AI agents transform the Machine Learning sector: Browse our directory of intelligent solutions from trusted AI agency partners and automation experts.

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MCP.so is a community-driven platform that collects and organizes third-party Model Context Protocol (MCP) Servers. It serves as a central directory where users can discover, share, and learn about various MCP Servers available for AI applications. The platform aims to facilitate the integration of AI models with external tools, data sources, and services by providing a comprehensive list of MCP Servers, enhancing the capabilities and functionalities of AI assistants.

MCP.so

mcp-so

A community-driven platform collecting and organizing third-party MCP Servers for AI applications.

Awesome MCP Servers is a GitHub-curated list compiling various MCP (Model Context Protocol) servers that allow large language models to interface with external tools, files, APIs, databases, and more. These servers empower AI agents to perform tasks beyond simple text generation by enabling them to access structured resources and execute contextual operations. The list features both production-ready and experimental implementations contributed by the community.

Awesome MCP Servers

awesome-mcp-servers

A curated list of Model Context Protocol (MCP) servers enhancing AI capabilities with external tools and data.

Smithery is a comprehensive platform designed to help developers find and deploy agentic services that comply with the Model Context Protocol (MCP). It serves as a centralized hub for discovering MCP servers, offering hosting and distribution services, and providing standardized interfaces for tool integration and configurations. By simplifying the integration of language model agents with external tools and data sources, Smithery accelerates the development of agentic AI applications.

Smithery

smithery

A platform assisting developers in discovering and deploying agentic services adhering to the Model Context Protocol (MCP).

LangMem is a software development kit (SDK) designed to enhance AI agents by integrating long-term memory capabilities. It allows agents to extract important information from interactions, optimize behavior through prompt refinement, and maintain knowledge over time. Compatible with various storage systems and natively integrating with LangGraph's storage layer, LangMem enables continuous improvement, personalized responses, and consistent behavior across sessions.

LangMem

langmem

An SDK enabling AI agents to learn and adapt through long-term memory integration.

HuggingGPT is an AI agent that leverages large language models (LLMs), such as ChatGPT, to connect with various AI models available in machine learning communities like Hugging Face. It functions by using an LLM to plan tasks based on user requests, selecting appropriate models according to their descriptions, executing each subtask with the chosen models, and summarizing the responses based on the execution results. This approach enables HuggingGPT to tackle a wide range of sophisticated AI tasks spanning different modalities and domains, including language, vision, and speech.

HuggingGPT

hugginggpt

LLM-powered agent connecting various AI models to solve complex tasks across multiple domains.

smolagents is a streamlined, open-source library developed by Hugging Face that enables developers to create powerful AI agents with just a few lines of code. The library emphasizes simplicity, containing approximately a thousand lines of code, and offers first-class support for Code Agents—agents that write their actions in Python code. smolagents is compatible with a wide range of language models, including those from OpenAI, Anthropic, and models hosted on the Hugging Face Hub. To ensure secure code execution, it provides options for running code in sandboxed environments via E2B. The library also integrates with the Hugging Face Hub, allowing users to share and load tools seamlessly.

smolagents

A lightweight, open-source library by Hugging Face for building AI agents with minimal code, supporting various language models and secure code execution.

MADS is an open-source framework designed to automate data science pipelines using a multi-agent system. By requiring only two inputs, it streamlines the process of generating machine learning models, performing analyses, and producing reports. MADS facilitates rapid prototyping and enables non-technical users to generate predictions effortlessly. Its modular architecture allows for the integration of various tools to enhance forecasting, model optimization, and data visualization, thereby accelerating business decision-making and automating compliance reporting.

MADS

mads

An open-source multi-agent framework that automates data science pipelines with minimal input.

Adala is an open-source framework designed to facilitate the creation of autonomous data labeling agents. These agents acquire skills through iterative learning, influenced by their operating environment, observations, and reflections. By providing ground truth datasets, users define the environment in which agents learn and apply their skills. Adala emphasizes modularity and extensibility, allowing AI engineers, machine learning researchers, and data scientists to build production-level agent systems that abstract low-level machine learning tasks to Adala and large language models (LLMs).

Adala

adala

An open-source framework for developing autonomous data labeling agents that learn and adapt through iterative processes.

Hugging Face Transformers is an open-source library that offers a wide range of pre-trained machine learning models for natural language processing (NLP) tasks. It provides easy-to-use APIs to integrate these models into applications, enabling tasks such as text classification, translation, summarization, and question-answering. The library supports various architectures, including BERT, GPT, and T5, making it a versatile tool for developers and researchers working on NLP projects.

Hugging Face Transformers

hugging-face-transformers

A library providing state-of-the-art machine learning models for natural language processing tasks.

AI Agents for "Machine Learning" Industry