Evo Wins AutoGPT Arena Hackathon

Last month, over 5,000 participants across 500 teams competed in the AutoGPT Arena Hacks, where agent performances were measured by the most comprehensive AI agent benchmark to-date. We’re proud to announce that Evo emerged victorious by scoring highest on these benchmarks.  You can check out Evo here.

Challenge Accepted!  Evo Takes on AutoGPT’s Arena Challenges

After winning the SuperAGI hackathon in September, we were determined to keep improving Evo. AutoGPT’s Arena Hacks was the perfect platform to showcase Evo’s capabilities.

AutoGPT’s Arena was not your typical hackathon. Over 4 weeks, the main task was to develop an AI agent that can handle AutoGPT’s rigorous challenges through natural language input.

The challenges were grouped into 3 benchmarking categories:

• Scrape & Synthesize: Extract data from the web and creating datasets
• Data Mastery: Perform essential data science tasks
• Coding Excellence: Master the art of coding

These categories were meant as specialization tracks – an agent would typically only do well in one category. Evo ended up scoring highest in all 3 categories. It also won the grand prize of best generalist agent! 🥇

Let’s dive into the technology and architecture that makes Evo so reliable.

Evo's Multi-Agent Approach

Evo uses a multi-agent architecture.  Each agent persona has its own specialization and capabilities to achieve users’ goals. The best suited personas are selected in Evo’s execution loop:

1. Predict. With each iteration of the execution loop, Evo starts by making an informed prediction about what the best next step should be.
2. Select. Based on this prediction, Evo selects a best-fit agent persona.
3. Contextualize. Based on the prediction from step 1 and the agent persona in step 2, the complete chat history is "contextualized" and only the most relevant messages are used for the final evaluation step.
4. Evaluate and Execute. A final evaluation step is run to determine what agent function is executed to try and further achieve the user's goal.

Learn More

Developers can check out our Github repo for instructions on how to run Evo locally and contribute to the project.

Special thanks to the AutoGPT team for hosting an incredible hackathon and for their continued support for Evo!