This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

Learn More

To learn more about Next.js, take a look at the following resources:

• Next.js Documentation - learn about Next.js features and API.
• Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Blog Roadmap

How to Pre-Configure files for Deep Agents

from deepagents import create_deep_agent
from deepagents.middleware.filesystem import FilesystemMiddleware  
from deepagents.backends.utils import create_file_data  
from deepagents.backends.state import StateBackend  
from langchain.tools import ToolRuntime
from langchain.chat_models import init_chat_model
from langgraph.store.memory import InMemoryStore  

# Pre-configure files  
initial_files = {  
	"/project/README.md": create_file_data("# My Project\n\nInitial documentation."),  
	"/project/src/app.py": create_file_data("def main():\n    print('Hello!')")  
}  

# Create runtime with pre-populated state  
runtime = ToolRuntime(  
	state={"messages": [], "files": initial_files},  
	context=None,  
	tool_call_id="tc",  
	store=InMemoryStore(),  
	stream_writer=lambda _: None,  
	config={},  
)  

# Create backend with pre-configured files  
backend = StateBackend(runtime)
model = init_chat_model(model="openai:gpt-4.1-mini")
agent = create_deep_agent(backend=backend, model=model)
input = {
	"messages": [{"role": "user", "content": "List the project files."}],
	"files": initial_files,
}
for chunk in agent.stream(  
	input,  
	config={"configurable": {"thread_id": "openai"}},  # Dual-mode for HITL support  
	stream_mode=["values"],

): 
	if "messages" in chunk:
		chunk["messages"][-1].pretty_print()