MindBridge is an AI orchestration MCP server that lets any app talk to any LLM — OpenAI, Anthropic, DeepSeek, Ollama, and more — through a single unified API. Route queries, compare models, get second opinions, and build smarter multi-LLM workflows.
MindBridge is your AI command hub — a Model Context Protocol (MCP) server built to unify, organize, and supercharge your LLM workflows.
Forget vendor lock-in. Forget juggling a dozen APIs.
MindBridge connects your apps to any model, from OpenAI and Anthropic to Ollama and DeepSeek — and lets them talk to each other like a team of expert consultants.
Need raw speed? Grab a cheap model.
Need complex reasoning? Route it to a specialist.
Want a second opinion? MindBridge has that built in.
This isn’t just model aggregation. It’s model orchestration.
| What it does | Why you should use it |
|---|---|
| Multi-LLM Support | Instantly switch between OpenAI, Anthropic, Google, DeepSeek, OpenRouter, Ollama (local models), and OpenAI-compatible APIs. |
| Reasoning Engine Aware | Smart routing to models built for deep reasoning like Claude, GPT-4o, DeepSeek Reasoner, etc. |
| getSecondOpinion Tool | Ask multiple models the same question to compare responses side-by-side. |
| OpenAI-Compatible API Layer | Drop MindBridge into any tool expecting OpenAI endpoints (Azure, Together.ai, Groq, etc.). |
| Auto-Detects Providers | Just add your keys. MindBridge handles setup & discovery automagically. |
| Flexible as Hell | Configure everything via env vars, MCP config, or JSON — it’s your call. |
“Every LLM is good at something. MindBridge makes them work together.”
Perfect for:
## Install globally
npm install -g @pinkpixel/mindbridge
## use with npx
npx @pinkpixel/mindbridge
To install mindbridge-mcp for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @pinkpixel-dev/mindbridge-mcp --client claude
Clone the repository:
git clone https://github.com/pinkpixel-dev/mindbridge.git
cd mindbridge
Install dependencies:
chmod +x install.sh
./install.sh
Configure environment variables:
cp .env.example .env
Edit .env and add your API keys for the providers you want to use.
The server supports the following environment variables:
OPENAI_API_KEY: Your OpenAI API keyANTHROPIC_API_KEY: Your Anthropic API keyDEEPSEEK_API_KEY: Your DeepSeek API keyGOOGLE_API_KEY: Your Google AI API keyOPENROUTER_API_KEY: Your OpenRouter API keyOLLAMA_BASE_URL: Ollama instance URL (default: http://localhost:11434)OPENAI_COMPATIBLE_API_KEY: (Optional) API key for OpenAI-compatible servicesOPENAI_COMPATIBLE_API_BASE_URL: Base URL for OpenAI-compatible servicesOPENAI_COMPATIBLE_API_MODELS: Comma-separated list of available modelsFor use with MCP-compatible IDEs like Cursor or Windsurf, you can use the following configuration in your mcp.json file:
{
"mcpServers": {
"mindbridge": {
"command": "npx",
"args": [
"-y",
"@pinkpixel/mindbridge"
],
"env": {
"OPENAI_API_KEY": "OPENAI_API_KEY_HERE",
"ANTHROPIC_API_KEY": "ANTHROPIC_API_KEY_HERE",
"GOOGLE_API_KEY": "GOOGLE_API_KEY_HERE",
"DEEPSEEK_API_KEY": "DEEPSEEK_API_KEY_HERE",
"OPENROUTER_API_KEY": "OPENROUTER_API_KEY_HERE"
},
"provider_config": {
"openai": {
"default_model": "gpt-4o"
},
"anthropic": {
"default_model": "claude-3-5-sonnet-20241022"
},
"google": {
"default_model": "gemini-2.0-flash"
},
"deepseek": {
"default_model": "deepseek-chat"
},
"openrouter": {
"default_model": "openai/gpt-4o"
},
"ollama": {
"base_url": "http://localhost:11434",
"default_model": "llama3"
},
"openai_compatible": {
"api_key": "API_KEY_HERE_OR_REMOVE_IF_NOT_NEEDED",
"base_url": "FULL_API_URL_HERE",
"available_models": ["MODEL1", "MODEL2"],
"default_model": "MODEL1"
}
},
"default_params": {
"temperature": 0.7,
"reasoning_effort": "medium"
},
"alwaysAllow": [
"getSecondOpinion",
"listProviders",
"listReasoningModels"
]
}
}
}
Replace the API keys with your actual keys. For the OpenAI-compatible configuration, you can remove the api_key field if the service doesn’t require authentication.
Development mode with auto-reload:
npm run dev
Production mode:
npm run build
npm start
When installed globally:
mindbridge
getSecondOpinion
{
provider: string; // LLM provider name
model: string; // Model identifier
prompt: string; // Your question or prompt
systemPrompt?: string; // Optional system instructions
temperature?: number; // Response randomness (0-1)
maxTokens?: number; // Maximum response length
reasoning_effort?: 'low' | 'medium' | 'high'; // For reasoning models
}
listProviders
listReasoningModels
// Get an opinion from GPT-4o
{
"provider": "openai",
"model": "gpt-4o",
"prompt": "What are the key considerations for database sharding?",
"temperature": 0.7,
"maxTokens": 1000
}
// Get a reasoned response from OpenAI's o1 model
{
"provider": "openai",
"model": "o1",
"prompt": "Explain the mathematical principles behind database indexing",
"reasoning_effort": "high",
"maxTokens": 4000
}
// Get a reasoned response from DeepSeek
{
"provider": "deepseek",
"model": "deepseek-reasoner",
"prompt": "What are the tradeoffs between microservices and monoliths?",
"reasoning_effort": "high",
"maxTokens": 2000
}
// Use an OpenAI-compatible provider
{
"provider": "openaiCompatible",
"model": "YOUR_MODEL_NAME",
"prompt": "Explain the concept of eventual consistency in distributed systems",
"temperature": 0.5,
"maxTokens": 1500
}
npm run lint: Run ESLintnpm run format: Format code with Prettiernpm run clean: Clean build artifactsnpm run build: Build the projectPRs welcome! Help us make AI workflows less dumb.
MIT — do whatever, just don’t be evil.
Made with ❤️ by Pink Pixel
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