This LLM Agents Hackathon, hosted by Berkeley RDI and in conjunction with the LLM agents MOOC, aims to bring together students, researchers, and practitioners to build and showcase innovative work in LLM agents, grow the AI agent community, and advance LLM agent technology. It is open to the public and will be held both virtually and in-person at UC Berkeley.
The hackathon is designed to have 5 tracks:
- Applications Track: Building innovative LLM agent applications across diverse domains, from coding assistants to personal AI companions.
- Benchmarks Track: Creating and improving benchmarks for AI agents, enabling standardized evaluation and comparison of different agent architectures and capabilities.
- Fundamentals Track: Enhancing core agent capabilities such as memory, planning, reasoning, and tool use through novel frameworks and techniques.
- Safety Track: Addressing critical safety concerns in AI agent deployment, including misuse prevention, privacy, interpretability, and broader societal impacts.
- Decentralized and Multi-Agents Track: Advancing tools, frameworks, and applications for decentralized multi-agent systems, focusing on enhanced capabilities, interactions, and deployment.
Each track specifies a set of designated tasks, each with a corresponding prize. Beyond the designated tasks, participants can also propose self-selected tasks to compete for prizes of the given track!
We hope this hackathon with these specially-designed tracks can help demonstrate that we are entering a new phase of maturity and practicality of LLM agent technology where:
- Every developer can learn to use LLM agent technology for building innovative applications (Applications Track)
- Decentralized community collaboration can effectively bring the community together to build key technologies and infrastructure for LLM agents, serving as important foundations and public good for the community in AI (Benchmarks, Fundamentals, and Safety Tracks)
HACKATHON TRACKS
Applications Track
Develop innovative LLM-based agents for various domains, including coding assistants, customer service, regulatory compliance, data science, AI scientists, and personal assistants. Focus on both hard-design problems (novel domain-specific tools) and soft-design problems (high-fidelity human simulations and improved AI agent interfaces).
Benchmarks Track
Create or improve AI agent benchmarks for novel tasks or extend existing ones. Focus on developing multi-modal or multi-agent benchmarks, improving evaluation methods, and creating more robust and efficient testing environments for AI agents.
Fundamentals Track
Enhance core agent capabilities in memory, planning, reasoning, tool-use, and multimodal interactions. Improve existing frameworks, design novel prompting schemes, and develop better methods for agents to interact with various tools and environments.
Safety Track
Address critical safety concerns in AI agent deployment, including preventing misuse, ensuring privacy, improving interpretability, and assessing broader societal impacts. Develop methods for better control, auditing, and accountability of AI agents in various applications and multi-agent systems.
Decentralized and Multi-Agents Track
Develop innovative tools, frameworks, and infrastructure to enhance multi-agent capabilities and decentralized deployment. Investigate how multiple agents interact with each other and how we can better leverage their capabilities. Design novel applications across domains, emphasizing decentralization benefits like robustness, scalability, and privacy.
TIMELINE (SUBJECT TO CHANGE)
| Date | Event |
|---|---|
| October 17 | Participant Sign up Open |
| October 21 | Begin LLM Agents Hackathon; Team/Track Sign Up |
| November 18 | Midpoint Project Check-in |
| December 12 | Final submissions |