chore: Spring AI 重构
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`s01 > s02 > [ s03 ] s04 > s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12`
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> *"An agent without a plan drifts"* -- list the steps first, then execute.
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> *"An agent without a plan drifts"* -- list the steps first, then execute. Doubles the completion rate.
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>
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> **Harness layer**: Planning -- keeping the model on course without scripting the route.
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## Problem
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On multi-step tasks, the model loses track. It repeats work, skips steps, or wanders off. Long conversations make this worse -- the system prompt fades as tool results fill the context. A 10-step refactoring might complete steps 1-3, then the model starts improvising because it forgot steps 4-10.
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On multi-step tasks, the model loses track -- repeats work, skips steps, or wanders off. Long conversations make this worse: tool results keep filling the context, gradually diluting the system prompt's influence. A 10-step refactoring might complete steps 1-3, then the model starts improvising because steps 4-10 have been pushed out of attention.
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## Solution
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@@ -28,69 +28,93 @@ On multi-step tasks, the model loses track. It repeats work, skips steps, or wan
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| [x] task C |
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+-----------------------+
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if rounds_since_todo >= 3:
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inject <reminder> into tool_result
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Inject latest todo state into
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system prompt via defaultSystem()
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on each request
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```
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## How It Works
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1. TodoManager stores items with statuses. Only one item can be `in_progress` at a time.
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```python
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class TodoManager:
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def update(self, items: list) -> str:
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validated, in_progress_count = [], 0
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for item in items:
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status = item.get("status", "pending")
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if status == "in_progress":
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in_progress_count += 1
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validated.append({"id": item["id"], "text": item["text"],
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"status": status})
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if in_progress_count > 1:
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raise ValueError("Only one task can be in_progress")
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self.items = validated
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return self.render()
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```
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```java
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public class TodoManager {
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2. The `todo` tool goes into the dispatch map like any other tool.
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public record TodoItem(String id, String text, String status) {}
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```python
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TOOL_HANDLERS = {
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# ...base tools...
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"todo": lambda **kw: TODO.update(kw["items"]),
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private List<TodoItem> items = new ArrayList<>();
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@Tool(description = "Update the full task list to track progress. "
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+ "Each item must have id, text, status (pending/in_progress/completed). "
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+ "Only one task can be in_progress at a time. Max 20 items.")
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public String updateTodos(
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@ToolParam(description = "The complete list of todo items")
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List<TodoItem> items) {
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if (items.size() > 20) return "Error: Max 20 todos allowed";
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List<TodoItem> validated = new ArrayList<>();
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int inProgressCount = 0;
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for (TodoItem item : items) {
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String status = (item.status() != null)
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? item.status().toLowerCase() : "pending";
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if ("in_progress".equals(status)) inProgressCount++;
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validated.add(new TodoItem(item.id(), item.text().trim(), status));
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}
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if (inProgressCount > 1)
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return "Error: Only one task can be in_progress at a time";
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this.items = validated;
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return render();
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}
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}
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```
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3. A nag reminder injects a nudge if the model goes 3+ rounds without calling `todo`.
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2. `TodoManager` is registered via `defaultTools()`; the `@Tool` annotated method is automatically exposed as a tool.
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```python
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if rounds_since_todo >= 3 and messages:
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last = messages[-1]
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if last["role"] == "user" and isinstance(last.get("content"), list):
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last["content"].insert(0, {
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"type": "text",
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"text": "<reminder>Update your todos.</reminder>",
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})
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```java
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ChatClient chatClient = ChatClient.builder(chatModel)
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.defaultSystem(system)
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.defaultTools(
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new BashTool(),
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new ReadFileTool(),
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new WriteFileTool(),
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new EditFileTool(),
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todoManager // @Tool annotated method auto-registered
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)
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.build();
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```
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The "one in_progress at a time" constraint forces sequential focus. The nag reminder creates accountability.
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3. System prompt injection: on each user input, inject the latest todo state into the system prompt with emphasis on update instructions.
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```java
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// Dynamic system prompt: includes current todo state
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String system = "You are a coding agent at " + workDir + ".\n"
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+ "Use the todo tool to plan multi-step tasks. "
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+ "Mark in_progress before starting, completed when done.\n"
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+ "IMPORTANT: You MUST call updateTodos regularly.\n\n"
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+ "<current-todos>\n" + todoManager.render() + "\n</current-todos>";
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```
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The "only one in_progress at a time" constraint forces sequential focus. Continuously injecting todo state into the system prompt creates accountability pressure -- the model sees its own plan every turn and won't forget to update it.
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> **TIP**: The Python version tracks `rounds_since_todo` inside the tool loop and injects `<reminder>` text after 3 consecutive rounds without a todo call. Spring AI's ChatClient manages the tool loop automatically and doesn't allow mid-loop injection, so system prompt injection is used instead to achieve the same effect.
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## What Changed From s02
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| Component | Before (s02) | After (s03) |
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|----------------|------------------|----------------------------|
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| Tools | 4 | 5 (+todo) |
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| Planning | None | TodoManager with statuses |
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| Nag injection | None | `<reminder>` after 3 rounds|
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| Agent loop | Simple dispatch | + rounds_since_todo counter|
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| Component | Before (s02) | After (s03) |
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|----------------|------------------|--------------------------------------|
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| Tools | 4 | 5 (+TodoManager `@Tool`) |
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| Planning | None | TodoManager with statuses |
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| State injection| None | System prompt injection `<current-todos>` |
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| ChatClient | Fixed system prompt | Rebuilt each turn, dynamic todo state injection |
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## Try It
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```sh
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cd learn-claude-code
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python agents/s03_todo_write.py
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mvn exec:java -Dexec.mainClass=io.mybatis.learn.s03.S03TodoWrite
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```
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1. `Refactor the file hello.py: add type hints, docstrings, and a main guard`
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2. `Create a Python package with __init__.py, utils.py, and tests/test_utils.py`
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3. `Review all Python files and fix any style issues`
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Try these prompts (English prompts work better with LLMs, but Chinese also works):
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1. `Refactor the file Hello.java: add JavaDoc, improve naming, and keep main method behavior unchanged`
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2. `Create a Java package with utils and tests`
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3. `Review all Java files and fix any style issues`
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