MACL is a modular Python framework designed to simplify the creation and orchestration of multiple AI agents. It lets you define individual agents with custom skills, set up communication channels, and schedule tasks across an agent network. Agents can exchange messages, negotiate responsibilities, and adapt dynamically based on shared data. With built-in support for popular LLMs and a plugin system for extensibility, MACL enables scalable and maintainable AI workflows across domains like customer service automation, data analysis pipelines, and simulation environments.

NaturalAgents is an open-source Python library designed to streamline the creation and deployment of LLM-powered agents. It provides modules for memory management, context tracking, and tool integration, allowing agents to store and recall information over long sessions. A hierarchical planner orchestrates multi-step reasoning and actions, while an extension system supports custom plugins and external API calls. Built-in logging and analytics enable developers to monitor agent performance and debug workflow issues. NaturalAgents also supports synchronous and asynchronous execution, making it flexible for both interactive use cases and automated pipelines.

OpenAI Agents Chatbot allows developers to integrate and manage multiple specialized AI agents (e.g., tools, knowledge retrieval, memory modules) into a single conversational application. features chain-of-thought orchestration, session-based memory, configurable tool endpoints, and seamless OpenAI API interactions. Users can customize each agent’s behavior, deploy locally or in cloud environments, and extend the framework with additional modules. This accelerates development of advanced chatbots, virtual assistants, and task automation systems.

OperAgents is a developer-oriented toolkit for building and orchestrating autonomous agents using large language models like GPT. It supports defining custom agent classes, integrating external tools (APIs, databases, code execution), and managing agent memory for context retention. Through configurable pipelines, agents can perform multi-step tasks—such as research, summarization, and decision support—while dynamically invoking tools and maintaining state. The framework includes modules for monitoring agent performance, handling errors automatically, and scaling agent executions. By abstracting LLM interactions and tool management, OperAgents accelerates the development of AI-driven workflows in domains like automated customer support, data analysis, and content generation.

Proactive AI Agents is a developer-centric framework designed to architect sophisticated autonomous agent ecosystems powered by large language models. It provides out-of-the-box capabilities for agent creation, task decomposition, and inter-agent communication, enabling seamless coordination on complex, multi-step objectives. Each agent can be equipped with custom tools, memory storage, and planning algorithms, empowering them to proactively anticipate user needs, schedule tasks, and adjust strategies dynamically. The framework supports modular integration of new language models, toolkits, and knowledge bases, while offering built-in logging and monitoring features. By abstracting the intricacies of agent orchestration, Proactive AI Agents accelerates the development of AI-driven workflows for research, automation, and enterprise applications.

pyafai is an open-source Python library designed to help developers architect, configure, and execute autonomous AI agents. It offers pluggable modules for memory management to retain context, tool integration for external API calls, observers for environment monitoring, planners for decision making, and an orchestrator to run agent loops. Logging and monitoring features provide visibility into agent performance and behavior. pyafai supports major LLM providers out of the box, enables custom module creation, and reduces boilerplate so teams can rapidly prototype virtual assistants, research bots, and automation workflows with full control over each component.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.

SimplerLLM provides developers a minimalistic API to compose LLM chains, define agent actions, and orchestrate tool calls. With built-in abstractions for memory retention, prompt templates, and output parsing, users can rapidly assemble conversational agents that maintain context across interactions. The framework seamlessly integrates with OpenAI, Azure, and HuggingFace models, and supports pluggable toolkits for searches, calculators, and custom APIs. Its lightweight core minimizes dependencies, allowing agile development and easy deployment on cloud or edge. Whether building chatbots, QA assistants, or task automators, SimplerLLM simplifies end-to-end LLM agent pipelines.

Steel is a developer-centric framework designed to accelerate the creation and operation of LLM-powered agents in production environments. It offers provider-agnostic connectors for major model APIs, an in-memory and persistent memory store, built-in tool invocation patterns, automatic caching of responses, and detailed tracing for observability. Developers can define complex agent workflows, integrate custom tools (e.g., search, database queries, and external APIs), and handle streaming outputs. Steel abstracts the complexity of orchestration, allowing teams to focus on business logic and rapidly iterate on AI-driven applications.

SuperBot is a comprehensive AI Agent framework enabling developers to deploy autonomous, context-aware assistants via Python and the command line. It integrates OpenAI’s chat models with a memory system, function-calling features, and plugin architecture. Agents can execute shell commands, run code, interact with files, perform web searches, and maintain conversation state. SuperBot supports multi-agent orchestration for complex workflows, all configurable through simple Python scripts and CLI commands. Its extensible design allows you to add custom tools, automate tasks, and integrate external APIs to build robust AI-driven applications.

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.

Team of AI Agents provides a modular architecture to build and deploy multi-agent systems. Each agent operates with distinct roles, utilizing a global memory store and local contexts for knowledge retention. The framework supports asynchronous messaging, tool usage via adapters, and dynamic task reassignment based on agent outcomes. Developers configure agents through YAML or Python scripts, enabling topic specialization, goal hierarchy, and priority handling. It includes built-in metrics for performance evaluation and debugging, facilitating rapid iteration. With extensible plugin architecture, users can integrate custom NLP models, databases, or external APIs. Team of AI Agents accelerates complex workflows by leveraging collective intelligence of specialized agents, making it ideal for research, automation, and simulation environments.

AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.

Autonomous Economic Agents (AEA) by Fetch.ai is a versatile framework that empowers developers to design, implement, and orchestrate autonomous software agents capable of interacting with each other, external environments, and digital ledgers. Leveraging a plugin-based architecture, AEA provides pre-built modules for communication protocols, cryptographic ledger APIs, decentralized identity, and customizable decision-making skills. Agents can discover and transact within decentralized marketplaces, perform goal-driven behaviors, and adapt through real-time data feeds. The framework supports simulation tools for testing and debugging multi-agent scenarios, as well as deployment onto live blockchains or peer-to-peer networks. With built-in interoperability and agent-to-agent messaging, AEA streamlines the development of complex autonomous economic applications such as energy trading, supply chain optimization, and smart IoT coordination.

A2A (Agent-to-Agent Architecture) is a Google open-source framework enabling the development and operation of distributed AI agents working together. It offers modular components to define agent roles, communication channels, and shared memory. Developers can integrate various LLM providers, customize agent behaviors, and orchestrate multi-step workflows. A2A includes built-in monitoring, error management, and replay capabilities to trace agent interactions. By providing a standardized protocol for agent discovery, message passing, and task allocation, A2A simplifies complex coordination patterns and enhances reliability when scaling agent-based applications across diverse environments.

AI Agents is a comprehensive Python framework designed to streamline the development of intelligent software agents. It offers plug-and-play toolkits for integrating external services such as web search, file I/O, and custom APIs. With built-in memory modules, agents maintain context across interactions, enabling advanced multi-step reasoning and persistent conversations. The framework supports multiple LLM providers, including OpenAI and open-source models, allowing developers to switch or combine models easily. Users define tasks, assign tools and memory policies, and the core engine orchestrates prompt construction, tool invocation, and response parsing for seamless agent operation.

ADK-Golang is an open-source Agent Development Kit for the Go ecosystem. It provides a modular framework to register and manage tools (APIs, databases, external services), build dynamic prompt templates, and maintain conversation memory for multi-turn interactions. With built-in orchestration patterns and logging support, developers can easily configure, test, and deploy AI agents that perform tasks such as data retrieval, automated workflows, and contextual chat. ADK-Golang abstracts low-level API calls and streamlines end-to-end agent lifecycles—from initialization and planning to execution and response handling—entirely in Go.

Agent Nexus offers a modular architecture for designing, configuring, and running interconnected AI agents that collaborate to solve complex tasks. Developers can register agents dynamically, customize behavior through Python modules, and define communication pipelines via simple YAML configurations. The built-in message router ensures reliable inter-agent data flow, while integrated logging and monitoring tools help track performance and debug workflows. With support for popular AI libraries like OpenAI and Hugging Face, Agent Nexus simplifies the integration of diverse models. Whether prototyping research experiments, building automated customer service assistants, or simulating multi-agent environments, Agent Nexus streamlines development and testing of collaborative AI systems, from academic research to commercial deployments.

Agentin is an open-source Python library designed to help developers build intelligent agents that can plan, act, and learn. It provides abstractions for managing conversational memory, integrating external tools or APIs, and orchestrating multiple agents in parallel or hierarchical workflows. With configurable planner modules and support for custom tool wrappers, Agentin enables rapid prototyping of autonomous data-processing agents, customer service bots, or research assistants. The framework also offers extensible logging and monitoring hooks, making it easy to track agent decisions and troubleshoot complex multi-step interactions.

Agenite is a Python-centric AI agent framework designed to streamline the creation, orchestration, and management of autonomous agents. It offers modular components such as memory stores, task schedulers, and event-driven communication channels, enabling developers to build agents capable of stateful interactions, multi-step reasoning, and asynchronous workflows. The platform provides adapters for connecting to external APIs, databases, and message queues, while its pluggable architecture supports custom modules for natural language processing, data retrieval, and decision-making. With built-in storage backends for Redis, SQL, and in-memory caches, Agenite ensures persistent agent state and enables scalable deployments. It also includes a command-line interface and JSON-RPC server for remote control, facilitating integration into CI/CD pipelines and real-time monitoring dashboards.