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Basic Information

Swarms is an open source framework and research project for orchestrating cooperative collections of language model agents. The repository provides a Python package and example code to instantiate a Swarms class, supply an OpenAI API key, define natural-language objectives, and run coordinated multi-agent workflows via methods such as run_swarms. The README emphasizes a vision of LLMs acting like a communicating swarm to improve planning, decomposition, reflection, memory, tool use, and inter-agent communication. Installation is supported via pip or by cloning the repository and installing requirements. The project is presented by Agora and includes a roadmap, bounty program, and planned integrations such as FastAPI endpoints, a Gradio conversational UI, vector database support, multi-agent debate frameworks, and tool chains for code generation and multimodal context gathering.

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Features
The repository exposes a Swarms class and example scripts demonstrating how to initialize the system with an OpenAI API key and run objectives. It supports pip installation and a downloadable repo with requirements, example.py, and usage snippets. The README documents architectural concepts including task decomposition, short-term and long-term memory, tool usage, self-reflection, and agent communication. Roadmap and TODO items list planned features: a Swarms API class with configurable worker counts, meta-prompting across workers, integration with debate frameworks, Ocean vector database for embeddings, FastAPI endpoints, a Gradio UI, multimodal screenshot worker, text-to-speech/text-to-script tools, and self-scaling worker swarms. The project also includes contribution guidance, a phased bounty program, and inspiration links to related multi-agent work.
Use Cases
Swarms helps developers and researchers build and experiment with multi-agent LLM orchestration by providing a starting framework, examples, and a clear roadmap for extending capabilities. It reduces the initial integration work by offering a packaged Swarms class, installation instructions, and example usage to run coordinated tasks with LLM workers. The project documents key agent-system components—planning, reflection, memory, and tool use—so implementers can design agents that decompose objectives, call external APIs, retain long-term knowledge via a vector store, and evaluate task completion. Planned integrations (debate frameworks, vector DB, FastAPI, Gradio) and a bounty program aim to accelerate practical deployments for use cases such as customer support, content generation, research workflows, and automated multi-step tasks. The open roadmap supports contributors who want to extend scalability, reliability, and UI options.

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