This SDK provides AI evaluations, prompt management, and observability for Python and TypeScript workflows.
Open source
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MLflow provides tools for AI engineering with agents, language models, and machine learning models, including debugging and monitoring.
About opensource
MLflow is an open source platform for agents, LLMs, and ML models that helps debug, evaluate, monitor, and optimize AI applications while managing costs and access to models and data.
It provides trace capture, evaluation metrics, prompt versioning and optimization, and a gateway for routing LLM requests. It supports Python, TypeScript/JavaScript, Java and other languages, integrates with OpenTelemetry and MCP, and includes a local server with a UI for viewing traces and metrics.
Key features
- Capture traces of LLM applications and agents via OpenTelemetry
- Run evaluations with built-in metrics or custom definitions
- Version, test, and deploy prompts with lineage tracking
- Route LLM requests through unified OpenAI-compatible gateway interface
- Manage rate limits, fallbacks, and traffic splitting for A/B testing
- Monitor production quality, costs, and safety
Best for: Teams developing LLM applications and AI agents that need debugging, evaluation, and monitoring.
Good for
- Open source with local server and UI for traces and metrics
- Supports Python, TypeScript/JavaScript, Java, and other languages
- Integrates with OpenTelemetry and MCP and supports multiple LLM providers
opensource at a glance
- Website
- github.com/mlflow/mlflow
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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opensource FAQ
How do I view traces and metrics locally?
Start the server with uvx mlflow server, set the tracking URI to http://localhost:5000, enable logging, then open the MLflow UI at http://localhost:5000.
Which programming languages does MLflow support?
Python, TypeScript/JavaScript, Java, and other programming languages, with integrations for OpenTelemetry and MCP.
What evaluation options are available?
Systematic evaluations using built-in metrics and LLM judges, with the option to define custom metrics.
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