This is a Next.js project bootstrapped with create-next-app.
See the canonical environment-variable guide before starting local development.
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun devOpen 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.
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!
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.
fromdeepagentsimportcreate_deep_agentfromdeepagents.middleware.filesystemimportFilesystemMiddlewarefromdeepagents.backends.utilsimportcreate_file_datafromdeepagents.backends.stateimportStateBackendfromlangchain.toolsimportToolRuntimefromlangchain.chat_modelsimportinit_chat_modelfromlanggraph.store.memoryimportInMemoryStore# 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,
}
forchunkinagent.stream( input, config={"configurable": {"thread_id": "openai"}}, # Dual-mode for HITL support stream_mode=["values"],
): if"messages"inchunk:
chunk["messages"][-1].pretty_print()