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302.AI

Last updated Sep 22, 2025

n8n community node for interacting with the 302.ai chat completion API

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69 Monthly Downloads

Included Nodes

302.AI

Description

@fengcch/n8n-nodes-302ai-chat

NPM Version
License: MIT
N8N Compatibility

This is an n8n community node for 302.AI AI service integration.

Prerequisites

You need to have a valid API key from 302.AI to use this node.

Installation

  1. Go to Settings > Community Nodes in your n8n instance.
  2. Select Install and enter @fengcch/n8n-nodes-302ai-chat in the search box.
  3. Click Install to add the node to your n8n instance.

Configuration

  1. In your n8n workflow, add the "302.AI" node.
  2. In the "Credentials" section, click on Create New.
  3. Give your credential a name.
  4. Enter your API key from 302.AI into the API Key field.
  5. Click Save to create the credential.

Usage

Chat Operation

  1. Model Name or ID: Select from available 302.ai models
  2. System Prompt: (Optional) Provide context for the AI
  3. Message: Your input message
  4. Additional Fields: Temperature, max tokens, etc.

Multimodal Support

  • Supports both text and image inputs in chat conversations
  • Compatible with vision-capable models for image understanding
  • Image URL: Optional field to include images in your conversation
  • Supported formats: HTTP/HTTPS image URLs or base64 encoded images
  • Use cases: Image analysis, visual question answering, content understanding from images

Output

  • Standard response: json.response contains the model's reply
  • Error handling: json.error for any API issues

Examples

Basic Chat Example

{
  "model": "gpt-3.5-turbo",
  "message": "Hello, how can AI help with automation?",
  "temperature": 0.7
}

Pseudo-stream Mode

  • Enable the Pseudo-stream Mode toggle (below Image URL) when the target model only supports streaming responses, for example Qwen3.
  • The node consumes the full stream and still returns a single JSON result so you can read the reply from json.response.
  • If the service emits reasoning_content, it is exposed as json.reasoning for easier debugging.

Multimodal Example (Text + Image)

{
  "model": "gpt-5",
  "message": "What do you see in this image?",
  "imageUrl": "https://example.com/image.jpg",
  "temperature": 0.5
}

Response Example

{
  "response": "AI can help with automation in many ways, including data processing, decision making, content generation, and workflow optimization..."
}

License

MIT