---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 427c4fcb0278983745a3371bb685595aef26923ea86c50bc48e65ee78d4ea834
  pageDirectory: concepts
  sources:
    - add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-typescript-tracing-sdk
    - MTTS
    - MLflow TypeScript SDK
    - TypeScript
    - TypeScript SDK
  citations:
    - file: add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md
title: MLflow TypeScript Tracing SDK
description: MLflow tracing support for TypeScript generative AI applications alongside Python.
tags:
  - mlflow
  - typescript
  - tracing
timestamp: "2026-06-19T13:54:20.276Z"
---

# MLflow TypeScript Tracing SDK

The **MLflow TypeScript Tracing SDK** provides a TypeScript/JavaScript library for instrumenting generative AI applications with distributed tracing. It supports both automatic and manual tracing approaches, enabling developers to capture execution traces from applications running in Node.js environments.

## Overview

MLflow offers three tracing approaches for TypeScript applications: automatic, manual, and combined. The TypeScript SDK follows the same conceptual model as the Python SDK but is optimized for TypeScript/JavaScript runtime environments. ^[add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md]

## Tracing Approaches

### Automatic Tracing

Automatic tracing uses a one-line configuration to automatically capture app logic. The SDK provides `autolog()` functionality that instruments supported libraries without requiring code changes to individual functions or methods. ^[add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md]

### Manual Tracing

Manual tracing is designed for custom logic and complex workflows. It gives developers full control over what gets traced through:

- **Function Decorator APIs**: Decorators that wrap functions to automatically create spans around their execution
- **Low-Level APIs**: Direct control over span creation, lifecycle management, and trace structure

Manual tracing is appropriate when you need fine-grained control over trace content, such as logging specific inputs, outputs, or intermediate state. ^[add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md]

### Combined Tracing

The combined approach mixes automatic and manual tracing for complete coverage. You can start with automatic tracing to capture most application activity and add manual instrumentation for specific code paths that need custom span data or additional context. ^[add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md]

## Recommended Approach

Start with automatic tracing as it is the fastest way to get traces working. Add manual tracing later if you need more control over specific components of your application. ^[add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md]

## Related Concepts

- [MLflow Tracing](/concepts/mlflow-tracing.md) — The framework for capturing and analyzing traces across Python and TypeScript
- [[MLflow Trace|MLflow Traces]] — The execution records generated by the tracing SDK
- Spans — Individual operations within a trace
- [Assessments on Traces](/concepts/assessments-on-traces.md) — Evaluation annotations attached to traces
- [Best Practices for MLflow Trace Context](/concepts/best-practices-for-mlflow-trace-context.md) — Guidelines for adding metadata to traces
- [Add Context to Traces](/concepts/best-practices-for-adding-context-to-mlflow-traces.md) — How to enrich traces with user, session, and environment information

## Sources

- add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md

# Citations

1. [add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws.md](/references/add-traces-to-applications-automatic-and-manual-tracing-databricks-on-aws-91d388aa.md)
