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**Title**: Waggle Dance: An Adversarial-Cooperative Multi-Agent Goal Solver

**Abstract**:
Waggle Dance is a highly experimental, adversarial-cooperative multi-agent goal solver implemented in JavaScript. Inspired by Auto-GPT and gpt-researcher, it is designed to achieve speed, accuracy, observability, and simplicity. The project utilizes a Planner Agent to generate an execution graph for sub-tasks, which are executed concurrently by Execution Agents. This paper discusses the project's design, implementation, and potential impact in the field of Artificial Intelligence and software development.

**Introduction**:
Artificial Intelligence (AI) has experienced rapid growth and development over the past few years. One area of particular interest is the development of multi-agent systems. Waggle Dance is a multi-agent goal solver that operates in an adversarial-cooperative manner. It was inspired by Auto-GPT and gpt-researcher, and aims to provide a counter-balance to the many agentic systems written in Python. The project is designed to be accessible to the large number of JavaScript developers and is freely available under the MIT license.

**Materials and Methods**:
Waggle Dance is implemented in JavaScript and uses a variety of technologies including Typescript, Langchain.js, T3, Prisma, tRPC, Pinecone, Postgres, OpenAI API, and MUI Joy. It employs a Planner Agent to generate an execution graph for sub-tasks, which are then executed concurrently by Execution Agents. This design aims to reduce poor results and hallucinations. The project also includes an API implemented within Next.js and an unstable API available via tRPC.

**Results and Discussion**:
Waggle Dance offers several features that set it apart from other multi-agent systems. It has a highly concurrent execution graph, adversarial agents that review results, a vector database for long-term memory, and a responsive user interface. The project also has a roadmap for future development, which includes implementing a Graph of Thoughts architecture, supporting Local LLMs and other LLM APIs, and developing desktop and mobile apps. The project's open core development process welcomes contributions from the community.

**Conclusion**:
Waggle Dance represents a significant contribution to the field of AI and software development. Its unique adversarial-cooperative approach, concurrent execution of tasks, and use of a vector database for long-term memory make it a promising tool for complex problem-solving. The project's ongoing development and future plans suggest that it will continue to evolve and improve, potentially influencing the development of multi-agent systems.

**Acknowledgments**:
We would like to thank the maintainers and contributors of Langchain.js, AutoGPT, AgentGPT, and big-AGI for their inspiration and contributions to this project. We also appreciate the support and contributions from our community.

**References**:

1. Auto-GPT. (n.d.). GitHub. https://github.com/Significant-Gravitas/Auto-GPT
2. gpt-researcher. (n.d.). GitHub. https://github.com/assafelovic/gpt-researcher
3. Liu, J. (n.d.). State & history of Agentic AI, context management. Apple Podcasts. https://podcasts.apple.com/us/podcast/the-twiml-ai-podcast-formerly-this-week-in-machine/id1116303051?i=1000612216800
4. OpenAI. (n.d.). GPT best practices. OpenAI. https://platform.openai.com/docs/guides/gpt-best-practices
5. Pinecone. (n.d.). Understand different types of memory and vector database techniques. Pinecone. https://www.pinecone.io/learn/hnsw/
