# Deep Search Agent

A minimal implementation of a deep search agent built from scratch using the LangChain framework and OpenAI API.  
The agent decides whether to answer a query directly or perform a web search using SerpAPI.

## Prerequisites

- Node.js 18+ (ESM support)
- An OpenAI API key
- A SerpAPI key (free tier available)

## Setup

```bash
# Clone the repository
git clone https://github.com/your-username/deep-search-agent.git
cd deep-search-agent

# Install dependencies
npm install

# Create a .env file with your API keys
cp .env.example .env
# Edit .env and replace the placeholders with your actual keys
```

## Usage

Run the agent with a query:

```bash
npm start -- "What is the capital of France?"
```

The agent will output either a direct answer or the results of a web search.

## How It Works

1. **Planner** – Uses an LLM to decide if the query requires a web search or can be answered directly.  
2. **Executor** – If a search is needed, the agent calls the SerpAPI tool and returns the results.  
3. **Memory** – Stores conversation context (optional for future extensions).

## Extending

- Add more tools (e.g., Wikipedia, Calculator) and update the planner prompt accordingly.
- Replace the planner with a more sophisticated planner (e.g., chain of thought).
- Persist memory to a database for long‑term context.

## License

MIT License