Open Source MCP Gateways in 2026: Top 5 Platforms Compared

TL;DR

• An open source MCP gateway is a self-hostable control layer that centralizes tool discovery, authentication, and access policy between AI agents and MCP servers.
• The five open source MCP gateways compared here are Bifrost, agentgateway, Docker MCP Gateway, IBM ContextForge, and Microsoft MCP Gateway.
• Bifrost combines an MCP gateway and an LLM gateway in one Go binary, with deny-by-default tool filtering per virtual key and 11 microseconds of overhead per request at 5,000 RPS.
• Bifrost Code Mode cut input tokens by 92.8% and estimated cost by 92.2% in a benchmark with 508 tools across 16 MCP servers, with a 100% pass rate.
• The right choice depends on scope: container isolation for local development, multi-protocol federation, Kubernetes lifecycle management, or unified model and tool governance for production agents.

An open source MCP gateway is a self-hostable control layer that sits between AI agents and Model Context Protocol (MCP) servers, centralizing tool discovery, authentication, and access policy in one place. Bifrost, the open-source MCP and AI gateway written in Go and built by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. This guide compares the five leading open source MCP gateways in 2026 on licensing, access control, transports, deployment model, and token efficiency, so platform teams can pick the right one for production agents.

What Is an Open Source MCP Gateway?

An open source MCP gateway is an inspectable, self-hosted proxy that aggregates many MCP servers behind a single endpoint. It authenticates the agents that connect, decides which tools each agent may see and call, and records tool activity. Teams choose open source gateways to keep tool traffic, credentials, and logs inside their own infrastructure.

The Model Context Protocol specification defines how clients discover and invoke tools, resources, and prompts on MCP servers. It does not define how an organization should govern hundreds of those servers across teams. That gap is what the gateway layer fills. A deeper walkthrough of the architecture is in our guide to MCP gateways for production AI agents.

Three jobs separate a gateway from a simple forwarding layer:

• Aggregation: Many MCP servers are exposed through one endpoint, so clients such as Claude Desktop, Cursor, and custom agents need a single configuration.
• Policy enforcement: Authentication and tool-level authorization are applied before any tool call reaches an upstream server.
• Visibility: Tool calls are logged with the identity of the caller, which is the foundation for audit and cost attribution.

If the distinction between these layers is still fuzzy, the comparison of an MCP gateway, an MCP proxy, and an MCP server breaks it down. Teams planning a production rollout can also review this MCP gateway reference architecture before shortlisting tools.

How to Evaluate an Open Source MCP Gateway

Evaluate an open source MCP gateway on five criteria: how it authenticates clients and upstream servers, how finely it controls tool access, which transports it supports, how it deploys, and how it manages token cost as tool counts grow. License terms and published performance data complete the picture for procurement and capacity planning.

Criterion	Why it matters	What to check
Authentication	Agents act on behalf of users, so credentials must map to real identities	Inbound client auth (headers, OAuth), upstream auth modes, per-user credentials
Tool-level access control	Server-level allow-lists over-privilege agents	Per-tool allow-lists, deny-by-default behavior, scoping per key, team, or user
Transports	MCP servers ship as STDIO processes, HTTP services, and SSE streams	STDIO, HTTP, SSE, and Streamable HTTP support
Deployment model	Regulated teams need tool traffic to stay in their network	Self-hosting, Kubernetes support, in-VPC options, operational footprint
Token efficiency	Tool definitions consume context on each request	Tool filtering, lazy tool loading, code-based orchestration
License and performance	Legal review and capacity planning depend on both	OSI license, published overhead or latency benchmarks

Authentication deserves the most scrutiny. The MCP security best practices call out confused-deputy and token-passthrough risks that a gateway is well placed to mitigate. Our roundup of MCP gateways for MCP authentication covers these patterns in more depth.

Best MCP Gateways Compared at a Glance

The five best MCP gateways in the open source category differ mainly in scope. Bifrost and agentgateway govern both model traffic and tool traffic. Docker MCP Gateway runs MCP servers as isolated containers, IBM ContextForge federates MCP, A2A, and REST or gRPC APIs, and Microsoft MCP Gateway manages MCP server lifecycles on Kubernetes.

Gateway	License	Core language	Protocol scope	Access control (as documented)	Deployment model	Published gateway overhead
Bifrost	Apache 2.0	Go	MCP + LLM gateway	Deny-by-default tool allow-lists per virtual key, OAuth 2.1 client auth, six upstream auth types	Self-hosted gateway, Go SDK, Kubernetes, in-VPC (enterprise)	11 µs per request at 5,000 RPS
agentgateway	Apache 2.0	Rust	MCP, A2A, and LLM gateway	JWT, API keys, OAuth; RBAC with CEL policies	Standalone YAML config or Kubernetes controller	Not published
Docker MCP Gateway	MIT	Go	MCP	Per-profile tool allowlists, Docker Desktop secrets, OAuth flows	Docker CLI plugin, with or without Docker Desktop	Not published
IBM ContextForge	Apache 2.0	Python	MCP, A2A, REST/gRPC federation	Basic, JWT, or custom auth; user-scoped OAuth tokens; rate limiting	PyPI or containers; multi-cluster Kubernetes	Not published
Microsoft MCP Gateway	MIT	.NET	MCP	Bearer token auth with Entra ID app roles	Kubernetes; local or Azure deployment	Not published

1. Bifrost

The Bifrost AI gateway is an open source MCP gateway and LLM gateway in a single Go binary. Bifrost acts as an MCP client that connects to upstream tool servers and as an MCP server that exposes those tools to Claude Desktop, Cursor, and custom applications. The same virtual keys that govern model access also govern tool access.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

Aggregation and transports

Bifrost connects to MCP servers over STDIO, HTTP, and SSE, with automatic retry logic for transient failures. In gateway mode, Bifrost exposes every connected tool through a single /mcp endpoint that accepts JSON-RPC 2.0 over POST and Server-Sent Events over GET.

Virtual MCPs bundle selected tools from one or more servers into a curated endpoint at /mcp/<slug>. A Virtual MCP is reachable only through the virtual keys it is attached to, and its slug never changes after creation, so client configurations do not break.

Authentication and tool-level access control

Bifrost authenticates in both directions:

• Inbound clients: Clients reach /mcp with virtual key headers or through browser-based OAuth 2.1, where Bifrost acts as the authorization server and issues short-lived JWTs.
• Upstream servers: Bifrost supports six upstream auth types: none, headers, OAuth 2.0, per-user OAuth, per-user headers, and token exchange (enterprise).
• Tool filtering: Three filtering levels stack at the client, request, and virtual key layers, and a tool must pass every applicable filter.

Tool access is deny-by-default. A virtual key with no MCP configuration exposes no tools, apart from clients an administrator marks as allowed by default. Bifrost also does not execute tool calls automatically: execution requires an explicit API call unless Agent Mode is enabled for specific tools.

Code Mode for token efficiency

Code Mode replaces large tool catalogs with four meta-tools, and the model writes Python (Starlark) that orchestrates tools in a sandbox. Tool definitions load on demand instead of on each turn, and intermediate results stay in the sandbox rather than flowing back through the model.

Bifrost benchmarked Code Mode against classic MCP across three rounds:

• 96 tools across 6 servers: Input tokens fell 58.2% and estimated cost fell 55.7%.
• 251 tools across 11 servers: Input tokens fell 84.5% and estimated cost fell 83.4%.
• 508 tools across 16 servers: Input tokens fell 92.8% and estimated cost fell 92.2%, with a 100% pass rate.

The full methodology is in the MCP gateway cost governance benchmark, including the per-round token and cost tables.

Performance, observability, and enterprise deployment

Bifrost adds 11 microseconds of overhead per request at 5,000 RPS in sustained benchmarks with a 100% success rate. The same gateway routes model traffic to 25+ providers and 10,000+ models through one OpenAI-compatible API, so agent teams run a single gateway for tools and models.

Built-in observability records both LLM and MCP log entries, with configurable request headers captured as metadata for tenant tracing. For production scale, Bifrost clustering replicates MCP tools and Virtual MCPs across nodes, and enterprise audit logs record administrative changes as events that can be HMAC-signed for verification. Teams in regulated environments can review the Bifrost Enterprise deployment options, including in-VPC installs.

2. agentgateway

agentgateway is an Apache 2.0 licensed proxy written in Rust and hosted as a Linux Foundation project. It covers agent-to-LLM, agent-to-tool, and agent-to-agent traffic through LLM, MCP, and A2A gateway components, and it runs either from a flat YAML configuration or through a Kubernetes controller with Gateway API support.

Best for: Kubernetes platform teams that want MCP and A2A traffic managed through Gateway API resources alongside inference routing for self-hosted models.

Key capabilities, as described in the project README:

• MCP gateway: Tool federation with STDIO, HTTP, SSE, and Streamable HTTP transports, OpenAPI integration, and OAuth authentication.
• Security: JWT, API key, and OAuth authentication, RBAC built on a CEL policy engine, rate limiting, and TLS.
• Guardrails: Content filtering through regex rules, OpenAI moderation, AWS Bedrock Guardrails, Google Model Armor, and custom webhooks.
• Inference routing: Kubernetes Inference Gateway extensions that route to self-hosted models based on GPU utilization, KV cache, and queue depth.

Considerations: The project describes itself as in active development, and it does not publish gateway overhead benchmarks. Teams comparing combined model and tool gateways can weigh these trade-offs against the criteria in our open source AI gateway comparison.

3. Docker MCP Gateway

Docker MCP Gateway is the MIT-licensed engine behind the docker mcp CLI plugin and the MCP Toolkit in Docker Desktop. It runs each MCP server as an isolated Docker container and gives MCP clients such as VS Code, Cursor, and Claude Desktop one shared gateway configuration.

Best for: Developers who already use Docker Desktop and want container-isolated MCP servers on a workstation or a shared development host.

Key capabilities, as described in the project README:

• Container isolation: Each local MCP server runs in its own container, and npx or uvx servers receive minimal host privileges.
• Profiles and catalogs: Servers are grouped into profiles that can be exported and shared through OCI registries, with catalogs sourced from Docker Hub, OCI images, or the community MCP registry.
• Tool allowlists: Individual tools can be enabled or disabled per profile.
• Secrets and OAuth: Credentials are handled through Docker Desktop secrets management, with built-in OAuth flows for services that need them.

Considerations: The gateway runs over STDIO by default and serves multiple clients over SSE or streaming transports. Identity-provider integration, per-user access control, and performance benchmarks are not described in the project README. Teams moving from a developer workstation to shared coding-agent infrastructure can compare this model with using an MCP gateway with Claude Code.

4. IBM ContextForge

IBM ContextForge, published as mcp-context-forge on GitHub, is an Apache 2.0 licensed registry and proxy written in Python. ContextForge federates MCP servers, A2A agents, and REST or gRPC APIs into one endpoint, and it includes an Admin UI, a plugin framework, and OpenTelemetry tracing.

Best for: Organizations that need to expose existing REST and gRPC services as MCP tools and federate them across multiple clusters.

Key capabilities, as described in the project README:

• Protocol translation: REST APIs and gRPC services (through server reflection) are wrapped as virtual MCP servers.
• Transports: HTTP, JSON-RPC, WebSocket, SSE, and Streamable HTTP, with STDIO available for server-side use.
• Auth and resilience: Basic, JWT, or custom auth schemes, user-scoped OAuth tokens, retries, and rate limiting.
• Scale-out: Redis-backed caching and multi-cluster federation on Kubernetes, plus an air-gapped Admin UI option.
• Extensibility: More than 40 plugins for additional transports, protocols, and integrations.

Considerations: ContextForge requires Python 3.11 or later, and a JWT secret and an encryption secret must be generated before the gateway starts. Running Redis-backed federation adds operational surface area. Latency benchmarks are not published in the README. Teams evaluating ContextForge for compliance workloads should confirm how tool calls are recorded against the patterns in auditing every AI tool call through an MCP gateway.

5. Microsoft MCP Gateway

Microsoft MCP Gateway is an MIT-licensed reverse proxy and management layer for MCP servers running on Kubernetes, built on .NET 8. It pairs a data plane with session-aware stateful routing and a control plane of REST APIs that deploy, update, and delete MCP servers and registered tools.

Best for: Teams running MCP servers on Kubernetes within the Azure ecosystem that want API-driven lifecycle management for those servers.

Key capabilities, as described in the project README:

• Session-aware routing: Requests with the same session ID route consistently to the same MCP server instance.
• Lifecycle management: REST endpoints for adapters and tools cover deployment, status, logs, updates, and removal.
• Tool gateway router: A router MCP server directs tool execution to registered tool servers based on tool definitions.
• Authorization: Bearer token authentication with Entra ID application roles for MCP servers and tools.

Considerations: The cloud deployment path requires an Azure subscription and an Entra ID app registration, and the optional agents and sessions features (in preview) depend on an Azure Foundry endpoint. Multi-cloud teams should factor in that identity dependency. Regulated industries weighing deployment constraints can compare it with the controls in our MCP gateway guide for regulated industries.

Common Challenges When Self-Hosting an MCP Proxy

Most teams begin with a lightweight MCP proxy and run into the same problems as agent usage grows: over-privileged tool access, credential sprawl, context windows filled with tool definitions, and no connection between tool calls and the identities or budgets behind them.

• Server-level permissions: Allowing an agent to reach a whole MCP server exposes every tool on it, including write and delete operations. Tool-level, deny-by-default allow-lists close that gap.
• Shared credentials: A single admin token for an upstream server means every user acts under one identity. Per-user OAuth and token exchange keep upstream access tied to the real caller.
• Context bloat: Connecting 10 or more servers can put hundreds of tool definitions into each request. Tool filtering and code-based orchestration keep prompts small.
• Split governance: Running separate gateways for models and tools means two policy engines, two sets of keys, and two audit trails.

The open-source Bifrost gateway addresses the last challenge directly by applying one governance model across model and tool traffic, where a single virtual key carries budgets, rate limits, and MCP tool permissions. For the conceptual background on why centralizing this layer matters, see how an MCP gateway centralizes agent tool access.

Which Open Source MCP Gateway Should You Choose?

Pick a gateway by matching its primary design goal to the constraint that matters most in your environment. Local development, multi-protocol federation, Kubernetes lifecycle management, and unified model and tool governance each point to a different project.

If your priority is	Consider
One gateway for models and tools, with per-key tool governance and low overhead	Bifrost
Token cost control across many MCP servers	Bifrost (Code Mode)
Gateway API resources and inference routing on Kubernetes	agentgateway
Container-isolated MCP servers on developer machines	Docker MCP Gateway
Converting REST and gRPC services into MCP tools	IBM ContextForge
API-driven MCP server lifecycle on Kubernetes with Entra ID	Microsoft MCP Gateway

For production agents that call models and tools in the same workflow, a unified gateway removes the integration work of stitching two control planes together. The LLM gateway buyer's guide lists the procurement questions to ask any vendor, and the Bifrost alternatives hub compares migration paths. To see a production reference design, review the MCP gateway architecture in Bifrost.

Frequently Asked Questions

What is an MCP gateway?

An MCP gateway is a control layer between AI agents and MCP servers. It aggregates many servers behind one endpoint, authenticates clients, enforces which tools each client can call, and logs tool activity. Bifrost implements this as both an MCP client and an MCP server, so agents connect to one /mcp endpoint instead of configuring each tool server separately.

Is there an MCP gateway?

Yes. Several self-hostable MCP gateways are available in 2026, including Bifrost, agentgateway, Docker MCP Gateway, IBM ContextForge, and Microsoft MCP Gateway. Each can be self-hosted from its public GitHub repository under an Apache 2.0 or MIT license. They differ in scope: some govern only MCP traffic, while Bifrost and agentgateway also route LLM traffic through the same gateway.

Is the MCP server open source?

The Model Context Protocol itself is an open standard that Anthropic introduced in November 2024, and its specification is public. Individual MCP servers vary: many are open source, while others are proprietary services from SaaS vendors. An open source MCP gateway can front both kinds, applying the same authentication and tool filtering rules regardless of who built the server.

What are some alternatives to Docker MCP gateway?

Alternatives to Docker MCP Gateway include Bifrost, agentgateway, IBM ContextForge, and Microsoft MCP Gateway. Docker's gateway centers on container-isolated servers for developer workflows. Bifrost suits teams that need per-key tool governance, OAuth-based client authentication, and model routing in production, while ContextForge and Microsoft's gateway target API federation and Kubernetes lifecycle management respectively.

Does an MCP gateway reduce token costs?

An MCP gateway can reduce token costs by limiting which tool definitions reach the model. Tool filtering removes unneeded definitions per request, and Bifrost goes further with on-demand tool loading in Code Mode, which exposes only four meta-tools. In Bifrost benchmarks with 508 tools across 16 servers, Code Mode reduced input tokens by 92.8% while maintaining a 100% pass rate.

Which open source MCP gateway is best for enterprises?

Bifrost is the strongest fit for enterprises that need production governance across both tools and models. Bifrost combines deny-by-default tool allow-lists per virtual key, six upstream authentication types, OAuth 2.1 client authentication, clustering, and signed audit logs. Enterprise deployments add role-based access control and in-VPC installation for regulated environments.

Try Bifrost as Your Open Source MCP Gateway

Bifrost gives platform teams an open source MCP gateway and LLM gateway in one deployment, with deny-by-default tool governance, OAuth 2.1 client authentication, Code Mode token reduction, and 11 microseconds of overhead at 5,000 RPS. Teams can start Bifrost with a single npx or Docker command and explore implementation patterns in the Bifrost resources library.

To see how Bifrost governs MCP tool access for enterprise AI agents at scale, book a demo with the Bifrost team.