Open-source alternatives to Fastapi Mcp

Skill Seekers is a toolset for generating large language model knowledge bases, featuring a multi-source content scraper and a dedicated RAG data pipeline. It extracts technical data from documentation, code, and video to create structured assets and configuration files for AI-powered IDE extensions. The project distinguishes itself through the ability to transform raw data into polished tutorials and specialized skills for AI plugin marketplaces. It utilizes abstract syntax tree parsing and optical character recognition to analyze GitHub repositories, PDFs, and video frames, converting these

mcp-agent is a framework for building AI agents that integrate with Model Context Protocol servers to execute tools and access data. It functions as a multi-agent orchestrator and protocol-compliant server, enabling the creation of agents that can discover and invoke tools from connected external servers. The project distinguishes itself through a durable workflow engine that supports long-running tasks capable of pausing, resuming, and surviving restarts. It implements complex orchestration patterns, including iterative evaluator-optimizer loops, hierarchical workflow nesting, and specialist

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 project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers

This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

RuView is a WiFi spatial sensing platform that uses radio frequency reflections to detect presence, track body poses, and monitor vital signs without the use of cameras. It functions as a 3D point-cloud spatial mapper, converting signal disturbances into coordinate sets to visualize physical environments and human movement. The system operates as a distributed sensing mesh where synchronized nodes use consensus and shared audit trails to maintain data consistency across a swarm. It further acts as an MQTT home automation bridge, streaming real-time spatial telemetry and occupancy data to smar

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

Wasp is a declarative full-stack web framework that enables developers to build and deploy applications by defining their architecture in a centralized configuration. By using a high-level specification, the framework automates the orchestration of frontend, backend, and database components, ensuring that infrastructure concerns like routing, authentication, and data modeling are handled consistently across the entire stack. The framework distinguishes itself through its compiler-driven approach, which translates declarative configurations into cohesive, production-ready codebases. It provide

This tool functions as a Model Context Protocol server that bridges artificial intelligence models with local development environments. It enables AI assistants to perform codebase analysis, execute command-line utilities, and apply automated code modifications directly to local project files. By integrating with the Gemini API, the system facilitates deep interaction between external models and local system resources. The project distinguishes itself through a robust security and reliability framework designed for automated development workflows. It enforces strict path-based access controls

PageIndex is an agent-ready knowledge engine that processes documents into hierarchical tree structures to enable reasoning-based information retrieval. By organizing content into logical trees rather than relying on traditional vector database chunking, the platform preserves the original structure and flow of complex documents. It functions as a Model Context Protocol server, allowing external AI agents to connect to and query indexed knowledge bases through standardized communication protocols. The platform distinguishes itself by using vision-language models to process raw document images

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

kubectl-ai is a natural language cluster operator and AI command assistant that translates plain-text prompts into executable Kubernetes commands. It serves as an interface between large language models and the Kubernetes API to enable cluster management through conversational text. The project implements a Model Context Protocol server to expose cluster operations as standardized tools for external AI clients. It uses a provider-agnostic model interface to support both cloud-based and local AI backends. The system covers natural language infrastructure control and AI-assisted DevOps through

container-use is a containerized AI execution environment and code sandbox designed to provide a secure space for AI coding agents to execute commands and build applications. It functions as a workspace orchestrator that provisions isolated containers mapped to git branches, allowing multiple agents to operate in parallel without state conflicts or affecting the host system. The project serves as a Model Context Protocol server, bridging AI agents to containerized environments for standardized tool access. It enables a workflow for reviewing and merging changes made by agents within these iso

ACI is a tool-calling platform and centralized system for managing and executing external service operations and custom scripts for agentic workflows. It functions as a unified Model Context Protocol server that enables AI agents and IDEs to dynamically discover and execute diverse toolsets. The platform distinguishes itself through a natural language capability index and intent matching to search for available tools based on task requirements. It provides an external service authenticator and account linking via OAuth-based credential management to permit secure tool execution on behalf of u

This project is a Model Context Protocol server and AI agent database connector. It provides a standardized communication layer that allows language models to interact with relational data stores, read database schemas, and manage PostgreSQL database resources. The implementation acts as a serverless host for the Model Context Protocol, deploying on distributed edge functions to connect AI assistants to a project. This enables AI agents to perform database administration, execute SQL queries, and handle schema migrations through an AI-compatible interface. The system covers broader capabilit

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

Micro-agent is a framework for AI-driven agents focused on automated test-driven development, design-to-code conversion, and external tool orchestration. It utilizes agents that iteratively write, test, and refine source code based on natural language prompts and design files. The system transforms visual design tokens and components into type-safe, linted code by comparing live URLs against reference screenshots to ensure visual parity. It also provides a protocol for linking agents to external commerce, search, and asset management services to synchronize data and expand functional capabili

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

This project is a Model Context Protocol server that functions as an automation tool for 3D design software. It acts as a bridge between creative applications and external intelligence agents, enabling users to manipulate geometry, materials, and lighting through natural language instructions. The tool distinguishes itself by providing a standardized interface for remote command execution and scene data exchange. By utilizing a protocol-based communication layer, it allows external models to query viewport status and object properties, facilitating automated decision-making and real-time scen

Klavis is a platform for managing Model Context Protocol (MCP) servers and providing sandboxed environments where AI agents can safely interact with external tools and services. It functions as an integration framework that orchestrates MCP server instances, exposes tools and resources for AI agents, and isolates agent interactions from production data through horizontally scalable sandbox environments. The platform distinguishes itself through its ability to generate long-horizon agentic tasks that simulate realistic tool-use workflows with live SaaS applications and production MCP servers.

grok-prompts is a library of system prompts and templates used to define the personality, safety boundaries, and operational logic of large language models. It provides a framework for managing model personas and the integration of external tools through structured configurations. The project focuses on persona management to ensure consistent response styles and professional tones. It includes specific configurations for safety guardrails to prevent harmful outputs and a tool integration framework that enables models to interact with services for web search, image generation, and document ana

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

Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

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