my-pal-mcp-server/docs/custom_models.md

Custom Models & API Setup

This guide covers setting up multiple AI model providers including OpenRouter, custom API endpoints, and local model servers. The Zen MCP server supports a unified configuration for all these providers through a single model registry.

Supported Providers

• OpenRouter - Unified access to multiple commercial models (GPT-4, Claude, Mistral, etc.)
• Custom API endpoints - Local models (Ollama, vLLM, LM Studio, text-generation-webui)
• Self-hosted APIs - Any OpenAI-compatible endpoint

When to Use What

Use OpenRouter when you want:

• Access to models not available through native APIs (GPT-4, Claude, Mistral, etc.)
• Simplified billing across multiple model providers
• Experimentation with various models without separate API keys

Use Custom URLs for:

• Local models like Ollama (Llama, Mistral, etc.)
• Self-hosted inference with vLLM, LM Studio, text-generation-webui
• Private/enterprise APIs that use OpenAI-compatible format
• Cost control with local hardware

Use native APIs (Gemini/OpenAI) when you want:

• Direct access to specific providers without intermediary
• Potentially lower latency and costs
• Access to the latest model features immediately upon release

Mix & Match: You can use multiple providers simultaneously! For example:

• OpenRouter for expensive commercial models (GPT-4, Claude)
• Custom URLs for local models (Ollama Llama)
• Native APIs for specific providers (Gemini Pro with extended thinking)

Note: When multiple providers offer the same model name, native APIs take priority over OpenRouter.

Model Aliases

Zen ships multiple registries:

• conf/openai_models.json – native OpenAI catalogue (override with OPENAI_MODELS_CONFIG_PATH)
• conf/gemini_models.json – native Google Gemini catalogue (GEMINI_MODELS_CONFIG_PATH)
• conf/xai_models.json – native X.AI / GROK catalogue (XAI_MODELS_CONFIG_PATH)
• conf/openrouter_models.json – OpenRouter catalogue (OPENROUTER_MODELS_CONFIG_PATH)
• conf/dial_models.json – DIAL aggregation catalogue (DIAL_MODELS_CONFIG_PATH)
• conf/custom_models.json – local/self-hosted OpenAI-compatible catalogue (CUSTOM_MODELS_CONFIG_PATH)

Copy whichever file you need into your project (or point the corresponding *_MODELS_CONFIG_PATH env var at your own copy) and edit it to advertise the models you want.

OpenRouter Models (Cloud)

The curated defaults in conf/openrouter_models.json include popular entries such as:

Alias	Canonical Model	Highlights
opus, claude-opus	anthropic/claude-opus-4.1	Flagship Claude reasoning model with vision
sonnet, sonnet4.5	anthropic/claude-sonnet-4.5	Balanced Claude with high context window
haiku	anthropic/claude-3.5-haiku	Fast Claude option with vision
pro, gemini	google/gemini-2.5-pro	Frontier Gemini with extended thinking
flash	google/gemini-2.5-flash	Ultra-fast Gemini with vision
mistral	mistralai/mistral-large-2411	Frontier Mistral (text only)
llama3	meta-llama/llama-3-70b	Large open-weight text model
deepseek-r1	deepseek/deepseek-r1-0528	DeepSeek reasoning model
perplexity	perplexity/llama-3-sonar-large-32k-online	Search-augmented model
gpt5.1, gpt-5.1, 5.1	openai/gpt-5.1	Flagship GPT-5.1 with reasoning and vision
gpt5.1-codex, codex-5.1	openai/gpt-5.1-codex	Agentic coding specialization (Responses API)
codex-mini, gpt5.1-codex-mini	openai/gpt-5.1-codex-mini	Cost-efficient Codex variant with streaming

Consult the JSON file for the full list, aliases, and capability flags. Add new entries as OpenRouter releases additional models.

Custom/Local Models

Alias	Maps to Local Model	Note
local-llama, local	llama3.2	Requires CUSTOM_API_URL configured

View the baseline OpenRouter catalogue in conf/openrouter_models.json and populate conf/custom_models.json with your local models.

Native catalogues (conf/openai_models.json, conf/gemini_models.json, conf/xai_models.json, conf/dial_models.json) follow the same schema. Updating those files lets you:

• Expose new aliases (e.g., map enterprise-pro to gpt-5-pro)
• Advertise support for JSON mode or vision if the upstream provider adds it
• Adjust token limits when providers increase context windows

Latest OpenAI releases

OpenAI's November 13, 2025 drop introduced gpt-5.1, gpt-5.1-codex, and gpt-5.1-codex-mini, all of which now ship in conf/openai_models.json:

Model	Highlights	Notes
gpt-5.1	400K context, 128K output, multimodal IO, configurable reasoning effort	Streaming enabled; use for balanced agent/coding flows
gpt-5.1-codex	Responses-only agentic coding version of GPT-5.1	Streaming disabled; use_openai_response_api=true; allow_code_generation=true
gpt-5.1-codex-mini	Cost-efficient Codex variant	Streaming enabled, retains 400K context and code-generation flag

These entries include pricing-friendly aliases (gpt5.1, codex-5.1, codex-mini) plus updated capability flags (supports_extended_thinking, allow_code_generation). Copy the manifest if you operate custom deployment names so downstream providers inherit the same metadata.

Because providers load the manifests on import, you can tweak capabilities without touching Python. Restart the server after editing the JSON files so changes are picked up.

To control ordering in auto mode or the listmodels summary, adjust the intelligence_score for each entry (or rely on the automatic heuristic described there).

Note: While you can use any OpenRouter model by its full name, models not in the config file will use generic capabilities (32K context window, no extended thinking, etc.) which may not match the model's actual capabilities. For best results, add new models to the config file with their proper specifications.

Quick Start

Option 1: OpenRouter Setup

1. Get API Key

1. Sign up at openrouter.ai
2. Create an API key from your dashboard
3. Add credits to your account

2. Set Environment Variable

# Add to your .env file
OPENROUTER_API_KEY=your-openrouter-api-key

Note: Control which models can be used directly in your OpenRouter dashboard at openrouter.ai. This gives you centralized control over model access and spending limits.

That's it! The setup script handles all necessary configuration automatically.

Option 2: Custom API Setup (Ollama, vLLM, etc.)

For local models like Ollama, vLLM, LM Studio, or any OpenAI-compatible API:

1. Start Your Local Model Server

# Example: Ollama
ollama serve
ollama pull llama3.2

# Example: vLLM
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf

# Example: LM Studio (enable OpenAI compatibility in settings)
# Server runs on localhost:1234

2. Configure Environment Variables

# Add to your .env file
CUSTOM_API_URL=http://localhost:11434/v1  # Ollama example
CUSTOM_API_KEY=                                      # Empty for Ollama (no auth needed)
CUSTOM_MODEL_NAME=llama3.2                          # Default model to use

Local Model Connection

The Zen MCP server runs natively, so you can use standard localhost URLs to connect to local models:

# For Ollama, vLLM, LM Studio, etc. running on your machine
CUSTOM_API_URL=http://localhost:11434/v1  # Ollama default port

3. Examples for Different Platforms

Ollama:

CUSTOM_API_URL=http://localhost:11434/v1
CUSTOM_API_KEY=
CUSTOM_MODEL_NAME=llama3.2

vLLM:

CUSTOM_API_URL=http://localhost:8000/v1
CUSTOM_API_KEY=
CUSTOM_MODEL_NAME=meta-llama/Llama-2-7b-chat-hf

LM Studio:

CUSTOM_API_URL=http://localhost:1234/v1
CUSTOM_API_KEY=lm-studio  # Or any value, LM Studio often requires some key
CUSTOM_MODEL_NAME=local-model

text-generation-webui (with OpenAI extension):

CUSTOM_API_URL=http://localhost:5001/v1
CUSTOM_API_KEY=
CUSTOM_MODEL_NAME=your-loaded-model

Using Models

Using model aliases (from the registry files):

# OpenRouter models:
"Use opus for deep analysis"         # → anthropic/claude-opus-4
"Use sonnet to review this code"     # → anthropic/claude-sonnet-4
"Use pro via zen to analyze this"    # → google/gemini-2.5-pro
"Use gpt4o via zen to analyze this"  # → openai/gpt-4o
"Use mistral via zen to optimize"    # → mistral/mistral-large

# Local models (with custom URL configured):
"Use local-llama to analyze this code"     # → llama3.2 (local)
"Use local to debug this function"         # → llama3.2 (local)

Using full model names:

# OpenRouter models:
"Use anthropic/claude-opus-4 via zen for deep analysis"
"Use openai/gpt-4o via zen to debug this"
"Use deepseek/deepseek-coder via zen to generate code"

# Local/custom models:
"Use llama3.2 via zen to review this"
"Use meta-llama/Llama-2-7b-chat-hf via zen to analyze"

For OpenRouter: Check current model pricing at openrouter.ai/models.
For Local models: Context window and capabilities are defined in conf/custom_models.json.

Model Provider Selection

The system automatically routes models to the appropriate provider:

1. Entries in conf/custom_models.json → Always routed through the Custom API (requires CUSTOM_API_URL)
2. Entries in conf/openrouter_models.json → Routed through OpenRouter (requires OPENROUTER_API_KEY)
3. Unknown models → Fallback logic based on model name patterns

Provider Priority Order:

1. Native APIs (Google, OpenAI) - if API keys are available
2. Custom endpoints - for models declared in conf/custom_models.json
3. OpenRouter - catch-all for cloud models

This ensures clean separation between local and cloud models while maintaining flexibility for unknown models.

Model Configuration

These JSON files define model aliases and capabilities. You can:

1. Use the default configuration - Includes popular models with convenient aliases
2. Customize the configuration - Add your own models and aliases
3. Override the config path - Set CUSTOM_MODELS_CONFIG_PATH environment variable to an absolute path on disk

Adding Custom Models

Edit conf/openrouter_models.json to tweak OpenRouter behaviour or conf/custom_models.json to add local models. Each entry maps directly onto ModelCapabilities.

Adding an OpenRouter Model

{
  "model_name": "vendor/model-name",
  "aliases": ["short-name", "nickname"],
  "context_window": 128000,
  "supports_extended_thinking": false,
  "supports_json_mode": true,
  "supports_function_calling": true,
  "description": "Model description"
}

Adding a Custom/Local Model

{
  "model_name": "my-local-model",
  "aliases": ["local-model", "custom"],
  "context_window": 128000,
  "supports_extended_thinking": false,
  "supports_json_mode": false,
  "supports_function_calling": false,
  "description": "My custom Ollama/vLLM model"
}

Field explanations:

• model_name: The model identifier (OpenRouter format like vendor/model or local name like llama3.2)
• aliases: Array of short names users can type instead of the full model name
• context_window: Total tokens the model can process (input + output combined)
• supports_extended_thinking: Whether the model has extended reasoning capabilities
• supports_json_mode: Whether the model can guarantee valid JSON output
• supports_function_calling: Whether the model supports function/tool calling
• description: Human-readable description of the model

Important: Keep OpenRouter and Custom models in their respective files so that requests are routed correctly.

Available Models

Popular models available through OpenRouter:

• GPT-4 - OpenAI's most capable model
• Claude 4 - Anthropic's models (Opus, Sonnet, Haiku)
• Mistral - Including Mistral Large
• Llama 3 - Meta's open models
• Many more at openrouter.ai/models

Troubleshooting

• "Model not found": Check exact model name at openrouter.ai/models
• "Insufficient credits": Add credits to your OpenRouter account
• "Model not available": Check your OpenRouter dashboard for model access permissions