Observability

Fluiq auto-instruments every supported library after a single instrument() call, free and unlimited on every tier. Multi-agent runs are captured as a real DAG (fan-out, joins, loop-backs). Select an integration below to see how traces flow into your dashboard.

Patches chat completions, streaming, embeddings, images, and audio, sync and async.

Python

import openai
from fluiq import instrument

instrument(api_key="fl_...")

client = openai.OpenAI()

# Chat completions: traced automatically
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarise this document"}],
)

# Streaming: also traced
with client.chat.completions.stream(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# Embeddings
client.embeddings.create(
    model="text-embedding-3-small",
    input=["Hello", "World"],
)

Observability

Patches chat completions, streaming, embeddings, images, and audio, sync and async.

Python

import openai
from fluiq import instrument

instrument(api_key="fl_...")

client = openai.OpenAI()

# Chat completions: traced automatically
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarise this document"}],
)

# Streaming: also traced
with client.chat.completions.stream(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a haiku"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

# Embeddings
client.embeddings.create(
    model="text-embedding-3-small",
    input=["Hello", "World"],
)