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Flowise → OpenObserve

Trace every prediction request made to a Flowise chatflow by wrapping the REST API calls in manual OpenTelemetry spans. Flowise is an open-source drag-and-drop LLM flow builder. This integration instruments a Python client that calls the Flowise prediction API and exports spans to OpenObserve.

Prerequisites

  • Docker installed
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • OpenAI API key (or another provider supported by Flowise)

Installation

pip install openobserve opentelemetry-sdk opentelemetry-exporter-otlp python-dotenv requests

Configuration

Start Flowise via Docker, passing your OpenObserve endpoint as the OTLP export target:

docker run -d \
  --name flowise \
  -p 3000:3000 \
  -e OTEL_EXPORTER_OTLP_ENDPOINT=http://host.docker.internal:5080/api/default/v1/traces \
  "-e OTEL_EXPORTER_OTLP_HEADERS=Authorization=Basic <your_base64_token>" \
  -e OTEL_SERVICE_NAME=flowise \
  flowiseai/flowise

Replace host.docker.internal with your OpenObserve host if it is not running on the same machine.

Once Flowise is running, open http://localhost:3000, build a chatflow, and copy the chatflow ID from the canvas URL.

Set the following in your .env file:

OPENOBSERVE_URL=http://localhost:5080/
OPENOBSERVE_ORG=default
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
FLOWISE_BASE_URL=http://localhost:3000
FLOWISE_CHATFLOW_ID=<your_chatflow_id>

Instrumentation

Wrap each prediction call in a manual span using the OpenTelemetry tracer:

from dotenv import load_dotenv
load_dotenv()

from openobserve import openobserve_init
openobserve_init()

from opentelemetry import trace
import os
import requests

tracer = trace.get_tracer(__name__)

base_url = os.environ["FLOWISE_BASE_URL"]
chatflow_id = os.environ["FLOWISE_CHATFLOW_ID"]

with tracer.start_as_current_span("flowise.chatflow_predict") as span:
    span.set_attribute("flowise_chatflow_id", chatflow_id)
    span.set_attribute("flowise_question", "Explain distributed tracing in one sentence.")
    resp = requests.post(
        f"{base_url}/api/v1/prediction/{chatflow_id}",
        headers={"Content-Type": "application/json"},
        json={"question": "Explain distributed tracing in one sentence."},
        timeout=30,
    )
    resp.raise_for_status()
    text = resp.json().get("text", "")
    span.set_attribute("flowise_answer_length", len(text))
    span.set_attribute("span_status", "OK")
    print(text)

What Gets Captured

Each prediction call emits one flowise.chatflow_predict span:

Attribute Description
flowise_chatflow_id The chatflow UUID that handled the request
flowise_question The question text sent to the chatflow
flowise_answer_length Character length of the response
span_status OK on success, ERROR on failure
error_message Error detail when the request fails
duration Total round-trip latency in microseconds

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Filter by operation_name = flowise.chatflow_predict to isolate Flowise spans
  3. Click a trace to inspect the chatflow ID, question, and response length
  4. Filter by span_status = ERROR to find failed predictions

Flowise trace in OpenObserve

Next Steps

With Flowise traces in OpenObserve, you can track chatflow latency over time, compare performance across different flow designs, and alert on prediction failures.

Read More