Deep Agents Planning and Todos

Master the write_todos tool for effective task planning and decomposition in Deep Agents.

Use This Skill When

• You need to break down complex multi-step tasks (3+ steps) into trackable subtasks.
• You want to show users the plan before executing (user approval workflow).
• You're debugging why todos aren't completing as expected.
• You need patterns for different task types (research, coding, analysis, document processing).
• You want to visualize todo progression from LangSmith traces.

When To Use write_todos

Decision rule: If you'd benefit from showing the user "Here's my plan..." before starting, use write_todos.

Quick Start

from deepagents import create_deep_agent

# TodoListMiddleware is included by default in create_deep_agent
agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    tools=[search_tool, summarize_tool],
    system_prompt="You are a research assistant. Use write_todos for multi-step tasks."
)

# Agent workflow:
# 1. Call write_todos with initial plan
# 2. Ask user: "Does this plan look good?"
# 3. User approves → start executing
# 4. Update the todo list as work progresses
# 5. Keep todos aligned with the current plan and execution state

Example todo creation:

# Agent calls write_todos internally:
{
  "name": "write_todos",
  "arguments": {
    "todos": [
      {"content": "Search for papers on LLM agents", "status": "pending"},
      {"content": "Read and extract findings from top 5 papers", "status": "pending"},
      {"content": "Identify common themes", "status": "pending"},
      {"content": "Write summary report", "status": "pending"}
    ]
  }
}

Todo Structure and API

Two-Field Structure

{
  "content": "Task description (clear, actionable)",
  "status": "pending" | "in_progress" | "completed"
}

Key Constraints

Full-list updates: Treat each write_todos call as a full state update and include all active todos.

Per-turn discipline: Prefer one write_todos update per model turn to avoid conflicting plan changes.

Best granularity: Keep lists to 3-6 items maximum (avoid over-fragmentation).

Tooling Note

Deep Agents documentation describes write_todos as the built-in interface for todo planning/tracking. Keep todo state accurate by rewriting the list with updated statuses as execution progresses.

Status Lifecycle

pending → in_progress → completed

Best practices:

1. Create todos with "status": "pending" for newly planned work.
2. Update to "in_progress" when starting work on a todo.
3. Mark "completed" when finished (don't delete - keeps context).
4. For interactive workflows, ask user approval ("Does this plan look good?") before starting execution.

Typical workflow:

# Step 1: Create initial plan (all pending)
write_todos([
  {"content": "Research topic", "status": "pending"},
  {"content": "Write summary", "status": "pending"}
])

# Step 2: Ask user approval
# User: "Yes, proceed"

# Step 3: Start first task
write_todos([
  {"content": "Research topic", "status": "in_progress"},
  {"content": "Write summary", "status": "pending"}
])

# Step 4: Complete first task, start second
write_todos([
  {"content": "Research topic", "status": "completed"},
  {"content": "Write summary", "status": "in_progress"}
])

# Step 5: Finish all tasks
write_todos([
  {"content": "Research topic", "status": "completed"},
  {"content": "Write summary", "status": "completed"}
])

Todo Patterns By Task Type

Quick Reference

For detailed patterns with code examples, see references/todo-patterns.md.

Best Practices

✅ DO

• Granularity: Keep lists to 3-6 items max (clear milestones, not micro-tasks).
• Naming: Use clear, action-oriented descriptions ("Search for X", "Analyze Y").
• User interaction: Always ask approval before executing plan.
• Status updates: Update promptly as items complete (don't skip status transitions).
• Context management: Use with filesystem tools for complex workflows.

⚠️ DON'T

• Over-fragment: Avoid 10+ todos (too granular, hard to track).
• Vague descriptions: "Do research" → "Search LangChain docs for Deep Agents overview".
• Skip approval: Don't start executing without user confirmation.
• Forget updates: Always update status when transitioning tasks.
• Drop existing context: Include existing active items when rewriting todos.

Troubleshooting

Todo Not Completing

Symptom: Todo stuck in in_progress, agent loops or gets confused.

Causes & fixes:

• Missing status update → Ensure agent updates status when task finishes.
• Unclear completion criteria → Make content more specific ("Read 5 papers" vs "Do research").
• Agent forgot about todos → Add to system prompt: "Use write_todos to maintain and update the plan as work progresses."

Agent Ignoring Todos

Symptom: Agent creates todos but doesn't follow them.

Causes & fixes:

• Missing system prompt guidance → Add: "Follow the todo list. Update status as you complete each item."
• One-off task (doesn't need todos) → Use direct execution for simple queries.
• Conflicting instructions → Remove competing planning instructions from prompt.

Too Many Todos

Symptom: 10+ todos, hard to track, agent overwhelmed.

Causes & fixes:

• Over-planning → Simplify to 3-6 high-level milestones.
• Nested subtasks → Use todo hierarchy pattern (see references/todo-patterns.md).
• Wrong abstraction → Consider breaking into multiple agent invocations.

Lost Context

Symptom: Agent loses track of what's been done.

Causes & fixes:

• No filesystem persistence → Use FilesystemBackend or StoreBackend for long sessions.
• Not maintaining todos → Update write_todos whenever status changes or scope shifts.
• Memory issues → Use MemoryMiddleware for long-term context.

Visualizing Todos

Use the included script to parse LangSmith traces and visualize todo progression:

# Export trace from LangSmith (download JSON)
# Then run:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json

# Show Mermaid diagram:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json --format mermaid

# Show full timeline:
uv run skills/deepagents-planning-todos/scripts/visualize_todos.py trace.json --show-timeline

Output example:

Todo Timeline for trace abc123:

Initial Plan (Step 1):
  ⏳ [pending] Search for papers on LLM agents
  ⏳ [pending] Read and extract findings
  ⏳ [pending] Identify common themes
  ⏳ [pending] Write summary report

Final State:
  ✅ [completed] Search for papers on LLM agents
  ✅ [completed] Read and extract findings
  ✅ [completed] Identify common themes
  ✅ [completed] Write summary report

Resources

References (detailed patterns):

• references/todo-patterns.md: Task-specific patterns with code examples

Examples (working code):

• assets/examples/todo-driven-agent/: Research agent demonstrating full workflow

Example structures (templates):

• assets/todo-structures/research-todos.json: Research task breakdown
• assets/todo-structures/coding-todos.json: Coding task breakdown

External docs:

• Deep Agents overview: https://docs.langchain.com/oss/python/deepagents/overview
• Deep Agents customization (middleware defaults): https://docs.langchain.com/oss/python/deepagents/customization
• Deep Agents harness (planning capabilities): https://docs.langchain.com/oss/python/deepagents/harness
• LangChain To-do middleware: https://docs.langchain.com/oss/python/langchain/middleware/built-in
• LangSmith tracing for Deep Agents: https://docs.langchain.com/langsmith/trace-deep-agents