Deep Research Agent

This directory contains the Deep Research Agent example for the Atomic Agents project. This example demonstrates how to create an intelligent research assistant that performs web searches and provides detailed answers with relevant follow-up questions.

Getting Started

Clone the main Atomic Agents repository:

git clone https://github.com/BrainBlend-AI/atomic-agents

Navigate to the Deep Research directory:

cd atomic-agents/atomic-examples/deep-research

Install dependencies using Poetry:
```
poetry install
```
Set up environment variables: Create a .env file in the deep-research directory with:
```
OPENAI_API_KEY=your_openai_api_key
SEARXNG_BASE_URL=http://localhost:8080
SEARXNG_API_KEY=your_searxng_secret_key
```
Important: To find your SearXNG secret key, check the secret_key value in your SearXNG settings.yml file, typically located at /etc/searxng/settings.yml or in the SearXNG installation directory.
Set up SearXNG:
- Install SearXNG from the official repository
- Default configuration expects SearXNG at http://localhost:8080
Run the Deep Research Agent:
```
poetry run python deep_research/main.py
```

File Explanation

1. Agents (`agents/`)

ChoiceAgent (choice_agent.py): Determines when new searches are needed
QueryAgent (query_agent.py): Generates optimized search queries
QuestionAnsweringAgent (qa_agent.py): Processes information and generates responses

2. Tools (`tools/`)

SearXNG Search (searxng_search.py): Performs web searches across multiple engines
Webpage Scraper (webpage_scraper.py): Extracts and processes web content

3. Main (`main.py`)

The entry point for the Deep Research Agent application. It orchestrates the research process:

Deciding when to perform new searches
Generating and executing search queries
Processing information and providing answers
Suggesting relevant follow-up questions

Example Usage

The agent can handle various research queries and provides:

Detailed answers based on current web information
Relevant citations and sources
Specific follow-up questions for deeper exploration
Context-aware responses that build on previous interactions

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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