Autonomous Research Agent

A modular AI pipeline that plans, executes, stores memory, and reports research—all orchestrated by main.py.

🚀 Features

• Planner: Breaks a research goal into actionable subtasks
• Executor: Uses an LLM to research each subtask
• Memory: Persists session results locally (or vector store)
• Reporter: Generates a Markdown report of findings
• Fully Tested: Unit and integration tests with pytest

📦 Installation

1. Clone the repo:

   git clone https://github.com/ramonbnuezjr/autogpt-research-agent.git
   cd autogpt-research-agent
   

2. Copy and configure your environment:

   cp .env.example .env
   # Edit `.env` to set your OpenAI API key and backend
   

3. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate
   

4. Install dependencies:

   pip install -r requirements.txt
   

🧠 Usage

Run the agent from the CLI:

python main.py --goal "How is AI being used in smart cities?"

Note: If not using CLI flags yet, just run python main.py and input the goal interactively.

📝 Sample Report

We’ve saved the latest research report in the reports/ folder.

Example:

• How_are_AI_agents_similar_to_new_species_20250512_184941.md

Browse the full folder:

• 📁 reports/

✅ Testing

We use pytest for all tests.

1. Install test dependencies:

   pip install pytest
   

2. Run all tests:

   pytest
   

3. (Optional) Enable live API testing:

   export LIVE_API=true
   pytest -m live
   

🔐 API Key Setup

This project requires access to an LLM (e.g., OpenAI). To run it, you must create a .env file using the template provided:

cp .env.example .env

Then edit .env with your personal API key:

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxx
LLM_BACKEND=openai

Never commit .env to version control.

🧪 CI & Linting

See .github/workflows/ci.yml and lint config files in the repo root for continuous integration, formatting, and type checking.