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  "chapters": [
    {
      "id": "chapter-1",
      "number": "01",
      "shortTitle": "Agent Loop",
      "title": "How the model cycles between actions and feedback",
      "question": "What does DSH's Agent Loop look like?",
      "codeGuide": {
        "title": "Put model actions behind a verifiable execution boundary",
        "description": "Start with one small baseline: a Step fixes its current input, the model proposes a tool call, and the runtime validates and executes it before passing the result to the next Step.",
        "observations": [
          {
            "title": "Argument checks and the constructor fix the execution boundary",
            "text": "validate_arguments covers only the JSON Schema subset used in this tutorial. The constructor keeps the model, tool table, and Step limit, so the loop does not need to rediscover those dependencies on every request.",
            "lines": [
              27,
              45
            ]
          },
          {
            "title": "One Step fixes a request and decides whether to stop",
            "text": "run first writes the user objective, then combines system rules, Tool Schema, current history, and the Step number into a request. When the model has no tool_calls, the current Turn ends.",
            "lines": [
              47,
              65
            ]
          },
          {
            "title": "Tool execution and failures both return to history",
            "text": "Unknown tools, invalid arguments, and execution exceptions all become tool results in messages. When max_steps is reached, the Harness raises an explicit error.",
            "lines": [
              67,
              79
            ]
          }
        ],
        "fills": [
          {
            "label": "Model protocol, Tool structure, and Agent shape",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                26
              ]
            ]
          },
          {
            "label": "Argument checks and the Agent's fixed dependencies",
            "kind": "body",
            "ranges": [
              [
                27,
                45
              ]
            ]
          },
          {
            "label": "run: build a request, receive a reply, and decide whether to stop",
            "kind": "body",
            "ranges": [
              [
                47,
                66
              ]
            ]
          },
          {
            "label": "Tool execution: validation, exceptions, and feedback",
            "kind": "body",
            "ranges": [
              [
                67,
                79
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Establish a verifiable Python execution baseline",
        "summary": "Python Nano DSH uses Agent.run() to advance several Steps within one Turn: the model returns a Tool Call, the runtime checks its arguments against a simplified Schema and executes the tool, then writes the result into the next request. It retains the limitation that tools, history, and records remain in memory for later chapters to address.",
        "harnessRole": "Execution baseline: convert model actions into validated tool results",
        "connection": "This chapter first closes the loop between model, tool, and feedback. Chapter 2 controls the request view, Chapter 3 manages installable capabilities, and Chapter 4 puts runtime facts into a common record.",
        "outcomes": [
          "Trace requests, Tool Calls, Tool Results, and the next Step through Agent.run()",
          "Explain why the runtime still needs to find tools, check arguments, and wrap exceptions after the model proposes an action",
          "Distinguish a Step, a Turn, and max_steps, and identify the runtime problems this baseline leaves open"
        ]
      },
      "lesson": "# One Tool Round-Trip: Where the Agent Loop Starts\n\nConsider one concrete task: the user asks for the current time in Beijing. The model understands the question, but it has no live data. It needs to call `get_time`, read the returned value, and then answer. That sequence puts the model, a tool, and the runtime on one execution path, which later DSH mechanisms rely on.\n\nMany Agent Harnesses can complete one tool call. DSH becomes distinct in the way it organizes the surrounding runtime: tools, prompts, sessions, and the Agent Loop can be assembled as plugins, while execution facts enter one shared log. The Python teaching version starts with the minimum loop and adds these responsibilities in later chapters.\n\n`agent.py` declares the `Llm` model protocol, the `Tool` data structure, an argument checker, and `Agent`. Keep one boundary in view before reading the implementation: **the model proposes the next step; the runtime prepares input, validates and executes tools, then saves feedback.**\n\n<!-- fill {\"index\":0} -->\n\n## 1.1 Why One Answer Can Span Multiple Steps\n\nOn its first request, the model receives system rules, the parameter definition for `get_time`, the user question, and the current Step number. None of that reveals the current time, so it returns a Tool Call. The runtime executes the query and adds its result to message history. A second request includes that new fact. When the model returns ordinary text without a tool call, the Turn ends.\n\n**A Step is one model request together with the tool execution it triggers; a Turn can contain several Steps.** `validate_arguments()` covers the small JSON Schema subset used in this tutorial, while `Agent.__init__()` fixes the model, tool table, and maximum Step count. Each iteration can then start from the same dependencies.\n\n<!-- fill {\"index\":1} -->\n\nOnly after that setup does `run()` process the user objective. It writes the objective to `messages`, builds a request for every Step, calls the model, and writes model text back into history. When there are no `tool_calls`, the current reply is final. When calls exist, the loop enters its tool-execution phase. Request construction, the model reply, and the stop check share one control flow.\n\n<!-- fill {\"index\":2} -->\n\n<!-- evidence {\"id\":\"m01-request-feedback\",\"tab\":\"request\",\"requestStep\":2,\"label\":\"How a tool result enters the next request\",\"description\":\"Compare the two requests: the first has the task and tool definitions; the second appends the tool call and query result to that content.\"} -->\n\n## 1.2 Tool Execution Needs One Feedback Path\n\nThe model can provide a tool name and arguments, but it cannot decide whether execution is valid or succeeds. The runtime must find the tool, check its arguments against a Schema, and await the tool function. A successful query, an unknown tool, invalid arguments, and a thrown exception all need to return through the same kind of tool-result message.\n\n`run()` keeps this work in the loop over `tool_calls`. **Successes and failures both return as tool results in message history.** Unknown tools and invalid arguments create an `error` first. Exceptions from a normal call are wrapped as an `error` as well. Every outcome returns as a message with `role: \"tool\"`, so the next request reads a consistent feedback shape and can adjust parameters or choose another action.\n\n<!-- fill {\"index\":3} -->\n\n## 1.3 Where This Loop Sits in DSH\n\nThis minimal implementation turns a tool round-trip into inspectable program flow, while still keeping the tool catalog and message history directly inside `Agent`. Longer tasks grow request input; runtime changes crowd the constructor; and history held only in memory cannot support replay after the process ends.\n\nFull DSH preserves this execution boundary while assigning input projection, capability assembly, and session recording to dedicated runtime mechanisms. Understanding the Step and Turn in this chapter makes it possible to see which part of the loop each later mechanism supports.\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,
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      "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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              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "get_time",
                  "description": "Return a fixed time for the requested city.",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "city": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "city"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 51
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "What time is it in Beijing?"
              },
              "approximateTokens": 14
            },
            {
              "id": "message-1",
              "kind": "message",
              "stability": "append-only",
              "label": "assistant",
              "value": {
                "role": "assistant",
                "content": "I will check the time first."
              },
              "approximateTokens": 16
            },
            {
              "id": "message-2",
              "kind": "message",
              "stability": "append-only",
              "label": "tool · get_time",
              "value": {
                "role": "tool",
                "name": "get_time",
                "content": "{\"city\":\"Beijing\",\"time\":\"14:30\"}"
              },
              "approximateTokens": 22
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 137,
          "prefix": {
            "sharedParts": 3,
            "sharedApproximateTokens": 97,
            "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": "Context and Cache Reuse",
      "title": "Keep the stable prefix as long as possible",
      "question": "How is context organized, and how does DSH optimize cache reuse?",
      "codeGuide": {
        "title": "Build a controllable model view from complete facts",
        "description": "The context module retains original messages, shortens long tool results only for the current request, and orders stable content, accumulated history, and Step-specific instructions by how often they change.",
        "observations": [
          {
            "title": "Projection preserves the original messages",
            "text": "Short results return in full. Long results retain their beginning and end and report the amount omitted. The function builds a model view while leaving the original content available to the caller.",
            "lines": [
              9,
              18
            ]
          },
          {
            "title": "Content that changes less often comes first",
            "text": "build_request places system rules and tools first, followed by projected history. The dynamicContext that changes in each Step comes last.",
            "lines": [
              21,
              38
            ]
          },
          {
            "title": "Prefix comparison provides a teaching estimate",
            "text": "Character counts provide an approximate token count. shared_prefix compares four top-level components in a fixed order until the first difference; it does not query the provider's actual cache.",
            "lines": [
              41,
              51
            ]
          }
        ],
        "fills": [
          {
            "label": "Projection and request-comparison function shapes",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                9
              ]
            ]
          },
          {
            "label": "project_tool_result: clip long results in the model view",
            "kind": "body",
            "ranges": [
              [
                10,
                20
              ]
            ]
          },
          {
            "label": "build_request: order stable content, history, and Step instructions",
            "kind": "body",
            "ranges": [
              [
                21,
                40
              ]
            ]
          },
          {
            "label": "Estimate text size and locate the first change",
            "kind": "body",
            "ranges": [
              [
                41,
                51
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Complete facts and the current request have different jobs",
        "summary": "Python Nano DSH copies messages while building a request and clips only the model view of Tool Results. System rules and tools come first, history appends in order, and Step instructions come last; shared_prefix gives a deterministic teaching comparison of four top-level components.",
        "harnessRole": "Input projection layer: determine what the current Step sends to the model",
        "connection": "Chapter 1 needs a request, and this chapter defines how to create one from complete history. Chapter 3 changes the available tools; Chapter 4 provides a fuller record structure for complete facts.",
        "outcomes": [
          "Distinguish original messages from the projected messages sent to the model",
          "Explain why project_tool_result() changes only the current request view",
          "Use the order of system, tools, messages, and dynamicContext to identify the first changed component"
        ]
      },
      "lesson": "# Keep the Shared Prefix as Long as Possible\n\nEach Step in the first chapter carries more messages into the next request. That is easy to follow in a short task. A long tool result can quickly fill the model's context window. At that point, the runtime needs to retain two forms of data: **complete facts for review and recovery, and a model view for the current inference.**\n\nDSH's context design first makes input controllable, then uses a stable request order to encourage prefix-cache reuse. **Control the input first; then keep the stable prefix as long as possible.** The Python teaching version reduces that design to message projection and comparison of four top-level request components. The model service remains the source of truth for actual cache hits.\n\n`context.py` defines the projection, request-building, and prefix-comparison functions. Together, they set the shared rules for later code: which tool results need clipping, how request components are ordered, and where two requests first differ.\n\n<!-- fill {\"index\":0} -->\n\n## 2.1 The Model View Can Shrink; Complete Facts Must Remain\n\nWhen an Agent answers a question about a memo, `read_note` may return a long text. Carrying the full value through every Step steadily increases input length. Overwriting it would make later review lose its evidence. The Python version therefore creates a projection while building a request. It changes only the Tool Results the model reads on that request and leaves the original `messages` list intact.\n\n`project_tool_result()` clips only strings over its limit. Short results remain unchanged. A long result retains the beginning, end, and the number of omitted characters. The beginning helps the model identify the source; the end often contains a conclusion, status, or error; the marker makes clear that the middle is absent from this view.\n\nThis implementation restricts clipping to the model view: **the current request can become shorter while the caller's complete messages stay unchanged.** Full DSH writes the original facts to a Session Log for later reconstruction, replay, and recovery.\n\n<!-- fill {\"index\":1} -->\n\n## 2.2 Request Order Determines Where Change Appears\n\nClipping bounds the size of one result. The order of request components determines where new content changes an otherwise shared request. Python's `build_request()` places system rules and tool definitions first, then the projected message history in time order, and finally the `dynamicContext` that changes on every Step. Comparing adjacent requests from the beginning leaves a longer shared prefix for slowly changing content.\n\n**Slow-changing content comes first; new content is appended near the end.** This function does not call a model service or manage a cache. It arranges current state into a unified request, so callers and the comparison function use the same order to understand one model call. When a tool round-trip adds messages, the new content naturally lands at the end of history.\n\n<!-- fill {\"index\":2} -->\n\n<!-- evidence {\"id\":\"m02-prefix\",\"tab\":\"request\",\"requestStep\":2,\"label\":\"Change starts at the end of history\",\"description\":\"Compare the stable opening and first changed position of two requests to see how a new tool round-trip affects their prefix.\"} -->\n\n## 2.3 Check Structural Stability with a Repeatable Comparison\n\nThe page can describe how much of two requests starts the same way, but it cannot know a model service's actual cache hit. `approximate_tokens()` gives a character-based teaching estimate. `shared_prefix()` checks `system`, `tools`, `messages`, and `dynamicContext` in a fixed order and finds the first different component. Neither helper contacts a service or changes a request.\n\nThis comparison is smaller than the TypeScript version: it checks four top-level components instead of normalizing every segment. **It still gives a stable explanation of which request component changes first.** When tools, prompts, or the model adapter change at runtime, the stable portion changes too; full DSH uses finer request descriptions and log reconstruction for those cases.\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,
        "additions": 51,
        "deletions": 0,
        "files": [
          {
            "path": "python_harness/context.py",
            "additions": 51,
            "deletions": 0
          }
        ]
      },
      "requests": [
        {
          "step": 1,
          "request": {
            "system": "Read the facts first, then answer briefly.",
            "tools": [
              {
                "name": "read_note",
                "description": "Read a note by name.",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "name": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "name"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "user",
                "content": "What does the note say?"
              }
            ],
            "dynamicContext": "step=1"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "Read the facts first, then answer briefly.",
              "approximateTokens": 11
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "read_note",
                  "description": "Read a note by name.",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "name": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "name"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 45
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "What does the note say?"
              },
              "approximateTokens": 13
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=1",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 71,
          "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": "Read the facts first, then answer briefly.",
            "tools": [
              {
                "name": "read_note",
                "description": "Read a note by name.",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "name": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "name"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "user",
                "content": "What does the note say?"
              },
              {
                "role": "assistant",
                "content": "I will read the note first."
              },
              {
                "role": "tool",
                "name": "read_note",
                "content": "Note today · meeting preparation\nBackground: Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes.\n… omitted 1513 characters …\nProject status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. \nConclusion: the meeting starts at 3:00 p.m."
              }
            ],
            "dynamicContext": "step=2"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "Read the facts first, then answer briefly.",
              "approximateTokens": 11
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "read_note",
                  "description": "Read a note by name.",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "name": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "name"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 45
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "What does the note say?"
              },
              "approximateTokens": 13
            },
            {
              "id": "message-1",
              "kind": "message",
              "stability": "append-only",
              "label": "assistant",
              "value": {
                "role": "assistant",
                "content": "I will read the note first."
              },
              "approximateTokens": 15
            },
            {
              "id": "message-2",
              "kind": "message",
              "stability": "append-only",
              "label": "tool · read_note",
              "value": {
                "role": "tool",
                "name": "read_note",
                "content": "Note today · meeting preparation\nBackground: Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes.\n… omitted 1513 characters …\nProject status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. Project status, open questions, and discussion notes. \nConclusion: the meeting starts at 3:00 p.m."
              },
              "approximateTokens": 150
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 236,
          "prefix": {
            "sharedParts": 3,
            "sharedApproximateTokens": 69,
            "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": "Everything Is a Plugin",
      "title": "Manage runtime capabilities through plugins",
      "question": "How does DSH make everything a plugin?",
      "codeGuide": {
        "title": "Give each capability a source, dependency, and exit path",
        "description": "Nano DSH mounts plugins through Context. When a plugin provides services, tools, prompts, or listeners, Context records the owner and adds the related cleanup action to one effect stack.",
        "observations": [
          {
            "title": "Context keeps owners and effects",
            "text": "The runtime registry keeps Contributions. The plugin currently being installed determines the owner, and each plugin has its own list of cleanup functions.",
            "lines": [
              25,
              38
            ]
          },
          {
            "title": "A successful mount takes effect; a failed one rolls back",
            "text": "When setup raises an error, Context removes registered content in reverse order. After setup succeeds, mount returns an idempotent unmount function. Explicit removal uses the same cleanup functions.",
            "lines": [
              39,
              65
            ]
          },
          {
            "title": "Registration also records a cleanup function",
            "text": "Tools and Prompts enter the registry only during installation. Each registration immediately adds a cleanup function that removes the owner's contribution, and inspect generates a view of the current runtime.",
            "lines": [
              67,
              89
            ]
          }
        ],
        "fills": [
          {
            "label": "Plugin protocol, contribution records, and Context shape",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                25
              ]
            ]
          },
          {
            "label": "Mount state, the effect stack, and reversible mounting",
            "kind": "body",
            "ranges": [
              [
                26,
                66
              ]
            ]
          },
          {
            "label": "Register Tools and Prompts while binding cleanup actions",
            "kind": "body",
            "ranges": [
              [
                67,
                78
              ]
            ]
          },
          {
            "label": "Inspect current capabilities and protect the installation boundary",
            "kind": "body",
            "ranges": [
              [
                79,
                89
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "The Python version keeps capability ownership and a reversible lifecycle",
        "summary": "Full DSH uses a Cordis plugin tree to organize models, tools, sessions, and the Agent Loop. The Python teaching version reduces that idea to owners and effects: it directly registers Tool and Prompt contributions, then cleans them in reverse order after a failed install or intentional unload. Its services and listeners containers reserve space for extension; the sample has no Service injection or dependency graph.",
        "harnessRole": "Runtime assembly layer: maintain the source and lifecycle of current tools and prompts",
        "connection": "Chapter 1 consumes a tool table, and Chapter 2 places it into a request. This chapter explains how capabilities enter and leave Context. Chapter 4 records runtime facts through messages and events.",
        "outcomes": [
          "Distinguish a Tool, an action available to the model, from a Plugin, a unit of installed capability",
          "Trace installation, failed rollback, and unloading through Context.mount(), effect(), and the returned unmount function",
          "Use inspect() owner records to explain where current Tools and Prompts came from"
        ]
      },
      "lesson": "# Every Runtime Capability Comes from a Plugin\n\nChapter 1 gives `Agent` its tools directly, and Chapter 2 places those tools into a request. That starting point makes the loop easy to understand. As capabilities multiply, changes begin to gather in a few constructors and global states: adding a tool, replacing a model adapter, or changing workspace settings can touch the same core code.\n\nFull DSH uses Cordis to organize this change. Cordis is the TypeScript plugin runtime used by DSH: **the model adapter, tool registry, Session Log, and Agent Loop all join one runtime tree as plugins.** The Python teaching version retains the shared lifecycle constraint: **every registered capability has an owner and can be withdrawn through a cleanup path.** It implements Tool and Prompt registration directly, while reserving `services` and `listeners` containers for future extensions.\n\n`runtime.py` first defines the plugin protocol, `Contribution` records with an owner, and the state held by `Context`. This Python sample has no `provide()`, `use()`, or dependency-relation API, so the chapter focuses on a capability's source, installation, and complete removal.\n\n<!-- fill {\"index\":0} -->\n\n## 3.1 Give a Capability a Lifecycle Before Installing It\n\nA Plugin is one unit of installation and removal. With a `Context`, it can register a Tool or contribute a Prompt inside `setup()`. `Context` stores the value and owner for every contribution and creates a separate effect stack for the plugin currently being installed. The runtime can then answer which tools and prompts exist and which installation supplied them.\n\nThe difficult state appears when installation fails halfway through. A plugin can register a tool and then throw while contributing a prompt. Leaving its earlier contribution in `Context` makes later state unreliable. `mount()` creates an effect list, runs `setup()`, and collects every cleanup action there. **A failed installation and an intentional unload follow the same reverse cleanup path.**\n\n<!-- fill {\"index\":1} -->\n\n## 3.2 Registering a Capability Also Records How It Leaves\n\n`effect()` is the common lifecycle entry point. It can run only during plugin installation and adds one cleanup function to the current plugin's list. `register_tool()` writes a tool and its owner to the table, then registers an action that deletes the entry. `contribute_prompt()` does the same for a prompt. **Registering a capability also registers its cleanup action and source.**\n\nThe order has a practical purpose. Later cleanup actions run first during an intentional unload, so related contributions leave in reverse registration order. The `disposed` flag in `mount()` also makes the returned unload function effective only once. This Python version does not implement the full DSH Service-injection and cascading-dependency behavior, yet it retains the reversible installation boundary those mechanisms rely on.\n\n<!-- fill {\"index\":2} -->\n\n## 3.3 One Record Covers Tools and Prompts in This Sample\n\nTools and prompts have different parameters, yet the runtime registers them in the same way: write a contribution with its owner, then give a removal action to `effect()`. `inspect()` derives a snapshot of current plugins, tools, and prompts from those containers. `_require_owner()` prevents a runtime mutation outside an installation.\n\nThis source segment keeps contribution registration, state reads, and rollback helpers inside `Context`. **Every plugin enters the current Python runtime through the same rule set.** Services, listeners, and dependency relationships should join the same owner-and-effect lifecycle if the implementation expands.\n\n<!-- fill {\"index\":3} -->\n\n## 3.4 Where DSH Separates from Many Harnesses\n\nTool loops and context projection are common in many Harnesses. **DSH also puts the capabilities that compose those mechanisms into the plugin tree.** Configuration describes the current assembly, plugin lifecycles manage contributions entering and leaving, and runtime inspection can trace ownership and dependencies.\n\nThe Python teaching version shows the smaller Tool-and-Prompt lifecycle. Full DSH also includes dependency injection, waiting for services to become ready, layered configuration, and isolated scopes. The difference is one of implementation scope; the central relationship remains the same: capabilities enter the runtime through an inspectable installation and leave through the same lifecycle.\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": "Making Every Run Traceable",
      "title": "Reconstruct execution from the log",
      "question": "How does DSH record and preserve an Agent run?",
      "codeGuide": {
        "title": "Use one event log to support requests and replay",
        "description": "Nano DSH appends numbered SessionEvents, then uses the target Step's request header, context checkpoints, and prior events to rebuild model input and the execution trace.",
        "observations": [
          {
            "title": "Events append with increasing identifiers",
            "text": "append uses the current length to assign a stable ID. request_step_ids reads header events to identify model Steps that can be reconstructed.",
            "lines": [
              16,
              26
            ]
          },
          {
            "title": "The request header defines the reconstruction range",
            "text": "step_id locates the target header first. Content before that event forms the current Step's history, and the latest checkpoint determines where reconstruction resumes.",
            "lines": [
              28,
              54
            ]
          },
          {
            "title": "Requests and Traces use the same log",
            "text": "Message projection reads recognized events, while trace formats every Event. Both views are generated from the Session Log.",
            "lines": [
              56,
              68
            ]
          }
        ],
        "fills": [
          {
            "label": "Event structure and the basic SessionLog shape",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                16
              ]
            ]
          },
          {
            "label": "Append facts and index reconstructable Steps",
            "kind": "body",
            "ranges": [
              [
                17,
                27
              ]
            ]
          },
          {
            "label": "Rebuild the current request from a header and checkpoint",
            "kind": "body",
            "ranges": [
              [
                28,
                55
              ]
            ]
          },
          {
            "label": "Restore messages from events",
            "kind": "body",
            "ranges": [
              [
                56,
                66
              ]
            ]
          },
          {
            "label": "Generate a Trace from the same event stream",
            "kind": "body",
            "ranges": [
              [
                67,
                68
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "The Python version derives requests and a timeline from one log",
        "summary": "Python Nano DSH appends Events to SessionLog, then uses a request/header, checkpoints, and prior events to rebuild model messages. trace() creates a display timeline from the same sequence. The minimum version asks its caller to append events explicitly and does not automatically attach the log to the plugin runtime or persistence.",
        "harnessRole": "Record layer: supply common input for request reconstruction and execution views",
        "connection": "Chapter 1 produces model and tool round-trips, Chapter 2 explains the request view, and Chapter 3 manages capability lifecycles. This chapter establishes a simple sequence of recordable facts that full DSH can extend in the final chapters.",
        "outcomes": [
          "Explain how increasing event identifiers preserve occurrence order",
          "Explain how request/header and context/checkpoint establish a reconstruction range",
          "Distinguish SessionLog, the request from build_request(), and the timeline from trace()"
        ]
      },
      "lesson": "# What the Model Sees Is What Gets Logged\n\nThe first three chapters produce several kinds of state: user messages and Tool Results make up model history, plugins change the available tools and prompts, and the page needs to show execution. If these details live separately in message arrays, debug output, and UI state, one run can quickly acquire conflicting versions of its own history.\n\n**Full DSH writes runtime facts to one append-only Session Log first, then derives model history, replay, UI, and persisted data from that log.** This order establishes a checkable constraint: **anything visible to the model should be reconstructable from the record.** The Python teaching version implements the smallest form of that relationship: events append in order, and requests and Traces read from the same set of events.\n\n`session.py` defines `Event` and `SessionLog`. Each Event contains only an increasing identifier, a type, and data. The caller explicitly decides when to append a user message, request header, or Tool Result. This reduction keeps the relationship between event order and request reconstruction visible.\n\n<!-- fill {\"index\":0} -->\n\n## 4.1 Save Facts in the Order They Occur\n\nOne tool round-trip passes through a user message, request header, model response, external tool execution, and Tool Result. This Python minimum version asks its caller to append the user message, request header, model response, and Tool Result; it does not automatically create a separate Tool Call event. `SessionLog.append()` uses the current length to create an increasing identifier and then appends the new Event. The identifier lets the page point to a definite event. **Append order preserves causality: a Tool Result follows external execution and precedes the next request.**\n\n`request_step_ids()` reads the identifiers of reconstructable Steps from `request/header` events. It does not copy history or infer an event's meaning; the index still points to the same append-only record. Keeping identifiers and this index in one small class prepares stable input for the reconstruction function that follows.\n\n<!-- fill {\"index\":1} -->\n\n<!-- evidence {\"id\":\"m04-events\",\"tab\":\"events\",\"eventType\":\"tool/result\",\"eventOccurrence\":1,\"label\":\"One shared record for a tool round-trip\",\"description\":\"Read user input, request header, model response, Tool Result, and the following request by event identifier to confirm their order.\"} -->\n\n## 4.2 The Log Must Cover Model-Visible Facts\n\nIn full DSH, plugin mounts and unloads change tools and prompts. Those changes need to enter the same record so that a later reader can explain why a Step saw a particular tool. The TypeScript implementation's `sessionPlugin()` listens for runtime changes and appends events.\n\nThe Python teaching version keeps a clearer boundary here: `SessionLog` does not attach to `Context` automatically, so its caller invokes `append()` when a fact occurs. **This code demonstrates a shared event source; it does not yet record plugin changes or execution stop reasons automatically.** When runtime listeners are added, their new events should join this same log instead of creating another history.\n\n<!-- fill {\"index\":2} -->\n\n## 4.3 A Request Header Marks the Boundary at That Time\n\nMessage history alone cannot describe a complete request. System rules, tool definitions, and Step instructions can all change over time. `build_request()` first locates the target `request/header` through `step_id` and reads only events before that header. **The request header stores the input boundary of a Step at the time it ran.**\n\nThe code then finds the newest `context/checkpoint`. When one exists, its summary becomes a system message and events after it continue to become messages one by one. `_message_from()` handles only user, assistant, and Tool Result events, preserving the tool name for a tool message. The Python minimum version does not record separate Tool Calls or apply Chapter 2's clipping again here; it shows the basic method for restoring the current request from event order.\n\n<!-- fill {\"index\":3} -->\n\n## 4.4 Trace and Requests Reuse the Same Facts\n\n`trace()` iterates through the same Events and creates display objects with an `event_id`, type, and data. **Request reconstruction and Trace share one event-based source of facts.** They serve different purposes without maintaining a second execution history.\n\n<!-- fill {\"index\":4} -->\n\nFull DSH writes SessionEvents to JSON Lines, or JSONL, where each line is one JSON record, or to SQLite, and connects the Session Log to its plugin runtime. The Python teaching version currently keeps facts in memory and lets the tutorial generator serialize samples. Both emphasize that requests and display views need the same event stream as evidence.\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": "Use a tool when the answer requires external facts.",
            "tools": [
              {
                "name": "get_time",
                "description": "Return a fixed time for the requested city.",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "user",
                "content": "What time is it in Beijing?"
              }
            ],
            "dynamicContext": "step=1"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "Use a tool when the answer requires external facts.",
              "approximateTokens": 13
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "get_time",
                  "description": "Return a fixed time for the requested city.",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "city": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "city"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 51
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "user",
              "value": {
                "role": "user",
                "content": "What time is it in Beijing?"
              },
              "approximateTokens": 14
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=1",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 80,
          "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": "Use a tool when the answer requires external facts.",
            "tools": [
              {
                "name": "get_time",
                "description": "Return a fixed time for the requested city.",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "messages": [
              {
                "role": "system",
                "content": "The Beijing time has been retrieved: 14:30."
              }
            ],
            "dynamicContext": "step=2"
          },
          "parts": [
            {
              "id": "system",
              "kind": "system",
              "stability": "stable",
              "label": "System prompt",
              "value": "Use a tool when the answer requires external facts.",
              "approximateTokens": 13
            },
            {
              "id": "tools",
              "kind": "tools",
              "stability": "stable",
              "label": "Tool schemas",
              "value": [
                {
                  "name": "get_time",
                  "description": "Return a fixed time for the requested city.",
                  "inputSchema": {
                    "type": "object",
                    "properties": {
                      "city": {
                        "type": "string"
                      }
                    },
                    "required": [
                      "city"
                    ],
                    "additionalProperties": false
                  }
                }
              ],
              "approximateTokens": 51
            },
            {
              "id": "message-0",
              "kind": "message",
              "stability": "append-only",
              "label": "system",
              "value": {
                "role": "system",
                "content": "The Beijing time has been retrieved: 14:30."
              },
              "approximateTokens": 19
            },
            {
              "id": "dynamic",
              "kind": "dynamic",
              "stability": "step-variable",
              "label": "Step context",
              "value": "step=2",
              "approximateTokens": 2
            }
          ],
          "totalApproximateTokens": 85,
          "prefix": {
            "sharedParts": 2,
            "sharedApproximateTokens": 64,
            "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": "What time is it in Beijing?"
          }
        },
        {
          "id": 2,
          "type": "request/header",
          "data": {
            "step_id": "step-1",
            "system": "Use a tool when the answer requires external facts.",
            "tools": [
              {
                "name": "get_time",
                "description": "Return a fixed time for the requested city.",
                "inputSchema": {
                  "type": "object",
                  "properties": {
                    "city": {
                      "type": "string"
                    }
                  },
                  "required": [
                    "city"
                  ],
                  "additionalProperties": false
                }
              }
            ],
            "dynamic_context": "step=1"
          }
        },
        {
          "id": 3,
          "type": "assistant/message",
          "data": {
            "content": "I will check the time first."
          }
        },
        {
          "id": 4,
          "type": "tool/result",
          "data": {
            "name": "get_time",
            "content": "{\"time\":\"14:30\"}"
          }
        },
        {
          "id": 5,
          "type": "context/checkpoint",
          "data": {
            "summary": "The Beijing time has been retrieved: 14:30."
          }
        },
        {
          "id": 6,
          "type": "request/header",
          "data": {
            "step_id": "step-2",
            "system": "Use a tool when the answer requires external facts.",
            "tools": [
              {
                "name": "get_time",
                "description": "Return a fixed time for the requested city.",
                "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": "What time is it in Beijing?"
        },
        {
          "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": "I will check the time first."
        },
        {
          "eventId": 4,
          "type": "tool/result",
          "title": "result get_time",
          "detail": "{\"time\":\"14:30\"}"
        },
        {
          "eventId": 5,
          "type": "context/checkpoint",
          "title": "context/checkpoint",
          "detail": "The Beijing time has been retrieved: 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": "Runtime Self-Evolution",
      "title": "Write and run a new Cordis plugin",
      "question": "How does DSH continuously evolve at runtime?",
      "codeGuide": {
        "title": "Close a capability gap through inspection, mounting, and release",
        "description": "Runtime Tools first lets the Agent inspect the current Context, then define and run a Cordis plugin. Dynamic plugins contribute tools and prompts through the same Context mount and unload flow as ordinary plugins.",
        "observations": [
          {
            "title": "The definition table stores code and runtime state",
            "text": "setup creates a definition table and increasing identifiers, then registers an effect that clears them during removal. cordis_inspect returns the current runtime view.",
            "lines": [
              29,
              40
            ]
          },
          {
            "title": "Definition and execution are separate actions",
            "text": "cordis_define compiles and stores Python plugin code submitted by the Agent. cordis_run retrieves the code by plugin_id, executes it to obtain a Plugin, and mounts it through context.mount().",
            "lines": [
              42,
              71
            ]
          },
          {
            "title": "Stopping and deletion share the unload function",
            "text": "cordis_stop calls the unload function while retaining the definition. cordis_undefine stops the plugin and deletes its definition. Context removes its contributions as part of the same lifecycle.",
            "lines": [
              73,
              94
            ]
          }
        ],
        "fills": [
          {
            "label": "Tool wrapper and Runtime Tools plugin shape",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                28
              ],
              [
                118,
                120
              ]
            ]
          },
          {
            "label": "Definition table, cleanup actions, and inspection interface",
            "kind": "body",
            "ranges": [
              [
                29,
                41
              ]
            ]
          },
          {
            "label": "Define and mount a dynamic Python plugin",
            "kind": "body",
            "ranges": [
              [
                42,
                72
              ]
            ]
          },
          {
            "label": "Stop, delete, register tools, and load plugin code",
            "kind": "body",
            "ranges": [
              [
                73,
                140
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "Inspect, mount, validate, and release a capability gap",
        "summary": "Python Nano DSH exposes cordis_inspect, cordis_define, cordis_run, cordis_stop, and cordis_undefine as ordinary tools. The caller confirms a gap, registers and mounts a Python plugin, retrieves its new tool from Context to validate the result, then stops or deletes the definition. Dynamic code loads through built-in compile() and exec() for trusted teaching samples only.",
        "harnessRole": "Capability evolution layer: add and release runtime capability during a task",
        "connection": "This path uses the earlier chapters: Runtime Tools expose capability changes and Context manages mounting and unloading. A caller must feed context.tools back into an Agent before later requests can see a new tool; this minimum version also does not automatically add definition and mounting events to SessionLog.",
        "outcomes": [
          "Describe a capability change in the order inspect, define, mount, validate, and release",
          "Explain the difference among code being defined, a plugin being running, and a new tool being verified",
          "Identify why dynamic code execution requires a trusted source and explicit security boundary"
        ]
      },
      "lesson": "# The Agent Installs Its Own Tool\n\nChapter 4's log can explain what happened, yet it does not add a capability. Suppose a task needs to count the words in a piece of text and the current `Context` has no `word_count` tool. Guessing tool names or repeatedly calling unrelated tools consumes Steps without closing that capability gap.\n\nDSH treats this situation as a controlled runtime change: **inspect the current capabilities and interfaces, mount a temporary plugin, call its new tool to verify the result, and release the plugin when the task ends.** The Python teaching version retains the same path and loads dynamic Python code with `compile()` and `exec()`. That path is appropriate only for trusted teaching samples; the code itself provides no security sandbox.\n\n`runtime_tools.py` begins with the common tool wrapper and the entry point for `runtime_tools_plugin()`. The following implementation works from one definition table, making three states easy to distinguish: plugin code is registered, the plugin is running, or the plugin has been released.\n\n<!-- fill {\"index\":0} -->\n\n## 5.1 Confirm the Gap Before Deciding What to Add\n\n`cordis_inspect` returns a snapshot of the plugins, tools, and prompts currently held by `Context`. **Confirm what is missing before deciding what to add.** The caller uses the result to establish that `word_count` is absent, then decides what the new plugin must provide. The baseline capability graph matters too: it gives a comparable state from before the change, so an existing capability is not mistaken for a gap.\n\nRuntime Tools also needs its own lifecycle. It keeps dynamic plugins in a definition table and, when removed, stops still-running plugins in reverse order before clearing their definitions. It also registers the inspection interface and a Prompt that explains when to inspect, define, and run a plugin. These preparations give a temporary extension a definite entry and exit.\n\n<!-- fill {\"index\":1} -->\n\n<!-- evidence {\"id\":\"m05-before\",\"tab\":\"graph\",\"requestStep\":1,\"label\":\"Confirm the capability gap before changing it\",\"description\":\"Inspect the current Context: it contains neither a word-count plugin nor a word_count tool.\"} -->\n\n## 5.2 Defining Code and Mounting a Capability Are Different Moments\n\nAfter inspection confirms the gap, the caller submits a plugin name, purpose, and Python code through `cordis_define`. The code must define `plugin_factory()` and return an object with `name` and `setup(context)`. **Defining code and mounting a capability happen at different moments.** The function checks code with `compile()` and saves the definition in its table, leaving the tool catalog unchanged. `cordis_run` retrieves the definition, executes the code to obtain a Plugin, and calls Chapter 3's `context.mount()`. The new tool joins the current Context only after mounting succeeds.\n\nThe distinction has practical value. Definition reports whether code can load; mounting reports whether the plugin really changed the runtime. A failure therefore has a precise location. `instantiate_plugin()` runs code in a new namespace, then checks the factory function and returned object's required shape. This path has no permission isolation, so deployment needs a constrained execution environment rather than this teaching loader.\n\n<!-- fill {\"index\":2} -->\n\n## 5.3 Verification and Release Close the Change Loop\n\nOnce mounting succeeds, `word_count` appears in the tool table. **It is now available for an outer layer to rebuild a later Agent request.** The Python `Agent` keeps its own tool table from construction and does not automatically subscribe to `Context`. The caller must read `context.tools` and create or update the Agent's tool definitions. A request already sent remains unchanged.\n\n<!-- evidence {\"id\":\"m05-installed\",\"tab\":\"graph\",\"requestStep\":2,\"label\":\"The new tool has joined the current Context\",\"description\":\"Compare this graph with the baseline: word-count registers word_count. Its rules enter later requests after the outer layer updates the Agent tool table.\"} -->\n\nThe outer layer retrieves `word_count` from `Context` and receives `words: 3`. That result proves that the new capability mounted and completed one call.\n\n<!-- evidence {\"id\":\"m05-verified\",\"tab\":\"events\",\"eventType\":\"tool/result\",\"eventOccurrence\":4,\"label\":\"A tool result verifies the new capability\",\"description\":\"Inspect word_count in the teaching call sequence to confirm that the new tool completed one real call.\"} -->\n\nAfter verification, `cordis_stop` invokes the unload function captured during mounting while retaining the definition for a later rerun. `cordis_undefine` stops a running plugin first and then removes its definition. The five `cordis_*` tools share one wrapper, while helpers read arguments and instantiate plugins so the main functions can focus on state transitions. This Python minimum version does not automatically write definition, mounting, or release events to SessionLog; full DSH should add those changes to the common record.\n\n<!-- evidence {\"id\":\"m05-removed\",\"tab\":\"graph\",\"requestStep\":3,\"label\":\"Release returns to the baseline\",\"description\":\"Inspect the final graph to confirm that word-count and word_count have both left the Context.\"} -->\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": {
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            "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\"}]}"
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              "name": "word-count",
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              "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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              "text": "one two three"
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            "name": "word_count",
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          "title": "result cordis_inspect",
          "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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          "type": "tool/call",
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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\":\"count words\"}"
        },
        {
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        },
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        },
        {
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          "type": "tool/call",
          "title": "call word_count",
          "detail": "{\"text\":\"one two three\"}"
        },
        {
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          "title": "result word_count",
          "detail": "{\"words\":3}"
        },
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          "title": "call cordis_stop",
          "detail": "{\"pluginId\":\"dyn-1\"}"
        },
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          "title": "result cordis_stop",
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        },
        {
          "eventId": 11,
          "type": "tool/call",
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        },
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          "title": "result cordis_undefine",
          "detail": "{\"ok\":true,\"pluginId\":\"dyn-1\",\"status\":\"undefined\"}"
        }
      ],
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            }
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            {
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            }
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    },
    {
      "id": "chapter-6",
      "number": "06",
      "shortTitle": "Continuing Long-Running Tasks",
      "title": "Advance a long-running task with Goals and Rounds",
      "question": "How does DSH keep long-running tasks moving to completion?",
      "codeGuide": {
        "title": "Let a Goal decide whether work continues outside a Turn",
        "description": "LongTaskRunner stores a Goal and starts Rounds in sequence. Each Round reuses the standard Agent Loop; the outer layer uses a structured result to decide whether work continues, completes, blocks, or reaches its limit.",
        "observations": [
          {
            "title": "Goal keeps cross-Round state in one place",
            "text": "The objective, status, number of started Rounds, and Round limit live in one object. Each Round's stage description is defined in advance outside the loop.",
            "lines": [
              13,
              26
            ]
          },
          {
            "title": "Every Round restates the objective and current stage",
            "text": "run_long_task continues only while the Goal is active and below its limit. Every call to run_round receives the same objective and the current stage description.",
            "lines": [
              29,
              33
            ]
          },
          {
            "title": "Status determines whether execution continues",
            "text": "accepted and blocked end the Goal. A Goal still active at the Round boundary becomes limit_reached.",
            "lines": [
              34,
              41
            ]
          }
        ],
        "fills": [
          {
            "label": "Goal data, callback type, and runner shape",
            "kind": "skeleton",
            "ranges": [
              [
                1,
                21
              ],
              [
                29,
                29
              ]
            ]
          },
          {
            "label": "Predefine each Round's task instruction",
            "kind": "body",
            "ranges": [
              [
                22,
                28
              ]
            ]
          },
          {
            "label": "Advance a Round and call run_round",
            "kind": "body",
            "ranges": [
              [
                30,
                33
              ]
            ]
          },
          {
            "label": "Finish or continue the Goal from result text",
            "kind": "body",
            "ranges": [
              [
                34,
                41
              ]
            ]
          }
        ],
        "folds": []
      },
      "changeStory": {
        "title": "The Python version uses a Goal to continue past an ordinary Turn",
        "summary": "Python Nano DSH keeps the objective, status, and Round count in Goal. run_long_task() passes the objective and stage instruction to an external run_round for each Round, then updates status from accepted or blocked in the returned text. A Goal still active at the limit becomes limit_reached.",
        "harnessRole": "Long-task coordination layer: let bounded Rounds advance one objective together",
        "connection": "Each run_round can continue to call the Chapter 1 Agent and reuse state retained by an outer layer. Compared with the TypeScript version, this teaching implementation has no structured RoundResult, progress check, or automatic SessionLog events.",
        "outcomes": [
          "Distinguish the four control layers: Goal, Round, Turn, and Step",
          "Explain how run_long_task() uses status and a Round count to start another attempt",
          "Identify accepted, blocked, and limit_reached as exits, and the limitation of text matching"
        ]
      },
      "lesson": "# Let the Task Continue Past a Turn\n\nChapter 5 can add a tool during a task. A Turn still ends when the current model stops calling tools. The task may remain incomplete: after one round of organizing three release notes, the Agent may have collected only two. A different layer has to decide whether work should continue.\n\nDSH manages long-term objectives outside the Agent Loop. **A Goal decides whether cross-Turn work continues; Turns and Steps continue to handle model requests and tool execution.** A Goal holds state that spans Rounds, while a Round is one new attempt to advance it. The Python teaching version uses a smaller `Goal` data class and `run_long_task()` to show that boundary; it updates status from the text returned by an external callback.\n\n`scenario.py` first defines Goal status and the `run_round` callback type, then gives the shape of `Goal` and each Round's instructions. It does not hold a particular Agent, Context, or Session Log. The caller decides whether to reuse those objects for a Round. This boundary lets long-task coordination do only what it needs to do: decide whether to start another ordinary Turn.\n\n<!-- fill {\"index\":0} -->\n\n## 6.1 A Goal Needs State That Can Be Checked Across Rounds\n\n`Goal` keeps the objective, maximum Round count, number of started Rounds, status, and reason in one object. `ROUND_INSTRUCTIONS` predefines stages for collection, implementation, and verification. **A Goal preserves cross-Round state; a Round describes the current stage of progress.** The outer layer can read one consistent status after each Round, and its caller can use that state when recording or displaying work.\n\nUnlike the TypeScript version, this Python minimum implementation does not automatically write a `goal/created` event or support persistence and recovery. The omission keeps the code focused on continuation decisions while identifying the record boundary that full DSH needs to supply.\n\n<!-- fill {\"index\":1} -->\n\n## 6.2 The Next Round Still Calls the Ordinary Agent Loop\n\nWhen a Goal is `active` and below its Round limit, `run_long_task()` reads the current stage instruction, increments the Round count, and calls external `run_round`. The callback receives the same long-term objective and the current instruction. It can give the Round to the same Agent, Context, workspace, and Session Log used before, leaving messages, Tool Results, and plugin changes available.\n\nThis layer does not duplicate Chapter 1's tool loop. **A Goal can start a new Turn without rewriting the Agent Loop.** It supplies the stage boundary for the next Turn; the ordinary Agent Loop continues to handle model requests, Tool Calls, and feedback. Keeping the loops separate lets the model stopping Tool Calls and the Goal continuing work be evaluated independently. `scenario.py` does not build a model request itself, so the actual message history belongs to the caller of `run_round`.\n\n<!-- fill {\"index\":2} -->\n\n## 6.3 Continuation Must Use Observable Results\n\nPython's `run_round` returns a result string. If it contains `accepted`, the Goal becomes `completed`; if it contains `blocked`, it becomes `blocked`; otherwise the loop continues or becomes `limit_reached` at the limit. These exits live in one loop, which updates the status and reason together.\n\n**This version can express completion, blockage, and a limit, yet it cannot distinguish no progress from progress that still needs another Round.** The returned `Goal` is the directly inspectable result here; the function does not create Session Events by itself. The TypeScript version uses a structured `RoundResult` with `progressed`, `completed`, and `blockedReason`, and writes status changes to the Session Log. A production implementation needs observable results of this kind instead of a text-contains-word check.\n\n<!-- fill {\"index\":3} -->\n\n## 6.4 The Six Chapters on One DSH Runtime Path\n\nOne Step completes a model-and-tool round-trip in the Agent Loop. The context layer decides which information that Step sees. The plugin runtime decides what capabilities currently exist. The Session Log saves inputs, actions, and capability changes as shared facts. Runtime Tools add a needed capability during work. Goals and Rounds decide whether work needs another Turn after the current one ends.\n\nThe Python teaching version retains the smallest interfaces along this path and explicitly leaves Service dependencies, automatic session recording, structured Round results, and persistence as extension points. Full DSH adds configuration layers, permissions, sandboxes, and more runtime providers on top of those interfaces.\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",
      "diffStats": {
        "filesChanged": 1,
        "additions": 41,
        "deletions": 0,
        "files": [
          {
            "path": "python_harness/scenario.py",
            "additions": 41,
            "deletions": 0
          }
        ]
      },
      "requests": [],
      "events": [],
      "trace": [],
      "graphs": []
    }
  ]
}