Best MCP Gateways for Python
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Compare the Top MCP Gateways that integrate with Python as of February 2026
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This a list of MCP Gateways that integrate with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
What are MCP Gateways for Python?
MCP gateways act as secure intermediaries that connect AI models with external tools, data sources, and services using the Model Context Protocol (MCP). They manage authentication, permissions, and request routing to ensure controlled and reliable access to contextual data. The gateways standardize how models discover, invoke, and interact with tools across different environments. Many MCP gateways include monitoring, logging, and policy enforcement features to maintain security and compliance. By centralizing tool access and context delivery, MCP gateways enable scalable, interoperable, and safer AI integrations. Compare and read user reviews of the best MCP Gateways for Python currently available using the table below. This list is updated regularly.
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Klavis AI
Klavis AI
Klavis AI provides open source infrastructure to simplify the use, building, and scaling of Model Context Protocols (MCPs) for AI applications. MCPs enable tools to be added dynamically at runtime in a standardized way, eliminating the need for preconfigured integrations during design time. Klavis AI offers hosted, secure MCP servers, eliminating the need for authentication management and client code. The platform supports integration with various tools and MCP servers. Klavis AI's MCP servers are stable and reliable, hosted on dedicated cloud infrastructure, and support OAuth and user-based authentication for secure access and management of user resources. The platform also offers MCP clients on Slack, Discord, and the web, allowing direct access to MCPs within these communication platforms. Additionally, Klavis AI provides a standardized RESTful API interface to interact with MCP servers, enabling developers to integrate MCP functionality into their applications.Starting Price: $99 per month -
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FastMCP
fastmcp
FastMCP is an open source, Pythonic framework for building Model Context Protocol (MCP) applications that makes creating, managing, and interacting with MCP servers simple and production-ready by handling the protocol’s complexity so developers can focus on business logic. The Model Context Protocol (MCP) is a standardized way for large language models to securely connect to tools, data, and services, and FastMCP provides a clean API to implement that protocol with minimal boilerplate, using Python decorators to register tools, resources, and prompts. A typical FastMCP server is created by instantiating a FastMCP object, decorating Python functions as tools (functions the LLM can invoke), and then running the server with built-in transport options like stdio or HTTP; this lets AI clients call into your code as if it were part of the model’s context.Starting Price: Free -
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Prefect Horizon
Prefect
Prefect Horizon is a managed AI infrastructure platform within the broader Prefect product suite that lets teams deploy, govern, and operate Model Context Protocol (MCP) servers and AI agents at enterprise scale with production-ready features such as managed hosting, authentication, access control, observability, and tool governance. It builds on the FastMCP framework to turn MCP from just a protocol into a platform with four core integrated pillars, Deploy (host and scale MCP servers quickly with CI/CD and monitoring), Registry (a centralized catalog of first-party, third-party, and curated MCP endpoints), Gateway (role-based access control, authentication, and audit logs for secure, governed access to tools), and Agents (permissioned, user-friendly agent interfaces that can be deployed in Horizon, Slack, or exposed over MCP so business users can interact with context-aware AI without needing MCP technical knowledge).Starting Price: Free -
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ContextForge MCP Gateway is an open source Model Context Protocol (MCP) gateway, registry, and proxy platform that provides a unified endpoint for AI clients to discover and access tools, resources, prompts, and REST or MCP services in complex AI ecosystems. It sits in front of multiple MCP servers and REST APIs to federate and unify discovery, authentication, rate-limiting, observability, and traffic routing across diverse backends, with support for transports such as HTTP, JSON-RPC, WebSocket, SSE, stdio, and streamable HTTP, and can virtualize legacy APIs as MCP-compliant tools. It includes an optional Admin UI for real-time configuration, monitoring, and log visibility, and is designed to scale from standalone deployments to multi-cluster Kubernetes environments with Redis-backed federation and caching for performance and resilience.
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