AG2 x GPT Researcher

AG2 is a framework for building multi-agent applications with LLMs. This example uses AG2 to orchestrate the GPT Researcher multi-agent workflow.

Use case

This example mirrors the LangGraph flow with the same set of agents and stages, but uses AG2 as the orchestration layer.

The Multi Agent Team

The research team is made up of 8 agents:

• Human - The human in the loop that oversees the process and provides feedback to the agents.
• Chief Editor - Oversees the research process and manages the team.
• Researcher (gpt-researcher) - A specialized autonomous agent that conducts in depth research on a given topic.
• Editor - Responsible for planning the research outline and structure.
• Reviewer - Validates the correctness of the research results given a set of criteria.
• Revisor - Revises the research results based on the feedback from the reviewer.
• Writer - Responsible for compiling and writing the final report.
• Publisher - Responsible for publishing the final report in various formats.

How it works

Stages:

1. Planning stage
2. Data collection and analysis
3. Review and revision
4. Writing and submission
5. Publication

How to run

1. Install dependencies:
   bashCopy

   pip install -r requirements.txt
   pip install -r multi_agents_ag2/requirements.txt
   

2. Update env variables:
   bashCopy

   export OPENAI_API_KEY={Your OpenAI API Key here}
   export TAVILY_API_KEY={Your Tavily API Key here}
   

3. Run the application:
   bashCopy

   python -m multi_agents_ag2.main
   

Usage

To change the research query and customize the report, edit multi_agents_ag2/task.json.

Task.json contains the following fields:

• query - The research query or task.
• model - The OpenAI LLM to use for the agents.
• max_sections - The maximum number of sections in the report. Each section is a subtopic of the research query.
• max_revisions - Maximum reviewer/reviser loops per section.
• include_human_feedback - If true, the user can provide feedback to the agents. If false, the agents will work autonomously.
• publish_formats - The formats to publish the report in. The reports will be written in the outputs directory.
• source - The location from which to conduct the research. Options: web or local. For local, please add DOC_PATH env var.
• follow_guidelines - If true, the research report will follow the guidelines below. It will take longer to complete. If false, the report will be generated faster but may not follow the guidelines.
• guidelines - A list of guidelines that the report must follow.
• verbose - If true, the application will print detailed logs to the console.