refactor: integrate TodoListMiddleware and update related components

- Removed the write_todos tool as it is now included by default through TodoListMiddleware in the deep agent.
- Updated the system prompt and documentation to reflect the integration of TodoListMiddleware, clarifying its capabilities for managing planning and todo lists.
- Enhanced the chat handling logic to extract todos directly from the deep agent's command output, ensuring seamless user experience.
- Refactored UI components to align with the new data structure and improve rendering of todo items, including updates to the Plan and TodoItem components.
- Cleaned up code for better maintainability and readability, following recent refactoring efforts.
This commit is contained in:
Anish Sarkar 2025-12-27 15:18:34 +05:30
parent 8a3ab3dfac
commit c28a90fc29
10 changed files with 172 additions and 419 deletions

View file

@ -69,6 +69,30 @@ def format_mentioned_documents_as_context(documents: list[Document]) -> str:
return "\n".join(context_parts)
def extract_todos_from_deepagents(command_output) -> dict:
"""
Extract todos from deepagents' TodoListMiddleware Command output.
deepagents returns a Command object with:
- Command.update['todos'] = [{'content': '...', 'status': '...'}]
Returns the todos directly (no transformation needed - UI matches deepagents format).
"""
todos_data = []
if hasattr(command_output, "update"):
# It's a Command object from deepagents
update = command_output.update
todos_data = update.get("todos", [])
elif isinstance(command_output, dict):
# Already a dict - check if it has todos directly or in update
if "todos" in command_output:
todos_data = command_output.get("todos", [])
elif "update" in command_output and isinstance(command_output["update"], dict):
todos_data = command_output["update"].get("todos", [])
return {"todos": todos_data}
async def stream_new_chat(
user_query: str,
search_space_id: int,
@ -557,9 +581,11 @@ async def stream_new_chat(
tool_name = event.get("name", "unknown_tool")
raw_output = event.get("data", {}).get("output", "")
# Extract content from ToolMessage if needed
# LangGraph may return a ToolMessage object instead of raw dict
if hasattr(raw_output, "content"):
# Handle deepagents' write_todos Command object specially
if tool_name == "write_todos" and hasattr(raw_output, "update"):
# deepagents returns a Command object - extract todos directly
tool_output = extract_todos_from_deepagents(raw_output)
elif hasattr(raw_output, "content"):
# It's a ToolMessage object - extract the content
content = raw_output.content
# If content is a string that looks like JSON, try to parse it
@ -721,12 +747,10 @@ async def stream_new_chat(
elif tool_name == "write_todos":
# Build completion items for planning
if isinstance(tool_output, dict):
plan_title = tool_output.get("title", "Plan")
todos = tool_output.get("todos", [])
todo_count = len(todos) if isinstance(todos, list) else 0
completed_items = [
*last_active_step_items,
f"Plan: {plan_title[:50]}{'...' if len(plan_title) > 50 else ''}",
f"Tasks: {todo_count} steps defined",
]
else:
@ -883,11 +907,10 @@ async def stream_new_chat(
)
# Send terminal message with plan info
if isinstance(tool_output, dict):
title = tool_output.get("title", "Plan")
todos = tool_output.get("todos", [])
todo_count = len(todos) if isinstance(todos, list) else 0
yield streaming_service.format_terminal_info(
f"Plan created: {title} ({todo_count} tasks)",
f"Plan created ({todo_count} tasks)",
"success",
)
else: