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.

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.

X AI Agent provides a modular architecture for building intelligent agents. It supports seamless integration with external tools and APIs, configurable memory modules, and multi-LLM orchestration. Developers can define custom skills, tool connectors, and workflows in code, then deploy agents that fetch data, generate content, automate processes, and handle complex dialogues autonomously.

AgenticRAG provides a modular architecture for creating autonomous agents that leverage retrieval-augmented generation (RAG). It offers components to index documents in vector stores, retrieve relevant context, and feed it into LLMs to generate context-aware responses. Users can integrate external APIs and tools, configure memory stores to track conversation history, and define custom workflows to orchestrate multi-step decision-making processes. The framework supports popular vector databases like Pinecone and FAISS, and LLM providers such as OpenAI, allowing seamless switching or multi-model setups. With built-in abstractions for agent loops and tool management, AgenticRAG simplifies development of agents capable of tasks like document QA, automated research, and knowledge-driven automation, reducing boilerplate code and accelerating time to deployment.

A2A4J is a lightweight Java framework designed for building autonomous AI agents. It offers abstractions for agents, tools, memories, and planners, supporting asynchronous execution of tasks and seamless integration with OpenAI and other LLM APIs. Its modular design lets you define custom tools and memory stores, orchestrate multi-step workflows, and manage decision loops. With built-in error handling, logging, and extensibility, A2A4J accelerates the development of intelligent Java applications and microservices.

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.

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.

AI Agents offers a structured toolkit for developers to build autonomous agents using large language models. It includes modules for integrating external APIs, managing conversational or long-term memory, orchestrating multi-step workflows, and chaining LLM calls. The framework provides templates for common agent types—data retrieval, question answering, and task automation—while allowing customization of prompts, tool definitions, and memory strategies. With asynchronous support, plugin architecture, and modular design, AI Agents enables scalable, maintainable, and extendable agentic applications.

AgentChat is an open-source AI Agent management platform that leverages OpenAI's GPT models to run versatile conversational agents. It provides a React front-end for interactive chat sessions, a Node.js back-end for API routing, and a plugin system for extending agent capabilities. Agents can be configured with role-based prompts, persistent memory storage, and pre-defined workflows to automate tasks such as summarization, scheduling, data extraction, and notifications. Users can create multiple agent instances, assign custom names, and switch between them in real-time. The system supports secure API key management, and developers can build or integrate new data connectors, knowledge bases, and third-party services to enrich agent interactions.

AgentChat is a developer-centric platform for building sophisticated multi-agent AI conversations. It combines a Python-based FastAPI backend and a React UI to allow users to define individual AI agents with distinct roles—such as data extractor, analyzer, and summarizer—that communicate to collaboratively complete complex tasks. Leveraging OpenAI's GPT models, AgentChat provides memory storage via Redis and supports custom tool integration for tasks like API calls, web scraping, and database querying. The platform offers real-time conversation monitoring, agent performance logs, and configurable agent pipelines. With its modular architecture, developers can extend agent capabilities by adding new tools or adjusting prompts, enabling customized automated workflows, decision-making processes, and knowledge discovery applications.

Aegix provides a robust SDK for orchestrating AI Agents capable of handling complex workflows through multi-step reasoning. With support for various LLM providers, it lets developers integrate custom tools—from database connectors to web scrapers—and maintain conversation state with memory modules such as vector stores. Aegix’s flexible agent loop architecture allows the specification of planning, execution, and review phases, enabling agents to refine outputs iteratively. Whether building document question-answering bots, code assistants, or automated support agents, Aegix simplifies development with clear abstractions, configuration-driven pipelines, and easy extension points. It’s designed to scale from prototypes to production, ensuring reliable performance and maintainable codebases for AI-driven applications.

AgentIn is a Python-based AI agent framework designed to accelerate the development of conversational and task-driven agents. It offers built-in memory modules to persist context, dynamic tool integration to call external APIs or local functions, and a flexible prompt templating system for customized interactions. Multi-agent orchestration enables parallel workflows, while logging and caching improve reliability and auditability. Easily configurable via YAML or Python code, AgentIn supports major LLM providers and can be extended with custom plugins for domain-specific capabilities.

Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

HackerGCLASS Agent API is an open-source Python framework that exposes RESTful endpoints to run AI agents. Developers can define custom tool integrations, configure prompt templates, and maintain agent state and memory across sessions. The framework supports orchestrating multiple agents in parallel, handling complex conversational flows, and integrating external services. It simplifies deployment via Uvicorn or other ASGI servers and offers extensibility with plugin modules, enabling rapid creation of domain-specific AI agents for diverse use cases.

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.

Agentic Solver provides a comprehensive toolkit for developing autonomous AI agents that leverage large language models (LLMs) to tackle real-world problems. It offers components for task decomposition, planning, execution, and result evaluation, enabling agents to break down high-level objectives into sequenced actions. Users can integrate external APIs, custom functions, and memory stores to extend agent capabilities, while built-in logging and retry mechanisms ensure resilience. Written in Python, the framework supports modular pipelines and flexible prompt templates, facilitating rapid experimentation. Whether automating customer support, data analysis, or content generation, Agentic Solver streamlines the end-to-end lifecycle, from initial configuration and tool registration to continuous agent monitoring and performance optimization.

Agentic-Systems is designed to streamline the development of sophisticated autonomous AI applications by offering a modular architecture composed of agent, tool, and memory components. Developers can define custom tools that encapsulate external APIs or internal functions, while memory modules retain contextual information across agent iterations. The built-in orchestration engine schedules tasks, resolves dependencies, and manages multi-agent interactions for collaborative workflows. By decoupling agent logic from execution details, the framework enables rapid experimentation, easy scaling, and fine-grained control over agent behavior. Whether prototyping research assistants, automating data pipelines, or deploying decision-support agents, Agentic-Systems provides the necessary abstractions and templates to accelerate end-to-end AI solution development.

Agentle provides a structured framework for developers to build custom AI agents with minimal boilerplate. It supports defining agent workflows as sequences of tasks, seamless integration with external APIs and tools, conversational memory management for context preservation, and built-in logging for auditability. The library also offers plugin hooks to extend functionality, multi-agent coordination for complex pipelines, and a unified interface to run agents locally or deploy via HTTP APIs.

AgentPilot provides a comprehensive monorepo solution for building, managing, and deploying autonomous AI agents. At its core, it features an extensible plugin system for integrating custom tools and LLMs, a memory management layer for preserving context across interactions, and a planning module that sequences agent tasks. Users can interact via a command-line interface or a web-based dashboard to configure agents, monitor execution, and review logs. By abstracting the complexity of agent orchestration, memory handling, and API integrations, AgentPilot enables rapid prototyping and production-ready deployment of multi-agent workflows in domains such as customer support automation, content generation, data processing, and more.

Agents Course by Justinvarghese511 is a structured learning program that equips developers with the skills to architect, implement, and deploy AI agents. Through step-by-step tutorials, participants learn to design agent decision flows, integrate external APIs, and manage context and memory. The course includes hands-on code examples, Jupyter notebooks, and practical exercises for building agents that automate data extraction, respond conversationally, and perform multi-step tasks. By the end, learners will have a portfolio of working AI agent projects and best practices for production deployment.