Browse all content tagged with Open Source
The mcp-local-rag MCP Server enables privacy-respecting, local Retrieval-Augmented Generation (RAG) web search for LLMs. It allows AI assistants to access, embed, and extract up-to-date information from the web without external APIs, enhancing research, content creation, and question answering workflows.
The Weather MCP Server connects AI assistants to real-time and historical weather data using the Open-Meteo API—no API keys needed. Enable AI-driven workflows with weather-aware tools for travel planning, event scheduling, automation, and more.
GitMCP is a free, open-source Model Context Protocol (MCP) server that connects AI assistants with any GitHub repository for code-aware completions, exploration, and development workflows. It reduces code hallucinations by providing live, contextual access to code, issues, PRs, and file structures for enhanced AI-driven productivity.
The Todos MCP Server is an open-source todo list application with Model Context Protocol (MCP) support, allowing AI assistants and chatbots to manage tasks programmatically via a standardized API. It features privacy-friendly local storage and practical tools for seamless task automation in AI-powered workflows.
The Agentset MCP Server is an open-source platform enabling Retrieval-Augmented Generation (RAG) with agentic capabilities, allowing AI assistants to connect with external data sources, APIs, and services for developing intelligent, document-based applications.
The Authenticator App MCP Server enables AI agents to securely access 2FA codes and passwords, streamlining automated login processes and credential management across platforms, while maintaining robust security practices.
The Figma-Context MCP Server connects AI coding agents with Figma design layouts by exposing Figma data via the Model Context Protocol (MCP). It enables AI assistants to fetch, analyze, and reason about Figma files for seamless design-to-code workflows, UI implementation, component extraction, and automated documentation.
Lspace MCP Server is an open-source backend and standalone application implementing the Model Context Protocol (MCP). It enables persistent, searchable knowledge by capturing insights from any AI session and integrating them across tools, empowering developers to build intelligent, context-enriched workflows.
The mcp-k8s-go MCP Server enables AI assistants to interact programmatically with Kubernetes clusters via Model Context Protocol, automating and streamlining DevOps workflows through a standardized interface.
The mcp-rag-local MCP Server empowers AI assistants with semantic memory, enabling storage and retrieval of text passages based on meaning, not just keywords. It uses Ollama for embeddings and ChromaDB for vector search, supporting advanced knowledge management and contextual recall in local workflows.
The Mifos X MCP Server bridges FlowHunt AI agents and Apache Fineract®, enabling seamless financial data automation, client onboarding, loan management, and workflow AI integration through a standardized protocol.
The Telegram MCP Server bridges Telegram’s API with AI assistants using the Model Context Protocol, enabling automated workflows for messaging, dialog management, and advanced communication scenarios within FlowHunt.
The TFT MCP Server connects AI assistants to the Riot Games API, enabling programmatic access to Team Fight Tactics (TFT) player match history and detailed match data for automated analysis, player tracking, and in-depth event commentary.
The Typesense MCP Server connects AI agents to Typesense, an open-source search engine, enabling LLMs to search, retrieve, and analyze structured data collections in real time. Integrate powerful document search, analytics, and schema discovery tools directly into your AI workflows with secure API key handling.
The TeamRetro MCP Server connects AI assistants with the TeamRetro platform, enabling automation of team management, retrospectives, health checks, and analytics through a secure Model Context Protocol interface. Integrate TeamRetro’s collaboration tools and analytics into your AI workflows for enhanced productivity.
Vectara MCP Server is an open source bridge between AI assistants and Vectara's Trusted RAG platform, enabling secure, efficient Retrieval-Augmented Generation (RAG) and enterprise search for generative AI workflows in FlowHunt.
AllenNLP is a robust open-source library for NLP research, built on PyTorch by AI2. It offers modular, extensible tools, pre-trained models, and easy integration with libraries like spaCy and Hugging Face, supporting tasks such as text classification, coreference resolution, and more.
Chainer is an open-source deep learning framework offering a flexible, intuitive, and high-performance platform for neural networks, featuring dynamic define-by-run graphs, GPU acceleration, and broad architecture support. Developed by Preferred Networks with major tech contributions, it’s ideal for research, prototyping, and distributed training, but is now in maintenance mode.
DL4J, or DeepLearning4J, is an open-source, distributed deep learning library for the Java Virtual Machine (JVM). Part of the Eclipse ecosystem, it enables scalable development and deployment of deep learning models using Java, Scala, and other JVM languages.
Discover the key announcements from Google I/O 2025, including Gemini 2.5 Flash, Project Astra, Android XR, AI agents in Android Studio, Gemini Nano, Gemma 3n, SignGemma, and how FlowHunt is leveraging these new AI-native capabilities.
Learn more about the Grok model by xAI, an advanced AI chatbot led by Elon Musk. Discover its real-time data access, key features, benchmarks, use cases, and how it compares to other AI models.
Jupyter Notebook is an open-source web application enabling users to create and share documents with live code, equations, visualizations, and narrative text. Widely used in data science, machine learning, education, and research, it supports over 40 programming languages and seamless integration with AI tools.
KNIME (Konstanz Information Miner) is a powerful open-source data analytics platform offering visual workflows, seamless data integration, advanced analytics, and automation for diverse industries.
Kubeflow is an open-source machine learning (ML) platform on Kubernetes, simplifying the deployment, management, and scaling of ML workflows. It offers a suite of tools covering the entire ML lifecycle, from model development to deployment and monitoring, enhancing scalability, reproducibility, and resource utilization.
LangChain is an open-source framework for developing applications powered by Large Language Models (LLMs), streamlining the integration of powerful LLMs like OpenAI’s GPT-3.5 and GPT-4 with external data sources for advanced NLP applications.
Find out more about Mistral AI and the LLM models they offer. Discover how these models are used and what sets them apart.
Apache MXNet is an open-source deep learning framework designed for efficient and flexible training and deployment of deep neural networks. Known for its scalability, hybrid programming model, and support for multiple languages, MXNet empowers researchers and developers to build advanced AI solutions.
Natural Language Toolkit (NLTK) is a comprehensive suite of Python libraries and programs for symbolic and statistical natural language processing (NLP). Widely used in academia and industry, it offers tools for tokenization, stemming, lemmatization, POS tagging, and more.
Open Neural Network Exchange (ONNX) is an open-source format for seamless interchange of machine learning models across different frameworks, enhancing deployment flexibility, standardization, and hardware optimization.
Plotly is an advanced open-source graphing library for creating interactive, publication-quality graphs online. Compatible with Python, R, and JavaScript, Plotly empowers users to deliver complex data visualizations and supports a wide range of chart types, interactivity, and web app integration.
Scikit-learn is a powerful open-source machine learning library for Python, providing simple and efficient tools for predictive data analysis. Widely used by data scientists and machine learning practitioners, it offers a broad range of algorithms for classification, regression, clustering, and more, with seamless integration into the Python ecosystem.
SciPy is a robust open-source Python library for scientific and technical computing. Building on NumPy, it offers advanced mathematical algorithms, optimization, integration, data manipulation, visualization, and interoperability with libraries like Matplotlib and Pandas, making it essential for scientific computing and data analysis.
TensorFlow is an open-source library developed by the Google Brain team, designed for numerical computation and large-scale machine learning. It supports deep learning, neural networks, and runs on CPUs, GPUs, and TPUs, simplifying data acquisition, model training, and deployment.
Discover Wan 2.1, the open-source AI video generation model by Alibaba. Learn how it works, explore its powerful features, and follow our step-by-step guide to run it locally on your own GPU.
Fastai is a deep learning library built on PyTorch, offering high-level APIs, transfer learning, and a layered architecture to simplify neural network development for vision, NLP, tabular data, and more. Developed by Jeremy Howard and Rachel Thomas, Fastai is open-source and community-driven, making state-of-the-art AI accessible for everyone.
