Registry › Compare › Langfuse vs MLflow

Langfuse vs MLflow

MLflow leads on trust: 88.6/100 (Grade A) against 78.5/100 (Grade B), a 10.1-point gap. Langfuse leads on transparency; MLflow leads on provenance and supply-chain integrity.

B Langfuse 78.5

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

langfuse/langfuse · #167 overall · #6 Observability & Evaluation · coverage A (4/5)

Choose Langfuse if transparency matters most.

+1.2Transparency: OSSF Scorecard 6.9 against 5.5

A MLflow 88.6

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

mlflow/mlflow · #33 overall · #2 Observability & Evaluation · coverage A (4/5)

Choose MLflow if provenance and supply-chain integrity matter most.

+7.2Identity / Provenance: package provenance attested, against none
+5.1Safety / Integrity: package provenance attested, against none

Where they differ

13.2

Safety / IntegrityMLflow +5.1

18.3

10.8

Identity / ProvenanceMLflow +7.2

18.0

14.4

TransparencyLangfuse +1.2

13.2

19.9

AdoptionLangfuse +0.3

19.6

+0.2

Runtime calibrationLangfuse +0.7

-0.5

1 dimension identical: Maintenance 20.0 · Full evidence table

An independent, evidence-based trust comparison of Langfuse and MLflow, two Observability & Evaluation projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.

Full evidence

Signal	Langfuselangfuse/langfuse	MLflowmlflow/mlflow
HVTrust score	78.5	88.6
Evidence grade	B	A
Coverage grade	A	A
Overall rank	#167	#33
Rank in Observability & Evaluation	#6	#2
GitHub stars	35.2k	28.2k
Last updated	today	today
Build provenance	No	Yes
OSSF Scorecard	6.9 / 10	5.5 / 10
License	NOASSERTION	Apache-2.0
Downloads	5.7M/wk	4.5M/wk
Trust dimensions (points earned)
Safety / integrity / 25	13.2	18.3
Identity & provenance / 18	10.8	18.0
Transparency / 17	14.4	13.2
Maintenance / 20	20.0	20.0
Adoption / 20	19.9	19.6
Runtime capability surface (full matrix)
MCP server	Implemented	Implemented
External providers	2 — Anthropic, OpenAI	3 — Amazon Bedrock, Anthropic, Postgres
Requires API keys	Yes	No
Plugin surface	plugins	plugins
Provenance drift	Unknown	Unknown

Open in the live compare tool → Langfuse profile MLflow profile More Observability & Evaluation →

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.3 →