Open Source MCP Gateways
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MCP Gateways
View 34 business solutionsBrowse free open source MCP Gateways and projects below. Use the toggles on the left to filter open source MCP Gateways by OS, license, language, programming language, and project status.
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LiteLLM
lightweight package to simplify LLM API calls
Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, Azure OpenAI etc.] liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response. Streaming is supported for OpenAI, Azure, Anthropic, and Huggingface models. -
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agentgateway
Next Generation Agentic Proxy for AI Agents and MCP servers
Agentgateway is an open-source “data plane” built specifically for agentic AI connectivity, focusing on how agents talk to other agents and to tools across different frameworks and environments. It presents itself as a complete connectivity solution that adds drop-in security, observability, and governance to agent-to-agent and agent-to-tool communication without requiring you to rebuild your agent stack. The project supports interoperable protocols designed for this ecosystem, including Agent2Agent (A2A) and Model Context Protocol (MCP), which helps standardize how tools and agents interoperate. It is designed for performance and scale, implemented in Rust and engineered to handle large throughput and multi-tenant deployments. Operationally, it emphasizes safety and control with an RBAC system tuned for MCP/A2A use cases, plus the ability to update configuration dynamically via xDS without downtime. -
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Agent Stack
Deploy and share agents with open infrastructure
Agent Stack is an open infrastructure platform designed to take AI agents from prototype to production, no matter how they were built. It includes a runtime environment, multi-tenant web UI, catalog of agents, and deployment flow that seeks to remove vendor lock-in and provide greater autonomy. Under the hood it’s built on the “Agent2Agent” (A2A) protocol, enabling interoperability between different agent ecosystems, runtime services, and frameworks. The platform supports agents built in frameworks like LangChain, CrewAI, etc., enabling them to be hosted, managed and shared through a unified interface. It also offers multi-model, multi-provider support (OpenAI, Anthropic, Gemini, IBM WatsonX, Ollama etc.), letting users compare performance and cost across models. For developers and organizations building AI-agent products or automations, Agent Stack gives a scaffold that handles the “plumbing”, so they can focus on logic and domain. -
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Octelium
A next-gen FOSS self-hosted unified zero trust secure access platform
Octelium is an open source, self-hosted unified secure-access platform built for modern infrastructure and hybrid environments. It positions itself as more than a typical VPN; it supports zero-trust network access (ZTNA), “BeyondCorp”-style access, API/AI gateway functionality, and even serves as a PaaS-like deployment surface. One of its key strengths is identity-based, application-layer (L7) aware control, meaning access decisions are made per request, with context and policy rather than simple network-level allow/block rules. It supports both client-based (e.g., WireGuard/QUIC tunnels) and client-less access models, which makes it flexible for both human users and automated workloads. The project also highlights self-hosted, no hidden “server-side” locked components, giving organizations greater ownership and control over access, rather than relying on proprietary SaaS. -
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kgateway
The Cloud-Native API Gateway and AI Gateway
kgateway is a mature, cloud-native API and ingress gateway designed to provide unified API connectivity for services, microservices, serverless workloads, and AI-centric systems running on Kubernetes clusters. It implements the Kubernetes Gateway API and can operate as both a lightweight in-cluster microgateway and a centralized gateway capable of handling billions of API calls with high performance and low latency. By integrating with Envoy and advanced data planes, it handles modern ingress concerns such as traffic routing, authentication, authorization, rate limiting, and observability for traditional HTTP/gRPC services and AI workloads alike. Beyond standard API traffic, kgateway also supports gateway patterns tailored for large language model (LLM) consumption, inference routing, and Model Context Protocol (MCP) orchestration, enabling secure access to models, tools, and agent interactions. -
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Bifrost
The Fastest LLM Gateway with built in OTel observability
Bifrost is an LLM gateway designed to provide a unified OpenAI-compatible API front for many different model providers. It abstracts away the complexity of working directly with multiple backend providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, etc.), enabling you to plug in providers and switch between them without touching your client code. It is built to be high performance: in benchmark tests at 5,000 requests per second, it reportedly adds only microseconds of overhead and achieves perfect success rates with no failed requests. Bifrost supports features such as automatic fallback (failover between providers), load balancing across API keys/providers, and semantic caching to reduce latency and cost. It also includes observability with built-in metrics, tracing, logging, and supports governance features like rate limiting, access control, and cost budgeting. The architecture is modular: there is a core engine, plugin layers, and transport layers (HTTP APIs). -
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Obot MCP Gateway
Hosting, Registry, Gateway, and Chat Client
Obot is an open-source platform built to help organizations adopt and operate Model Context Protocol (MCP) capabilities in a centralized, production-friendly way. It combines multiple MCP building blocks into one system, including hosting for MCP servers, a registry for discovery, a gateway layer to route access, and a standards-compliant chat client experience. The project is aimed at solving common enterprise rollout problems such as reliably hosting servers for internal and external users, curating “approved” MCP servers for employees to find, and enforcing authentication, access control, and auditable activity. It also supports building richer agents and chatbots that can leverage MCP servers while keeping operations manageable for IT and platform teams. The platform is designed to work with a variety of workflows and clients, so MCP servers managed inside Obot can be used by automation/agent frameworks as well as popular chat clients that speak MCP. -
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Portkey AI Gateway
A blazing fast AI Gateway with integrated guardrails
Portkey AI Gateway aims to offer a blazing fast, secure, and flexible gateway for interacting with a wide variety of models and enforcing guardrails. It presents a single, friendly API through which you can route to 200+ LLMs, while applying configurable input/output guardrails to enforce policies or restrict certain content. It supports automatic retries, fallbacks, load balancing across providers or keys, and request timeouts to avoid latency spikes. The gateway is multimodal: it can handle text, vision, audio, and image models under a common interface. It also offers features for governance: role-based access, compliance with standards (SOC2, HIPAA, GDPR), secure key management, and logging/analytics of usage, latency, errors, and cost. The system integrates with agent frameworks like LangChain, Autogen, and others, enabling the building of more complex AI applications. It’s lightweight and optimized for low latency with a small footprint. -
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ContextForge MCP Gateway
A Model Context Protocol (MCP) Gateway & Registry
MCP Context Forge is a feature-rich gateway and registry that federates Model Context Protocol (MCP) servers and traditional REST services behind a single, governed endpoint. It exposes an MCP-compliant interface to clients while handling discovery, authentication, rate limiting, retries, and observability on the server side. The gateway scales horizontally, supports multi-cluster deployments on Kubernetes, and uses Redis for federation and caching across instances. Operators can define virtual servers, wire multiple transports, and optionally enable an admin UI for management and monitoring. Packaged for quick starts via PyPI and Docker, it targets production reliability with health checks, metrics, and structured logs. The project positions itself as an integration hub so agentic apps can “connect once, use many” backends with consistent policy and lifecycle control. -
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DeployStack
Centralized credential vault, governance, and token optimization
DeployStack is an open-source framework that helps developers and teams define and deploy production infrastructure stacks using modular, reusable templates, often with IaC (infrastructure as code) principles. It provides a structured way to compose resources such as cloud networking, compute, and managed services into coherent deployment blueprints that can be versioned and reused across projects. By abstracting common deployment patterns and capturing them as templates, Deploystack reduces duplication of effort that typically occurs when setting up stacks for different applications or environments. The project emphasizes repeatability and clarity, enabling teams to follow best practices for scalability, security, and operational reliability without hand-crafting deployment scripts for every new service. It supports integration with popular cloud providers and infrastructure tooling, streamlining workflows that span local development through staging and production environments. -
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Docker MCP Gateway
Docker mcp CLI plugin / MCP Gateway
Docker’s MCP Gateway project is a Docker CLI plugin and supporting gateway system designed to run, manage, and securely expose MCP servers using container isolation. It underpins the MCP Toolkit experience in Docker Desktop, but it can also be used independently as a general-purpose MCP operational layer. The core idea is to treat MCP servers like containerized services, giving each server controlled privileges and a lifecycle you can inspect, enable/disable, and reset as needed. Instead of having each AI client manage its own MCP server configuration, the gateway provides a unified interface so multiple clients can connect consistently to the same configured tool surface. The project emphasizes security and operational hygiene by supporting secrets management (to avoid leaking credentials via plain environment variables) and providing built-in OAuth flows for MCP servers that require authenticated service access. -
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Gate22
Open-source MCP gateway and control plane for teams
Gate22 is an open-source governance and control plane for Model Context Protocol (MCP) environments that helps teams define and enforce policies about which tools and capabilities AI agents can access, how they can interact with those tools, and how usage is logged and audited. It provides a centralized layer where organizations can configure permission boundaries, role-based access, and operational constraints that govern agent behavior and tool invocation across agentic IDEs or custom agent stacks. By integrating with MCP-aware systems, Gate22 helps maintain security and compliance while enabling teams to scale agent-enabled workflows without losing observability into what actions are taken and why. It can be used to enforce fine-grained policies that restrict dangerous or unauthorized operations, track which agents are calling which tools, and record metadata for auditing and debugging. -
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HasMCP
Convert API into MCP Server in seconds
HasMCP empowers AI development by seamlessly connecting your existing APIs to Large Language Models. Its Automated OpenAPI Mapping instantly translates API documentation into LLM-usable tools, eliminating manual coding. Security is paramount, with Native MCP Elicitation Auth managing complex authentication flows like OAuth2, ensuring user credentials are never exposed. To enhance efficiency, Context Window Optimization intelligently prunes API responses using JMESPath and Goja (JS) logic, dramatically reducing costs and improving response times. The platform is built for dynamic environments; Real-time Dynamic Tooling allows the system to adapt on the fly as APIs change or go offline. For robust security, it includes Secure Secret & Gateway Management to safeguard sensitive credentials in an encrypted vault. MCP Composition enables the construction of high-performance, modular AI systems. Finally, comprehensive Observability & Telemetry tools offer deep insights into tool usages. -
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Kong
The Cloud-Native API Gateway
Kong is a next generation cloud-native API platform for multi-cloud and hybrid organizations. When building for the web, mobile, or Internet of Things, you’ll need a common functionality to run your software, and Kong is that solution. Kong acts as a gateway, connecting microservices requests and APIs natively while also providing load balancing, logging, monitoring, authentication, rate-limiting, and so much more through plugins. Kong is highly extensible as well as platform agnostic, connecting APIs across different environments, platforms and patterns. Achieve architectural freedom with Kong today. -
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Kong Konnect MCP
A Model Context Protocol server for interacting with Kong Konnect
MCP Konnect is a Model Context Protocol (MCP) server implementation that enables AI assistants and agents to interact with Kong Konnect, the API management and gateway platform from Kong. This server exposes Kong Konnect’s APIs through standardized MCP interfaces so that tools like conversational AI or agent systems can query analytics, inspect configuration, and manage Kong Gateway resources using natural language or programmable agents. By bridging MCP clients to Kong’s control plane, the project allows autonomous agents to retrieve data on traffic metrics, route definitions, services, plugins, and consumer settings without writing custom API integration code. It also groups tools into categories—such as analytics, configuration, and control plane management—to organize capabilities logically for AI workflows. -
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MCP Server and GW
An MCP stdio to HTTP SSE transport gateway with example server
mcp-server-and-gw is a Model Context Protocol (MCP) gateway that bridges standard input/output to HTTP Server-Sent Events (SSE) transport. It includes an example MCP server and client, facilitating the development and testing of MCP implementations. This tool is particularly useful for integrating MCP servers with applications like Claude Desktop that may not natively support remote server connections. -
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MCPJungle
Self-hosted MCP Gateway and Registry for AI agents
MCPJungle is a self-hosted gateway and registry for the Model Context Protocol (MCP), aimed at managing tool/integration servers for AI agents within organizations. It offers a “single source of truth” registry where developers can register MCP servers and the tools they provide, and MCP clients (such as AI agents) discover and consume those tools through one gateway endpoint. This greatly simplifies the architecture when you have many MCP servers; agents only need to connect to one gateway rather than multiple endpoints. The platform supports enterprise-grade workflows; centralized tool management, access control, self-hosting so that internal servers and tools remain under your organization’s control, and registry metadata to track what tools exist and who can use them. For organizations building internal AI automation systems, MCPJungle helps enforce governance, tool discovery, and integration scalability. -
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MetaMCP
MCP Aggregator, Orchestrator, Middleware, Gateway in one docker
MetaMCP is an all-in-one MCP aggregator, orchestrator, and middleware that merges many MCP servers into a single, policy-aware server you can point any MCP client at. It’s built to centralize discovery, apply middlewares (e.g., auth, rate limits, transforms), and present a unified catalog of tools and resources from diverse backends. The repository and site describe it as “the one MCP to manage all your MCPs,” with diagrams and examples showing how to compose fleets behind a single endpoint. It ships Dockerized for quick deployment and emphasizes dynamic aggregation so teams can register or remove servers without restarting clients. The org maintains related repos and a GUI app for cloud and self-hosted setups, with a note that the cloud demo is outdated while the open-source v2 evolves. Overall, MetaMCP aims to simplify multi-server MCP operations for individuals and organizations. -
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Microsoft MCP Gateway
MCP Gateway is a reverse proxy and management layer for MCP servers
Microsoft’s MCP Gateway is a reverse proxy and management layer for MCP servers that adds session-aware routing, lifecycle management, and scalable orchestration. It centralizes how clients connect to many MCP services, enabling multi-tenant isolation and consistent policies while keeping each service independently deployable. The architecture targets Kubernetes, with manifests and guidance for cluster-native scaling, health probes, and rollout strategies. It also includes local and Azure deployment recipes, making it straightforward to try on a laptop and then promote to cloud infrastructure. Core concepts—like session routing, server pools, and pluggable policies—are documented to help teams reason about stateful agent sessions at scale. By standardizing discovery and control, it reduces operational toil and improves reliability for agent platforms built on MCP. -
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Peta Core
Secure vault, managed runtime, audit trail, and policy-based approvals
Peta Core is an open-source project that aims to provide foundational infrastructure for building extensible, distributed applications and services, particularly in the context of agentic systems and modern tooling. It typically encompasses core abstractions, protocols, and utilities that other modules in the PETA ecosystem can build upon, offering patterns for service discovery, messaging, workflow orchestration, and plugin integration. PETA-Core may include frameworks for managing agent lifecycles, standard interfaces for connectors, and support for consistent configuration across components, making it easier to assemble complex, multi-part applications with clear boundaries and reusable logic. The project’s core role is to reduce integration friction for developers by establishing conventions and shared libraries that accelerate development in distributed, AI-augmented contexts. -
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Unla
Gateway service that instantly transforms existing MCP Servers
Unla is a lightweight, highly available MCP gateway written in Go that turns existing MCP servers or ordinary HTTP APIs into MCP-compliant services through configuration, not code changes. Its goal is to let teams “wire up” tools they already run—internal REST endpoints, third-party APIs, or local MCP servers—and present a single, reliable MCP interface to clients like Claude Desktop, Cursor, and IDEs. The gateway focuses on operational concerns you’d expect in production: multi-instance availability, health checking, and declarative routing that maps upstreams to MCP tools and resources. A quick-start and CLI make it easy to stand up an API server, while the package structure exposes helpers for people who want to embed or extend the gateway. Because it is itself MCP-speaking, Unla can sit in front of mixed fleets and normalize transports and schemas for clients. Documentation and pkg.go.dev pages reinforce the positioning as a stable, Go-native building block for MCP deployments.
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Guide to Open Source MCP Gateways
Open source MCP gateways are designed to help organizations manage communication between AI models, external services, and connected tools using standardized protocols. These gateways simplify how requests are routed, authenticated, monitored, and controlled across different environments while giving teams greater flexibility over deployment and customization. Because the source code is publicly available, organizations can adapt features, extend integrations, and tailor security measures to meet their operational requirements.
Many businesses adopt open source MCP gateways to reduce vendor dependency while maintaining greater visibility into how AI interactions are processed. Development teams can configure policies for access control, logging, rate limiting, and request validation without being restricted by proprietary architectures. This flexibility makes the technology suitable for experimentation as well as production environments that demand transparency and control.
As AI ecosystems continue to expand, open source MCP gateways play an important role in connecting models with enterprise applications, cloud services, and internal tools. They help improve interoperability, simplify governance, and support consistent communication across multiple systems. Organizations evaluating these gateways often focus on scalability, security, maintainability, and compatibility with their existing technology stack.
Features of Open Source MCP Gateways
- Protocol Translation: Connects different communication methods, enabling compatible data exchange between AI tools and external services.
- Authentication Controls: Verifies identities and permissions before allowing access to connected resources.
- Request Routing: Directs incoming requests to appropriate endpoints for efficient processing.
- Traffic Monitoring: Tracks gateway activity, helping administrators review performance and usage patterns.
- Access Policies: Applies configurable rules that control which users, tools, or services reach connected resources.
- Logging Capabilities: Records gateway events for troubleshooting, auditing, and operational visibility.
- Scalability Options: Supports growing workloads by handling additional connections without major infrastructure changes.
- API Integration: Connects with APIs, allowing AI tools to exchange information across multiple business systems.
What Are the Different Types of Open Source MCP Gateways?
- API Gateway Integration: Connects AI tools with APIs while managing authentication, routing, and request handling.
- Multi-Protocol Gateways: Support different communication methods, enabling connections across varied environments and services.
- Edge Deployment Gateways: Process requests closer to users, reducing latency and improving response times.
- Enterprise Integration Gateways: Link AI tools with internal business applications, databases, and infrastructure.
- Security-Focused Gateways: Emphasize access control, encryption, auditing, and policy enforcement for connected environments.
- Cloud-Native Gateways: Operate within cloud environments using scalable architectures and automated resource management.
- Hybrid Deployment Gateways: Bridge on-premises and cloud resources while maintaining consistent communication between connected systems.
Open Source MCP Gateways Benefits
- Reduce integration complexity: Connect multiple AI tools and external services through standardized communication methods, simplifying deployment across different environments.
- Improve flexibility: Modify configurations and extend functionality to match changing business requirements without depending on a single provider.
- Increase transparency: Review underlying components to better understand how requests, permissions, and data exchanges are handled.
- Support interoperability: Enable different AI systems and compatible tools to exchange information more efficiently.
- Encourage customization: Adapt workflows, authentication methods, and routing logic for specialized operational needs.
- Lower long-term costs: Minimize licensing expenses while allowing organizations to invest in implementation and support instead.
- Strengthen innovation: Community contributions introduce enhancements, integrations, and fixes that continuously expand available capabilities.
Who Uses Open Source MCP Gateways?
- Application developers: Build and manage connections between AI models, services, and external tools through standardized gateway implementations.
- IT administrators: Oversee deployment, access controls, and ongoing maintenance across connected environments.
- AI engineering teams: Integrate multiple AI services into streamlined workflows using centralized gateway capabilities.
- DevOps teams: Automate deployments and maintain reliable gateway performance across development and production environments.
- Technology consultants: Recommend and implement gateway solutions that support client integration requirements.
- Research organizations: Connect experimental AI environments with external resources for testing and analysis.
- Enterprise architects: Design scalable infrastructures that support secure communication between AI tools and business systems.
- Educational institutions: Teach AI integration concepts through practical gateway implementations.
How Much Do Open Source MCP Gateways Cost?
Open source MCP gateways are often available without licensing fees, making them an attractive option for organizations looking to reduce upfront expenses. While the gateway itself may be free to use, organizations should still budget for infrastructure, hosting, security, maintenance, monitoring, and technical expertise. Costs can increase if advanced enterprise capabilities, managed hosting, or professional support are required.
Organizations with larger deployments may also incur expenses related to cloud resources, integrations, compliance, training, and ongoing administration. Some providers offer optional subscription plans or commercial licensing for advanced features, while others charge for managed services instead of the gateway itself. The overall investment depends on deployment size, performance requirements, and internal technical resources.
What Do Open Source MCP Gateways Integrate With?
Open source MCP gateways can integrate with AI assistants, workflow automation tools, identity and access management platforms, API management tools, developer environments, monitoring solutions, logging platforms, and cloud infrastructure services. They also work with messaging platforms, data storage systems, and security tools to support authentication, routing, and governance. Many organizations connect them with orchestration platforms and backend services to simplify communication between AI models and business applications. Integration capabilities often depend on supported protocols, deployment architecture, and customization requirements. Well-designed integrations help create consistent data flows, improve operational visibility, and reduce manual configuration while maintaining security and scalability.
Recent Trends Related to Open Source MCP Gateways
- Standardized connectivity is improving: More projects focus on consistent interfaces that simplify communication between AI models, tools, and external services.
- Security features are expanding: Developers increasingly add authentication, authorization, and encrypted communications to strengthen gateway deployments.
- Container-ready deployments are growing: Many gateways support cloud-native environments for easier scaling, portability, and maintenance.
- Community contributions continue increasing: Active contributors regularly enhance compatibility, fix issues, and introduce useful integrations across diverse ecosystems.
- Performance optimization remains a priority: Projects reduce latency and improve request handling to support demanding AI workloads.
- Flexible deployment options are becoming common: Organizations can run gateways on premises, in private clouds, or hybrid environments.
- Observability capabilities are improving: Built-in logging, monitoring, and tracing help administrators troubleshoot issues and optimize operations.
Getting Started With Open Source MCP Gateways
Selecting the right open source MCP gateways starts with understanding how they will connect AI models, data sources, and external services. Look for software that supports the protocols, authentication methods, and deployment environments your organization already uses. Strong security controls, flexible access management, and detailed logging can help protect sensitive information while improving visibility into gateway activity.
Scalability is equally important. Evaluate how well the gateway handles increasing workloads, supports multiple integrations, and fits into existing infrastructure. Review documentation quality, update frequency, and community activity to gauge long-term reliability. Testing the gateway with real workloads before deployment can confirm that it delivers the performance, compatibility, and management capabilities your team requires.
