{"slug": "open-source-mcp-gateways-for-claude-code-in-2026", "title": "Open Source MCP Gateways for Claude Code in 2026", "summary": "Maxim AI's Bifrost, an open-source AI gateway written in Go, is being positioned as a centralized MCP gateway for Anthropic's Claude Code CLI, aggregating multiple Model Context Protocol tool servers behind a single authenticated endpoint. The project addresses context-window flooding, credential sprawl, and missing audit trails that arise when developers connect directly to dozens of discrete MCP servers. Bifrost adds schema aggregation, virtual keys and budgets, code-mode execution, and content guardrails for agentic coding workflows.", "body_md": "**TL;DR**\n\nAn open source mcp gateway for Claude Code is a reverse proxy and control plane that aggregates multiple Model Context Protocol tool servers into a unified, authenticated interface for agentic coding workflows. Without a centralized gateway, developers running Anthropic's [Claude Code CLI](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview) must manage individual connections, credentials, and schema overhead across dozens of discrete tool endpoints. [Bifrost](https://www.getmaxim.ai/bifrost), an [open-source AI gateway](https://github.com/maximhq/bifrost) developed in Go by Maxim AI, provides a centralized architecture to govern tool access, cut context overhead, and route model requests from a single service. This guide analyzes the leading open-source options available for Claude Code, compares their core architectures, and outlines how to select the right platform for engineering teams.\n\nClaude Code relies on the [Model Context Protocol](https://modelcontextprotocol.io/) to interact with local filesystems, remote databases, GitHub repositories, issue trackers, and internal APIs. While connecting two or three servers directly via the terminal interface works during initial development, scaling agentic workflows across an engineering organization introduces systemic infrastructure challenges.\n\n```\n+-----------------------------------------------------------------------+\n|                     Direct Connections (Unmanaged)                    |\n|                                                                       |\n|  +-------------+  --> stdio  --> GitHub MCP (50+ tools, schemas)      |\n|  | Claude Code |  --> HTTP   --> PostgreSQL MCP (15+ tools, schemas)  |\n|  |  Terminal   |  --> SSE    --> Jira/Confluence MCP (30+ tools)      |\n|  +-------------+  --> stdio  --> Local Filesystem MCP (12 tools)      |\n|         |                                                             |\n|         +--> Result: Context window flooded with 100+ schemas         |\n|                      Fragile auth, secret sprawl, zero audit trails    |\n+-----------------------------------------------------------------------+\n\n+-----------------------------------------------------------------------+\n|                    Gateway Architecture (Governed)                    |\n|                                                                       |\n|  +-------------+                                                      |\n|  | Claude Code |                                                      |\n|  |  Terminal   |                                                      |\n|  +-------------+                                                      |\n|         |                                                             |\n|         | Single SSE/HTTP endpoint (Virtual Key + Tool Filtering)     |\n|         v                                                             |\n|  +-----------------------------------------------------------------+  |\n|  |                        BIFROST MCP GATEWAY                      |  |\n|  |  - Schema Aggregation       - Centralized Secrets & OAuth       |  |\n|  |  - Code Mode Execution      - Immutable Audit Logging           |  |\n|  |  - Virtual Keys & Budgets   - Content & Secrets Guardrails      |  |\n|  +-----------------------------------------------------------------+  |\n|         |                                                             |\n|         +--> stdio/HTTP --> GitHub MCP Server                         |\n|         +--> HTTP/SSE   --> PostgreSQL MCP Server                     |\n|         +--> stdio      --> Internal Microservices                    |\n+-----------------------------------------------------------------------+\n```\n\nEvery standard MCP server exposes JSON schemas describing its available tools, input parameters, and return types. Whenever Claude Code initiates a conversation turn, client implementations inject all active tool schemas directly into the prompt context.\n\nWhen a developer connects five servers that each expose 15 to 20 tools, the agent loads between 75 and 100 complete schemas before reading any project source code. Because each schema averages 150 to 300 tokens, tool definitions alone consume 15,000 to 30,000 tokens per request. In multi-step coding loops where Claude Code inspects files, runs tests, and applies patches across dozens of iterations, these redundant tool definitions compound API costs and displace valuable codebase context.\n\nConnecting Claude Code directly to raw MCP servers distributes sensitive credentials across developer workstations. Personal access tokens for GitHub, database connection strings, and internal API keys end up stored in plaintext configuration files such as `~/.claude.json` or `.mcp.json`. \n\nFurthermore, agentic tools that fetch untrusted web content or parse dynamic documentation expose the environment to prompt injection attacks. If an external payload instructs the model to call a destructive database tool, an unmanaged terminal client executes the command based solely on local user approvals.\n\nEnterprise compliance frameworks (including SOC 2, ISO 27001, and HIPAA) require comprehensive visibility into data modifications and automated actions. Direct MCP connections create distributed silos: tool execution logs exist only within local terminal output or individual server processes. Security teams cannot track which developer accessed specific customer records, what queries were executed against production databases, or when credentials were used outside designated parameters.\n\nSelecting an open source mcp gateway for Claude Code requires evaluating how effectively each platform resolves operational bottlenecks while preserving developer velocity.\n\n| Evaluation Dimension | Description | Production Requirement | \n|---|---|---|\n| **Tool Aggregation & Discovery** | Consolidates multiple upstream MCP servers (stdio, SSE, HTTP) into a single client connection. | Dynamic tool registration, cross-server deduplication, and zero-restart configuration updates. | \n| **Token Optimization** | Mechanisms to prevent tool schema bloat inside the Claude Code prompt context. | Dynamic tool filtering, on-demand loading, or programmatic execution modes (such as Python orchestration). | \n| **Authentication & Governance** | Enforces identity management, virtual keys, rate limits, and spend boundaries. | Per-user virtual keys, OAuth 2.0 PKCE token brokering, lazy authentication, and project budgets. | \n| **Execution Latency & Overhead** | Processing delay added by the proxy layer during tool discovery and execution. | Sub-millisecond routing overhead to avoid degrading interactive CLI performance. | \n| **Multi-Provider LLM Routing** | Ability to proxy inference requests alongside MCP tool traffic. | Native Anthropic API compatibility, provider fallbacks, and multi-model routing. | \n| **Security & Guardrails** | Inspection of inputs and outputs to prevent data leakage and command injection. | Native secrets detection, PII masking, regex filtering, and integration with enterprise guardrail engines. | \n\nThe open-source ecosystem provides several specialized architectures for managing MCP infrastructure. Below is an overview comparing the leading platforms for Claude Code deployments.\n\n| Feature / Metric | Bifrost | Docker MCP Gateway | IBM ContextForge | LiteLLM Proxy | \n|---|---|---|---|---|\n| **Primary Focus** | High-performance AI & MCP gateway | Local container isolation | Enterprise tool federation & registry | Universal LLM proxy with tool routing | \n| **Core Language** | Go | Go / Docker Engine | Python | Python | \n| **Claude Code Setup** | Native CLI / one-line environment flag | Docker CLI plugin / profile config | Custom MCP client config | Standard Anthropic base URL config | \n| **Token Reduction Mode** | Yes (Code Mode cuts tokens up to 92.8%) | No (Full schema passthrough) | Partial (Catalog search / filtering) | Partial (Static tool allowlists) | \n| **Upstream Transports** | stdio, SSE, HTTP | Docker containers, stdio, SSE | REST, gRPC, SSE, stdio | SSE, HTTP | \n| **Auth & Access Control** | Virtual keys, OAuth 2.0, lazy per-user auth | Container-level isolation | Keycloak, JWT, OIDC | Master keys, team budgets | \n| **Routing Overhead** | 11 µs at 5,000 RPS | Variable (Container spin-up) | 5-15 ms (Python runtime) | 8-25 ms (Python runtime) | \n| **Model Routing Included** | Yes (20+ providers, 1000+ models) | No (MCP transport only) | No (Requires external LLM gateway) | Yes (100+ providers) | \n\n[Bifrost](https://www.getmaxim.ai/bifrost) is a high-performance, open-source AI gateway built in Go that unifies model routing and MCP governance into a single, cohesive deployment. In sustained [benchmarks](https://www.getmaxim.ai/bifrost/resources/benchmarks), Bifrost introduces only 11 microseconds of overhead per request at 5,000 requests per second, making it the fastest gateway architecture available for latency-sensitive terminal workflows.\n\n```\n                       +-----------------------------+\n                       |   Claude Code CLI Session   |\n                       +-----------------------------+\n                                      |\n                                      | ANTHROPIC_BASE_URL (Inference)\n                                      | /mcp (Aggregated Tools)\n                                      v\n                       +-----------------------------+\n                       |     BIFROST CONTROL PLANE   |\n                       +-----------------------------+\n                        /             |             \\\n                       /              |              \\\n                      v               v               v\n            +---------------+ +---------------+ +---------------+\n            |  Inference    | |  MCP Gateway  | |  Governance   |\n            |  - Anthropic  | |  - Code Mode  | |  - Virtual Key|\n            |  - Bedrock    | |  - Agent Mode | |  - Rate Limits|\n            |  - Vertex AI  | |  - Filtering  | |  - Guardrails |\n            +---------------+ +---------------+ +---------------+\n                      |               |               |\n                      +---------------+---------------+\n                                      |\n         +----------------------------+----------------------------+\n         |                            |                            |\n         v                            v                            v\n  +--------------+             +--------------+             +--------------+\n  |  GitHub MCP  |             | Postgres MCP |             | Internal API |\n  +--------------+             +--------------+             +--------------+\n```\n\nBifrost operates simultaneously as an [MCP gateway](https://www.getmaxim.ai/bifrost/resources/mcp-gateway) and a model routing proxy. When configuring Claude Code, developers do not need separate tools for proxying inference calls and orchestrating tool servers. \n\nAs an MCP client, Bifrost connects to external tool servers across stdio, Server-Sent Events (SSE), and streamable HTTP transports. Concurrently, it acts as an MCP server, presenting a single `/mcp` endpoint back to Claude Code. \n\nOn the model side, Bifrost exposes an Anthropic-compatible endpoint that intercepts Claude Code inference calls, allowing developers to switch between Anthropic, AWS Bedrock, Google Vertex AI, and local inference engines without touching client code.\n\n```\n# Launch Claude Code directly through the Bifrost CLI\nnpx -y @maximhq/bifrost-cli\n```\n\nThe interactive Bifrost CLI automatically inspects available gateway routes, writes configuration profiles to `~/.claude/settings.json`, and attaches the `/mcp` server endpoint to Claude Code.\n\nTo address the context window bloat inherent in tool-heavy setups, Bifrost provides two distinct execution strategies:\n\nBecause intermediate data stays inside the gateway execution environment instead of bouncing back and forth across multiple model turns, [Code Mode documentation](https://docs.getbifrost.ai/mcp/code-mode) highlights token reductions of up to 92.8% alongside a 40% reduction in end-to-end task latency.\n\nBifrost enforces security using [virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys). Platform engineers configure virtual keys with explicit tool allowlists using [MCP tool filtering](https://docs.getbifrost.ai/features/governance/mcp-tools). A frontend engineer's virtual key can be restricted to Figma and GitHub tools, while a backend engineer's key enables PostgreSQL and Kubernetes operations. \n\nFor enterprise environments, Bifrost supports [MCP authentication](https://docs.getbifrost.ai/mcp/auth/overview) with per-user lazy OAuth 2.0 PKCE. When Claude Code attempts to execute a restricted tool, Bifrost prompts the developer to complete an SSO sign-in via the browser, binding the active session to corporate identity providers like Okta or Microsoft Entra without storing static API keys on the local disk.\n\nBeyond gateway-level routing, Bifrost applies [governance](https://www.getmaxim.ai/bifrost/resources/governance) and security controls (virtual keys, budgets, guardrails, audit logs) centrally, and [Bifrost Edge](https://www.getmaxim.ai/bifrost/edge) extends that same governance and security to AI traffic on employee machines, with [endpoint enforcement](https://docs.getbifrost.ai/edge/security) on each device.\n\nThe [Docker MCP Gateway](https://docs.docker.com/ai/mcp-catalog-and-toolkit/) is an open-source solution distributed as part of Docker Desktop's MCP Toolkit and the standalone `docker-mcp` CLI plugin. It focuses primarily on the local execution lifecycle and sandboxing of tool servers.\n\n```\n+-------------------------------------------------------------+\n|                      DOCKER MCP GATEWAY                     |\n|                                                             |\n|  +-------------+                                            |\n|  | Claude Code |                                            |\n|  +-------------+                                            |\n|         |                                                   |\n|         | stdio / HTTP                                      |\n|         v                                                   |\n|  +------------------------+                                 |\n|  | docker-mcp CLI Plugin  |                                 |\n|  +------------------------+                                 |\n|         |                                                   |\n|         +--> Container Sandbox 1: GitHub MCP (Isolated FS)  |\n|         +--> Container Sandbox 2: Postgres MCP (Bridge Net) |\n|         +--> Container Sandbox 3: Custom Node.js Tool       |\n+-------------------------------------------------------------+\n```\n\nThe primary architectural advantage of Docker's gateway is container isolation. Standard community MCP servers are typically distributed as raw Node.js (`npx`) or Python (` uvx`) packages. Running these packages directly on host machines gives untrusted code access to local filesystems, environment variables, and network interfaces.\n\nDocker MCP Gateway encapsulates each upstream server within an isolated container. Administrators define fine-grained volume mounts and network policies, ensuring that an MCP server configured to analyze code cannot access SSH keys or query internal subnets unless explicitly permitted.\n\nThe gateway dynamically manages container lifecycles. Rather than keeping dozens of background processes running permanently on a developer's laptop, Docker MCP Gateway inspects incoming calls from Claude Code and starts containers on demand. Once idle, servers spin down to conserve memory and compute resources.\n\nWhile Docker MCP Gateway excels at local isolation, it is not designed as a distributed, multi-user control plane. It lacks centralized virtual key management, does not offer multi-provider LLM routing, and does not provide programmatic token optimization techniques like Code Mode. It passes standard JSON schemas directly to the client, leaving context saturation unaddressed.\n\n**Best for:** Solo developers and small technical teams seeking safe, local sandboxing for community-authored MCP servers without deploying distributed infrastructure.\n\n[IBM ContextForge](https://github.com/IBM/mcp-context-forge) is an open-source gateway, registry, and proxy designed to bridge Model Context Protocol servers with traditional enterprise architectures.\n\n```\n+-------------------------------------------------------------+\n|                     IBM CONTEXTFORGE                        |\n|                                                             |\n|  +-------------+                                            |\n|  | Claude Code |                                            |\n|  +-------------+                                            |\n|         |                                                   |\n|         | MCP JSON-RPC                                      |\n|         v                                                   |\n|  +-------------------------------------------------------+  |\n|  | ContextForge Proxy & Translation Layer                |  |\n|  | - Protocol Adapter: REST / gRPC -> MCP Tools           |  |\n|  | - Enterprise Registry & Metadata Catalog              |  |\n|  | - JWT / Keycloak Identity Management                  |  |\n|  +-------------------------------------------------------+  |\n|         |                                                   |\n|         +--> Legacy REST APIs (Transformed into MCP Tools)  |\n|         +--> Enterprise gRPC Endpoints                      |\n|         +--> Standard Upstream MCP Servers                  |\n+-------------------------------------------------------------+\n```\n\nMany enterprise utilities exist as internal REST services or gRPC endpoints rather than native MCP servers. ContextForge provides protocol adaptation layers that translate legacy OpenAPI specifications and gRPC definitions into MCP-compliant tool interfaces.\n\nThis enables Claude Code to query legacy service meshes without requiring developers to write and maintain custom MCP wrapper servers for each internal service.\n\nContextForge features a centralized metadata catalog where teams publish, version, and document tools. Claude Code queries the registry to discover available tools across distinct business units. Access policies are enforced via JWT tokens and integration with enterprise identity platforms like Keycloak.\n\nContextForge is a heavier framework written in Python that requires auxiliary datastores (such as Redis) for caching and state management. Because it is implemented as a general-purpose service mesh for AI, routing latency ranges between 5 and 15 milliseconds, which is noticeably higher than Go-based alternatives when handling high-frequency tool queries.\n\n**Best for:** Enterprises with extensive legacy REST/gRPC infrastructure that need to expose internal services to Claude Code as standardized MCP endpoints.\n\n[LiteLLM Proxy](https://docs.litellm.ai/docs/mcp) is widely recognized as a multi-provider LLM load balancer and router. In recent releases, LiteLLM has expanded its feature set to include basic MCP gateway functionality.\n\n```\n+-------------------------------------------------------------+\n|                        LITELLM PROXY                        |\n|                                                             |\n|  +-------------+                                            |\n|  | Claude Code |                                            |\n|  +-------------+                                            |\n|         |                                                   |\n|         | Unified Base URL (Inference + Tools)              |\n|         v                                                   |\n|  +-------------------------------------------------------+  |\n|  | LiteLLM Proxy (Python)                                |  |\n|  | - Upstream MCP Pass-Through                           |  |\n|  | - Master Key & Team Spend Tracking                     |  |\n|  | - Model Fallbacks & Load Balancing                    |  |\n|  +-------------------------------------------------------+  |\n|         |                                                   |\n|         +--> Model Providers (Anthropic, Bedrock, OpenAI)   |\n|         +--> Static Upstream MCP Servers (SSE, HTTP)        |\n+-------------------------------------------------------------+\n```\n\nLiteLLM allows teams to define both LLM model providers and upstream MCP servers inside a single `config.yaml` file. Developers point Claude Code's `ANTHROPIC_BASE_URL` to the LiteLLM proxy, allowing the gateway to handle model failover while exposing connected MCP tools through configured routes.\n\nOrganizations using LiteLLM can track token spending across teams and assign budget limits to specific proxy keys. For MCP servers, LiteLLM supports static header pass-through, allowing teams to forward centralized tokens to downstream tools like GitHub or Jira.\n\nBecause LiteLLM's core architecture was engineered for LLM completion requests rather than complex agentic state machines, its MCP implementation is largely a routing pass-through.\n\nIt lacks on-demand Python code execution (Code Mode), does not support dynamic local container lifecycle management, and relies on static YAML configuration files that make real-time tool filtering across hundreds of developers difficult to maintain.\n\n**Best for:** Teams already running LiteLLM for model proxying that want to add basic upstream MCP server connections without deploying a dedicated MCP platform.\n\nWhen running Claude Code in continuous development loops, two operational metrics determine viability: token consumption and gateway routing overhead.\n\n```\nContext Window Consumption (5 Connected Servers / ~85 Tools)\n==============================================================================\nDirect Connections:    [########################################] 18,200 tokens\nStandard MCP Gateway:  [########################################] 18,200 tokens\nBifrost (Code Mode):   [####] 1,450 tokens (92% Reduction)\n==============================================================================\n\nGateway Routing Overhead (P99 Latency Added Per Call)\n==============================================================================\nBifrost:               [#] 0.011 ms (11 microseconds)\nDocker MCP Gateway:    [#####] 1.20 ms\nIBM ContextForge:      [############################] 12.50 ms\nLiteLLM Proxy:         [######################################] 18.20 ms\n==============================================================================\n```\n\nIn a benchmark setup connecting five typical engineering MCP servers (GitHub, PostgreSQL, Filesystem, Jira, and Slack) exposing a cumulative 85 tools, standard proxy setups inject approximately 18,200 tokens of schema definitions into every single prompt.\n\nStandard gateways (including Docker MCP Gateway, basic LiteLLM configurations, and ContextForge pass-through mode) do not alter this schema footprint: they forward the full tool definitions directly to Claude Code.\n\nIn contrast, Bifrost with Code Mode replaces schema injection with an abstract execution interface. The model receives a compact Python environment definition, inspecting tool parameters programmatically only when necessary. This contracts the context footprint from 18,200 tokens down to roughly 1,450 tokens, freeing context space for complex code refactoring.\n\nThe following table summarizes performance metrics observed across the four platforms under identical workloads.\n\n| Performance Dimension | Bifrost | Docker MCP Gateway | IBM ContextForge | LiteLLM Proxy | \n|---|---|---|---|---|\n| **P99 Proxy Overhead** | **11 microseconds** | 1.2 milliseconds | 12.5 milliseconds | 18.2 milliseconds | \n| **Max Concurrent Requests (Single Core)** | **> 4,500 RPS** | ~350 RPS | ~120 RPS | ~95 RPS | \n| **Runtime Language** | Compiled Go | Go / Docker daemon | Python (AsyncIO) | Python (FastAPI/AsyncIO) | \n| **Context Memory Footprint** | ~35 MB | ~250 MB (Engine dependent) | ~420 MB | ~310 MB | \n\nSetting up an open source mcp gateway for Claude Code involves routing both the agent's LLM inference traffic and its tool connection interface through the gateway.\n\nUsing Bifrost as the deployment target, start the gateway locally or within a private cloud environment using Docker or the pre-compiled binary:\n\n```\n# Run Bifrost locally via Docker\ndocker run -d \\\n  -p 8080:8080 \\\n  -v $(pwd)/bifrost-data:/app/data \\\n  -e BIFROST_PORT=8080 \\\n  --name bifrost-gateway \\\n  maximhq/bifrost:latest\n```\n\nOnce running, configure upstream MCP servers in Bifrost via the administrative web UI or configuration API:\n\n```\n{\n  \"mcp_servers\": {\n    \"github\": {\n      \"transport\": \"stdio\",\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@modelcontextprotocol/server-github\"],\n      \"env\": {\n        \"GITHUB_PERSONAL_ACCESS_TOKEN\": \"ghp_upstreamTokenSecret\"\n      }\n    },\n    \"postgres\": {\n      \"transport\": \"sse\",\n      \"url\": \"http://internal-db-mcp.internal:3001/sse\"\n    }\n  }\n}\n```\n\nClaude Code reads global defaults from `~/.claude/settings.json`. Update this configuration to route model calls and tool queries through the gateway:\n\n```\n{\n  \"env\": {\n    \"ANTHROPIC_BASE_URL\": \"http://localhost:8080/anthropic\",\n    \"ANTHROPIC_API_KEY\": \"bk_live_virtualKeyFrontendTeam\"\n  }\n}\n```\n\nInstead of adding each server individually via `claude mcp add`, register the Bifrost aggregation endpoint:\n\n```\n# Connect Claude Code to the single aggregated gateway interface\nclaude mcp add --transport sse bifrost-gateway http://localhost:8080/mcp\n```\n\nClaude Code now accesses all upstream tools through a single, governed connection. Administrators can add or revoke upstream tools at the gateway level without requiring developers to edit local files or restart their CLI sessions.\n\nAn AI gateway routes and load-balances API calls between client applications and large language model providers, managing model tokens, fallbacks, and rate limits. An MCP gateway specifically routes communication between AI agents and external tool servers over the Model Context Protocol, handling tool schema aggregation, tool authentication, and execution permissions. Bifrost integrates both capabilities within a single binary.\n\nStandard MCP implementations inject the complete JSON schema of every connected tool into the prompt context on every request. Advanced gateways reduce these costs by applying tool allowlists per virtual key or by using programmatic execution modes like Bifrost Code Mode, which lets models write orchestration scripts instead of loading static schemas, reducing token overhead by up to 92.8%.\n\nYes. When running Claude Code through a dual AI and MCP gateway like Bifrost, the gateway translates Claude Code's Anthropic-formatted Messages API requests into provider-compatible calls for AWS Bedrock, Google Vertex AI, Azure OpenAI, or local vLLM instances. This allows developers to run Claude Code against enterprise cloud agreements.\n\nHigh-performance gateways written in compiled languages like Go introduce negligible latency. Bifrost adds only 11 microseconds of overhead per request under sustained load. Gateways built in interpreted languages like Python typically add between 5 and 20 milliseconds, which remains acceptable for interactive tool calling but can impact high-frequency batch loops.\n\nYes. Open-source gateways like Bifrost, Docker MCP Gateway, and ContextForge can be self-hosted entirely within private VPCs, Kubernetes clusters, or air-gapped data centers. This ensures that sensitive source code, internal schemas, and authentication keys never traverse public cloud infrastructure.\n\nWhile an MCP gateway operates as a centralized proxy for configured servers, [Bifrost Edge](https://www.getmaxim.ai/bifrost/edge) runs locally across employee machines to discover and govern shadow AI applications and unmanaged local MCP servers. Edge extends the gateway's access policies and guardrails directly to the endpoint operating system.\n\nSelecting the right open source mcp gateway for Claude Code depends on your organization's scale, security posture, and infrastructure complexity:\n\nTo evaluate how a unified AI and MCP gateway streamlines agentic coding workflows, explore the [Bifrost GitHub repository](https://github.com/maximhq/bifrost) or [schedule a Bifrost technical demonstration](https://getmaxim.ai/bifrost/book-a-demo).", "url": "https://wpnews.pro/news/open-source-mcp-gateways-for-claude-code-in-2026", "canonical_source": "https://dev.to/kamya_shah_e69d5dd78f831c/open-source-mcp-gateways-for-claude-code-in-2026-4h11", "published_at": "2026-09-21 07:13:28+00:00", "updated_at": "2026-09-21 07:53:13.195344+00:00", "lang": "en", "topics": ["ai-agents", "agent-protocols", "ai-infrastructure", "developer-tools", "ai-tools"], "entities": ["Bifrost", "Maxim AI", "Claude Code", "Anthropic", "Model Context Protocol", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/open-source-mcp-gateways-for-claude-code-in-2026", "markdown": "https://wpnews.pro/news/open-source-mcp-gateways-for-claude-code-in-2026.md", "text": "https://wpnews.pro/news/open-source-mcp-gateways-for-claude-code-in-2026.txt", "jsonld": "https://wpnews.pro/news/open-source-mcp-gateways-for-claude-code-in-2026.jsonld"}}