33rd of 41 in Gateways and observability

MLflow

Tracing, evals, prompt registry and AI gateway plus classic ML tracking

Stars
28.5k
License
Apache-2.0
Last commit
Oct 2026
Last release
Oct 2026
Language
Python
Stars, last 30 weeks+3,772
27 Mar 2026: 24,741 stars3 Apr 2026: 24,899 stars10 Apr 2026: 25,062 stars17 Apr 2026: 25,219 stars24 Apr 2026: 25,350 stars1 May 2026: 25,478 stars8 May 2026: 25,635 stars15 May 2026: 25,769 stars22 May 2026: 25,890 stars29 May 2026: 26,037 stars5 Jun 2026: 26,178 stars12 Jun 2026: 26,347 stars19 Jun 2026: 26,513 stars26 Jun 2026: 26,624 stars3 Jul 2026: 26,742 stars10 Jul 2026: 26,868 stars17 Jul 2026: 26,989 stars24 Jul 2026: 27,115 stars31 Jul 2026: 27,228 stars7 Aug 2026: 27,335 stars14 Aug 2026: 27,447 stars21 Aug 2026: 27,544 stars28 Aug 2026: 27,662 stars4 Sept 2026: 27,765 stars11 Sept 2026: 27,874 stars18 Sept 2026: 27,994 stars25 Sept 2026: 28,100 stars2 Oct 2026: 28,208 stars9 Oct 2026: 28,315 stars16 Oct 2026: 28,413 stars23 Oct 2026: 28,513 stars

24,741 on 27 Mar 202628,513 on 23 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Single mlflow server (port 5000) that records OpenTelemetry traces from 60+ frameworks via one-line autolog, runs evaluations with 50+ metrics and LLM judges, versions and optimizes prompts, and fronts providers through an OpenAI-compatible AI Gateway with rate limits, fallbacks and traffic splitting. Keeps the original experiment tracking, model registry and deployment tooling. For teams wanting one platform for GenAI and ML.

Who it is for: Teams wanting one platform for GenAI tracing and ML tracking

Ranked among open-source alternatives to LangSmith (#1 of 10).

Strengths

  • One-line autolog for 60+ frameworks in Python, TypeScript and Java; MCP and OTel native
  • Starts with uvx mlflow server; no separate database needed to begin
  • AI Gateway adds credential management, guardrails and A/B traffic splitting
  • Setup wizard lets Claude Code, Codex or OpenCode add tracing to a project

Weaknesses

  • README covers the quickstart; production backend store and auth setup live in docs
  • Broad scope (ML tracking plus GenAI) means a large install and UI surface
  • No Dockerfile or compose file at the repo root
  • TypeScript and Java coverage is smaller than Python (5 TS and 2 Java frameworks listed)

What it needs

  • no GPU
  • Docker + Compose
  • Models: any LLM provider via autolog or the AI Gateway
  • port 5000

Quick answers

Is MLflow open source?

Yes. Its code is on GitHub under the Apache-2.0 license.

Can you run MLflow with Docker?

Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.

Does MLflow need a GPU?

No, it runs without one.

Head to head

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