Components Observability

Helicone

Core Stack

Open-source observability platform for LLM applications

Version

3.0.0

Last Updated

2024-01-08

Difficulty

Beginner

Reading Time

2 min

GitHub Documentation PyPI

Helicone

Helicone is an open-source observability platform specifically designed for LLM applications. It provides comprehensive monitoring, cost tracking, and performance analytics for your AI systems.

Key Features

LLM-Specific Monitoring: Purpose-built for language model applications
Cost Tracking: Monitor and optimize your LLM API costs
Request/Response Logging: Complete visibility into LLM interactions
Performance Analytics: Track latency, throughput, and success rates
Open Source: Self-hostable with full control over your data

Installation

1

pip install helicone

Quick Start

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from helicone import Helicone
import openai

# Initialize Helicone
helicone = Helicone(
    api_key="your-helicone-api-key",
    base_url="https://oai.hconeai.com/v1"
)

# Use with OpenAI
openai.api_base = "https://oai.hconeai.com/v1"
openai.api_key = "your-openai-api-key"

# Add Helicone headers
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}],
    headers={
        "Helicone-Auth": f"Bearer {helicone.api_key}",
        "Helicone-Cache-Enabled": "true"
    }
)

Use Cases

LLM Cost Monitoring: Track spending across different models and providers
Performance Optimization: Identify bottlenecks and optimize response times
Usage Analytics: Understand user patterns and application behavior
Debugging LLM Applications: Trace issues through complete request/response logs

Best Practices

Enable Caching: Use Helicone’s caching to reduce costs and improve performance
Set Up Alerts: Configure alerts for cost thresholds and error rates
Use Custom Properties: Tag requests with user IDs or session information
Monitor Rate Limits: Track API rate limit usage to avoid throttling
Regular Analysis: Review analytics regularly to optimize your LLM usage

Integration Examples

With LangChain

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from langchain.llms import OpenAI
from langchain.callbacks import HeliconeCallbackHandler

# Set up Helicone callback
helicone_callback = HeliconeCallbackHandler(
    api_key="your-helicone-api-key",
    app_name="my-langchain-app"
)

# Use with LangChain
llm = OpenAI(
    openai_api_base="https://oai.hconeai.com/v1",
    callbacks=[helicone_callback]
)

response = llm("What is machine learning?")

Custom Properties

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import openai

response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}],
    headers={
        "Helicone-Auth": "Bearer your-api-key",
        "Helicone-Property-User": "user-123",
        "Helicone-Property-Session": "session-456",
        "Helicone-Property-Environment": "production"
    }
)

Resources

Alternatives

Weights & Biases

LangSmith

LLM application observability platform

Key Strengths:

• LangChain native integration
• Excellent debugging tools

Best For:

• LangChain application debugging
• LLM chain optimization

Intermediate

Quick Decision Guide

Choose Helicone for the recommended stack with proven patterns and comprehensive support.

Choose LangSmith if you need langchain application debugging or similar specialized requirements.

View detailed comparisons →

#python #monitoring #llm #observability