Flowise Integration

Flowise is an open-source low-code platform for building customized LLM orchestration flows and AI agents. You can integrate Flowise with Evolution API to deploy visual AI workflows on WhatsApp.

What is Flowise?

Flowise provides:

Drag-and-drop LLM workflow builder
Support for multiple LLM providers (OpenAI, Anthropic, local models)
Vector store integrations for RAG
Agent capabilities with tools
Memory and conversation management
Custom chains and workflows
API endpoints for each flow

Enable Flowise Integration

Add this environment variable to your .env file:

# Enable Flowise integration
FLOWISE_ENABLED=true

Configuration Settings

Create Default Settings

Before creating Flowise bots, configure default behavior settings for your instance.

curl -X POST https://your-api.com/flowise/settings/{instance} \
  -H "apikey: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "expire": 300,
    "keywordFinish": "#EXIT",
    "delayMessage": 1000,
    "unknownMessage": "I did not understand your message.",
    "listeningFromMe": false,
    "stopBotFromMe": false,
    "keepOpen": false,
    "debounceTime": 10,
    "ignoreJids": [],
    "flowiseIdFallback": ""
  }'

Settings Parameters

expire

number

required

Session expiration time in seconds. Default: 300 (5 minutes)

keywordFinish

string

required

Keyword to terminate the bot session. Example: “#EXIT”, “bye”

delayMessage

number

required

Delay in milliseconds between messages. Default: 1000

unknownMessage

string

required

Message sent when bot encounters an error or cannot respond

listeningFromMe

boolean

required

Whether bot responds to messages sent by the instance owner

stopBotFromMe

boolean

required

Whether instance owner can stop the bot session

keepOpen

boolean

required

Keep session open after completion. Default: false

debounceTime

number

required

Seconds to wait before processing rapid messages. Default: 10

ignoreJids

array

required

List of JIDs (phone numbers) to ignore

flowiseIdFallback

string

Fallback Flowise bot ID to use when primary bot fails

Create a Flowise Bot

Create Flowise Flow

Design your AI workflow in Flowise using the visual builder. Deploy the flow and note the API endpoint.

Get API credentials

From Flowise, copy your chatflow API endpoint URL and API key (if authentication is enabled).

Create bot in Evolution API

Test the integration

Send a message matching your trigger to start a Flowise conversation.

curl -X POST https://your-api.com/flowise/create/{instance} \
  -H "apikey: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "enabled": true,
    "description": "Customer Support Flow",
    "apiUrl": "https://flowise.yourdomain.com/api/v1/prediction/chatflow-id-here",
    "apiKey": "your-flowise-api-key",
    "triggerType": "keyword",
    "triggerOperator": "equals",
    "triggerValue": "support",
    "expire": 300,
    "keywordFinish": "#EXIT",
    "delayMessage": 1000,
    "unknownMessage": "Sorry, I encountered an error.",
    "listeningFromMe": false,
    "stopBotFromMe": false,
    "keepOpen": false,
    "debounceTime": 10
  }'

Request Parameters

enabled

boolean

required

Enable or disable this bot

description

string

required

Description of the bot purpose

apiUrl

string

required

Flowise chatflow prediction API endpoint (e.g., “https://flowise.yourdomain.com/api/v1/prediction/chatflow-id”)

apiKey

string

Flowise API key for authentication (optional, only if you enabled API key auth in Flowise)

triggerType

string

required

Trigger type: “all”, “keyword”, or “advanced”

triggerOperator

string

For keyword triggers: “equals”, “contains”, “startsWith”, “endsWith”, “regex”

triggerValue

string

The keyword or regex pattern to trigger this bot

Trigger Types

All Messages

Respond to every message (only one “all” trigger allowed per instance):

{
  "triggerType": "all"
}

Keyword Trigger

Trigger based on specific keywords:

{
  "triggerType": "keyword",
  "triggerOperator": "contains",
  "triggerValue": "help"
}

Available operators:

equals - Exact match
contains - Contains the keyword
startsWith - Message starts with keyword
endsWith - Message ends with keyword
regex - Regular expression match

Advanced Trigger

Use regex for complex pattern matching:

{
  "triggerType": "advanced",
  "triggerValue": "^(how|what|when)\\s"
}

Update Flowise Bot

Modify an existing Flowise bot configuration:

curl -X PUT https://your-api.com/flowise/update/{instance}/{botId} \
  -H "apikey: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "enabled": true,
    "description": "Updated Support Flow",
    "triggerValue": "info"
  }'

Delete Flowise Bot

Remove a Flowise bot from your instance:

curl -X DELETE https://your-api.com/flowise/delete/{instance}/{botId} \
  -H "apikey: your-api-key"

Fetch Flowise Bots

Retrieve all Flowise bots configured for your instance:

curl -X GET https://your-api.com/flowise/fetch/{instance} \
  -H "apikey: your-api-key"

Session Management

Change Session Status

Update the status of an active Flowise session:

curl -X POST https://your-api.com/flowise/changeStatus/{instance} \
  -H "apikey: your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "remoteJid": "[email protected]",
    "status": "closed"
  }'

Fetch Sessions

Get all active Flowise sessions:

curl -X GET https://your-api.com/flowise/fetchSessions/{instance} \
  -H "apikey: your-api-key"

Flowise Workflow Examples

RAG Chatflow

Create a knowledge-base powered bot:

Add Document Loader (PDF, text, web scraping)
Connect to Text Splitter
Add Vector Store (Pinecone, Weaviate, etc.)
Connect to Conversational Retrieval Chain
Add Chat Model (OpenAI, Anthropic, etc.)
Deploy and copy API endpoint

Agent with Tools

Build an autonomous agent:

Add Agent node (OpenAI Functions, ReAct)
Connect tools (Calculator, Search, Custom API)
Add Memory for context retention
Configure LLM model
Deploy and copy API endpoint

Custom Chain

Design complex workflows:

Add LLM Chain node
Add prompt templates
Connect multiple chains
Add conditional logic
Deploy and copy API endpoint

Use Cases

Knowledge Base Assistant

Deploy RAG-powered support:

Answer questions from your documentation
Provide accurate, sourced information
Handle multi-turn conversations
Update knowledge base without retraining

Multi-Step Workflows

Build complex conversation flows:

Data collection and validation
Multi-stage processes
Conditional branching
Integration with multiple services

AI Agents with Tools

Create autonomous assistants:

Web search and information retrieval
API integrations and data fetching
Calculations and data processing
Multi-step reasoning and planning

Hybrid LLM Solutions

Combine multiple AI providers:

Use different models for different tasks
Fallback between providers
Cost optimization strategies
Local model integration

Best Practices

Test your Flowise chatflows thoroughly in the Flowise UI before deploying to WhatsApp.

Secure your Flowise API endpoints with API keys, especially if they’re publicly accessible.

Design modular flows: Break complex workflows into reusable components
Handle errors gracefully: Add error handling nodes in your Flowise chains
Optimize for WhatsApp: Keep responses concise and conversational
Use memory appropriately: Configure conversation memory based on use case
Monitor performance: Track latency and success rates in Flowise analytics
Version your flows: Maintain different versions for testing and production

Troubleshooting

Bot not responding

Verify FLOWISE_ENABLED=true in environment
Check that the bot is enabled (enabled: true)
Ensure trigger conditions match the incoming message
Verify Flowise API URL is correct and accessible
Test the Flowise chatflow directly in Flowise UI
Check Flowise logs for errors

Authentication errors

Verify API key is correct (if using authentication)
Check that API key is properly configured in Flowise
Ensure API key has necessary permissions
Test API endpoint with curl or Postman

Slow response times

Optimize your Flowise chatflow (reduce unnecessary nodes)
Check vector store query performance
Monitor LLM provider response times
Consider using faster models for time-sensitive use cases
Check network latency between Evolution API and Flowise

Context not maintained

Verify conversation memory is enabled in Flowise chatflow
Increase expire time if sessions are timing out
Set keepOpen: true for longer conversations
Check that session IDs are being passed correctly

Unexpected responses

Review chatflow logic in Flowise visual builder
Check prompt templates for clarity
Verify tool configurations and outputs
Test with the same input in Flowise UI
Review Flowise execution logs

Advanced Configuration

Multiple Chatflows

You can deploy multiple Flowise chatflows with different triggers:

// Technical support chatflow
{
  apiUrl: 'https://flowise.example.com/api/v1/prediction/tech-support-id',
  triggerType: 'keyword',
  triggerValue: 'technical'
}

// Sales inquiry chatflow
{
  apiUrl: 'https://flowise.example.com/api/v1/prediction/sales-id',
  triggerType: 'keyword',
  triggerValue: 'buy'
}

// General assistant (fallback)
{
  apiUrl: 'https://flowise.example.com/api/v1/prediction/general-id',
  triggerType: 'all'
}

Self-Hosted Flowise

For self-hosted Flowise installations:

{
  "apiUrl": "http://localhost:3000/api/v1/prediction/your-chatflow-id",
  "apiKey": "your-api-key"
}

Ensure your Flowise instance is accessible from the Evolution API server.

Flowise Features in WhatsApp

Conversation Memory

Flowise automatically maintains conversation context:

Buffer memory for recent messages
Summary memory for long conversations
Entity memory for tracking information

Document Chat

Chat with uploaded documents:

PDF parsing and indexing
Semantic search across documents
Citation and source tracking
Multi-document support

Agent Tools

Integrate external tools:

Web search (SerpAPI, Brave Search)
Calculations and math
Custom API calls
Database queries

Dify - Alternative LLM orchestration platform
OpenAI - Direct OpenAI integration
N8N - Workflow automation platform
EvoAI - Evolution AI platform

Get Started

Core Concepts

WhatsApp Integration

Chatbot Integrations

Event Systems

Storage & Media

Deployment

​Flowise Integration

​What is Flowise?

​Enable Flowise Integration

​Configuration Settings

​Create Default Settings

​Settings Parameters

​Create a Flowise Bot

​Request Parameters

​Trigger Types

​All Messages

​Keyword Trigger

​Advanced Trigger

​Update Flowise Bot

​Delete Flowise Bot

​Fetch Flowise Bots

​Session Management

​Change Session Status

​Fetch Sessions

​Flowise Workflow Examples

​RAG Chatflow

​Agent with Tools

​Custom Chain

​Use Cases

​Best Practices

​Troubleshooting

​Advanced Configuration

​Multiple Chatflows

​Self-Hosted Flowise

​Flowise Features in WhatsApp

​Conversation Memory

​Document Chat

​Agent Tools

​Related Integrations

Build docs developers (and LLMs) love

Flowise Integration

What is Flowise?

Enable Flowise Integration

Configuration Settings

Create Default Settings

Settings Parameters

Create a Flowise Bot

Request Parameters

Trigger Types

All Messages

Keyword Trigger

Advanced Trigger

Update Flowise Bot

Delete Flowise Bot

Fetch Flowise Bots

Session Management

Change Session Status

Fetch Sessions

Flowise Workflow Examples

RAG Chatflow

Agent with Tools

Custom Chain

Use Cases

Best Practices

Troubleshooting

Advanced Configuration

Multiple Chatflows

Self-Hosted Flowise

Flowise Features in WhatsApp

Conversation Memory

Document Chat

Agent Tools

Related Integrations