MDLR is an innovative, open-source framework that utilizes AI to analyze and organize unstructured content like comments and notes. This powerful platform helps transform scattered data into actionable, real-time, and continually evolving summaries. Ideal for personal diaries and collaborative efforts, MDLR provides users with AI-driven summaries that adapt as new data is introduced, ensuring insights remain current and relevant. Easily integrated into various platforms, MDLR enhances project management with auto-updating notes and summaries, perfect for both individual and group usage.

Multi-Agent-Seller provides a modular environment for simulating e-commerce negotiations using AI agents. It includes pre-built buyer and seller agents with customizable negotiation strategies, such as dynamic pricing, time-based concessions, and utility-based decision-making. Users can define custom protocols, message formats, and market conditions. The framework handles session management, offer tracking, and result logging with built-in visualization tools for analyzing agent interactions. It integrates easily with machine learning libraries for strategy development, enabling experimentation with reinforcement learning or rule-based agents. Its extensible architecture allows adding new agent types, negotiation rules, and visualization plugins. Multi-Agent-Seller is ideal for testing multi-agent algorithms, studying negotiation behaviors, and teaching concepts in AI and e-commerce domains.

Multi-Agent Systems is designed to simplify the creation, configuration, and execution of distributed agent-based architectures. Developers can define agent behaviors, communication ontologies, and service descriptions within Java classes. The framework handles container setup, message transport, and life-cycle management for agents. Built on standard FIPA protocols, it supports peer-to-peer negotiation, collaborative planning, and modular extension. Users can run, monitor, and debug multi-agent scenarios on a single machine or across networked hosts, making it ideal for research, education, and small-scale deployments.

OpenSilver is a free, open-source UI framework designed for building modern .NET web applications using C# and XAML. It supports WPF and Silverlight applications and facilitates seamless transition away from legacy Silverlight technology. The framework ensures 100% code reusability, compatibility with various modern web technologies like Blazor, React, and Angular, and offers an AI-enhanced XAML designer for Visual Studio Code. With OpenSilver, developers can build cross-platform applications that run on any browser and device, preserving the original look and feel of the apps, while significantly reducing the migration cost and time.

PerfAgents is an AI-powered synthetic monitoring and testing platform designed to simplify the setup and management of monitoring your critical applications. It supports multiple open-source frameworks like Selenium, Puppeteer, Cypress, and Playwright. By leveraging your existing scripts or effortlessly creating new ones, PerfAgents provides continuous testing, reducing downtime and ensuring optimal application performance. It seamlessly integrates with tools like Slack, Microsoft Teams, Jira, and PagerDuty for real-time alerts and notifications.

Promptix Python is an open-source framework for building autonomous AI agents in Python. With a simple installation via pip, you can instantiate agents powered by any major LLM, register domain-specific tools, configure in-memory or persistent data stores, and orchestrate multi-step decision loops. The SDK supports real-time streaming of token outputs, callback handlers for logging or custom processing, and built-in memory modules to retain context across interactions. Developers can leverage this library to prototype chatbot assistants, automations, data pipelines, or research agents in minutes. Its modular design allows swapping models, adding custom tools, and extending memory backends, providing flexibility for a wide range of AI agent use cases.

The RAG-based Intelligent Conversational AI Agent combines a vector store-backed retrieval layer with Google’s Gemini LLM via LangChain to power context-rich, conversational knowledge extraction. Users ingest and index documents—PDFs, web pages, or databases—into a vector database. When a query is posed, the agent retrieves top relevant passages, feeds them into a prompt template, and generates concise, accurate answers. Modular components allow customization of data sources, vector stores, prompt engineering, and LLM backends. This open-source framework simplifies the development of domain-specific Q&A bots, knowledge explorers, and research assistants, delivering scalable, real-time insights from large document collections.

SoccerAgent is a specialized AI framework designed for developing and training autonomous soccer agents using state-of-the-art multi-agent reinforcement learning (MARL) techniques. It simulates realistic soccer matches in 2D or 3D environments, offering tools to define reward functions, customize player attributes, and implement tactical strategies. Users can integrate popular RL algorithms (such as PPO, DDPG, and MADDPG) via built-in modules, monitor training progress through dashboards, and visualize agent behaviors in real time. The framework supports scenario-based training for offense, defense, and coordination protocols. With an extensible codebase and detailed documentation, SoccerAgent empowers researchers and developers to analyze team dynamics and refine AI-driven gameplay strategies for academic and commercial projects.

Step Functions Agent is an open-source toolkit enabling developers to construct intelligent serverless workflows on AWS. By leveraging Large Language Models like OpenAI's GPT, this agent dynamically generates AWS Step Functions state machine definitions based on natural language prompts or structured instructions. It supports invoking Lambda functions, passing context between steps, implementing conditional branching, parallelization, retries, and error handling. The framework abstracts AWS service integrations, automatically provisions resources, and offers observability through CloudWatch. Users can customize prompts, integrate custom functions, and monitor workflow executions. With built-in fallback strategies and audit logging, Step Functions Agent streamlines building scalable, resilient AI-driven automation pipelines, accelerating development for data processing, ETL, and decision support applications.

ToolAgents is a modular open-source AI agent framework that integrates large language models with external tools to automate complex workflows. Developers register tools via a centralized registry, defining endpoints for tasks such as API calls, database queries, code execution, and document analysis. Agents can plan multi-step operations, dynamically invoking or chaining tools based on LLM outputs. The framework supports both sequential and parallel task execution, error handling, and extensible plug-ins for custom tool integrations. With Python-based APIs, ToolAgents simplifies building, testing, and deploying intelligent agents that fetch data, generate content, execute scripts, and process documents, enabling rapid prototyping and scalable automation across analytics, research, and business operations.

Trinity-RFT (Retrieval Fine-Tuning) is a unified open-source framework designed to enhance model accuracy and efficiency by combining retrieval and fine-tuning workflows. Users can prepare a corpus, build a retrieval index, and plug the retrieved context directly into training loops. It supports multi-modal retrieval for text, images, and video, integrates with popular vector stores, and offers evaluation metrics and deployment scripts for rapid prototyping and production deployment.

VMAS is a comprehensive toolkit for building and training multi-agent systems using deep reinforcement learning. It supports GPU-based parallel simulation of hundreds of environment instances, enabling high-throughput data collection and scalable training. VMAS includes implementations of popular MARL algorithms like PPO, MADDPG, QMIX, and COMA, along with modular policy and environment interfaces for rapid prototyping. The framework facilitates centralized training with decentralized execution (CTDE), offers customizable reward shaping, observation spaces, and callback hooks for logging and visualization. With its modular design, VMAS seamlessly integrates with PyTorch models and external environments, making it ideal for research in cooperative, competitive, and mixed-motive tasks across robotics, traffic control, resource allocation, and game AI scenarios.

Cloudflare Agents is designed to help developers build, deploy, and manage autonomous AI agents at the network edge using Cloudflare Workers. By leveraging a unified SDK, you can define agent behaviors, custom actions, and conversational flows in JavaScript or TypeScript. The framework seamlessly integrates with major LLM providers like OpenAI and Anthropic, and offers built-in support for HTTP requests, environment variables, and streaming responses. Once configured, agents can be deployed globally in seconds, providing ultra-low latency interactions to end-users. Cloudflare Agents also includes tools for local development, testing, and debugging, ensuring a smooth development experience.

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.

AAGPT is an extensible, open-source AI agent framework designed for building autonomous agents. It enables you to define high-level objectives, manage conversational memory, plan multi-step tasks, and integrate external tools or APIs. Using a simple configuration file and Python SDK, you can customize agent behavior, define custom actions, and deploy agents that can interact with data sources, execute commands, and learn from past interactions to improve performance over time.

AI-Agentic Machine Translation is an open-source framework designed for research and development in machine translation. It orchestrates three core agents—a generator, an evaluator, and a refiner—to collaboratively produce, assess, and refine translations. Built on PyTorch and transformer models, the system supports supervised pre-training, reinforcement learning optimization, and configurable agent policies. Users can benchmark on standard datasets, track BLEU scores, and extend the pipeline with custom agents or reward functions to explore agentic collaboration in translation tasks.

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.

Agent Starter Pack is a developer toolkit that scaffolds intelligent, interactive agents on Google Cloud. It offers templates in Node.js and Python to manage conversation flows, maintain long-term memory, and perform tool and API invocations. Built on Vertex AI and Cloud Functions or Cloud Run, it supports multi-step planning, dynamic routing, observability, and logging. Developers can extend connectors to custom services, build domain-specific assistants, and deploy scalable agents in minutes.

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.