Deep Research Assistant

A CopilotKit Deep Agents demo showcasing planning, memory/files, and generative UI using Tavily for web research.

https://github.com/user-attachments/assets/68d5729f-91f9-4fd9-a579-cd1a8f4aad8d

What This Demo Shows

This demo showcases all key Deep Agents capabilities:

Planning (Todos) - Visible research plan with status indicators (pending, in progress, completed)
Memory/Files - Markdown files created by the agent, viewable in the workspace with download option
Generative UI - Rich tool call rendering with result summaries and expandable details
Web Research - Tavily-powered search for real-time information

Architecture

[User asks research question]
        ↓
Next.js Frontend (CopilotChat + Workspace)
        ↓
CopilotKit Runtime → LangGraphHttpAgent
        ↓
Python Backend (FastAPI + AG-UI)
        ↓
Deep Agent (research_assistant)
    ├── write_todos        (planning, built-in)
    ├── write_file         (filesystem, built-in)
    ├── read_file          (filesystem, built-in)
    └── research(query)
            └── internal Deep Agent [thread-isolated]
                    └── internet_search (Tavily)

Project Structure

deep-research-v2/
├── src/                              # Next.js frontend
│   ├── app/
│   │   ├── layout.tsx               # CopilotKit provider
│   │   ├── page.tsx                 # Main page with useDefaultTool
│   │   ├── globals.css              # Glassmorphism styles
│   │   └── api/copilotkit/route.ts  # CopilotRuntime endpoint
│   ├── components/
│   │   ├── Workspace.tsx            # Research progress display
│   │   ├── ToolCard.tsx             # Generative UI for tools
│   │   └── FileViewerModal.tsx      # Markdown file viewer
│   └── types/
│       └── research.ts              # TypeScript types
│
├── agent/                           # Python backend
│   ├── main.py                      # FastAPI server + AG-UI
│   ├── agent.py                     # Deep Agent definition
│   ├── tools.py                     # Tavily search tools
│   └── pyproject.toml               # Python dependencies
│
├── .env.example                     # Environment variables
└── README.md                        # This file

Environment Variables

Variable	Required	Default	Description
`OPENAI_API_KEY`	Yes	-	Get API key
`TAVILY_API_KEY`	Yes	-	Get API key
`OPENAI_MODEL`	No	`gpt-5.2`	Model to use (gpt-5.2, gpt-5, etc.)
`LANGGRAPH_DEPLOYMENT_URL`	No	`http://localhost:8123`	Backend URL
`SERVER_HOST`	No	`0.0.0.0`	Backend host
`SERVER_PORT`	No	`8123`	Backend port

Setup & Installation

Backend (Python)

bash

cd agent
uv venv && source .venv/bin/activate
uv pip install -e .

Or with pip:

bash

cd agent
python -m venv .venv && source .venv/bin/activate
pip install -e .

Frontend (Node.js)

bash

npm install

Environment

Copy .env.example to .env in both the root directory and agent/ directory, then fill in your API keys.

Running Locally

Terminal 1 - Backend:

bash

cd agent
uv run python main.py

Terminal 2 - Frontend:

bash

npm run dev

Open http://localhost:3000 and ask the assistant to research any topic.

Key Patterns

Frontend: useDefaultTool (not useCoAgent)

This demo uses local React state with useDefaultTool instead of useCoAgent to avoid type mismatches between Python's FilesystemMiddleware (Dict) and TypeScript (Array):

typescript

const [state, setState] = useState<ResearchState>(INITIAL_STATE);

useDefaultTool({
  render: (props) => {
    // Update local state based on tool results
    if (name === "write_todos" && status === "complete") {
      setState(prev => ({ ...prev, todos: result.todos }));
    }
    return <ToolCard {...props} />;
  },
});

Backend: Deep Agents with research tool

python

agent_graph = create_deep_agent(
    model=ChatOpenAI(model="gpt-5.2"),
    system_prompt=MAIN_SYSTEM_PROMPT,
    tools=[research],
    middleware=[CopilotKitMiddleware()],
    checkpointer=MemorySaver(),
)

Learn More

License

MIT