Open WebUI
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline.
Funcitons are like plugins for Open WebUI. We can create a custom Pipe Function that process inputs and generate responses by invoking Flowise Prediction API before returning results to the user. Through this, Flowise can be used in Open WebUI.
Setup
First, have Open WebUI up and running, you can refer to the Quickstart guide. From the left bottom, click your profile and Admin Panel

Open Functions tab, and add a new Function.

Name the Function, and add the following code:
"""
title: Flowise Integration for OpenWebUI
Requirements:
- Flowise API URL (set via FLOWISE_API_URL)
- Flowise API Key (set via FLOWISE_API_KEY)
"""
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, List, Union, Generator, Iterator
import requests
import json
import os
class Pipe:
class Valves(BaseModel):
flowise_url: str = Field(
default=os.getenv("FLOWISE_API_URL", ""),
description="Flowise URL",
)
flowise_api_key: str = Field(
default=os.getenv("FLOWISE_API_KEY", ""),
description="Flowise API key for authentication",
)
def __init__(self):
self.type = "manifold"
self.id = "flowise_chat"
self.valves = self.Valves()
# Validate required settings
if not self.valves.flowise_url:
print(
"⚠️ Please set your Flowise URL using the FLOWISE_API_URL environment variable"
)
if not self.valves.flowise_api_key:
print(
"⚠️ Please set your Flowise API key using the FLOWISE_API_KEY environment variable"
)
def pipes(self):
if self.valves.flowise_api_key and self.valves.flowise_url:
try:
headers = {
"Authorization": f"Bearer {self.valves.flowise_api_key}",
"Content-Type": "application/json",
}
r = requests.get(
f"{self.valves.flowise_url}/api/v1/chatflows?type=AGENTFLOW",
headers=headers,
)
models = r.json()
return [
{
"id": model["id"],
"name": model["name"],
}
for model in models
]
except Exception as e:
return [
{
"id": "error",
"name": e,
},
]
else:
return [
{
"id": "error",
"name": "API Key not provided.",
},
]
def _process_message_content(self, message: dict) -> str:
"""Process message content, handling text for now"""
if isinstance(message.get("content"), list):
processed_content = []
for item in message["content"]:
if item["type"] == "text":
processed_content.append(item["text"])
return " ".join(processed_content)
return message.get("content", "")
def pipe(
self, body: dict, __user__: Optional[dict] = None, __metadata__: dict = None
) -> Union[str, Generator, Iterator]:
"""Process chat messages through Flowise"""
try:
print("\nProcessing Flowise request:")
print(f"Request body: {json.dumps(body, indent=2)}")
stream_enabled = body.get("stream", True)
session_id = __metadata__['chat_id']
# Extract model id from the model name
model_id = body["model"][body["model"].find(".") + 1 :]
# Extract messages from the body
messages = body.get("messages", [])
if not messages:
raise Exception("No messages found in request body")
# Get the current message (last message)
current_message = messages[-1]
question = self._process_message_content(current_message)
# Prepare request payload according to Flowise API format
data = {
"question": question, # Current message
"overrideConfig": {
"sessionId": session_id
}, # Optional configuration,
"streaming": stream_enabled
}
headers = {
"Authorization": f"Bearer {self.valves.flowise_api_key}",
"Content-Type": "application/json",
}
print("\nMaking Flowise API request:")
print(f"URL: {self.valves.flowise_url}")
print(f"Headers: {headers}")
print(f"Data: {json.dumps(data, indent=2)}")
# Make the API request
r = requests.post(
url=f"{self.valves.flowise_url}/api/v1/prediction/{model_id}",
json=data,
headers=headers
)
r.raise_for_status()
# Return response based on streaming preference
if stream_enabled:
for line in r.iter_lines(decode_unicode=True):
if line and line.startswith('data:'):
try:
# Remove 'data:' prefix and parse JSON
json_data = line[5:] # Remove 'data:' prefix
response = json.loads(json_data)
# Only yield content from token events
if isinstance(response, dict) and response.get("event") == "token":
token_data = response.get("data", "")
if token_data: # Only yield non-empty tokens
yield token_data
except json.JSONDecodeError:
# Skip malformed JSON lines
continue
else:
response = r.json()
# Only return the text field from the response
if isinstance(response, dict) and "text" in response:
return response["text"]
return ""
except Exception as e:
error_msg = f"Error in Flowise pipe: {str(e)}"
print(error_msg)
return error_msg
After Function has been saved, enable it, and click the settings button to put in your Flowise URL and Flowise API Key:


Now when you refresh and click New Chat, you will be able to see the list of flows. You can modify the code to show:
Only Agentflows V2:
f"{self.valves.flowise_url}/api/v1/chatflows?type=AGENTFLOW"
Only Chatflows:
f"{self.valves.flowise_url}/api/v1/chatflows?type=CHATFLOW"
Only Assistants:
f"{self.valves.flowise_url}/api/v1/chatflows?type=ASSISTANT"

Test:

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