---
title: "mlflow"
type: "tool"
slug: "mlflow-mlflow"
canonical_url: "https://www.graphcanon.com/tools/mlflow-mlflow"
github_url: "https://github.com/mlflow/mlflow"
homepage_url: "https://mlflow.org"
stars: 26921
forks: 5961
primary_language: "Python"
license: "Apache-2.0"
categories: ["llm-frameworks", "developer-tools", "model-training", "evaluation-observability"]
tags: ["evaluation", "agents", "ai", "agentops", "apache-spark", "langchain", "llm-evaluation", "ai-governance"]
updated_at: "2026-07-07T18:23:17.189964+00:00"
---

# mlflow

> The open source AI engineering platform for agents, LLMs, and ML models.

MLflow is an open-source platform designed to streamline the process of debugging, evaluating, monitoring, and optimizing production-quality AI applications. It supports teams in managing costs and securing model access through its robust feature set for observability, evaluation, prompt management, optimization, and AI Gateway capabilities.

## Facts

- Repository: https://github.com/mlflow/mlflow
- Homepage: https://mlflow.org
- Stars: 26,921 · Forks: 5,961 · Open issues: 2,021 · Watchers: 318
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T16:43:54+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Developer Tools](/categories/developer-tools.md)
- [Model Training](/categories/model-training.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)

## Tags

evaluation, agents, ai, agentops, apache-spark, langchain, llm-evaluation, ai-governance

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## README (excerpt)

```text
<h1 align="center" style="border-bottom: none">
    <a href="https://mlflow.org/">
        <img alt="MLflow logo" src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/logo.svg" width="200" />
    </a>
</h1>
<h2 align="center" style="border-bottom: none">The Open Source AI Engineering Platform for Agents, LLMs & Models</h2>

MLflow is the largest open source **AI engineering platform for agents, LLMs, and ML models**. MLflow enables teams of all sizes to [debug](https://mlflow.org/llm-tracing),
[evaluate](https://mlflow.org/llm-evaluation), [monitor](https://mlflow.org/ai-monitoring), and [optimize](https://mlflow.org/prompt-optimization) production-quality AI applications while
controlling costs and managing access to models and data. With over **60 million monthly downloads**,
thousands of organizations rely on MLflow each day to ship AI to production with confidence.

MLflow's comprehensive feature set for agents and LLM applications includes production-grade [observability](https://mlflow.org/docs/latest/genai/tracing), [evaluation](https://mlflow.org/docs/latest/genai/eval-monitor),
[prompt management](https://mlflow.org/docs/latest/genai/prompt-registry), [prompt optimization](https://mlflow.org/prompt-optimization) and an [AI Gateway](https://mlflow.org/docs/latest/genai/governance/ai-gateway) for managing costs and model access.
Learn more at [MLflow for LLMs and Agents](https://mlflow.org/docs/latest/genai).

<div align="center">




<a href="https://twitter.com/intent/follow?screen_name=mlflow" target="_blank">
<img src="https://img.shields.io/twitter/follow/mlflow?logo=X&color=%20%23f5f5f5"
      alt="follow on X(Twitter)"></a>
<a href="https://www.linkedin.com/company/mlflow-org/" target="_blank">
<img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
      alt="follow on LinkedIn"></a>


</div>

<div align="center">
   <div>
      <a href="https://mlflow.org/"><strong>Website</strong></a> ·
      <a href="https://demo.mlflow.org/"><strong>Try Demo</strong></a> ·
      <a href="https://mlflow.org/docs/latest"><strong>Docs</strong></a> ·
      <a href="https://mlflow.org/blog"><strong>News</strong></a> ·
      <a href="https://lu.ma/mlflow?k=c"><strong>Events</strong></a>
   </div>
</div>

<br>

## Get Started in 3 Simple Steps

From zero to full-stack LLMOps in minutes. No complex setup or major code changes required. [Get Started →](https://mlflow.org/docs/latest/genai/tracing/quickstart/)

> **Fastest start — set up tracing with our CLI**
>
> ```bash
> uvx mlflow@latest agent setup
> ```
>
> One command installs the MLflow skills and launches your coding agent of choice to add tracing to your app. Prefer to wire it up yourself? Follow the three steps below.

**1. Start MLflow Server**

```bash
uvx mlflow server
```

**2. Enable Logging**

```python
import mlflow

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.openai.autolog()
```

**3. Run Your Code**

```python
from openai import OpenAI

client = OpenAI()
client.responses.create(
    model="gpt-5.4-mini",
    input="Hello!",
)
```

Explore traces and metrics in the MLflow UI at `http://localhost:5000`.

## LLMs & Agents

MLflow provides everything you need to build, debug, evaluate, and deploy production-quality LLM applications and AI agents. Supports Python, TypeScript/JavaScript, Java and any other programming language. MLflow also natively integrates with [OpenTelemetry](https://opentelemetry.io/) and MCP.

<table>
  <tr>
    <td width="50%">
    <img src="https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/assets/readme-tracing.png" alt="Observability" width=100%>
    <div align="center">
        <br>
        <a href="https://mlflow.org/docs/latest/genai/tracing/"><strong>Observability</strong></a>
        <br><br>
        <div>Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM pr
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/mlflow-mlflow`](/api/graphcanon/tools/mlflow-mlflow)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_