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GET
/
api
/
v1
/
nodes
List Components
curl --request GET \
  --url https://api.example.com/api/v1/nodes
{
  "401": {},
  "500": {},
  "name": "<string>",
  "label": "<string>",
  "description": "<string>",
  "version": 123,
  "category": "<string>",
  "badge": "<string>",
  "iconSrc": "<string>",
  "inputs": [
    {}
  ],
  "outputs": [
    {}
  ]
}
Retrieve all available component nodes that can be used in your flows. Components include chat models, embeddings, vector stores, tools, agents, and more.

​
Response

​
name
string
Unique identifier/name of the component
​
label
string
Display name for the component
​
description
string
Description of what the component does
​
version
number
Version number of the component
​
category
string
Category the component belongs to (e.g., Chat Models, Embeddings, Vector Stores)
​
badge
string
Optional badge to display (e.g., NEW, DEPRECATED)
​
iconSrc
string
Path to the component’s icon
​
inputs
array
Array of input parameters the component accepts
​
outputs
array
Array of output types the component produces

​
Example Request

curl -X GET \
  'https://your-flowise-instance.com/api/v1/nodes' \
  -H 'Authorization: Bearer YOUR_API_KEY'

​
Example Response

[
  {
    "name": "chatOpenAI",
    "label": "ChatOpenAI",
    "description": "Wrapper around OpenAI large language models",
    "version": 3,
    "category": "Chat Models",
    "iconSrc": "/nodes/openai.svg",
    "inputs": [
      {
        "label": "Model Name",
        "name": "modelName",
        "type": "options",
        "options": [
          {"label": "gpt-4", "name": "gpt-4"},
          {"label": "gpt-3.5-turbo", "name": "gpt-3.5-turbo"}
        ],
        "default": "gpt-3.5-turbo"
      },
      {
        "label": "Temperature",
        "name": "temperature",
        "type": "number",
        "default": 0.9
      }
    ],
    "outputs": [
      {
        "label": "ChatOpenAI",
        "name": "chatOpenAI",
        "baseClasses": ["ChatOpenAI", "BaseChatModel"]
      }
    ]
  },
  {
    "name": "pinecone",
    "label": "Pinecone",
    "description": "Upsert embedded data to Pinecone",
    "version": 2,
    "category": "Vector Stores",
    "badge": "POPULAR",
    "iconSrc": "/nodes/pinecone.svg",
    "inputs": [
      {
        "label": "Pinecone Index",
        "name": "pineconeIndex",
        "type": "string"
      },
      {
        "label": "Embeddings",
        "name": "embeddings",
        "type": "Embeddings"
      }
    ],
    "outputs": [
      {
        "label": "Pinecone Retriever",
        "name": "retriever",
        "baseClasses": ["VectorStoreRetriever", "BaseRetriever"]
      }
    ]
  }
]

​
Component Categories

Available categories include:
  • Chat Models - LLM providers (OpenAI, Anthropic, etc.)
  • Embeddings - Text embedding models
  • Vector Stores - Vector database integrations
  • Document Loaders - Load documents from various sources
  • Text Splitters - Split text into chunks
  • Memory - Conversation memory implementations
  • Tools - Built-in and custom tools
  • Agents - Agent implementations
  • Chains - Pre-built chains
  • Output Parsers - Parse LLM outputs

​
Use Cases

  1. Build Flow UI - Use this endpoint to populate component palettes
  2. Validation - Verify component configurations
  3. Documentation - Generate component documentation
  4. Migration - Handle component version updates

​
Error Responses

​
401
error
Unauthorized - Invalid or missing API key
​
500
error
Internal Server Error - Error retrieving components

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