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HoneyHive is a comprehensive AI observability platform that helps you monitor, evaluate, and improve your LLM applications. When combined with Portkey, you get powerful observability features alongside Portkey’s advanced AI gateway capabilities. This integration allows you to:
  • Automatically trace all LLM requests through Portkey’s gateway
  • Use Portkey’s 250+ LLM providers with HoneyHive observability
  • Implement advanced features like caching, fallbacks, and load balancing
  • Maintain detailed traces and analytics in both platforms

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Quick Start Integration

HoneyHive automatically traces requests to popular LLM providers, making the integration with Portkey seamless. Simply initialize HoneyHive and point your LLM clients to Portkey’s gateway.

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Installation

pip install portkey-ai honeyhive openai

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Basic Setup

from openai import OpenAI
from honeyhive import HoneyHiveTracer, trace

# Initialize HoneyHive tracer at the beginning of your application
HoneyHiveTracer.init(
    api_key='YOUR_HONEYHIVE_API_KEY',
    project='your-project-name',
    source='production',  # Optional: dev, staging, production
    session_name='Portkey Integration'  # Optional
)

# Create OpenAI client pointing to Portkey
client = OpenAI(
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1"
)

# Make requests - automatically traced by HoneyHive
response = client.chat.completions.create(
    model="@openai-provider-slug/gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

print(response.choices[0].message.content)
HoneyHive automatically traces all requests to popular LLM providers, so you get observability data without additional configuration.

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Using Portkey Features with HoneyHive

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1. Trace Functions with @trace Decorator

Use HoneyHive’s @trace decorator to monitor specific functions: 
@trace
def call_openai():
    client = OpenAI(
        api_key="YOUR_PORTKEY_API_KEY",
        base_url="https://api.portkey.ai/v1"
    )

    completion = client.chat.completions.create(
        model="@openai-provider-slug/gpt-4o-mini",
        messages=[{"role": "user", "content": "What is the meaning of life?"}]
    )

    return completion.choices[0].message.content

# Call the traced function
result = call_openai()

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2. Multiple Providers

Switch between 250+ LLM providers while maintaining HoneyHive observability:
  • OpenAI
  • Anthropic
@trace
def call_openai():
    client = OpenAI(
        api_key="YOUR_PORTKEY_API_KEY",
        base_url="https://api.portkey.ai/v1"
    )
    return client.chat.completions.create(
        model="@openai-provider-slug/gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}]
    )

​
3. Advanced Routing with Configs

Use Portkey’s config system for advanced features while tracking in HoneyHive: 
@trace
def advanced_llm_call():
    # Reference a saved config from Portkey dashboard
    client = OpenAI(
        api_key="YOUR_PORTKEY_API_KEY",
        base_url="https://api.portkey.ai/v1"
    )

    return client.chat.completions.create(
        model="@config-slug/model-name",  # Your saved config ID
        messages=[{"role": "user", "content": "Analyze this data..."}]
    )
Example config for fallback between providers: 
{
  "strategy": {
    "mode": "fallback"
  },
  "targets": [
    {
      "provider": "openai",
      "api_key": "YOUR_OPENAI_KEY",
      "override_params": {"model": "gpt-4o"}
    },
    {
      "provider": "anthropic",
      "api_key": "YOUR_ANTHROPIC_KEY",
      "override_params": {"model": "claude-3-opus-20240229"}
    }
  ]
}

​
4. Caching for Cost Optimization

Enable caching to reduce costs while maintaining full observability: 
@trace
def cached_llm_call():
    client = OpenAI(
        api_key="YOUR_PORTKEY_API_KEY",
        base_url="https://api.portkey.ai/v1"
    )

    return client.chat.completions.create(
        model="@cache-config-slug/gpt-4o-mini",
        messages=[{"role": "user", "content": "What is machine learning?"}]
    )

​
5. Custom Metadata and Tracing

Add custom metadata visible in both HoneyHive and Portkey: 
@trace
def contextualized_llm_call(user_id, query):
    client = OpenAI(
        api_key="YOUR_PORTKEY_API_KEY",
        base_url="https://api.portkey.ai/v1"
    )

    return client.chat.completions.create(
        model="@openai-provider-slug/gpt-4o-mini",
        messages=[{"role": "user", "content": query}],
        # Metadata can be passed directly in the request
        user=user_id
    )

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

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Observability Features

With this integration, you get:

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In HoneyHive:

  • Automatic request/response tracing
  • Function-level performance metrics
  • Session-based analytics
  • Custom event tracking
  • Error monitoring and debugging

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In Portkey:

  • Request logs with provider details
  • Advanced analytics across providers
  • Cost tracking and budgets
  • Performance metrics
  • Custom dashboards
  • Token usage analytics

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Migration Guide

If you’re already using HoneyHive with OpenAI, migrating to use Portkey is simple: 
from openai import OpenAI
from honeyhive import HoneyHiveTracer, trace

HoneyHiveTracer.init(
    api_key='YOUR_HONEYHIVE_API_KEY',
    project='my-project'
)

@trace
def call_openai():
    client = OpenAI(api_key="YOUR_OPENAI_KEY")
    return client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello!"}]
    )

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Resources

For enterprise support and custom features, contact our enterprise team.
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