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better doc
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# s03: TodoWrite
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> A TodoManager lets the agent track its own progress, and a nag reminder injection forces it to keep updating when it forgets.
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`s01 > s02 > [ s03 ] s04 > s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12`
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## The Problem
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> *"Plan before you act"* -- visible plans improve task completion.
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When an agent works on a multi-step task, it often loses track of what it
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has done and what remains. Without explicit planning, the model might repeat
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work, skip steps, or wander off on tangents. The user has no visibility
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into the agent's internal plan.
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## Problem
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This is worse than it sounds. Long conversations cause the model to "drift"
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-- the system prompt fades in influence as the context window fills with
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tool results. A 10-step refactoring task might complete steps 1-3, then
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the model starts improvising because it forgot steps 4-10 existed.
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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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The solution is structured state: a TodoManager that the model writes to
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explicitly. The model creates a plan, marks items in_progress as it works,
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and marks them completed when done. A nag reminder injects a nudge if the
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model goes 3+ rounds without updating its todos.
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Note: the nag threshold of 3 rounds is low for visibility. Production systems tune higher. From s07, this course switches to the Task board for durable multi-step work; TodoWrite remains available for quick checklists.
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## The Solution
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## Solution
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```
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+----------+ +-------+ +---------+
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| User | ---> | LLM | ---> | Tools |
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| prompt | | | | + todo |
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+----------+ +---+---+ +----+----+
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^ |
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| tool_result |
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+---------------+
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+-----------+-----------+
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| TodoManager state |
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| [ ] task A |
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| [>] task B <- doing |
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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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+--------+ +-------+ +---------+
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| User | ---> | LLM | ---> | Tools |
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| prompt | | | | + todo |
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+--------+ +---+---+ +----+----+
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^ |
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| tool_result |
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+----------------+
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+-----------+-----------+
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| TodoManager state |
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| [ ] task A |
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| [>] task B <- doing |
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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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```
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## How It Works
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1. The TodoManager validates and stores a list of items with statuses.
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Only one item can be `in_progress` at a time.
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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 __init__(self):
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self.items = []
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def update(self, items: list) -> str:
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validated = []
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in_progress_count = 0
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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({
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"id": item["id"],
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"text": item["text"],
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"status": status,
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})
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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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2. The `todo` tool is added to the dispatch map like any other tool.
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2. The `todo` tool goes into the dispatch map like any other tool.
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```python
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TOOL_HANDLERS = {
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"bash": lambda **kw: run_bash(kw["command"]),
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# ...other tools...
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"todo": lambda **kw: TODO.update(kw["items"]),
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# ...base tools...
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"todo": lambda **kw: TODO.update(kw["items"]),
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}
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```
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3. The nag reminder injects a `<reminder>` tag into the tool_result
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messages when the model goes 3+ rounds without calling `todo`.
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3. A nag reminder injects a nudge if the model goes 3+ rounds without calling `todo`.
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```python
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def agent_loop(messages: list):
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rounds_since_todo = 0
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while True:
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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"
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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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# ... rest of loop ...
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rounds_since_todo = 0 if used_todo else rounds_since_todo + 1
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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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```
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4. The system prompt instructs the model to use todos for planning.
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```python
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SYSTEM = f"""You are a coding agent at {WORKDIR}.
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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.
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Prefer tools over prose."""
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```
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## Key Code
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The TodoManager and nag injection (from `agents/s03_todo_write.py`,
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lines 51-85 and 158-187):
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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 = []
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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({
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"id": item["id"],
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"text": item["text"],
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"status": status,
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})
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if in_progress_count > 1:
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raise ValueError("Only one in_progress")
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self.items = validated
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return self.render()
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# In agent_loop:
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if rounds_since_todo >= 3:
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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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```
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The "one in_progress at a time" constraint forces sequential focus. The nag reminder creates accountability.
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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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| 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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## Design Rationale
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Visible plans improve task completion because the model can self-monitor progress. The nag mechanism creates accountability -- without it, the model may abandon plans mid-execution as conversation context grows and earlier instructions fade. The "one in_progress at a time" constraint enforces sequential focus, preventing context-switching overhead that degrades output quality. This pattern works because it externalizes the model's working memory into structured state that survives attention drift.
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## Try It
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```sh
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@ -161,8 +89,6 @@ cd learn-claude-code
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python agents/s03_todo_write.py
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```
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Example prompts to try:
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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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