The Multi-LLM Dynamic Agent Router is an open-architecture framework for building AI agent collaborations. It features a dynamic router that directs sub-requests to the optimal language model, and a GraphQL interface to define composite prompts, query results, and merge responses. This enables developers to break complex tasks into micro-prompts, route them to specialized LLMs, and recombine outputs programmatically, yielding higher relevance, efficiency, and maintainability.

mini-agi is designed to simplify the creation of autonomous AI agents by providing a minimal, modular framework. Built in Python, it leverages OpenAI’s language models to interpret high-level goals, decompose them into sub-tasks, and orchestrate tool calls such as HTTP requests, file operations, or custom actions. The framework includes memory storage to track agent state and results, a planner module for task decomposition with cost-based heuristics, and an executor module that sequentially invokes tools. With configuration files, users can inject custom tools, define prompt templates, and adjust planning depth. mini-agi’s lightweight architecture makes it ideal for prototyping AI agents that perform research queries, automate workflows, or generate code autonomously.

Odin AI is an advanced AI agent that offers a suite of functionalities including automated task management, intelligent scheduling, and personalized assistance. Designed to optimize productivity, it assists users in coordinating their tasks, provides timely reminders, and adapts to user preferences for maximum efficiency. The AI agent continuously learns from user interactions, ensuring that it becomes increasingly effective in managing workflows while providing real-time support whenever needed.

Semantic Kernel Copilot Demo is an end-to-end reference application illustrating how to build advanced AI agents with Microsoft’s Semantic Kernel framework. The demo features prompt chaining for multi-step reasoning, memory management to recall context across sessions, and a plugin-based skill architecture enabling integration with external APIs or services. Developers can configure connectors for Azure OpenAI or OpenAI models, define custom prompt templates, and implement domain-specific skills such as calendar access, file operations, or data retrieval. The sample shows how to orchestrate these components to create a conversational Copilot capable of understanding user intents, executing tasks, and maintaining context over time, fostering rapid development of personalized AI assistants.

uAgents is a modular JavaScript framework that empowers developers to build autonomous, decentralized AI agents which can discover peers, exchange messages, collaborate on tasks, and adapt through learning. Agents communicate over libp2p-based gossip protocols, register capabilities via on-chain registries, and negotiate service-level agreements using smart contracts. The core library handles agent lifecycle events, message routing, and extensible behaviors such as reinforcement learning and market-driven task allocation. Through customizable plugins, uAgents can integrate with Fetch.ai’s ledger, external APIs, and oracle networks, enabling agents to perform real-world actions, data acquisition, and decision-making in distributed environments without centralized orchestration.

AI-Agents provides a flexible agent architecture that orchestrates language model planners, persistent memory modules, and pluggable toolkits. Developers define tools for HTTP requests, file operations, and custom logic, then configure an LLM planner to decide which tool to invoke. Memory stores context and conversation history. The framework handles asynchronous execution, error recovery, and logging, enabling rapid prototyping of intelligent assistants, data analyzers, or automation bots without reinventing core orchestration logic.

Agent Forge provides a modular architecture for defining, executing, and coordinating AI agents. It offers built-in task orchestration APIs to sequence and parallelize operations, memory modules for long-term context retention, and a plugin system to integrate external services (e.g., LLMs, databases, third-party APIs). Developers can rapidly prototype, test, and deploy agents in production, weaving together complex workflows without managing low-level infrastructure.

AgentLab is a ServiceNow framework for creating AI agents—also called digital workers—using a visual, drag-and-drop editor. Users link large language models with ServiceNow tables, define intents and actions, and orchestrate workflows for tasks like incident resolution, change approvals, and knowledge retrieval. Agents can be tested in built-in sandboxes, versioned, and monitored in real time. With connectors for external APIs and chat interfaces, AgentLab enables deployment across portals, Microsoft Teams, and Slack. The platform offers governance controls, audit trails, and analytics dashboards to ensure compliance and performance at scale.

Agent-FLAN is designed to simplify the creation of sophisticated AI agent-driven applications by segmenting tasks into planning and execution roles. Users define agent behaviors and workflows via configuration files, specifying input formats, tool interfaces, and communication protocols. The planning agent generates high-level task plans, while execution agents carry out specific actions, such as calling APIs, processing data, or generating content with large language models. Agent-FLAN’s modular architecture supports plug-and-play tool adapters, custom prompt templates, and real-time monitoring dashboards. It seamlessly integrates with popular LLM providers like OpenAI, Anthropic, and Hugging Face, enabling developers to quickly prototype, test, and deploy multi-agent workflows for scenarios such as automated research assistants, dynamic content generation pipelines, and enterprise process automation.

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.

Agno AI Agent is designed to help developers quickly build autonomous agents powered by large language models. It provides a modular tool registry, memory management, planning and execution loops, and seamless integration with external APIs (such as web search, file systems, and databases). Users can define custom tool interfaces, configure agent personalities, and orchestrate complex, multi-step workflows. Agents can plan tasks, call tools dynamically, and learn from previous interactions to improve performance over time.

10x Rules is a comprehensive AI agent framework allowing organizations to construct and deploy smart agents based on custom rule sets and business logic. By defining triggers, conditions, and actions in an intuitive interface, users can instruct AI agents to perform tasks such as extracting data from documents, scoring leads, sending personalized emails, and updating CRM records. The platform seamlessly integrates with popular services through pre-built connectors, supports real-time monitoring and debugging, and provides analytics on agent performance. Both technical and non-technical users can streamline repetitive workflows, reduce manual errors, and accelerate operations with AI-driven automation.

AutoGen Hands-On provides a structured environment to learn AutoGen framework usage through practical Python examples. It guides users on cloning the repository, installing dependencies, and configuring API keys to deploy multi-agent setups. Each script demonstrates key features such as defining agent roles, session memory, message routing, and task orchestration patterns. The code includes logging, error handling, and extensible hooks that allow customization of agents’ behavior and integration with external services. Users gain hands-on experience in building collaborative AI workflows where multiple agents interact to complete complex tasks, from customer support chatbots to automated data processing pipelines. The tutorial fosters best practices in multi-agent coordination and scalable AI development.

Autogen Studio Research is a GitHub-hosted research prototype for building, visualizing, and iterating on multi-agent AI applications. It offers a web-based UI that lets you drag and drop agent components, define communication channels, and configure execution pipelines. Under the hood, it uses a Python SDK to connect to various LLM backends (OpenAI, Azure, local models) and provides real-time logging, metrics, and debugging tools. The platform is designed for rapid prototyping of collaborative agent systems, decision-making workflows, and automated task orchestration.

autoMate leverages OpenAI's GPT models and a modular tooling system to perform end-to-end automation workflows. Users define objectives in natural language, and autoMate breaks them into subtasks such as reading or writing files, scraping web pages, summarizing data, and composing emails. It dynamically invokes the appropriate functions, handles API interactions, logs progress, and outputs results in the desired format. Its extensible architecture allows adding custom tools, enabling scalable automation across data processing, content generation, and system operations.

Council provides a structured environment for designing AI agents by defining roles, chaining tasks, and integrating external tools or APIs. Users can configure memory stores, manage agent state, and implement custom reasoning pipelines. Council’s plugin architecture allows seamless integration with NLP services, data sources, and third-party tools, enabling you to rapidly prototype and deploy multi-agent systems that coordinate to perform complex tasks reliably.

Dial4JaCa is a Java library plugin for the JaCaMo multi-agent platform that intercepts inter-agent messages, encodes agent intentions, and routes them through LLM backends (OpenAI, local models). It manages dialogue context, updates belief bases, and integrates response generation directly into AgentSpeak(L) reasoning cycles. Developers can customize prompts, define dialogue artifacts, and handle asynchronous calls, enabling agents to interpret user utterances, coordinate tasks, and retrieve external information in natural language. Its modular design supports error handling, logging, and multi-LLM selection, ideal for research, education, and rapid prototyping of conversational MAS.

GPTSwarm acts as a collective intelligence platform where multiple AI agents interact and collaborate to solve complex problems and execute tasks more efficiently. Users can create workflows by coordinating various agents to perform specific roles, leading to improved productivity and time savings. This system is designed to streamline processes in project management, automation, and various workflows, providing scalable solutions tailored to individual and organizational needs.

Swarms.ai provides a visual interface to define and connect multiple AI agents into intelligent workflows. Each agent can be configured with specific roles, data sources, and custom API integrations. Agents collaborate by passing messages, triggering actions, and sharing context to handle complex tasks end to end. The platform offers role-based access control, versioning, and real-time analytics to monitor swarm performance. No coding is required: users drag and drop components, set triggers, and link outputs to design automated processes for support, sales, operations, and more.

MyGuy is an advanced AI assistant that streamlines everyday tasks by managing appointments, making calls, and handling various inquiries for you. Whether you need to set reminders, book appointments, or delegate tasks, MyGuy is there 24/7 to provide quick and reliable assistance. With a user-friendly interface and intelligent functionalities, it helps users maximize their productivity while minimizing stress associated with routine tasks. It’s perfect for those who juggle multiple responsibilities and need an extra hand.