Team Coordination is a lightweight Python library designed to simplify the orchestration of multiple AI agents working together on complex tasks. By defining specialized agent roles—such as planners, executors, evaluators, or communicators—users can decompose a high-level objective into manageable sub-tasks, delegate them to individual agents, and facilitate structured communication between them. The framework handles asynchronous execution, protocol routing, and result aggregation, allowing teams of AI agents to collaborate efficiently. Its plugin system supports integration with popular LLMs, APIs, and custom logic, making it ideal for applications in automated customer service, research, game AI, and data processing pipelines. With clear abstractions and extensible components, Team Coordination accelerates the development of scalable multi-agent workflows.

Swarms SDK simplifies creation, configuration, and execution of collaborative multi-agent systems using large language models. Developers define agents with distinct roles—researcher, synthesizer, critic—and group them into swarms that exchange messages via a shared bus. The SDK handles scheduling, context persistence, and memory storage, enabling iterative problem solving. With native support for OpenAI, Anthropic, and other LLM providers, it offers flexible integrations. Utilities for logging, result aggregation, and performance evaluation help teams prototype and deploy AI-driven workflows for brainstorming, content generation, summarization, and decision support.

ReasonChain provides a modular pipeline for constructing sequences of LLM-driven operations, allowing each step’s output to feed into the next. Users can define custom chain nodes for prompt generation, API calls to different LLM providers, conditional logic to route workflows, and aggregation functions for final outputs. The framework includes built-in debugging and logging to trace intermediate states, support for vector database lookups, and easy extension through user-defined modules. Whether solving multi-step reasoning tasks, orchestrating data transformations, or building conversational agents with memory, ReasonChain offers a transparent, reusable, and testable environment. Its design encourages experimentation with chain-of-thought strategies, making it ideal for research, prototyping, and production-ready AI solutions.