{ "markdown": "

\n \n \"MLflow\n \n

\n

The Open Source AI Engineering Platform for Agents, LLMs & Models

\n\nMLflow 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),\n[evaluate](https://mlflow.org/llm-evaluation), [monitor](https://mlflow.org/ai-monitoring), and [optimize](https://mlflow.org/prompt-optimization) production-quality AI applications while\ncontrolling costs and managing access to models and data. With over **60 million monthly downloads**,\nthousands of organizations rely on MLflow each day to ship AI to production with confidence.\n\nMLflow'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),\n[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.\nLearn more at [MLflow for LLMs and Agents](https://mlflow.org/docs/latest/genai).\n\n
\n\n[![Python SDK](https://img.shields.io/pypi/v/mlflow)](https://pypi.org/project/mlflow/)\n[![PyPI Downloads](https://img.shields.io/pypi/dm/mlflow)](https://pepy.tech/projects/mlflow)\n[![License](https://img.shields.io/github/license/mlflow/mlflow)](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt)\n\n\"follow\n\n\"follow\n[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/mlflow/mlflow)\n\n
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\n Website ·\n Try Demo ·\n Docs ·\n News ·\n Events\n
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\n\n## Get Started in 3 Simple Steps\n\nFrom 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/)\n\n> **Fastest start — set up tracing with our CLI**\n>\n> ```bash\n> uvx mlflow@latest agent setup\n> ```\n>\n> 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.\n\n**1. Start MLflow Server**\n\n```bash\nuvx mlflow server\n```\n\n**2. Enable Logging**\n\n```python\nimport mlflow\n\nmlflow.set_tracking_uri(\"http://localhost:5000\")\nmlflow.openai.autolog()\n```\n\n**3. Run Your Code**\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI()\nclient.responses.create(\n model=\"gpt-5.4-mini\",\n input=\"Hello!\",\n)\n```\n\nExplore traces and metrics in the MLflow UI at `http://localhost:5000`.\n\n## LLMs & Agents\n\nMLflow 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.\n\n\n \n \n \n \n \n \n \n \n
\n \"Observability\"\n
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\n Observability\n

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Capture complete traces of your LLM applications and agents for deep behavioral insights. Built on OpenTelemetry, supporting any LLM provider and agent framework. Monitor production quality, costs, and safety.

\n Getting Started →\n
\n Try Demo →\n

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\n 		\n \"Evaluation\"\n
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\n Evaluation\n

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Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own.

\n Getting Started →\n
\n Try Demo →\n

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\n \"Prompts\n
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\n Prompts & Optimization\n

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Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance.

\n Getting Started →\n
\n Try Demo →\n

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\n 		\n \"AI\n
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\n AI Gateway\n

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Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through an OpenAI-compatible interface with built-in credential management, guardrails and traffic splitting for A/B testing.

\n Getting Started →\n

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\n\n## Model Training\n\nFor machine learning and deep learning model development, MLflow provides a full suite of tools to manage the ML lifecycle:\n\n- [**Experiment Tracking**](https://mlflow.org/docs/latest/ml/tracking/) — Track models, parameters, metrics, and evaluation results across experiments\n- [**Model Evaluation**](https://mlflow.org/docs/latest/ml/evaluation/) — Automated evaluation tools integrated with experiment tracking\n- [**Model Registry**](https://mlflow.org/docs/latest/ml/model-registry/) — Collaboratively manage the full lifecycle of ML models\n- [**Deployment**](https://mlflow.org/docs/latest/ml/deployment/) — Deploy models to batch and real-time scoring on Docker, Kubernetes, Azure ML, AWS SageMaker, and more\n\nLearn more at [MLflow for Model Training](https://mlflow.org/docs/latest/ml).\n\n## Integrations\n\nMLflow supports all agent frameworks, LLM providers, tools, and programming languages. We offer one-line automatic tracing for more than 60 frameworks. See the [full integrations list](https://mlflow.org/docs/latest/genai/tracing/integrations/).\n\n### OpenTelemetry\n\n\n \n \n \n

OpenTelemetry
\n\n### Agent Frameworks (Python)\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

LangChain
LangGraph
OpenAI Agent
DSPy
PydanticAI
Google ADK

Microsoft Agent
CrewAI
LlamaIndex
AutoGen
Strands
LiveKit Agents

Agno
Bedrock AgentCore
Smolagents
Semantic Kernel
DeepAgent
AG2

Haystack
Koog
txtai
Pipecat
Watsonx
\n\n### Agent Frameworks (TypeScript)\n\n\n \n \n \n \n \n \n \n

LangChain
LangGraph
Vercel AI SDK
Mastra
VoltAgent
\n\n### Agent Frameworks (Java)\n\n\n \n \n \n \n

Spring AI
Quarkus LangChain4j
\n\n### Model Providers\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

OpenAI
Anthropic
Databricks
Gemini
Amazon Bedrock
LiteLLM

Mistral
xAI / Grok
Ollama
Groq
DeepSeek
Qwen

Moonshot AI
Cohere
BytePlus
Novita AI
FireworksAI
Together AI
\n\n### Gateways\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n

Databricks
LiteLLM Proxy
Vercel AI Gateway
OpenRouter
Portkey

Helicone
Kong AI Gateway
PydanticAI Gateway
TrueFoundry
\n\n### Tools & No-Code\n\n\n \n \n \n \n \n \n \n \n \n \n \n

Instructor
Claude Code
Opencode
Langfuse
Arize / Phoenix
Goose

Langflow
\n\n## Hosting MLflow\n\nMLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is **vendor-neutral** — whether you're building AI agents, LLM applications, or ML models, you have access to MLflow's core capabilities.\n\n\n \n \n \n \n \n \n \n \n

Databricks
Amazon SageMaker
Azure ML
Nebius
Red Hat OpenShift AI
Self-Hosted
\n\n## 💭 Support\n\n- For help or questions about MLflow usage (e.g. \"how do I do X?\") visit the [documentation](https://mlflow.org/docs/latest).\n- In the documentation, you can ask the question to our AI-powered chat bot. Click on the **\"Ask AI\"** button at the right bottom.\n- Join the [virtual events](https://lu.ma/mlflow?k=c) like office hours and meetups.\n- To report a bug, file a documentation issue, or submit a feature request, please [open a GitHub issue](https://github.com/mlflow/mlflow/issues/new/choose).\n- For release announcements and other discussions, please subscribe to our mailing list (mlflow-users@googlegroups.com)\n or join us on [Slack](https://mlflow.org/slack).\n\n## 🤝 Contributing\n\nWe happily welcome contributions to MLflow!\n\n- Submit [bug reports](https://github.com/mlflow/mlflow/issues/new?template=bug_report_template.yaml) and [feature requests](https://github.com/mlflow/mlflow/issues/new?template=feature_request_template.yaml)\n- Contribute for [good-first-issues](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/mlflow/mlflow/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22)\n- Writing about MLflow and sharing your experience\n\nPlease see our [contribution guide](CONTRIBUTING.md) to learn more about contributing to MLflow.\n\n## ⭐️ Star History\n\n\n \n \n \n \"Star\n \n\n\n## ✏️ Citation\n\nIf you use MLflow in your research, please cite it using the \"Cite this repository\" button at the top of the [GitHub repository page](https://github.com/mlflow/mlflow), which will provide you with citation formats including APA and BibTeX.\n\n## 👥 Core Members\n\nMLflow is currently maintained by the following core members with significant contributions from hundreds of exceptionally talented community members.\n\n- [Ben Wilson](https://github.com/BenWilson2)\n- [Corey Zumar](https://github.com/dbczumar)\n- [Daniel Lok](https://github.com/daniellok-db)\n- [Gabriel Fu](https://github.com/gabrielfu)\n- [Harutaka Kawamura](https://github.com/harupy)\n- [Humair Khan](https://github.com/HumairAK)\n- [Joel Robin P](https://github.com/joelrobin18)\n- [Matthew Prahl](https://github.com/mprahl)\n- [Pat Sukprasert](https://github.com/PattaraS)\n- [Serena Ruan](https://github.com/serena-ruan)\n- [Tomu Hirata](https://github.com/TomeHirata)\n- [Weichen Xu](https://github.com/WeichenXu123)\n- [Yuki Watanabe](https://github.com/B-Step62)\n", "bytes": 32891, "sha": "3a00e5bbeeeb6246915d0c937f9ecedf8a44a7ff4b81f76eff88f26c3b00b73a", "repo_slug": "mlflow/mlflow", "fonte": "repo", "truncated": false, "api": "https://agentalog.com/api/listings/skl_mlflow_mlflow_claude_skills_fetch_unreso_fdc1aebd/readme" }