OpenAI Agent Swarm is a modular framework designed to streamline the coordination of multiple GPT-powered agents across diverse tasks. Each agent operates independently with customizable prompts and role definitions, while the Swarm core manages agent lifecycle, message passing, and task scheduling. The platform includes tools for defining complex workflows, monitoring agent interactions in real time, and aggregating results into coherent outputs. By distributing workloads across specialized agents, users can tackle complex problem-solving scenarios, from content generation and research analysis to automated debugging and data summarization. OpenAI Agent Swarm integrates seamlessly with the OpenAI API, allowing developers to rapidly deploy multi-agent systems without building orchestration infrastructure from scratch.

AgentSmith is a modular agent orchestration framework built in Python that enables developers to define, configure, and run multiple AI agents collaboratively. Each agent can be assigned specialized roles—such as researcher, planner, coder, or reviewer—and communicate via an internal message bus. AgentSmith supports memory management through vector stores like FAISS or Pinecone, task decomposition into subtasks, and automated supervision to ensure goal completion. Agents and pipelines are configured via human-readable YAML files, and the framework integrates seamlessly with OpenAI APIs and custom LLMs. It includes built-in logging, monitoring, and error handling, making it ideal for automating software development workflows, data analysis, and decision support systems.

Hive is a robust AI agent orchestration platform built for Node.js environments. It provides a modular system for defining, managing, and executing multiple AI agents in parallel or sequential workflows. Each agent can be configured with specific roles, prompt templates, memory stores, and external tool integrations such as APIs or plugins. Hive streamlines communication paths between agents, enabling data sharing, decision-making, and task delegation. Its extensible design allows developers to implement custom utilities, monitor execution logs, and deploy agents at scale. Hive also includes features like error handling, retry policies, and performance optimizations to ensure reliable automation. With minimal setup, teams can prototype complex AI-driven services, including chatbots, data analysis pipelines, and content generators.

Tech Research Agent operates by first receiving a research query, then dispatching web searches via Google Serp API. It crawls result URLs, extracts code samples and textual content, applies natural language processing for summarization, and builds a knowledge graph of key concepts. Using OpenAI GPT, it synthesizes findings into coherent technical reports in markdown format. It supports customization of search depth, summarization granularity, and output templates. With built-in caching and parallel processing, the agent accelerates large-scale literature reviews, API explorations, and competitive analysis, enabling users to quickly identify trends, best practices, and relevant code examples for technology evaluation.