Agentcy: Multi-Agent Collaboration with AutoGen

This code demonstrates the power of multi-agent collaboration using the AutoGen library. Instead of relying on a single agent to handle tasks, multiple specialized agents work together, each bringing its expertise to the table.

📖 Overview

The code sets up a collaborative environment where multiple agents, each with its unique role and expertise, come together to discuss, plan, and execute tasks. This collaboration ensures that different aspects of a task are handled by the most qualified agent, leading to more efficient and accurate outcomes.

🕵🏽 Agents

The agents involved in the collaboration include:

1. Agency Researcher: Conducts research on user pain points, market opportunities, and prevailing market conditions.
2. Writing Assistant: Utilizes research and content writing functions to generate content.
3. Agency Strategist: Drafts strategic briefs for effective brand positioning in the market.
4. Agency Copywriter: Crafts compelling narratives and messages that align with the brand's strategy.
5. Agency Media Planner: Identifies the best mix of media channels for advertising.
6. Agency Marketer: Transforms strategy and insights into marketable ideas.
7. Agency Manager: Oversees the entire project lifecycle.
8. Agency Director: Guides the creative vision of the project.
9. User Proxy: Acts as an intermediary between the human user and the agents.

(Note: Some agents from the original list like Designer and Account Manager were commented out in the provided code updates.)

🤝 Collaboration Flow

1. The GroupChat class establishes a collaborative environment for agent communication.
2. The GroupChatManager oversees group chat, ensuring smooth agent interaction.
3. The initiate_chat method kickstarts the collaboration process.

⚙️ Setup & Configuration

1. Ensure required libraries are installed:

pip install pyautogen

2. Set up the OpenAI configuration list by either providing an environment variable OAI_CONFIG_LIST or specifying a file path.

[
    {
        "model": "gpt-3.5-turbo", #or whatever model you prefer
        "api_key": "INSERT_HERE"
    }
]

3. Setup api keys in .env:

OPENAI_API_KEY="XXX"
SERPAPI_API_KEY="XXX"
SERPER_API_KEY="XXX"
BROWSERLESS_API_KEY="XXX"

4. Launch in CLI:

python3 main.py

⏯️ Conclusion

In the realm of creative agencies, the multi-agent collaboration approach revolutionizes the way projects are handled. By tapping into the distinct expertise of various agency roles, from strategists to media planners, we can guarantee that each facet of a project is managed by those best suited for the task. This methodology not only ensures precision and efficiency but also showcases its versatility, as it can be tailored to suit diverse project requirements, whether it's brand positioning, content creation, or any other creative endeavor.

Credit to Jason Zhou's RAG example.

📈 Roadmap

1. Refine workflow and data pass through to agents
2. Reduce unnecessaery back and forth
3. Save files to local folder
4. Implement other agents, see commented out agents
5. Create and train fine-tuned agents for each domain specific task

📝 License

MIT License. See LICENSE for more information.