Registry › Compare › LangWatch vs MLflow

LangWatch vs MLflow

Both are Grade A and 2.8 points apart, so choose on what you weigh most. MLflow leads on supply-chain integrity and adoption.

A LangWatch 86.7

Open-Source Agent Observability & Evals for AI Platform teams. Trace, test, route and govern every agent & coding assistant in the company.

langwatch/langwatch · #76 overall · #3 Observability & Evaluation · coverage A (4/5)

LangWatch doesn't lead on any scored dimension in this pair.

A MLflow 89.5

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

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

+4.2Adoption: 5.2M weekly downloads against 83.1k
+1.7Safety / Integrity: 100% of recent commits signed, against 82%
+0.5Transparency: OSSF Scorecard 5.5 against 4.9

Where they differ

17.7

Safety / IntegrityMLflow +1.7

19.4

12.7

TransparencyMLflow +0.5

13.2

20.0

MaintenanceLangWatch +0.1

19.9

15.4

AdoptionMLflow +4.2

19.6

+2.9

Runtime calibrationLangWatch +3.5

-0.6

1 dimension identical: Identity 18.0 · Full evidence table

An independent, evidence-based trust comparison of LangWatch 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	LangWatchlangwatch/langwatch	MLflowmlflow/mlflow
HVTrust score	86.7	89.5
Evidence grade	A	A
Coverage grade	A	A
Overall rank	#76	#32
Rank in Observability & Evaluation	#3	#2
GitHub stars	5.0k	28.6k
Last updated	today	1d ago
Build provenance	Yes	Yes
OSSF Scorecard	4.9 / 10	5.5 / 10
License	Apache-2.0	Apache-2.0
Downloads	83k/wk	5.2M/wk
Trust dimensions (points earned)
Safety / integrity / 25	17.7	19.4
Identity & provenance / 18	18.0	18.0
Transparency / 17	12.7	13.2
Maintenance / 20	20.0	19.9
Adoption / 20	15.4	19.6
Runtime capability surface (full matrix)
MCP server	Implemented	Implemented
External providers	1 — Anthropic	3 — Amazon Bedrock, Anthropic, Postgres
Requires API keys	No	No
Plugin surface	plugins	plugins
Provenance drift	Partial	Unknown

Open in the live compare tool → LangWatch 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 v4.4 →