AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.

AgentSpeak is an open-source Java-based implementation of the AgentSpeak(L) programming language, designed to facilitate the creation and management of BDI (Belief-Desire-Intention) autonomous agents. It features a runtime environment that parses AgentSpeak(L) code, maintains agents’ belief bases, triggers events, and selects and executes plans based on current beliefs and goals. The interpreter supports concurrent agent execution, dynamic plan updates, and customizable semantics. With a modular architecture, programmers can extend core components such as plan selection and belief revision. AgentSpeak enables developers in academia and industry to prototype, simulate, and deploy intelligent agents in simulations, IoT systems, and multi-agent scenarios.

Agent Zero is a next-generation AI assistant that allows users to run their own autonomous AI agents on a virtual computer. It is open-source and fully customizable, meaning that users can tailor its functionalities to meet their specific needs. With Agent Zero, you can bypass the limitations imposed by traditional AI systems and enjoy a streamlined, transparent experience. This AI assistant embodies the principles of decentralization and autonomy, making it accessible to everyone, regardless of their technical background.

AIAgentWorkshop is an open-source Python project demonstrating how to build autonomous AI agents capable of planning, decision-making, and tool usage. It includes examples of integrating web search, file management, and system commands, along with simple memory and reasoning modules. Developers can follow guided exercises to create agents that interpret user goals, generate multi-step plans, execute tasks across different tools, and maintain context. The modular architecture makes it easy to swap or extend tools and chain agent actions for complex workflows, turning AI research concepts into runnable prototypes.

Amico is an AI agent framework that lets users create custom autonomous assistants for various business processes. Through a visual flow builder, users define triggers such as email arrival or webhook events, connect to third-party APIs, and program logic for data processing, classification, and response generation. The platform supports integrations with Slack, Zendesk, Google Sheets, and more, enabling agents to ingest data, perform sentiment analysis, schedule meetings, or generate reports. Agents can run on schedules or react to events in real time, executing tasks without human intervention. Advanced features include conversational conversations, multi-step workflows, and performance analytics dashboards. With built-in security and role-based access, Amico offers a scalable solution for automating repetitive tasks and improving efficiency across teams.

Amon is a platform and framework for building autonomous AI agents that execute multi-step tasks without human intervention. Users define agent behaviors, data sources, and integrations via simple configuration files or an intuitive UI. Amon’s runtime manages agent lifecycles, error handling, and retry logic. It supports real-time monitoring, logging, and scaling across cloud or on-premise environments, making it ideal for automating customer support, data processing, code reviews, and more.

APLib is designed to simplify the development of AI-driven autonomous agents within gaming and simulation environments. Utilizing a Belief-Desire-Intention (BDI) inspired architecture, it offers modular components for perception, decision-making, and action execution. Developers define agent beliefs, goals, and behaviors via intuitive APIs and behavior trees. APLib agents can interpret game state through customizable sensors, formulate plans using built-in planners, and interact with the environment via actuators. The library supports integration with Unity, Unreal, and pure Java environments, facilitating automated testing, AI research, and simulations. It promotes reuse of behavior modules, rapid prototyping, and robust QA workflows by automating repetitive test scenarios and simulating complex player behaviors without manual intervention.

BabyAGI UI provides a streamlined, browser-based front end for the open-source BabyAGI autonomous agent. Users input an overall objective and initial task; the system then leverages large language models to generate subsequent tasks, prioritize them based on relevance to the main goal, and execute each step. Throughout the process, BabyAGI UI maintains a history of completed tasks, shows outputs for each run, and updates the task queue dynamically. Users can adjust parameters like model type, memory retention, and execution limits, offering a balance of automation and control in self-directed workflows.

TinyAuton provides a minimal, extensible architecture for building autonomous agents that plan, execute, and refine tasks using OpenAI’s GPT models. It offers built-in modules for defining objectives, managing conversation context, invoking custom tools, and logging agent decisions. Through iterative self-reflection loops, the agent can analyze outcomes, adjust plans, and retry failed steps. Developers can integrate external APIs or local scripts as tools, set up memory or state, and customize the agent’s reasoning pipeline. TinyAuton is optimized for rapid prototyping of AI-driven workflows, from data extraction to code generation, all within a few lines of Python.

Devon provides a comprehensive suite of tools for defining, orchestrating, and running autonomous agents within Python applications. Users can outline agent goals, specify callable tasks, and chain actions based on conditional logic. Through seamless integration with language models like GPT and local vector stores, agents ingest and interpret user inputs, retrieve contextual knowledge, and generate plans. The framework supports long-term memory via pluggable storage backends, enabling agents to recall past interactions. Built-in monitoring and logging components allow real-time tracking of agent performance, while a CLI and SDK facilitate rapid development and deployment. Suitable for automating customer support, data analysis pipelines, and routine business operations, Devon accelerates the creation of scalable digital workers.

Divine Agent is a comprehensive AI agent platform that simplifies the design, development, and deployment of autonomous digital workers. Through its intuitive visual workflow builder, users can define agent behavior as a sequence of nodes, connect to any REST or GraphQL API, and select from supported LLMs like OpenAI and Google PaLM. The built-in memory module preserves context across sessions, while real-time analytics track usage, performance, and errors. Once tested, agents can be deployed as HTTP endpoints or integrated with channels like Slack, email, and custom applications, enabling rapid automation of customer support, sales, and knowledge tasks.

The E-Commerce Multi-Agent System on JADE demonstrates how autonomous agents can manage online shopping workflows. Buyer agents search products and negotiate prices with seller agents. Seller agents handle inventory and pricing strategies. Logistics agents schedule shipments and update order status. The system showcases inter-agent communication via ACL, behavior extension, and container deployment on the JADE platform.

ElizaOS provides a robust suite of tools to design, test, and deploy autonomous AI agents within TypeScript projects. Developers define agent personalities, goals, and memory hierarchies, then leverage ElizaOS's planning system to outline task workflows. Its modular connector architecture simplifies integrating with communication platforms—Discord, Telegram, Slack, X—and blockchain networks via Web3 adapters. ElizaOS supports multiple LLM backends (OpenAI, Anthropic, Llama, Gemini), allowing seamless switching between models. Plugin support extends functionality with custom skills, logging, and observability features. Through its CLI and SDK, teams can iterate on agent configurations, monitor live performance, and scale deployments in cloud environments or on-premises. ElizaOS empowers companies to automate customer interactions, social media engagement, and business processes with autonomous digital workers.

Industrial AI Agents is a Python-based toolkit designed to streamline industrial operations by deploying autonomous AI agents specialized in manufacturing tasks. It features a Data Ingest Agent for collecting sensor streams via MQTT and OPC-UA, a Diagnostics Agent for anomaly and fault detection, a Quality Agent for visual inspection using computer vision, and a Scheduling Agent for dynamic production planning. The framework supports containerized deployment with Docker and Kubernetes, enabling integration with existing IoT platforms and scalable real-time analytics.

Java Action Generic is a lightweight, modular library that allows developers to implement autonomous agent behaviors in Java by defining generic actions. Actions are parameterized units of work that agents can execute, schedule, and compose at runtime. The framework offers a consistent action interface, allowing developers to create custom actions, handle action parameters, and integrate with LightJason’s agent lifecycle management. With support for event-driven execution and concurrency, agents can perform tasks such as dynamic decision-making, interaction with external services, and complex behavior orchestration. The library promotes reusability and modular design, making it suitable for research, simulations, IoT, and game AI applications on any JVM-supported platform.

openai-agents-python is a comprehensive Python package designed to help developers construct fully autonomous AI agents. It provides abstractions for agent planning, tool integration, memory states, and execution loops. Users can register custom tools, specify agent goals, and let the framework orchestrate step-by-step reasoning. The library also includes utilities for testing and logging agent actions, making it easier to iterate on behaviors and troubleshoot complex multi-step tasks.

The LLM Agents Simulation Framework enables the design, execution, and analysis of simulated environments where autonomous agents interact through large language models. Users can register multiple agent instances, assign customizable prompts and roles, and specify communication channels such as message passing or shared state. The framework orchestrates simulation cycles, collects logs, and calculates metrics like turn-taking frequency, response latency, and success rates. It supports seamless integration with OpenAI, Hugging Face, and local LLMs. Researchers can create complex scenarios—negotiation, resource allocation, or collaborative problem-solving—to observe emergent behaviors. Extensible plugin architecture allows addition of new agent behaviors, environment constraints, or visualization modules, fostering reproducible experiments.

Maux is a SaaS AI agent platform that lets teams design, configure, and launch intelligent autonomous agents without deep infrastructure management. Users can choose from modular templates, customize prompt chains, and integrate with APIs like Slack, CRM systems, or databases. Maux supports multi-agent orchestration, letting agents communicate and coordinate on complex tasks. Built-in monitoring dashboards and logs provide insight into performance, usage metrics, and error handling. The platform also offers version control, role-based access, and webhook triggers, enabling seamless deployment of production-grade AI agents for customer support, research automation, data processing, and workflow automation.

MultiAgent2 provides a comprehensive toolkit for orchestrating autonomous AI agents powered by large language models. Developers can define agents with customizable personas, strategies, and memory contexts, enabling them to converse, share information, and collectively solve problems. The framework supports pluggable storage options for long-term memory, role-based access to shared data, and configurable communication channels for synchronous or asynchronous dialogue. Its CLI and Python SDK facilitate rapid prototyping, testing, and deployment of multi-agent systems for use cases spanning research experiments, automated customer support, content generation pipelines, and decision support workflows. By abstracting inter-agent communication and memory management, MultiAgent2 accelerates the development of complex AI-driven applications.

Nova provides a comprehensive toolkit for creating autonomous AI agents in Python. It offers a planner that decomposes goals into actionable steps, a plugin system to integrate any external tools or APIs, and a memory module to store and recall conversation context. Developers can configure custom behaviors, track agent decisions through logs, and extend functionality with minimal code. Nova streamlines the entire agent lifecycle from design to deployment.