# AG2

[AG2](https://github.com/ag2ai/ag2) is a framework for building multi-agent applications with LLMs.
This example uses AG2 to orchestrate the GPT Researcher multi-agent workflow.

Check out our blog posts:
- [Deep Web Research with AG2 and GPT Researcher](https://docs.ag2.ai/latest/docs/blog/#deep-web-research-with-ag2-and-gpt-researcher) (AG2 Blog)

## Use case
By using AG2, the research process can be significantly improved in depth and quality by leveraging multiple agents with specialized skills.
Inspired by the recent [STORM](https://arxiv.org/abs/2402.14207) paper, this example showcases how a team of AI agents can work together to conduct research on a given topic, from planning to publication.

An average run generates a 5-6 page research report in multiple formats such as PDF, Docx and Markdown.

Please note: This example uses the OpenAI API only for optimized performance.

## 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

![AG2 Pipeline](/img/ag2-pipeline.webp)

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

## How to run
1. Install required packages:
    ```bash
    pip install -r requirements.txt
    pip install -r multi_agents_ag2/requirements.txt
    ```
2. Update env variables:
    ```bash
    export OPENAI_API_KEY={Your OpenAI API Key here}
    export TAVILY_API_KEY={Your Tavily API Key here}
    ```
3. Run the application:
    ```bash
    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.
