\n@@ -0,0 +1,140 @@\n+\"\"\"Chapter 5: define, run, stop, and undefine dynamic plugins.\"\"\"\n+\n+from __future__ import annotations\n+\n+from types import SimpleNamespace\n+from typing import Any, Awaitable, Callable\n+\n+from .runtime import Context, Plugin\n+\n+Tool = SimpleNamespace\n+\n+\n+def make_tool(\n+    parameters: dict[str, Any],\n+    execute: Callable[[dict[str, Any]], Awaitable[Any]],\n+) -> Tool:\n+    \"\"\"Bundle a JSON-Schema parameters object with an async execute function.\n+\n+    The agent loop validates arguments against parameters, then awaits\n+    execute(arguments), so every registered tool needs this shape.\n+    \"\"\"\n+    return Tool(parameters=parameters, execute=execute)\n+\n+\n+def runtime_tools_plugin() -> Plugin:\n+    \"\"\"A plugin exposing cordis_* tools for dynamic plugin experiments.\"\"\"\n+\n+    def setup(context: Context) -> None:\n+        definitions: dict[str, dict[str, Any]] = {}\n+        next_plugin_id = 1\n+\n+        def cleanup() -> None:\n+            for definition in reversed(list(definitions.values())):\n+                dispose = definition.get(\"dispose\")\n+                if dispose is not None:\n+                    dispose()\n+            definitions.clear()\n+\n+        async def inspect_runtime(arguments: dict[str, Any]) -> dict[str, Any]:\n+            return context.inspect()\n+\n+        async def define(arguments: dict[str, Any]) -> dict[str, Any]:\n+            name = arguments[\"name\"]\n+            code = arguments[\"code\"]\n+            compile(code, f\"<dynamic:{name}>\", \"exec\")\n+            nonlocal next_plugin_id\n+            plugin_id = f\"dyn-{next_plugin_id}\"\n+            next_plugin_id += 1\n+            definitions[plugin_id] = {\n+                \"plugin_id\": plugin_id,\n+                \"name\": name,\n+                \"purpose\": arguments[\"purpose\"],\n+                \"code\": code,\n+            }\n+            return {\"ok\": True, \"pluginId\": plugin_id, \"name\": name, \"status\": \"defined\"}\n+\n+        async def run(arguments: dict[str, Any]) -> dict[str, Any]:\n+            plugin_id = arguments[\"pluginId\"]\n+            definition = definitions.get(plugin_id)\n+            if definition is None:\n+                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n+            if definition.get(\"dispose\") is not None:\n+                return {\"ok\": False, \"error\": f\"{plugin_id} is already running\"}\n+            plugin = instantiate_plugin(definition)\n+            definition[\"dispose\"] = context.mount(plugin)\n+            return {\n+                \"ok\": True,\n+                \"pluginId\": plugin_id,\n+                \"status\": \"running\",\n+                \"tools\": list(context.tools),\n+            }\n+\n+        async def stop(arguments: dict[str, Any]) -> dict[str, Any]:\n+            plugin_id = arguments[\"pluginId\"]\n+            definition = definitions.get(plugin_id)\n+            if definition is None:\n+                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n+            dispose = definition.get(\"dispose\")\n+            if dispose is None:\n+                return {\"ok\": False, \"error\": f\"{plugin_id} is not running\"}\n+            dispose()\n+            definition[\"dispose\"] = None\n+            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"stopped\"}\n+\n+        async def undefine(arguments: dict[str, Any]) -> dict[str, Any]:\n+            plugin_id = arguments[\"pluginId\"]\n+            definition = definitions.get(plugin_id)\n+            if definition is None:\n+                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n+            dispose = definition.get(\"dispose\")\n+            if dispose is not None:\n+                dispose()\n+            definitions.pop(plugin_id, None)\n+            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"undefined\"}\n+\n+        context.effect(cleanup)\n+        context.contribute_prompt(\n+            \"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\n+        )\n+        context.register_tool(\"cordis_inspect\", make_tool({\"type\": \"object\", \"properties\": {}, \"additionalProperties\": False}, inspect_runtime))\n+        context.register_tool(\"cordis_define\", make_tool(\n+            {\n+                \"type\": \"object\",\n+                \"properties\": {\n+                    \"name\": {\"type\": \"string\", \"minLength\": 1},\n+                    \"purpose\": {\"type\": \"string\", \"minLength\": 1},\n+                    \"code\": {\"type\": \"string\", \"minLength\": 1},\n+                },\n+                \"required\": [\"name\", \"purpose\", \"code\"],\n+                \"additionalProperties\": False,\n+            },\n+            define,\n+        ))\n+        context.register_tool(\"cordis_run\", make_tool(plugin_id_schema(), run))\n+        context.register_tool(\"cordis_stop\", make_tool(plugin_id_schema(), stop))\n+        context.register_tool(\"cordis_undefine\", make_tool(plugin_id_schema(), undefine))\n+\n+    return SimpleNamespace(name=\"runtime-tools\", setup=setup)\n+\n+\n+def instantiate_plugin(definition: dict[str, Any]) -> Plugin:\n+    \"\"\"Execute the stored code and require a plugin_factory() returning a Plugin.\"\"\"\n+    namespace: dict[str, Any] = {}\n+    exec(compile(definition[\"code\"], f\"<dynamic:{definition['name']}>\", \"exec\"), namespace)\n+    factory = namespace.get(\"plugin_factory\")\n+    if not callable(factory):\n+        raise RuntimeError(\"dynamic code must define plugin_factory() returning a Plugin\")\n+    plugin = factory()\n+    if not hasattr(plugin, \"name\") or not callable(getattr(plugin, \"setup\", None)):\n+        raise RuntimeError(\"plugin_factory() must return a Plugin with name and setup(context)\")\n+    return plugin\n+\n+\n+def plugin_id_schema() -> dict[str, Any]:\n+    return {\n+        \"type\": \"object\",\n+        \"properties\": {\"pluginId\": {\"type\": \"string\", \"pattern\": r\"^dyn-[1-9][0-9]*$\"}},\n+        \"required\": [\"pluginId\"],\n+        \"additionalProperties\": False,\n+    }\n",
      "diffStats": {
        "filesChanged": 1,
        "additions": 140,
        "deletions": 0,
        "files": [
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            "path": "python_harness/runtime_tools.py",
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      "events": [
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          "data": {
            "name": "cordis_inspect",
            "content": "{\"plugins\":[\"runtime-tools\"],\"tools\":[{\"name\":\"cordis_inspect\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_define\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_run\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_stop\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_undefine\",\"plugin\":\"runtime-tools\"}],\"prompts\":[{\"text\":\"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\"plugin\":\"runtime-tools\"}]}"
          }
        },
        {
          "id": 3,
          "type": "tool/call",
          "data": {
            "name": "cordis_define",
            "arguments": {
              "name": "word-count",
              "purpose": "统计单词",
              "code": "def plugin_factory():\n    from types import SimpleNamespace\n    async def execute(arguments):\n        return {'words': len(arguments['text'].split())}\n    def setup(ctx):\n        ctx.register_tool('word_count', SimpleNamespace(\n            parameters={'type': 'object', 'properties': {'text': {'type': 'string'}}, 'required': ['text']},\n            execute=execute,\n        ))\n    return SimpleNamespace(name='word-count', setup=setup)"
            }
          }
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          }
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        {
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          "type": "tool/call",
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            "name": "word_count",
            "arguments": {
              "text": "one two three"
            }
          }
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          "type": "tool/result",
          "data": {
            "name": "word_count",
            "content": "{\"words\":3}"
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            "name": "cordis_stop",
            "arguments": {
              "pluginId": "dyn-1"
            }
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          "data": {
            "name": "cordis_stop",
            "content": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"status\":\"stopped\"}"
          }
        },
        {
          "id": 11,
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          "detail": "{\"plugins\":[\"runtime-tools\"],\"tools\":[{\"name\":\"cordis_inspect\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_define\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_run\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_stop\",\"plugin\":\"runtime-tools\"},{\"name\":\"cordis_undefine\",\"plugin\":\"runtime-tools\"}],\"prompts\":[{\"text\":\"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\"plugin\":\"runtime-tools\"}]}"
        },
        {
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          "detail": "{\"code\":\"def plugin_factory():\\n    from types import SimpleNamespace\\n    async def execute(arguments):\\n        return {'words': len(arguments['text'].split())}\\n    def setup(ctx):\\n        ctx.register_tool('word_count', SimpleNamespace(\\n            parameters={'type': 'object', 'properties': {'text': {'type': 'string'}}, 'required': ['text']},\\n            execute=execute,\\n        ))\\n    return SimpleNamespace(name='word-count', setup=setup)\",\"name\":\"word-count\",\"purpose\":\"统计单词\"}"
        },
        {
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          "type": "tool/result",
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          "detail": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"name\":\"word-count\",\"status\":\"defined\"}"
        },
        {
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          "type": "tool/call",
          "title": "call cordis_run",
          "detail": "{\"pluginId\":\"dyn-1\"}"
        },
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          "type": "tool/result",
          "title": "result cordis_run",
          "detail": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"status\":\"running\",\"tools\":[\"cordis_inspect\",\"cordis_define\",\"cordis_run\",\"cordis_stop\",\"cordis_undefine\",\"word_count\"]}"
        },
        {
          "eventId": 7,
          "type": "tool/call",
          "title": "call word_count",
          "detail": "{\"text\":\"one two three\"}"
        },
        {
          "eventId": 8,
          "type": "tool/result",
          "title": "result word_count",
          "detail": "{\"words\":3}"
        },
        {
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          "type": "tool/call",
          "title": "call cordis_stop",
          "detail": "{\"pluginId\":\"dyn-1\"}"
        },
        {
          "eventId": 10,
          "type": "tool/result",
          "title": "result cordis_stop",
          "detail": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"status\":\"stopped\"}"
        },
        {
          "eventId": 11,
          "type": "tool/call",
          "title": "call cordis_undefine",
          "detail": "{\"pluginId\":\"dyn-1\"}"
        },
        {
          "eventId": 12,
          "type": "tool/result",
          "title": "result cordis_undefine",
          "detail": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"status\":\"undefined\"}"
        }
      ],
      "graphs": [
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            },
            {
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              "owner": "runtime-tools"
            }
          ],
          "prompts": [
            {
              "text": "Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.",
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            }
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            }
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              "text": "Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.",
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            }
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              "text": "Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.",
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            }
          ],
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          "nodes": [
            {
              "id": "runtime-tools",
              "label": "runtime-tools",
              "kind": "plugin"
            },
            {
              "id": "tool:cordis_inspect",
              "label": "cordis_inspect",
              "kind": "tool"
            },
            {
              "id": "tool:cordis_define",
              "label": "cordis_define",
              "kind": "tool"
            },
            {
              "id": "tool:cordis_run",
              "label": "cordis_run",
              "kind": "tool"
            },
            {
              "id": "tool:cordis_stop",
              "label": "cordis_stop",
              "kind": "tool"
            },
            {
              "id": "tool:cordis_undefine",
              "label": "cordis_undefine",
              "kind": "tool"
            },
            {
              "id": "prompt:0",
              "label": "Prompt",
              "kind": "prompt"
            }
          ],
          "edges": [
            {
              "from": "runtime-tools",
              "to": "tool:cordis_inspect",
              "label": "contributes"
            },
            {
              "from": "runtime-tools",
              "to": "tool:cordis_define",
              "label": "contributes"
            },
            {
              "from": "runtime-tools",
              "to": "tool:cordis_run",
              "label": "contributes"
            },
            {
              "from": "runtime-tools",
              "to": "tool:cordis_stop",
              "label": "contributes"
            },
            {
              "from": "runtime-tools",
              "to": "tool:cordis_undefine",
              "label": "contributes"
            },
            {
              "from": "runtime-tools",
              "to": "prompt:0",
              "label": "contributes"
            }
          ]
        }
      ]
    },
    {
      "id": "chapter-6",
      "number": "06",
      "shortTitle": "长程任务续行",
      "title": "用 Goal 与 Round 推进长程任务",
      "question": "DSH 是如何持续完成长程任务的？",
      "codeGuide": {
        "title": "Goal 怎样在 Turn 之外决定是否续行",
        "description": "LongTaskRunner 保存目标并依次启动 Round。每个 Round 复用普通 Agent Loop，外层根据结构化结果决定继续、完成、受阻或到达上限。",
        "observations": [
          {
            "title": "Goal 集中保存跨轮状态",
            "text": "目标、状态、已启动轮数和轮数上限保存在同一对象中；Round 的阶段说明由外部预先定义。",
            "lines": [
              13,
              26
            ]
          },
          {
            "title": "每轮都重申目标与当前阶段",
            "text": "run_long_task 只在 Goal 为 active 且未达上限时继续；每次调用 run_round 都携带同一个 objective 与当前阶段说明。",
            "lines": [
              29,
              33
            ]
          },
          {
            "title": "状态决定是否续行",
            "text": "accepted 和 blocked 会结束 Goal；状态仍为 active 的 Goal 在轮数边界标记为 limit_reached。",
            "lines": [
              34,
              41
            ]
          }
        ],
        "fills": [
          {
            "label": "Goal 数据、回调类型与运行器轮廓",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                21
              ],
              [
                29,
                29
              ]
            ]
          },
          {
            "label": "预先定义每轮任务说明",
            "kind": "body",
            "ranges": [
              [
                22,
                28
              ]
            ]
          },
          {
            "label": "推进轮次并调用 run_round",
            "kind": "body",
            "ranges": [
              [
                30,
                33
              ]
            ]
          },
          {
            "label": "根据结果文本结束或继续 Goal",
            "kind": "body",
            "ranges": [
              [
                34,
                41
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Python 版用 Goal 跨过普通 Turn",
        "summary": "Python Nano DSH 的 Goal 保存目标、状态和轮数。run_long_task() 每轮把目标和阶段说明交给外部 run_round，并根据返回文本中的 accepted 或 blocked 更新状态；仍为 active 的 Goal 在上限处成为 limit_reached。",
        "harnessRole": "长程协调层：让有限轮次共同推进一个目标",
        "connection": "每个 run_round 可以继续调用第一章的 Agent，并复用外层保留的状态。与 TypeScript 版相比，这个教学版本没有结构化 RoundResult、进展检查或自动 SessionLog 事件。",
        "outcomes": [
          "能区分 Goal、Round、Turn 和 Step 四个控制层",
          "能说明 run_long_task() 如何按照状态和轮数启动下一轮",
          "能指出 accepted、blocked 和 limit_reached 三种退出路径，以及文本匹配的教学局限"
        ]
      },
      "lesson": "# 让任务跨过 Turn 继续\n\n第五章可以在任务中补齐工具，当前模型停止调用工具时，一个 Turn 仍会结束。任务是否完成需要另一层判断。整理三条发布说明时，第一轮可能只收集到两条；模型暂时给出回复，目标依然处于进行中。\n\nDSH 将长期目标放在 Agent Loop 外管理。**Goal 决定跨轮任务是否继续，Turn 和 Step 继续承担模型请求与工具执行。**Goal 保存跨轮状态，Round 代表一次新的推进尝试。Python 教学版用较小的 `Goal` 数据类和 `run_long_task()` 展示这条边界；它根据外部回调返回的文本更新状态。\n\n`scenario.py` 先定义 Goal 状态和 `run_round` 回调类型，再给出 `Goal` 与每轮任务说明。它不持有具体 Agent、Context 或 Session Log，这些对象由调用方在每轮中决定是否复用。这个边界让长程协调只做它需要做的事：决定是否再启动一次普通 Turn。\n\n<!-- fill {\"index\":0} -->\n\n## 6.1 Goal 需要保存能跨轮检查的状态\n\n`Goal` 把目标文本、最大轮数、已经开始的轮数、状态和原因放在一个对象中。`ROUND_INSTRUCTIONS` 则预先定义收集、实施和验证三个阶段说明。**Goal 保存跨轮状态，Round 只描述当前推进的阶段。**外层可以在每轮后读取统一状态，调用方也可以据此决定如何记录或展示任务。\n\n与 TypeScript 版不同，Python 最小实现没有在创建 Goal 时自动写入 `goal/created` 事件，也没有持久化恢复流程。这个省略让代码聚焦在续行判断，同时也指出完整 DSH 需要补上的记录边界。\n\n<!-- fill {\"index\":1} -->\n\n## 6.2 下一轮仍然调用普通 Agent Loop\n\nGoal 处于 `active` 且未达到轮数上限时，`run_long_task()` 读取当前阶段说明，递增轮数，并调用外部 `run_round`。回调收到同一个长期目标和本轮说明，可以将这一轮交给与前一轮相同的 Agent、Context、工作区和 Session Log；前几章保存的消息、工具结果和插件变化因而仍然可用。\n\n这一层没有复制第一章的工具循环。**Goal 可以启动新的 Turn，却不重写 Agent Loop。**它只负责为下一次 Turn 提供阶段边界，普通 Agent Loop 继续处理模型请求、工具调用和反馈。两层循环分开后，模型停止工具调用与 Goal 是否继续推进可以独立判断。`scenario.py` 本身不构造模型请求，因此真正的消息历史仍属于 `run_round` 的调用方。\n\n<!-- fill {\"index\":2} -->\n\n## 6.3 续行要依据可观察的结果\n\nPython 版 `run_round` 返回一段结果文本。文本包含 `accepted` 时，Goal 变为 `completed`；包含 `blocked` 时，Goal 变为 `blocked`；其余情况下，循环继续到下一轮或到达上限后变为 `limit_reached`。这些出口都写在一个循环中，状态和原因一起更新。\n\n**这个版本能够表达完成、受阻和上限，却无法区分没有进展与仍需继续。**函数返回的 `Goal` 是这里可直接检查的结果；它不会自行创建 Session Event。TypeScript 版使用结构化 `RoundResult` 的 `progressed`、`completed` 与 `blockedReason`，并把状态变化写入 Session Log。生产实现应采用这类可观察的结果，而不是依赖文本包含某个词。\n\n<!-- fill {\"index\":3} -->\n\n## 6.4 六章放回同一条 DSH 运行时主线\n\n一次 Step 先在 Agent Loop 中完成模型与工具的往返；上下文层决定该 Step 能看到哪些信息；插件运行时决定当前有哪些能力；Session Log 将输入、动作和能力变化保存为共同事实；Runtime Tools 在任务中补齐能力；Goal 和 Round 则在一个 Turn 结束后判断任务是否需要继续。\n\nPython 教学版保留了这条主线的最小接口，并明确留下了 Service 依赖、自动会话记录、结构化轮次结果和持久化等扩展点。完整 DSH 在这些接口之上增加配置层、权限、沙箱和更多运行时提供方。\n",
      "source": {
        "path": "python_harness/scenario.py",
        "content": "\"\"\"Chapter 6: carry one goal through bounded rounds.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Awaitable, Callable, Literal\n\n\nGoalStatus = Literal[\"active\", \"completed\", \"blocked\", \"limit_reached\"]\nRunRound = Callable[[str], Awaitable[str]]\n\n\n@dataclass\nclass Goal:\n    objective: str\n    max_rounds: int = 3\n    rounds_started: int = 0\n    status: GoalStatus = \"active\"\n    reason: str = \"\"\n\n\nROUND_INSTRUCTIONS = (\n    \"Collect: obtain the facts needed to complete the goal, then end the Round after making measurable progress.\",\n    \"Implement: complete the main action from the available facts, record the result, and end the Round.\",\n    \"Verify: check that the deliverable satisfies the goal and remove temporary capabilities.\",\n)\n\n\nasync def run_long_task(goal: Goal, run_round: RunRound) -> Goal:\n    while goal.status == \"active\" and goal.rounds_started < goal.max_rounds:\n        instruction = ROUND_INSTRUCTIONS[goal.rounds_started]\n        goal.rounds_started += 1\n        result = await run_round(f\"Goal: {goal.objective}\\nThis Round: {instruction}\")\n        if \"accepted\" in result:\n            goal.status, goal.reason = \"completed\", \"The goal passed verification\"\n        elif \"blocked\" in result:\n            goal.status, goal.reason = \"blocked\", result\n\n    if goal.status == \"active\":\n        goal.status, goal.reason = \"limit_reached\", \"The maximum number of Rounds was reached\"\n    return goal\n",
        "excerpt": "@dataclass\nclass Goal:\n    objective: str\n    max_rounds: int = 3\n    rounds_started: int = 0\n    status: GoalStatus = \"active\"\n    reason: str = \"\"\n\n\nROUND_INSTRUCTIONS = (\n    \"Collect: obtain the facts needed to complete the goal, then end the Round after making measurable progress.\",\n    \"Implement: complete the main action from the available facts, record the result, and end the Round.\",\n    \"Verify: check that the deliverable satisfies the goal and remove temporary capabilities.\",\n)\n\n\nasync def run_long_task(goal: Goal, run_round: RunRound) -> Goal:\n    while goal.status == \"active\" and goal.rounds_started < goal.max_rounds:\n        instruction = ROUND_INSTRUCTIONS[goal.rounds_started]\n        goal.rounds_started += 1\n        result = await run_round(f\"Goal: {goal.objective}\\nThis Round: {instruction}\")\n        if \"accepted\" in result:\n            goal.status, goal.reason = \"completed\", \"The goal passed verification\"\n        elif \"blocked\" in result:\n            goal.status, goal.reason = \"blocked\", result\n\n    if goal.status == \"active\":\n        goal.status, goal.reason = \"limit_reached\", \"The maximum number of Rounds was reached\"\n    return goal",
        "startLine": 13,
        "endLine": 41
      },
      "extraFiles": [
        {
          "path": "python_harness/agent.py",
          "content": "\"\"\"Chapter 1: the smallest useful agent loop.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any, Awaitable, Callable, Protocol\n\n\nJson = dict[str, Any] | list[Any] | str | int | float | bool | None\n\n\nclass Llm(Protocol):\n    async def complete(self, request: dict[str, Any]) -> dict[str, Any]: ...\n\n\nToolHandler = Callable[[dict[str, Any]], Awaitable[Json]]\n\n\n@dataclass(frozen=True)\nclass Tool:\n    name: str\n    description: str\n    parameters: dict[str, Any]\n    execute: ToolHandler\n\n\ndef validate_arguments(schema: dict[str, Any], arguments: dict[str, Any]) -> str | None:\n    \"\"\"Validate the small JSON-Schema subset used by this tutorial.\"\"\"\n    required = schema.get(\"required\", [])\n    missing = [name for name in required if name not in arguments]\n    if missing:\n        return f\"missing required fields: {', '.join(missing)}\"\n    properties = schema.get(\"properties\", {})\n    if schema.get(\"additionalProperties\") is False:\n        unknown = [name for name in arguments if name not in properties]\n        if unknown:\n            return f\"unknown fields: {', '.join(unknown)}\"\n    return None\n\n\nclass Agent:\n    def __init__(self, llm: Llm, tools: list[Tool], *, max_steps: int = 8) -> None:\n        self.llm = llm\n        self.tools = {tool.name: tool for tool in tools}\n        self.max_steps = max_steps\n\n    async def run(self, objective: str) -> str:\n        messages: list[dict[str, Any]] = [{\"role\": \"user\", \"content\": objective}]\n        schemas = [\n            {\"name\": tool.name, \"description\": tool.description, \"parameters\": tool.parameters}\n            for tool in self.tools.values()\n        ]\n\n        for step in range(1, self.max_steps + 1):\n            request = {\n                \"system\": \"Use the provided tools to complete the goal. Continue from each tool result and stop calling tools when the work is complete.\",\n                \"tools\": schemas,\n                \"messages\": messages,\n                \"dynamic_context\": f\"step={step}\",\n            }\n            response = await self.llm.complete(request)\n            messages.append({\"role\": \"assistant\", \"content\": response.get(\"content\", \"\")})\n            calls = response.get(\"tool_calls\", [])\n            if not calls:\n                return str(response.get(\"content\", \"\"))\n\n            for call in calls:\n                name, arguments = call[\"name\"], call.get(\"arguments\", {})\n                tool = self.tools.get(name)\n                error = \"unknown tool\" if tool is None else validate_arguments(tool.parameters, arguments)\n                result: Json = {\"error\": error}\n                if tool is not None and error is None:\n                    try:\n                        result = await tool.execute(arguments)\n                    except Exception as exc:  # Tool errors become model-readable results.\n                        result = {\"error\": str(exc)}\n                messages.append({\"role\": \"tool\", \"name\": name, \"content\": result})\n\n        raise RuntimeError(f\"agent exceeded {self.max_steps} steps\")\n"
        },
        {
          "path": "python_harness/context.py",
          "content": "\"\"\"Chapter 2: deterministic context projection and prefix estimates.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nfrom typing import Any\n\n\ndef project_tool_result(value: Any, *, limit: int = 520) -> Any:\n    \"\"\"Keep full values in the session, but send bounded text to the model.\"\"\"\n    if not isinstance(value, str) or len(value) <= limit:\n        return value\n\n    omitted = len(value) - limit\n    marker = f\"\\n… omitted {omitted} characters …\\n\"\n    head_size = limit // 2\n    tail_size = limit - head_size\n    return value[:head_size] + marker + value[-tail_size:]\n\n\ndef build_request(\n    system: str,\n    tools: list[dict[str, Any]],\n    messages: list[dict[str, Any]],\n    dynamic_context: str,\n) -> dict[str, Any]:\n    projected = []\n    for message in messages:\n        copy = dict(message)\n        if copy.get(\"role\") == \"tool\":\n            copy[\"content\"] = project_tool_result(copy.get(\"content\"))\n        projected.append(copy)\n    return {\n        \"system\": system,\n        \"tools\": tools,\n        \"messages\": projected,\n        \"dynamicContext\": dynamic_context,\n    }\n\n\ndef approximate_tokens(value: Any) -> int:\n    text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False)\n    return max(1, (len(text) + 3) // 4)\n\n\ndef shared_prefix(left: dict[str, Any] | None, right: dict[str, Any]) -> dict[str, Any]:\n    if left is None:\n        return {\"shared_parts\": 0, \"first_invalidation\": \"first request\"}\n    ordered = (\"system\", \"tools\", \"messages\", \"dynamicContext\")\n    count = next((index for index, key in enumerate(ordered) if left.get(key) != right.get(key)), len(ordered))\n    return {\"shared_parts\": count, \"first_invalidation\": ordered[count] if count < len(ordered) else None}\n"
        },
        {
          "path": "python_harness/runtime.py",
          "content": "\"\"\"Chapter 3: a tiny plugin runtime with ownership and rollback.\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\nfrom dataclasses import dataclass\nfrom typing import Any, Protocol\n\n\nCleanup = Callable[[], None]\n\n\nclass Plugin(Protocol):\n    name: str\n\n    def setup(self, context: \"Context\") -> None: ...\n\n\n@dataclass\nclass Contribution:\n    value: Any\n    owner: str\n\n\nclass Context:\n    def __init__(self) -> None:\n        self.services: dict[str, Contribution] = {}\n        self.tools: dict[str, Contribution] = {}\n        self.prompts: list[Contribution] = []\n        self.listeners: dict[str, list[Contribution]] = {}\n        self._installing: str | None = None\n        self._effects: dict[str, list[Cleanup]] = {}\n\n    def effect(self, cleanup: Cleanup) -> None:\n        if self._installing is None:\n            raise RuntimeError(\"contributions must be registered while mounting a plugin\")\n        self._effects[self._installing].append(cleanup)\n\n    def mount(self, plugin: Plugin) -> Cleanup:\n        if plugin.name in self._effects:\n            raise ValueError(f\"plugin already mounted: {plugin.name}\")\n        self._effects[plugin.name] = []\n        self._installing = plugin.name\n        try:\n            plugin.setup(self)\n        except Exception:\n            self._rollback(plugin.name)\n            raise\n        finally:\n            self._installing = None\n\n        disposed = False\n\n        def unmount() -> None:\n            nonlocal disposed\n            if disposed:\n                return\n            disposed = True\n            self._rollback(plugin.name)\n\n        return unmount\n\n    def _rollback(self, owner: str) -> None:\n        for cleanup in reversed(self._effects.pop(owner, [])):\n            cleanup()\n\n    def register_tool(self, name: str, tool: Any) -> None:\n        owner = self._require_owner()\n        if name in self.tools:\n            raise ValueError(f\"tool already registered: {name}\")\n        self.tools[name] = Contribution(tool, owner)\n        self.effect(lambda: self.tools.pop(name, None))\n\n    def contribute_prompt(self, text: str) -> None:\n        contribution = Contribution(text, self._require_owner())\n        self.prompts.append(contribution)\n        self.effect(lambda: self.prompts.remove(contribution))\n\n    def inspect(self) -> dict[str, Any]:\n        return {\n            \"plugins\": list(self._effects),\n            \"tools\": [{\"name\": name, \"plugin\": item.owner} for name, item in self.tools.items()],\n            \"prompts\": [{\"text\": item.value, \"plugin\": item.owner} for item in self.prompts],\n        }\n\n    def _require_owner(self) -> str:\n        if self._installing is None:\n            raise RuntimeError(\"no plugin is being installed\")\n        return self._installing\n"
        },
        {
          "path": "python_harness/session.py",
          "content": "\"\"\"Chapter 4: one append-only history for requests and traces.\"\"\"\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Any\n\n\n@dataclass(frozen=True)\nclass Event:\n    id: int\n    type: str\n    data: dict[str, Any]\n\n\nclass SessionLog:\n    def __init__(self) -> None:\n        self.events: list[Event] = []\n\n    def append(self, event_type: str, **data: Any) -> Event:\n        event = Event(len(self.events) + 1, event_type, data)\n        self.events.append(event)\n        return event\n\n    def request_step_ids(self) -> list[str]:\n        return [event.data[\"step_id\"] for event in self.events if event.type == \"request/header\"]\n\n    def build_request(self, step_id: str) -> dict[str, Any]:\n        header_index = next(\n            index\n            for index, event in enumerate(self.events)\n            if event.type == \"request/header\" and event.data[\"step_id\"] == step_id\n        )\n        header = self.events[header_index].data\n        history = self.events[:header_index]\n        checkpoint_index = max(\n            (index for index, event in enumerate(history) if event.type == \"context/checkpoint\"),\n            default=-1,\n        )\n        messages: list[dict[str, Any]] = []\n        if checkpoint_index >= 0:\n            checkpoint = history[checkpoint_index]\n            messages.append({\"role\": \"system\", \"content\": checkpoint.data[\"summary\"]})\n            history = history[checkpoint_index + 1 :]\n        for event in history:\n            message = self._message_from(event)\n            if message is not None:\n                messages.append(message)\n        return {\n            \"system\": header[\"system\"],\n            \"tools\": header[\"tools\"],\n            \"messages\": messages,\n            \"dynamicContext\": header[\"dynamic_context\"],\n        }\n\n    @staticmethod\n    def _message_from(event: Event) -> dict[str, Any] | None:\n        roles = {\"user/message\": \"user\", \"assistant/message\": \"assistant\", \"tool/result\": \"tool\"}\n        role = roles.get(event.type)\n        if role is None:\n            return None\n        message = {\"role\": role, \"content\": event.data.get(\"content\", \"\")}\n        if role == \"tool\":\n            message[\"name\"] = event.data[\"name\"]\n        return message\n\n    def trace(self) -> list[dict[str, Any]]:\n        return [{\"event_id\": event.id, \"type\": event.type, **event.data} for event in self.events]\n"
        },
        {
          "path": "python_harness/runtime_tools.py",
          "content": "\"\"\"Chapter 5: define, run, stop, and undefine dynamic plugins.\"\"\"\n\nfrom __future__ import annotations\n\nfrom types import SimpleNamespace\nfrom typing import Any, Awaitable, Callable\n\nfrom .runtime import Context, Plugin\n\nTool = SimpleNamespace\n\n\ndef make_tool(\n    parameters: dict[str, Any],\n    execute: Callable[[dict[str, Any]], Awaitable[Any]],\n) -> Tool:\n    \"\"\"Bundle a JSON-Schema parameters object with an async execute function.\n\n    The agent loop validates arguments against parameters, then awaits\n    execute(arguments), so every registered tool needs this shape.\n    \"\"\"\n    return Tool(parameters=parameters, execute=execute)\n\n\ndef runtime_tools_plugin() -> Plugin:\n    \"\"\"A plugin exposing cordis_* tools for dynamic plugin experiments.\"\"\"\n\n    def setup(context: Context) -> None:\n        definitions: dict[str, dict[str, Any]] = {}\n        next_plugin_id = 1\n\n        def cleanup() -> None:\n            for definition in reversed(list(definitions.values())):\n                dispose = definition.get(\"dispose\")\n                if dispose is not None:\n                    dispose()\n            definitions.clear()\n\n        async def inspect_runtime(arguments: dict[str, Any]) -> dict[str, Any]:\n            return context.inspect()\n\n        async def define(arguments: dict[str, Any]) -> dict[str, Any]:\n            name = arguments[\"name\"]\n            code = arguments[\"code\"]\n            compile(code, f\"<dynamic:{name}>\", \"exec\")\n            nonlocal next_plugin_id\n            plugin_id = f\"dyn-{next_plugin_id}\"\n            next_plugin_id += 1\n            definitions[plugin_id] = {\n                \"plugin_id\": plugin_id,\n                \"name\": name,\n                \"purpose\": arguments[\"purpose\"],\n                \"code\": code,\n            }\n            return {\"ok\": True, \"pluginId\": plugin_id, \"name\": name, \"status\": \"defined\"}\n\n        async def run(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            if definition.get(\"dispose\") is not None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is already running\"}\n            plugin = instantiate_plugin(definition)\n            definition[\"dispose\"] = context.mount(plugin)\n            return {\n                \"ok\": True,\n                \"pluginId\": plugin_id,\n                \"status\": \"running\",\n                \"tools\": list(context.tools),\n            }\n\n        async def stop(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is None:\n                return {\"ok\": False, \"error\": f\"{plugin_id} is not running\"}\n            dispose()\n            definition[\"dispose\"] = None\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"stopped\"}\n\n        async def undefine(arguments: dict[str, Any]) -> dict[str, Any]:\n            plugin_id = arguments[\"pluginId\"]\n            definition = definitions.get(plugin_id)\n            if definition is None:\n                return {\"ok\": False, \"error\": f\"unknown dynamic plugin: {plugin_id}\"}\n            dispose = definition.get(\"dispose\")\n            if dispose is not None:\n                dispose()\n            definitions.pop(plugin_id, None)\n            return {\"ok\": True, \"pluginId\": plugin_id, \"status\": \"undefined\"}\n\n        context.effect(cleanup)\n        context.contribute_prompt(\n            \"Inspect the current Context, define a small Cordis Plugin when the task needs a new capability, run it, use its tools, then stop or undefine it after the experiment.\",\n        )\n        context.register_tool(\"cordis_inspect\", make_tool({\"type\": \"object\", \"properties\": {}, \"additionalProperties\": False}, inspect_runtime))\n        context.register_tool(\"cordis_define\", make_tool(\n            {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"name\": {\"type\": \"string\", \"minLength\": 1},\n                    \"purpose\": {\"type\": \"string\", \"minLength\": 1},\n                    \"code\": {\"type\": \"string\", \"minLength\": 1},\n                },\n                \"required\": [\"name\", \"purpose\", \"code\"],\n                \"additionalProperties\": False,\n            },\n            define,\n        ))\n        context.register_tool(\"cordis_run\", make_tool(plugin_id_schema(), run))\n        context.register_tool(\"cordis_stop\", make_tool(plugin_id_schema(), stop))\n        context.register_tool(\"cordis_undefine\", make_tool(plugin_id_schema(), undefine))\n\n    return SimpleNamespace(name=\"runtime-tools\", setup=setup)\n\n\ndef instantiate_plugin(definition: dict[str, Any]) -> Plugin:\n    \"\"\"Execute the stored code and require a plugin_factory() returning a Plugin.\"\"\"\n    namespace: dict[str, Any] = {}\n    exec(compile(definition[\"code\"], f\"<dynamic:{definition['name']}>\", \"exec\"), namespace)\n    factory = namespace.get(\"plugin_factory\")\n    if not callable(factory):\n        raise RuntimeError(\"dynamic code must define plugin_factory() returning a Plugin\")\n    plugin = factory()\n    if not hasattr(plugin, \"name\") or not callable(getattr(plugin, \"setup\", None)):\n        raise RuntimeError(\"plugin_factory() must return a Plugin with name and setup(context)\")\n    return plugin\n\n\ndef plugin_id_schema() -> dict[str, Any]:\n    return {\n        \"type\": \"object\",\n        \"properties\": {\"pluginId\": {\"type\": \"string\", \"pattern\": r\"^dyn-[1-9][0-9]*$\"}},\n        \"required\": [\"pluginId\"],\n        \"additionalProperties\": False,\n    }\n"
        }
      ],
      "diff": "diff --git a/python_harness/scenario.py b/python_harness/scenario.py\nnew file mode 100644\n--- /dev/null\n+++ b/python_harness/scenario.py\n@@ -0,0 +1,41 @@\n+\"\"\"Chapter 6: carry one goal through bounded rounds.\"\"\"\n+\n+from __future__ import annotations\n+\n+from dataclasses import dataclass\n+from typing import Awaitable, Callable, Literal\n+\n+\n+GoalStatus = Literal[\"active\", \"completed\", \"blocked\", \"limit_reached\"]\n+RunRound = Callable[[str], Awaitable[str]]\n+\n+\n+@dataclass\n+class Goal:\n+    objective: str\n+    max_rounds: int = 3\n+    rounds_started: int = 0\n+    status: GoalStatus = \"active\"\n+    reason: str = \"\"\n+\n+\n+ROUND_INSTRUCTIONS = (\n+    \"Collect: obtain the facts needed to complete the goal, then end the Round after making measurable progress.\",\n+    \"Implement: complete the main action from the available facts, record the result, and end the Round.\",\n+    \"Verify: check that the deliverable satisfies the goal and remove temporary capabilities.\",\n+)\n+\n+\n+async def run_long_task(goal: Goal, run_round: RunRound) -> Goal:\n+    while goal.status == \"active\" and goal.rounds_started < goal.max_rounds:\n+        instruction = ROUND_INSTRUCTIONS[goal.rounds_started]\n+        goal.rounds_started += 1\n+        result = await run_round(f\"Goal: {goal.objective}\\nThis Round: {instruction}\")\n+        if \"accepted\" in result:\n+            goal.status, goal.reason = \"completed\", \"The goal passed verification\"\n+        elif \"blocked\" in result:\n+            goal.status, goal.reason = \"blocked\", result\n+\n+    if goal.status == \"active\":\n+        goal.status, goal.reason = \"limit_reached\", \"The maximum number of Rounds was reached\"\n+    return goal\n",
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