n8n-nodes-openrouter-reasoning

n8n-nodes-openrouter-reasoning

Community node for n8n that adds an OpenRouter Chat Model (Reasoning) sub-node for the AI Agent. It behaves like the official OpenRouter Chat Model, but adds OpenRouter-specific reasoning, provider routing, and extra request body support through LangChain modelKwargs.

This package does not create a new Agent and does not use @openrouter/agent. It keeps the visual n8n AI Agent and replaces only the connected chat model sub-node.

Attribution

This node is an independent community package. Its behavior is based on the public n8n OpenRouter Chat Model integration and adapts the OpenRouter tool-call argument normalization pattern so AI Agent tool calling keeps working with models that return empty tool arguments.

Features

• Sub-node output: AiLanguageModel
• Compatible with the n8n AI Agent
• Uses the official n8n OpenRouter credential type: openRouterApi
• Dynamic OpenRouter model selector with richer labels when metadata is available
• Preserves JSON mode through response_format
• Preserves tool calling behavior, including OpenRouter empty tool argument normalization
• Compatible with Structured Output Parser
• Supports OpenRouter provider routing
• Supports OpenRouter reasoning options
• Supports advanced Extra OpenRouter Body JSON
• Optional runtime validation of model reasoning capabilities

Options

provider_default means the node does not send that parameter to OpenRouter. OpenRouter, the selected model, or the selected provider decides the default behavior.

Option	Internal name	Sent to OpenRouter
Provider Sort	providerSort	provider.sort
Allow Fallbacks	providerAllowFallbacks	provider.allow_fallbacks
Require Parameters	providerRequireParameters	provider.require_parameters
Data Collection	providerDataCollection	provider.data_collection
ZDR Only	providerZdr	provider.zdr
Reasoning Enabled	reasoningEnabled	reasoning.enabled = true; disabled sends reasoning.effort = "none"
Reasoning Effort	reasoningEffort	reasoning.effort
Reasoning Max Tokens	reasoningMaxTokens	reasoning.max_tokens
Exclude Reasoning From Response	reasoningExclude	reasoning.exclude
Extra OpenRouter Body JSON	extraBodyJson	Raw top-level OpenRouter request body fields
Validate Model Capabilities	validateModelCapabilities	Local validation before model creation

Reasoning Enabled = Disabled is implemented as reasoning.effort = "none" for compatibility. OpenRouter documents enabled: true, but enabled: false is not treated here as the portable way to disable reasoning.

Extra OpenRouter Body JSON must be a valid JSON object. Arrays, strings, numbers, and invalid JSON throw:

Extra OpenRouter Body JSON must be a valid JSON object.

Explicit UI fields override conflicting values from Extra OpenRouter Body JSON.

Examples

Low reasoning for a support assistant

{
  "reasoning": {
    "effort": "low",
    "exclude": true
  },
  "provider": {
    "sort": "latency",
    "require_parameters": true,
    "allow_fallbacks": true
  }
}

Configure this in the UI with:

• Reasoning Effort: Low
• Exclude Reasoning From Response: True
• Provider Sort: Latency
• Require Parameters: True
• Allow Fallbacks: True

Extra OpenRouter Body JSON

{
  "session_id": "support-session-123",
  "provider": {
    "sort": "price"
  }
}

If the UI also sets Provider Sort = Latency, the final request body uses:

{
  "session_id": "support-session-123",
  "provider": {
    "sort": "latency"
  }
}

JSON mode with reasoning

JSON mode is sent independently from reasoning:

{
  "response_format": {
    "type": "json_object"
  },
  "reasoning": {
    "effort": "low"
  }
}

Model capabilities

The model selector keeps the OpenRouter model id as the option value. When metadata is available, labels/descriptions include context length, pricing, reasoning support, and supported parameters. OpenRouter prices are returned as USD per token and displayed as USD per 1M tokens, for example $0.25/M input · $1.50/M output.

Reasoning Effort is kept as a static dropdown for compatibility with n8n community nodes. Dynamic effort filtering by selected model is not implemented because load options cannot be relied on to safely reshape nested collection options across all supported n8n versions. Use Validate Model Capabilities for optional runtime checks.

When Validate Model Capabilities is enabled:

• If the selected model declares reasoning.supported_efforts, the selected effort must be listed.
• If the selected model declares reasoning.mandatory = true, none/disabled reasoning is blocked.
• If the selected model does not provide reasoning metadata, execution is not blocked.

Install dependencies

On Windows, use npm.cmd if PowerShell blocks npm.ps1:

npm.cmd install

On Linux/macOS:

npm install

Build

npm.cmd run build

Generate a local test package

npm.cmd pack

This generates a local .tgz file, for example:

n8n-nodes-openrouter-reasoning-0.2.0.tgz

Test locally with n8n

Option 1: n8n self-hosted on the host

Install the generated package in your n8n custom nodes folder or test project:

npm.cmd install C:\path\to\n8n-nodes-openrouter-reasoning-0.2.0.tgz

Restart n8n after installing.

Option 2: Docker

Copy the .tgz into a folder mounted by your n8n container, then install it inside the container:

docker cp n8n-nodes-openrouter-reasoning-0.2.0.tgz <n8n-container>:/tmp/
docker exec -it <n8n-container> sh
cd /home/node/.n8n
npm install /tmp/n8n-nodes-openrouter-reasoning-0.2.0.tgz
exit
docker restart <n8n-container>

Option 3: Coolify

1. Add the .tgz to a persistent volume or build context available to the n8n container.
2. Run an install command in the n8n data directory:

cd /home/node/.n8n
npm install /path/to/n8n-nodes-openrouter-reasoning-0.2.0.tgz

1. Restart the n8n service in Coolify.

Example workflow in n8n

1. Add an AI Agent node.
2. Connect OpenRouter Chat Model (Reasoning) to the AI Agent model input.
3. Select an OpenRouter credential using the existing OpenRouter credential type.
4. Select a model that supports tool calling and the reasoning mode you want to use.
5. Configure reasoning, provider routing, JSON mode, or extra body options as needed.
6. Add a Code Tool connected to the AI Agent. A simple test tool can return:

return [{ result: `received: ${query}` }];

1. Add a Structured Output Parser and connect it to the AI Agent output parser input.
2. Test a prompt that asks the agent to call the Code Tool and return structured JSON.

Publish to npm later

Do not publish during local testing. When you are ready, use the n8n release flow:

npm run release -- --publish