Principle:Mlflow Mlflow LLM Autologging
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, LLM_Observability |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Automatically capturing invocations to large language model APIs as structured traces without requiring manual code instrumentation.
Description
LLM Autologging is a design principle that enables transparent observability of large language model interactions by automatically intercepting API calls and recording them as traces. Rather than requiring developers to manually annotate every function call with tracing logic, autologging uses monkey-patching techniques to wrap library methods at runtime. When enabled, each call to a supported LLM provider is captured as a span within a trace, including input prompts, output completions, token usage, and latency data.
The principle addresses a fundamental tension in production LLM systems: the need for comprehensive observability without imposing instrumentation burden on application developers. By operating at the library integration layer, autologging captures data at the boundary between application code and external API calls, which is precisely where the most valuable debugging and monitoring information resides.
Autologging integrations are designed to be composable and non-intrusive. They can be selectively enabled or disabled per library, configured to work alongside manual instrumentation, and toggled without code changes beyond a single function call. This makes it practical to adopt tracing incrementally, starting with automatic capture and then layering in manual spans for application-specific logic as needed.
Usage
Use LLM autologging when you want to gain immediate visibility into LLM API calls across an application without modifying the calling code. This is particularly valuable during initial development to understand API behavior, in staging environments to validate prompt configurations, and in production to monitor latency, cost, and failure rates. Autologging is the recommended starting point for LLM tracing before adding more targeted manual instrumentation.
Theoretical Basis
The autologging principle is grounded in aspect-oriented programming (AOP), where cross-cutting concerns such as logging and monitoring are separated from core business logic. By applying patches at the library interface level, the tracing concern is injected transparently. This aligns with the Open/Closed Principle: the application code remains closed to modification but open to extension through the autologging layer. The approach also draws on the OpenTelemetry concept of automatic instrumentation, where telemetry data is collected through library hooks rather than explicit API calls.