HoneyHive Python SDK

A comprehensive Python SDK for HoneyHive, providing LLM observability, evaluation, and tracing capabilities with OpenTelemetry integration.

🚀 Features

OpenTelemetry Integration - Full OTEL compliance with custom span processor and exporter
Automatic Session Management - Seamless session creation and management
Decorator Support - Easy-to-use @trace (unified sync/async), @atrace, and @trace_class decorators
Context Managers - start_span and enrich_span for manual span management
HTTP Instrumentation - Automatic HTTP request tracing
Baggage Support - Context propagation across service boundaries
Experiment Harness Integration - Automatic experiment tracking with MLflow, Weights & Biases, and Comet support
Real-time API Integration - Direct integration with HoneyHive backend services
Comprehensive Testing - Full test suite with 203 passing tests

📦 Installation

Choose Your Instrumentor Type:

HoneyHive supports both OpenInference (lightweight) and OpenLLMetry (enhanced metrics) instrumentors.

Option A: OpenInference (Recommended for Beginners)

# Install with OpenAI integration (most common)
pip install honeyhive[openinference-openai]

# Install with Anthropic integration
pip install honeyhive[openinference-anthropic]

# Install with Google AI integration
pip install honeyhive[openinference-google-ai]

# Install with multiple providers
pip install honeyhive[openinference-openai,openinference-anthropic,openinference-google-ai]

# Install all OpenInference integrations
pip install honeyhive[all-openinference]

Option B: OpenLLMetry (Enhanced Metrics)

# Install with OpenAI integration (enhanced metrics)
pip install honeyhive[traceloop-openai]

# Install with Anthropic integration
pip install honeyhive[traceloop-anthropic]

# Install with Google AI integration
pip install honeyhive[traceloop-google-ai]

# Install with multiple providers
pip install honeyhive[traceloop-openai,traceloop-anthropic,traceloop-google-ai]

# Install all OpenLLMetry integrations
pip install honeyhive[all-traceloop]

Option C: Mix Both Types

# Strategic mixing based on your needs
pip install honeyhive[traceloop-openai,openinference-anthropic]

Basic Installation (manual instrumentor setup required):

pip install honeyhive

📋 Including in Your Project

For detailed guidance on including HoneyHive in your pyproject.toml, see our pyproject.toml Integration Guide.

🔧 Quick Start

Basic Usage

from honeyhive import HoneyHiveTracer, trace

# Initialize tracer
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production"
)

# Use unified decorator for automatic tracing (works with both sync and async)
@trace(event_type="demo", event_name="my_function")
def my_function():
    return "Hello, World!"

@trace(event_type="demo", event_name="my_async_function")
async def my_async_function():
    await asyncio.sleep(0.1)
    return "Hello, Async World!"

# Manual span management
with tracer.start_span("custom-operation"):
    # Your code here
    pass

# With HTTP tracing enabled (new simplified API)
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production",
    disable_http_tracing=False  # project derived from API key
)

Initialization

The HoneyHiveTracer.init() method is the recommended way to initialize the tracer:

from honeyhive import HoneyHiveTracer

# Standard initialization
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production"  # project derived from API key
)

# With custom server URL for self-hosted deployments
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production",
    server_url="https://custom-server.com"  # project derived from API key
)

Enhanced Features Available

from honeyhive import HoneyHiveTracer
from openinference.instrumentation.openai import OpenAIInstrumentor

# All features are available in the init method
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production",
    test_mode=True,  # Test mode support
    instrumentors=[OpenAIInstrumentor()],  # Auto-integration
    disable_http_tracing=True  # Performance control
)

✅ The init method now supports ALL constructor features!

OpenInference Integration

from honeyhive import HoneyHiveTracer
from openinference.instrumentation.openai import OpenAIInstrumentor

# Initialize tracer with OpenInference instrumentor (recommended pattern)
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
    source="production",
    instrumentors=[OpenAIInstrumentor()]  # Auto-integration
)

# OpenInference automatically traces OpenAI calls
import openai
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

Enriching Spans and Sessions

v1.0+ Recommended Pattern: Instance Methods

from honeyhive import HoneyHiveTracer

# Initialize tracer
tracer = HoneyHiveTracer.init(
    api_key="your-api-key",
)

# Use instance methods for enrichment (PRIMARY - Recommended)
@tracer.trace(event_type="tool")
def my_function(input_data):
    result = process_data(input_data)

    # ✅ Instance method (PRIMARY pattern in v1.0+)
    tracer.enrich_span(
        metadata={"input": input_data, "result": result},
        metrics={"processing_time_ms": 150}
    )

    return result

# Enrich session with user properties
tracer.enrich_session(
    user_properties={"user_id": "user-123", "plan": "premium"}
)

Legacy Pattern: Free Functions (Backward Compatibility)

For backward compatibility, the free function pattern from v0.2.x still works:

from honeyhive import trace, enrich_span, enrich_session

# Free functions with automatic tracer discovery (LEGACY)
@trace(event_type="tool")
def my_function(input_data):
    result = process_data(input_data)

    # Free function with auto-discovery (backward compatible)
    enrich_span(
        metadata={"input": input_data, "result": result},
        metrics={"processing_time_ms": 150}
    )

    return result

# Enrich session via free function
enrich_session(user_properties={"user_id": "user-123"})

⚠️ Deprecation Notice: Free functions will be deprecated in v2.0. We recommend migrating to instance methods for new code.

Why Instance Methods?

✅ Explicit tracer reference (no auto-discovery overhead)
✅ Better multi-instance support (multiple tracers in same process)
✅ Clearer code (explicit is better than implicit)
✅ Future-proof (primary pattern going forward)

🏗️ Architecture

Core Components

src/honeyhive/
├── api/                    # API client implementations
│   ├── client.py          # Main API client
│   ├── configurations.py  # Configuration management
│   ├── datapoints.py      # Data point operations
│   ├── datasets.py        # Dataset operations
│   ├── events.py          # Event management
│   ├── evaluations.py     # Evaluation operations
│   ├── metrics.py         # Metrics operations
│   ├── projects.py        # Project management
│   ├── session.py         # Session operations
│   └── tools.py           # Tool operations
├── tracer/                 # OpenTelemetry integration
│   ├── otel_tracer.py     # Main tracer implementation
│   ├── span_processor.py  # Custom span processor
│   ├── span_exporter.py   # Custom span exporter
│   ├── decorators.py      # Tracing decorators
│   └── http_instrumentation.py # HTTP request tracing
├── evaluation/             # Evaluation framework
│   └── evaluators.py      # Evaluation decorators
├── models/                 # Pydantic models
│   └── generated.py       # Auto-generated from OpenAPI
└── utils/                  # Utility functions
    ├── config.py          # Configuration management
    ├── connection_pool.py # HTTP connection pooling
    ├── retry.py           # Retry mechanisms
    └── logger.py          # Logging utilities

Key Design Principles

Singleton Pattern - Single tracer instance per application
Environment Configuration - Flexible configuration via environment variables
Graceful Degradation - Fallback mechanisms for missing dependencies
Test Isolation - Comprehensive test suite with proper isolation
OpenTelemetry Compliance - Full OTEL standard compliance

⚙️ Configuration

Environment Variables

Variable	Description	Default
`HH_API_KEY`	HoneyHive API key	Required
`HH_INGESTION_API_KEY`	Ingestion API key (`hh_ingst_...`) used to send traces and events; `HH_API_KEY` is used when unset	Optional
`HH_API_URL`	API base URL	`https://api.dp1.us.honeyhive.ai`
`HH_SOURCE`	Source environment	`production`
`HH_DISABLE_TRACING`	Disable tracing completely	`false`
`HH_DISABLE_HTTP_TRACING`	Disable HTTP request tracing	`false`
`HH_TEST_MODE`	Enable test mode	`false`
`HH_DEBUG_MODE`	Enable debug mode	`false`
`HH_VERBOSE`	Enable verbose API logging	`false`
`HH_OTLP_ENABLED`	Enable OTLP export	`true`

Experiment Harness Variables

Variable	Description	Default
`HH_EXPERIMENT_ID`	Unique experiment identifier	`None`
`HH_EXPERIMENT_NAME`	Human-readable experiment name	`None`
`HH_EXPERIMENT_VARIANT`	Experiment variant/treatment	`None`
`HH_EXPERIMENT_GROUP`	Experiment group/cohort	`None`
`HH_EXPERIMENT_METADATA`	JSON experiment metadata	`None`

HTTP Client Configuration

Variable	Description	Default
`HH_MAX_CONNECTIONS`	Maximum HTTP connections	`100`
`HH_MAX_KEEPALIVE_CONNECTIONS`	Keepalive connections	`20`
`HH_KEEPALIVE_EXPIRY`	Keepalive expiry (seconds)	`30.0`
`HH_POOL_TIMEOUT`	Connection pool timeout	`30.0`
`HH_RATE_LIMIT_CALLS`	Rate limit calls per window	`1000`
`HH_RATE_LIMIT_WINDOW`	Rate limit window (seconds)	`60.0`
`HH_HTTP_PROXY`	HTTP proxy URL	`None`
`HH_HTTPS_PROXY`	HTTPS proxy URL	`None`
`HH_NO_PROXY`	Proxy bypass list	`None`
`HH_VERIFY_SSL`	SSL verification	`true`

🤝 Contributing

Want to contribute to HoneyHive? See CONTRIBUTING.md for development setup and guidelines.

all-openinference all-traceloop aws-strands dev docs openinference-anthropic openinference-aws-bedrock openinference-azure-openai openinference-claude-agent-sdk openinference-google-adk openinference-google-ai openinference-langchain openinference-litellm openinference-llm-providers openinference-mcp openinference-openai openinference-openai-agents traceloop-anthropic traceloop-aws-bedrock traceloop-azure-openai traceloop-google-ai traceloop-langchain traceloop-llm-providers traceloop-mcp traceloop-openai

Release files for honeyhive 1.6.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for honeyhive 1.6.0
File	Size	Uploaded
honeyhive-1.6.0.tar.gz	1.1 MB	Sep 17, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for honeyhive 1.6.0
File	Interpreter	ABI	Platform	Reset
honeyhive-1.6.0-py3-none-any.whl 446.0 kB Sep 17, 2026	Python 3	none	any	Details

Total release size: 1.5 MB

Release files / honeyhive-1.6.0.tar.gz

Download URL	honeyhive-1.6.0.tar.gz
Size	1.1 MB
Tags	Source
SHA-256 checksum How to use checksums	`11f30bc78c251b3cafbb4e4b91fd2e5a0a9a9361c8da6720ebfd427af2c8c2a4`
BLAKE2b-256 checksum How to use checksums	`6965c3b5455b3b4c2df437faa611573586b354e4795c68c54e9b27b13e48a623`
Upload date	Sep 17, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.16`

Release files / honeyhive-1.6.0-py3-none-any.whl

Download URL	honeyhive-1.6.0-py3-none-any.whl
Size	446.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`13ff59ba2f70b160c7e2964b8abbbdaab4b59a97603f43a529c420ce4b40918b`
BLAKE2b-256 checksum How to use checksums	`c0a21bf0682528316a6d06d0ba7640a23c53604cf6bd12dbdd51ae1e6ad81961`
Upload date	Sep 17, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.16`