Cerebellum offers a modular platform where developers define agents using declarative plans composed of sequential steps or tool invocations. Each plan can call built-in or custom tools—such as API connectors, retrievers, or data processors—through a unified interface. Memory modules allow agents to store, retrieve, and forget information across sessions, enabling context-aware and stateful interactions. It integrates with popular LLMs (OpenAI, Hugging Face), supports custom tool registration, and features an event-driven execution engine for real-time control flow. With logging, error handling, and plugin hooks, Cerebellum boosts productivity, facilitating rapid agent development for automation, virtual assistants, and research applications.

Duet GPT is a Python-based open source framework for orchestrating multi-agent conversations between two GPT models. You define distinct agent roles, customized with system prompts, and the framework manages turn-taking, message passing, and conversation history automatically. This cooperative structure accelerates complex task resolution, enabling comparative reasoning, critique cycles, and iterative refinement through back-and-forth exchanges. Its seamless OpenAI API integration, simple configuration, and built-in logging make it ideal for research, prototyping, and production workflows in coding assistance, decision support, and creative ideation. Developers can extend the core classes to integrate new LLM services, adjust the iterator logic, and export transcripts in JSON or Markdown formats for post-analysis.

GoLC provides developers with a comprehensive toolkit for constructing language model chains and agents in Go. At its core, it includes chain management, customizable prompt templates, and seamless integration with major LLM providers. Through document loaders and vector stores, GoLC enables embedding-based retrieval, powering RAG workflows. The framework supports stateful memory modules for conversational contexts and a lightweight agent architecture to orchestrate multi-step reasoning and tool invocations. Its modular design allows plugging in custom tools, data sources, and output handlers. With Go-native performance and minimal dependencies, GoLC streamlines AI pipeline development, making it ideal for building chatbots, knowledge assistants, automated reasoning agents, and production-grade backend AI services in Go.

AgentMesh provides a modular infrastructure for developers to create networks of AI agents, each focusing on a specific task or domain. Agents can be dynamically discovered and registered at runtime, exchange messages asynchronously, and follow configurable routing rules. The framework handles retries, fallbacks, and error recovery, allowing multi-agent pipelines for data processing, decision support, or conversational use cases. It integrates easily with existing LLMs and custom models via a simple plugin interface.

AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.

AtomicAgent provides a structured framework for defining, composing, and executing AI agent tasks. Core modules include a tool registry to register and invoke external services, a memory manager to persist conversational or task context, and an orchestration engine that drives LLM interactions step by step. Developers can define reusable tools, configure decision logic, and leverage asynchronous execution for long-running tasks. AtomicAgent’s modular design promotes maintainability, testability, and rapid iteration of complex AI-driven workflows, from chatbots to data processing pipelines.

AutoGen UI is a frontend toolkit designed to render and manage multi-agent conversational flows. It offers ready-made components such as chat windows, agent selectors, message timelines, and debugging panels. Developers can configure multiple AI agents, stream responses in real time, log each step of the conversation, and apply custom styling. It integrates easily with backend orchestration libraries to provide a complete end-to-end interface for building and monitoring AI agent interactions.