The MCP LLM Bridge is a tool that acts as a bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs. It provides a bidirectional protocol translation layer between MCP and OpenAI's function-calling interface, enabling any OpenAI-compatible language model to leverage MCP-compliant tools through a standardized interface. The tool supports primary integration with the OpenAI API and offers additional compatibility for local endpoints that implement the OpenAI API specification. Users can configure the tool for different endpoints and models, facilitating the execution of complex queries and tasks using cloud-based or local models like Ollama and LM Studio.

Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

mcpd is a tool developed by Mozilla AI to declaratively manage Model Context Protocol (MCP) servers, enabling consistent interface for defining and running tools across different environments. It bridges the gap between local development and enterprise deployment by providing secure secrets management, declarative configuration, and seamless environment promotion. mcpd simplifies the developer experience by offering zero-config tool setup, language-agnostic tooling, version-controlled configuration files, enterprise-ready secrets management, and smooth transition from local to production environments.

mcp-graphql is a Model Context Protocol server that enables Large Language Models (LLMs) to interact with GraphQL APIs. It provides schema introspection and query execution capabilities, allowing models to dynamically discover and use GraphQL APIs. The server offers tools for retrieving the GraphQL schema and executing queries against the endpoint. Mutations are disabled by default for security reasons. Users can install mcp-graphql via Smithery or manually to Claude Desktop. It is recommended to carefully consider enabling mutations in production environments to prevent unauthorized data modifications.

Action MCP is a powerful tool for managing and automating your cloud infrastructure. It provides a user-friendly interface to easily create, update, and delete resources on popular cloud platforms. With Action MCP, you can streamline your deployment process, reduce manual errors, and improve overall efficiency. The tool supports various cloud providers and offers a wide range of features to meet your infrastructure management needs. Whether you are a developer, system administrator, or DevOps engineer, Action MCP can help you simplify and optimize your cloud operations.

Fast MCP is a Ruby gem that simplifies the integration of AI models with your Ruby applications. It provides a clean implementation of the Model Context Protocol, eliminating complex communication protocols, integration challenges, and compatibility issues. With Fast MCP, you can easily connect AI models to your servers, share data resources, choose from multiple transports, integrate with frameworks like Rails and Sinatra, and secure your AI-powered endpoints. The gem also offers real-time updates and authentication support, making AI integration a seamless experience for developers.

Shell Sage is an intelligent terminal companion and AI-powered terminal assistant that enhances the terminal experience with features like local and cloud AI support, context-aware error diagnosis, natural language to command translation, and safe command execution workflows. It offers interactive workflows, supports various API providers, and allows for custom model selection. Users can configure the tool for local or API mode, select specific models, and switch between modes easily. Currently in alpha development, Shell Sage has known limitations like limited Windows support and occasional false positives in error detection. The roadmap includes improvements like better context awareness, Windows PowerShell integration, Tmux integration, and CI/CD error pattern database.

Stripe AI is a repository that serves as a hub for building AI-powered products and businesses on top of Stripe. It provides SDKs for integrating Stripe with LLMs and agent frameworks, including tools like @stripe/agent-toolkit, @stripe/ai-sdk, and @stripe/token-meter. The Model Context Protocol (MCP) server hosted by Stripe allows secure client access via OAuth, and the Agent Toolkit enables integration with popular agent frameworks like OpenAI's Agent SDK, LangChain, CrewAI, and Vercel's AI SDK through function calling. The toolkit supports Python and TypeScript and is built on top of the Stripe Python and Node SDKs.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

LLM-Term is a Rust-based CLI tool that generates and executes terminal commands using OpenAI's language models or local Ollama models. It offers configurable model and token limits, works on both PowerShell and Unix-like shells, and provides a seamless user experience for generating commands based on prompts. Users can easily set up the tool, customize configurations, and leverage different models for command generation.

ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

RA.Aid is an AI software development agent powered by `aider` and advanced reasoning models like `o1`. It combines `aider`'s code editing capabilities with LangChain's agent-based task execution framework to provide an intelligent assistant for research, planning, and implementation of multi-step development tasks. It handles complex programming tasks by breaking them down into manageable steps, running shell commands automatically, and leveraging expert reasoning models like OpenAI's o1. RA.Aid is designed for everyday software development, offering features such as multi-step task planning, automated command execution, and the ability to handle complex programming tasks beyond single-shot code edits.

PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. It is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. The tool provides secure and isolated operations in a sandboxed Docker environment, fully autonomous AI-powered agent for penetration testing steps, a suite of 20+ professional security tools, smart memory system for storing research results, web intelligence for gathering information, integration with external search systems, team delegation system, comprehensive monitoring and reporting, modern interface, API integration, persistent storage, scalable architecture, self-hosted solution, flexible authentication, and quick deployment through Docker Compose.

Perplexity-mcp is a Model Context Protocol (MCP) server that provides web search functionality using Perplexity AI's API. It works with the Anthropic Claude desktop client. The server allows users to search the web with specific queries and filter results by recency. It implements the perplexity_search_web tool, which takes a query as a required argument and can filter results by day, week, month, or year. Users need to set up environment variables, including the PERPLEXITY_API_KEY, to use the server. The tool can be installed via Smithery and requires UV for installation. It offers various models for different contexts and can be added as an MCP server in Cursor or Claude Desktop configurations.

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.

The `any-llm` repository provides a unified API to access different LLM (Large Language Model) providers. It offers a simple and developer-friendly interface, leveraging official provider SDKs for compatibility and maintenance. The tool is framework-agnostic, actively maintained, and does not require a proxy or gateway server. It addresses challenges in API standardization and aims to provide a consistent interface for various LLM providers, overcoming limitations of existing solutions like LiteLLM, AISuite, and framework-specific integrations.

The xNomad.fun repository is an open-source codebase for the website xNomad.fun. The project aims to provide a reference for developing AI-NFT applications based on the MCV project and to engage the community in transforming the AI and blockchain industries. The repository includes instructions for setting up the core service and configuring endpoints in the .env file. It also offers optional features like airdrop support and Twitter integration. For more information, users can refer to the xNomad Documentation. The project is licensed under the MIT License and is developed by the xNomad Team.

The MCP LLM Bridge is a tool that acts as a bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs. It provides a bidirectional protocol translation layer between MCP and OpenAI's function-calling interface, enabling any OpenAI-compatible language model to leverage MCP-compliant tools through a standardized interface. The tool supports primary integration with the OpenAI API and offers additional compatibility for local endpoints that implement the OpenAI API specification. Users can configure the tool for different endpoints and models, facilitating the execution of complex queries and tasks using cloud-based or local models like Ollama and LM Studio.

DataLine is an AI-driven data analysis and visualization tool designed for technical and non-technical users to explore data quickly. It offers privacy-focused data storage on the user's device, supports various data sources, generates charts, executes queries, and facilitates report building. The tool aims to speed up data analysis tasks for businesses and individuals by providing a user-friendly interface and natural language querying capabilities.

aiosql is a Python module that allows you to organize SQL statements in .sql files and load them into your Python application as methods to call. It supports various database drivers like SQLite, PostgreSQL, MySQL, MariaDB, and DuckDB. The project is an implementation of Kris Jenkins' yesql library to the Python ecosystem, allowing users to easily reuse SQL code in SQL GUIs or CLI tools. With aiosql, you can write, version control, comment, and run SQL code using files without losing the ability to use them as you would any other SQL file. It provides support for PEP 249 and asyncio based drivers, enabling users to execute parametric SQL queries from Python methods.

DBHub is a universal database gateway that implements the Model Context Protocol (MCP) server interface. It allows MCP-compatible clients to connect to and explore different databases. The gateway supports various database resources and tools, providing capabilities such as executing queries, listing connectors, generating SQL, and explaining database elements. Users can easily configure their database connections and choose between different transport modes like stdio and sse. DBHub also offers a demo mode with a sample employee database for testing purposes.

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making database exploration and analysis safer and more structured. It provides features such as listing available MySQL tables as resources, reading table contents, executing SQL queries with proper error handling, secure database access through environment variables, and comprehensive logging. The tool ensures security best practices by never committing environment variables or credentials, using a database user with minimal required permissions, implementing query whitelisting for production use, and monitoring and logging all database operations.

mcp-graphql is a Model Context Protocol server that enables Large Language Models (LLMs) to interact with GraphQL APIs. It provides schema introspection and query execution capabilities, allowing models to dynamically discover and use GraphQL APIs. The server offers tools for retrieving the GraphQL schema and executing queries against the endpoint. Mutations are disabled by default for security reasons. Users can install mcp-graphql via Smithery or manually to Claude Desktop. It is recommended to carefully consider enabling mutations in production environments to prevent unauthorized data modifications.

Laravel Boost accelerates AI-assisted development by providing essential context and structure for generating high-quality, Laravel-specific code. It includes an MCP server with specialized tools, AI guidelines, and a Documentation API. Boost is designed to streamline AI-assisted coding workflows by offering precise, context-aware results and extensive Laravel-specific information.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.