Evolving Agents provides a genetic programming–based framework for constructing and evolving modular AI agents. Users assemble agent architectures from interchangeable components, define environment simulations and fitness metrics, then run evolutionary cycles to automatically generate improved agent behaviors. The library includes tools for mutation, crossover, population management, and evolution monitoring, allowing researchers and developers to prototype, test, and refine autonomous agents in diverse simulated environments.

Goated Agents simplifies building sophisticated AI-driven autonomous systems in Go. By embedding chain-of-thought processing directly in the language runtime, developers can implement multi-step reasoning with transparent intermediate reasoning logs. The library offers a tool definition API, allowing agents to call external services, databases, or custom code modules. Memory management support enables persistent context across interactions. Plugin architecture facilitates extending core capabilities such as tool wrappers, logging, and monitoring. Goated Agents leverages Go’s performance and static typing to deliver efficient, reliable agent execution. Whether constructing chatbots, automation pipelines, or research prototypes, Goated Agents provides the building blocks to orchestrate complex reasoning flows and integrate LLM-driven intelligence seamlessly into Go applications.

Invicta AI is a cutting-edge platform that allows businesses to create, manage, and deploy autonomous AI agents. These agents can handle a variety of tasks, including customer service inquiries, data analysis, and workflow automation. The platform provides a user-friendly interface where users can design custom AI agents suited to their business needs. With seamless integration capabilities, Invicta AI makes it easy to connect various data sources, enhancing the overall productivity and efficiency of business operations.

Octagon Agents is an enterprise-grade platform that enables developers and organizations to create, orchestrate, and scale autonomous AI Agents. It features a visual workflow editor and SDKs for Python and JavaScript, allowing users to configure agent behaviors, integrate external APIs, and manage stateful memories. Agents can be chained into complex pipelines, enabling decision-making across multiple tasks such as data extraction, analysis, and automated responses. With real-time monitoring dashboards, logging, and retry mechanisms, Octagon Agents ensures reliability and traceability in production environments. Moreover, built-in authentication and encryption provide robust security, making it suitable for sensitive business applications. Teams can deploy agents on cloud or on-premise infrastructure, achieving high availability and performance.

Open ACN is a robust AI platforms and frameworks solution designed for building decentralized multi-agent systems. It offers a suite of consensus protocols tailored for agent cooperation, ensuring reliable decision-making across geodistributed nodes. The framework includes modular communication layers, customizable strategy plug-ins, and a built-in simulation environment for end-to-end testing. Developers can define agent behaviors, deploy across Linux, macOS, Windows, or Docker, and leverage real-time logging and monitoring tools. By providing extensible APIs and seamless integration with existing machine learning models, Open ACN simplifies complex orchestration tasks, fostering interoperable, resilient autonomous networks suitable for applications in robotics, supply chain automation, decentralized finance, and IoT.

Pony.ai offers a cutting-edge autonomous driving platform that combines advanced AI algorithms, computer vision, and real-time data processing to enable vehicles to navigate complex urban environments safely. Their technology is aimed at providing ride-hailing services, goods delivery, and enhancing transportation safety. By leveraging their expertise in autonomous systems, Pony.ai delivers products and solutions for both consumers and businesses seeking innovative transportation methods.

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.

StableAgents provides a comprehensive toolkit to create autonomous AI agents that can plan, execute, and adapt complex workflows using large language models. It supports modular components including planners, memory stores, tools, and evaluators. Agents can access external APIs, perform retrieval-augmented tasks, and store conversation or interaction context. The framework comes with a CLI and Python SDK, enabling local development or cloud deployment. Through its plugin architecture, StableAgents integrates with popular LLM providers and vector databases and includes monitoring dashboards and logging for performance tracing.

Thufir is a Python-based open-source agent framework designed to facilitate the creation of autonomous AI agents capable of complex task planning and execution. At its core, Thufir provides a planning engine that decomposes high-level objectives into actionable steps, a memory module for storing and retrieving contextual information across sessions, and a plug-and-play tool interface allowing agents to interact with external APIs, databases, or code execution environments. Developers can leverage Thufir’s modular components to customize agent behaviors, define custom tools, manage agent state, and orchestrate multi-agent workflows. By abstracting away low-level infrastructure concerns, Thufir accelerates the development and deployment of intelligent agents for use cases like virtual assistants, workflow automation, research, and digital workers.

AI-Agents provides a modular toolkit to create autonomous AI agents capable of task planning, execution, and self-monitoring. It offers built-in support for tool integration—such as web search, data processing, and custom APIs—and features a memory component to retain and recall context across interactions. With a flexible plugin system, agents can dynamically load new capabilities, while asynchronous execution ensures efficient multi-step workflows. The framework leverages LangChain for advanced chain-of-thought reasoning and simplifies deployment in Python environments on macOS, Windows, or Linux.

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.

Agentic-AI is an open-source Python framework that streamlines building autonomous agents leveraging large language models such as OpenAI GPT. It provides core modules for task planning, memory persistence, and tool integration, allowing agents to decompose high-level goals into executable steps. The framework supports plugin-based custom tools—APIs, web scraping, database queries—enabling agents to interact with external systems. It features a chain-of-thought reasoning engine coordinating planning and execution loops, context-aware memory recalls, and dynamic decision-making. Developers can easily configure agent behaviors, monitor action logs, and extend functionality, achieving scalable, adaptable AI-driven automation for diverse applications.

Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.

ai-agents-trial is an open-source Python project demonstrating how to build autonomous AI agents using LLMs. It provides modular abstractions for agent planning, tool invocation (e.g., web search, calculators), and memory management. Developers can define custom tools, chain actions across multiple steps, and persist context across sessions. The codebase uses OpenAI APIs alongside helper utilities to orchestrate workflows, making it ideal for rapid prototyping of chat-based assistants, research bots, or domain-specific automation agents. Integration points allow extending functionality with new connectors and data sources without altering core logic.

Automata is a developer-focused framework that enables creation of autonomous AI agents in JavaScript and TypeScript. It offers a modular architecture including planners for task decomposition, memory modules for context retention, and tool integrations for HTTP requests, database queries, and custom API calls. With support for asynchronous execution, plugin extensions, and structured outputs, Automata streamlines the development of agents that can perform multi-step reasoning, interact with external systems, and dynamically update their knowledge base.

ModelScope Agent provides a modular, Python‐based framework to orchestrate autonomous AI agents. It features plugin integration for external tools (APIs, databases, search), conversation memory for context preservation, and customizable agent chains to handle complex tasks such as knowledge retrieval, document processing, and decision support. Developers can configure agent roles, behaviors, and prompts, as well as leverage multiple LLM backends to optimize performance and reliability in production.

Dive is a Python-based open-source framework designed for creating and running autonomous AI agents that can perform multi-step tasks with minimal manual intervention. By defining agent profiles in simple YAML configuration files, developers can specify APIs, tools, and memory modules for tasks such as data retrieval, analysis, and pipeline orchestration. Dive manages context, state, and prompt engineering, allowing flexible workflows with built-in error handling and logging. Its pluggable architecture supports a wide range of language models and retrieval systems, making it easy to assemble agents for customer service automation, content generation, and DevOps processes. The framework scales from prototype to production, offering CLI commands and API endpoints to integrate agents seamlessly into existing systems.

FAgent offers a modular architecture for constructing AI agents, including environment abstractions, policy interfaces, and tool connectors. It supports integration with popular LLM services, implements memory management for context retention, and provides an observability layer for logging and monitoring agent actions. Developers can define custom tools and actions, orchestrate multi-step workflows, and run simulation-based evaluations. FAgent also includes plugins for data collection, performance metrics, and automated testing, making it suitable for research, prototyping, and production deployments of autonomous agents in various domains.

GenAI Agents SDK is an open-source Python library designed to help developers create self-driven AI agents using large language models. It offers a core agent template with pluggable modules for memory storage, tool interfaces, planning strategies, and execution loops. You can configure agents to call external APIs, read/write files, run searches, or interact with databases. Its modular design ensures easy customization, rapid prototyping, and seamless integration of new capabilities, empowering the creation of dynamic, autonomous AI applications that can reason, plan, and act in real-world scenarios.

Microsoft AutoGen is designed to facilitate the end-to-end development of autonomous AI agents by providing modular components for memory management, task planning, tool integration, and communication. Developers can define custom tools with structured schemas and connect to major LLM providers such as OpenAI and Azure OpenAI. The framework supports both single-agent and multi-agent orchestration, enabling collaborative workflows where agents coordinate to complete complex tasks. Its plug-and-play architecture allows easy extension with new memory stores, planning strategies, and communication protocols. By abstracting the low-level integration details, AutoGen accelerates prototyping and deployment of AI-driven applications across domains like customer support, data analysis, and process automation.