Open-source alternatives to Csharp Sdk

This is a software development kit for integrating the Model Context Protocol into Java applications. It serves as a framework for building AI servers and communication layers that exchange prompts, resources, and tool definitions between AI clients and servers. The SDK provides a transport-agnostic communication layer, allowing bidirectional data exchange over standard I/O, HTTP, or Server-Sent Events. It includes a generative AI resource manager for exposing structured data and prompt templates, and a standardized interface for implementing protocol clients and servers. The project covers

This is a software development kit and framework for implementing the Model Context Protocol in Go. It provides a standardized system for building servers and clients that exchange external resources, proprietary data, and executable tools to provide context for large language models. The SDK includes a JSON-RPC communication library and an integration framework to expose local data, prompt templates, and typed functions to AI models. It enables the development of both protocol servers that provide external context and clients that consume these remote tools and resources. The project covers

mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents. The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protoco

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

This project provides a TypeScript software development kit for the Model Context Protocol, a standard designed to facilitate bidirectional communication between AI applications and external data sources or tools. It serves as a foundational framework for building both clients and servers, enabling language models to interact with external systems through a unified, decoupled interface. The SDK distinguishes itself by implementing a transport-agnostic connection layer that supports both local standard input-output streams and remote HTTP endpoints. It utilizes a JSON-RPC message bus to manage

mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for

This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inte

TablePro is a cross-platform database management client designed for browsing, querying, and administering both SQL and NoSQL databases. It functions as a unified workspace that integrates a code-centric SQL editor with schema visualization tools, allowing developers to manage complex data models and execute queries across diverse database engines. The application distinguishes itself through an agentic AI integration layer that connects language models directly to database tools, enabling automated query generation, optimization, and error fixing with configurable approval gates. It features

fastmcp is a Python library and framework for building servers and clients that implement the Model Context Protocol. It serves as a tool integration library designed to connect large language models to external tools and data sources. The framework features an interactive tool user interface renderer, which allows for the display of visual interfaces for tools directly within a conversational flow. It also provides a library for automatically generating schemas and validation for tools used by language models. The project covers server and client development, including tool and resource exp

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

Mods is a terminal-based AI client that sends prompts to large language models and streams responses back to the command line. It functions as a multi-provider AI gateway, routing queries to OpenAI, Cohere, Groq, Gemini, and local endpoints, and includes a conversation history manager that saves, caches, branches, and resumes text-based interactions. The tool also operates as a Model Context Protocol client, connecting to external MCP servers via stdio, SSE, or HTTP to extend model capabilities with specialized tools and data. The project distinguishes itself through a config-driven provider

This project is a Model Context Protocol server that acts as a programmatic bridge between large language models and private messaging accounts. It provides an automation interface for interacting with WhatsApp by exposing messaging and data retrieval capabilities as tools for AI assistants. The system utilizes browser automation to control the web application interface, allowing for stateful session management to maintain authentication. It enables the transmission of various content types, including plain text, documents, and audio files formatted as voice messages. The server covers conve

ddgs is a metasearch engine and web content extractor that provides a toolkit for programmatically retrieving search results from DuckDuckGo. It functions as a search API server and a Model Context Protocol server to integrate web search capabilities directly into large language model environments. The project distinguishes itself by aggregating text, image, news, and video results from multiple providers into a single interface. It includes a utility for fetching URLs and converting HTML content into markdown, plain text, or structured data. The system covers a broad range of search capabil

This project is a Model Context Protocol server that provides large language models with neural web search and webpage content extraction capabilities. It implements a standardized interface to expose research tools and resources to compatible clients. The server integrates a neural search engine to retrieve real-time internet data using semantic embeddings rather than keyword matching. It includes specialized utilities for company intelligence and reasoning-based deep research, enabling the collection and synthesis of organizational data and professional profiles. The system covers a broad

Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for se

This project is an autonomous workflow engine and orchestration platform designed to coordinate specialized AI agents. It functions as a development framework that manages the end-to-end lifecycle of complex, multi-step tasks, including persona definition, persistent memory management, and the execution of automated coding workflows. By acting as a Model Context Protocol server, it enables standardized communication between development tools and external AI models. The platform distinguishes itself through an event-driven architecture that routes typed messages between agent personas, allowin

Headroom is an AI gateway proxy and token optimizer designed to reduce the cost and latency of large language model interactions. It functions as an intermediary that intercepts traffic between clients and providers to apply context compression, request routing, and format translation. The system differentiates itself through a Model Context Protocol server implementation that delivers compression and retrieval tools to compatible AI hosts. It employs a content-aware compression pipeline and tiered importance scoring to trim redundant data from logs and tool outputs while preserving essential

This project is a Model Context Protocol server designed to bridge the gap between local frontend component libraries and language models. It functions as a development assistant that provides AI tools with the structural context, dependency requirements, and installation patterns necessary to generate accurate, framework-specific UI code. The server distinguishes itself by utilizing schema-driven metadata extraction and static file system analysis to interpret component structures without requiring runtime execution. By decoupling component definitions from specific UI libraries, it supports

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

OpenSandbox is a secure execution environment and runtime designed for running untrusted code and scripts generated by AI agents. It utilizes a containerized code execution engine and microVM-based isolation to protect host systems from malicious actions while providing isolated virtual environments. The project features a sandbox server based on the Model Context Protocol to automate the creation and control of virtual workspaces. It supports the deployment of secure remote desktop hosts, including headless web browsers and editor instances, for automated interaction. The system includes an

Web3.py is a Python library that provides a comprehensive interface for interacting with the Ethereum blockchain. It functions as a JSON-RPC client, allowing applications to connect to blockchain nodes via HTTP, WebSocket, or IPC to read network state and send transactions. The library includes a dedicated smart contract interface that uses Application Binary Interface definitions to deploy contracts and execute on-chain functions. It also features a cryptography toolkit for signing transactions and hashing data with Keccak, alongside utilities for translating Ethereum Name Service domain nam

This project is a Model Context Protocol server that enables Large Language Models to control Playwright browsers for web automation, scraping, and end-to-end testing. It functions as a programmable interface for executing JavaScript, capturing screenshots, and interacting with web elements across multiple browser engines. The server exposes browser automation capabilities as a set of standardized tools that models can discover and invoke. It supports session-based browser isolation to ensure unique contexts for each client connection and provides a transport layer using either standard input

Magic MCP is a Model Context Protocol server and AI component generator that translates natural language descriptions into functional user interface code. It acts as an LLM design orchestrator, producing responsive web elements and layouts anchored on utility-first CSS styling patterns. The system features a side-by-side variation engine that generates multiple stylistic interpretations of a single prompt for comparative selection. It incorporates SVG-based asset integration for branding and iconography and utilizes template-based assembly to combine pre-defined style patterns with user-speci

This project is a multi-protocol API simulation and mocking system designed to replace external dependencies during development and testing. It provides an API mocking server, a network traffic proxy, and specialized simulators for language model services and identity providers. The system distinguishes itself through deep AI simulation capabilities, including the emulation of language model providers and Model Context Protocol servers using JSON-RPC 2.0. It supports multi-turn conversational logic, state tracking for AI chat APIs, and the visualization of agent execution through call graphs

This project is a curated library of community-driven prompt templates and personas designed to improve interactions with large language models. It functions as a prompt engineering guide, providing interactive tutorials and examples to teach advanced design and reasoning techniques. The library can operate as a Model Context Protocol server, providing a standardized interface for AI tools and agents to access prompt data as a service. For organizations, it offers a self-hosted repository option that allows for private deployment on internal infrastructure with custom authentication and data

gptel is an LLM Emacs client and multi-backend AI integration system that allows users to interact with large language models directly inside the Emacs text editor. It serves as an AI-powered text refactoring tool and a context-aware prompt manager, providing a unified interface to connect with various AI providers, including local Ollama instances, AWS Bedrock, and Gemini. The project distinguishes itself as a Model Context Protocol client, connecting to MCP servers to provide language models with external tools and data sources. It enables context-augmented prompting by aggregating text fro