Open-source alternatives to Java Sdk

mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers that connect large language model applications to external tools and data sources. It serves as a developer kit for implementing bidirectional communication and structured data exchange between AI clients and servers. The framework enables the creation of executable tools with structured output schemas, reusable prompt templates, and data resource exposure via URI templates. It supports multiple transport layers, including stdio, HTTP, and Server-Sent Events, using a transport

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

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-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

FastMCP is a Python framework designed for building servers that expose functions, resources, and prompts to AI models using the Model Context Protocol. It simplifies the development process by automatically deriving tool metadata, input schemas, and documentation directly from Python function signatures and type hints. The framework provides a unified container for managing these components, allowing developers to build modular applications that integrate seamlessly with AI assistants. The project distinguishes itself through its support for interactive, server-defined user interface compone

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

OpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a

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

Roo-Code is an editor extension and AI agent orchestrator designed to automate software engineering tasks. It functions as an LLM-powered tool that generates source code from natural language descriptions and manages autonomous agents directly within the development environment. The system distinguishes itself through the use of role-based behavioral profiles, allowing the agent to switch between personas such as Architect or Debugger to align with different project phases. It also operates as a Model Context Protocol client, connecting to external servers to expand the data sources and tools

This project is a Model Context Protocol server that connects large language models to web scraping and crawling tools. It functions as a bridge, allowing LLM clients to utilize a web crawling engine and scraping utilities to extract and process web data. The server integrates a markdown web converter that transforms dynamic web pages and PDF documents into clean markdown to optimize consumption by AI models. It also provides a browser automation interface for controlling headless sessions and bypassing access restrictions. The system covers broad capabilities including large-scale website d

This project is a self-hosted AI monitoring stack that functions as an LLM observability platform, AI evaluation framework, and OpenTelemetry trace analyzer. It is designed to capture and analyze LLM traces, sessions, and telemetry to monitor AI agent performance. The platform distinguishes itself as a Model Context Protocol server, exposing workspace functions as tools for AI coding agents. It enables the conversion of failing production traces into test datasets for regression testing and utilizes semantic-based session clustering to discover emerging user behavior patterns. The system cov

This project provides a collection of infrastructure components for multichain wallet integration, including a cryptographic library for cross-chain transaction signing and a curated repository for cryptocurrency asset metadata. It serves as a central hub for managing token logos, contract addresses, and technical specifications for digital assets across multiple blockchains. The system includes a Model Context Protocol server that exposes real-time blockchain data and technical documentation to large language models. It further extends this AI integration by providing a standardized tool-cal

Ever Gauzy is an integrated business management suite providing an ERP and CRM framework for professional services automation. It functions as a multi-tenant SaaS platform that combines time tracking, billing, and human resource management into a unified system. The project is distinguished by its headless architecture, utilizing a REST and GraphQL API gateway to expose business operations. It features a Model Context Protocol server that allows AI assistants to interact with system data and execute functional tools for automated business workflows. The platform covers a broad operational su

OpenSandbox is a secure sandbox runtime and containerized code execution engine designed to run AI-generated code and scripts in isolated environments. It serves as a workload orchestrator that prevents host system contamination by utilizing kernel-level isolation to execute arbitrary commands and scripts. The project distinguishes itself by providing a model context server that bridges large language models to the sandbox for performing file operations and system commands. It also includes a remote GUI sandbox that supports browser automation and desktop interfaces via remote access protocol

MoviePilot is a self-hosted media orchestrator and NAS media library automator. It coordinates workflows between downloaders, metadata scrapers, and file systems to automate the discovery, downloading, renaming, and organization of movie and television content. The system functions as an LLM media management agent, allowing users to control subscriptions, searches, and file organization through conversational text commands. It also acts as a Model Context Protocol server, exposing internal media management tools via a standardized interface for external AI clients and agents. The project inc

btrace is a JVM dynamic tracing tool and performance profiler used for injecting safe instrumentation scripts into a running Java Virtual Machine without requiring a process restart. It functions as a Java agent framework and a Model Context Protocol server, exposing JVM diagnostic operations and tracing tools to large language models and AI assistants. The project distinguishes itself by enabling real-time code injection and bytecode-level instrumentation via a secure binary protocol. It ensures production stability through a static safety analysis engine that blocks unstable code patterns,

This project is a Model Context Protocol server that enables artificial intelligence assistants to interact directly with Microsoft Excel files. It functions as a bridge, allowing external systems to read, write, and modify spreadsheet data through a standardized interface. By supporting both direct file manipulation and headless application automation, the server provides a comprehensive utility for programmatic workbook management. The server distinguishes itself by combining data processing capabilities with a visual rendering pipeline. It can generate image snapshots of specific spreadshe

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

This project is a comprehensive suite of AI tools and frameworks, featuring an LLM multi-agent orchestrator, an autonomous agent runtime, and a stateful application framework. It provides the infrastructure to build and manage specialized AI agents capable of coordinating complex tasks through graph-based workflows and shared state. The system is distinguished by its implementation of the Model Context Protocol, allowing for standardized resource discovery and communication between AI clients and servers. It further includes an AI-powered documentation generator designed to analyze source cod

autoMate is an AI agent tool server and multi-model AI gateway that exposes local system tools and data to chat clients via a standard protocol. It functions as a computer use agent capable of controlling desktop interfaces and browsers to automate local system tasks through natural language. The project serves as a SaaS integration platform, bridging AI agents to third-party services such as GitHub, Notion, Slack, and Jira. It also operates as a personal knowledge management system that stores markdown notes and files using a hybrid keyword and full-text search memory system. The software f

This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizi

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

Higress is an AI API gateway and cloud-native traffic manager that functions as a Kubernetes ingress controller. It provides a centralized system for routing, securing, and optimizing traffic directed toward large language models, AI agents, and microservice architectures. The project distinguishes itself through deep AI orchestration, including the ability to host and manage Model Context Protocol servers that transform REST APIs into tools for AI agents. It features specialized AI infrastructure for model request proxying, protocol translation across multiple providers, and semantic-based c

code2prompt is a codebase-to-prompt converter and LLM context generator that transforms source code and directory structures into formatted text blocks for large language models. It functions as both a utility for generating prompts and an AI agent context server that exposes codebase files and metadata to coding assistants via a standardized server protocol. The tool distinguishes itself through git-aware capabilities, integrating commit messages and branch diffs to provide version control context for AI-generated code changes. It also utilizes the Model Context Protocol to allow external AI

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

This project is a Model Context Protocol server that acts as a bridge between large language models and Obsidian. It provides a standardized interface for external tools to read, search, and modify markdown files and folder structures within a local knowledge base. The server functions as an Obsidian REST API connector, communicating with a community plugin to perform programmatic vault operations. This enables the integration of language model context with private vault content for automated note-taking and knowledge management. The system covers content and media management through the ret