Multi-Agent ColComp is an extensible, open-source framework for orchestrating a team of AI agents to work together on complex tasks. Developers can define distinct agent roles, configure communication channels, and share contextual data through a unified memory store. The library includes plug-and-play components for negotiation, coordination, and consensus building. Example setups demonstrate collaborative text generation, distributed planning, and multi-agent simulation. Its modular design supports easy extension, enabling teams to prototype and evaluate multi-agent strategies rapidly in research or production environments.

Multi-Agent Coordination provides a lightweight API to define AI agents, register them with a central coordinator, and dispatch tasks for collaborative problem solving. It handles message routing, concurrency control, and result aggregation. Developers can plug in custom agent behaviors, extend communication channels, and monitor interactions through built-in logging and hooks. This framework simplifies the development of distributed AI workflows, where each agent specializes in a subtask and the coordinator ensures smooth collaboration.

MultiAgent2 provides a comprehensive toolkit for orchestrating autonomous AI agents powered by large language models. Developers can define agents with customizable personas, strategies, and memory contexts, enabling them to converse, share information, and collectively solve problems. The framework supports pluggable storage options for long-term memory, role-based access to shared data, and configurable communication channels for synchronous or asynchronous dialogue. Its CLI and Python SDK facilitate rapid prototyping, testing, and deployment of multi-agent systems for use cases spanning research experiments, automated customer support, content generation pipelines, and decision support workflows. By abstracting inter-agent communication and memory management, MultiAgent2 accelerates the development of complex AI-driven applications.