# Deep Agent with StateGraph

This repository contains a minimal implementation of a **Deep Agent** based on the
`deep-agents-from-scratch` notebook.  The agent uses a LangGraph
`StateGraph` to orchestrate the following steps:

1. **Think** – decide what to search for.
2. **Search** – perform a web search with Tavily.
3. **Summarize** – summarize each search result and store the raw content in a
   virtual file system.
4. **Write File** – dump all virtual files to disk.

The implementation follows the modern LangChain API guidelines:

* All imports use the current modular packages (`langchain_openai`,
  `langgraph`, `langchain_core`, etc.).
* No legacy `langchain.chat_models` or `langchain.llms` imports.
* The agent can be run from the command line.

## Setup

```bash
# Create a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the agent interactively
python src/deep_agents_from_scratch/deep_agent.py
```

## Usage

After starting the script you will be prompted to enter a research question.
The agent will perform a web search, summarize the results, and write the
raw content to the `output/` directory.

## Project Structure

```
src/
├── deep_agents_from_scratch/
│   ├── __init__.py
│   ├── deep_agent.py
│   ├── research_tools.py
│   └── state.py
```

- `state.py` – defines the `DeepAgentState` dataclass used by the graph.
- `research_tools.py` – contains the Tavily search, think, and summarization
  tools.
- `deep_agent.py` – builds the `StateGraph`, defines node functions, and
  provides a CLI entry point.

## Extending the Agent

You can add more tools or nodes by following the pattern used in
`deep_agent.py`.  For example, to add a node that writes the summaries to a
PDF you would:

1. Create a new tool that formats the summaries.
2. Add a node function that calls the tool.
3. Connect the node in the graph.

## License

MIT License.