Getting started with tracing in HoneyHive.
Distributed tracing is a critical observability technique for modern AI systems, providing a hierarchical view of execution across complex architectures.
Visualization of a trace in HoneyHive
HoneyHive’s tracing capabilities enable you to:
This guide will walk you through implementing HoneyHive tracing, from basic instrumentation to advanced techniques for distributed AI systems.
HoneyHive’s tracing system represents your code’s execution flow across different processes and services as a hierarchical tree of events. This structure provides a comprehensive view of your application’s execution flow:
Visualization of a trace in HoneyHive: Tree structure (left) and detailed view (right)
The root event in the tree is called a session
event, which is equivalent to trace
in Application Performance Monitoring (APM) tools. A session represents a complete interaction or process within your AI application, grouping together all subsequent events in that trace.
Each session
is composed of nested event
s, which are equivalent to span
s in APM tools. Events represent discrete operations or steps in your application’s execution. They can be of different types:
Model
Events:
Tool
Events:
Chain
Events:
Segmenting execution by these different event types enables quicker debugging, dataset curation, and granular evaluations down the line. This hierarchical structure allows for detailed analysis and efficient troubleshooting of your AI application’s execution flow, providing insights at every level of your system. You can learn more about our data model here.
For those looking to get started quickly, we recommend our automatic tracing method. This method automatically instruments major LLM providers and even vector database requests with minimal setup using OpenTelemetry’s Semantic Conventions.
Begin tracing sessions with HoneyHive in minutes
Explore our wide range of pre-built integrations
For a comprehensive list of packages supported by our automatic tracer, please refer to our compatibility guide.
While automatic tracing covers many use cases, you may need to instrument custom logic or code not captured automatically. Custom Spans allow you to trace any function in your codebase.
To maximize the value of your traces, you can enrich any event with additional properties such as user feedback
, user properties
, evaluations
, configs
, metadata
, and more.
For mature AI teams with complex requirements, we offer advanced tracing capabilities:
Track execution across multiple services
Instrument multi-modal AI pipelines effectively
Trace multi-threaded Python applications
Conduct A/B tests on metrics and feedback
For scenarios requiring fine-grained control over tracing or when using languages outside of Python and JS/TS, we offer manual instrumentation options:
Implement custom tracing logic via our APIs
Explore our comprehensive API documentation
Now that you’re familiar with HoneyHive’s tracing capabilities, we recommend:
For any questions or support, please don’t hesitate to reach out to our support team or join our community forum.