AI Agent Monitoring 2026: LangFuse vs Arize vs Helicone Compared

Once AI Agents hit production and the call volume ramps up, things start to get hazy—how many tokens are being spent? Is latency high? Did the model answer users' questions correctly? None of this is clear. AI Agents simply cannot go without monitoring.

💡 What You Will Learn

Once AI Agents hit production and the call volume ramps up, things start to get hazy—how many tokens are being spent? Is latency high? Did the model answer users' questions correctly? None of this is

from langfuse import Langfuse

langfuse = Langfuse(
    secret_key="sk-lf-",
    public_key="pk-lf-",
    host="http://localhost:3000"
)

trace = langfuse.trace(name="Conversation")
span = trace.span(name="Generate", input=user_message, output=ai_response)
span.end()

# 
https://api.openai.com/v1/chat/completions

# Helicone
https://api.openai.com/v1/chat/completions?helicone=true

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