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Head to head · Evaluations · October 2026 research run

DeepEval vs MLflow Tracing

DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance & community. Both do evaluations.

Best agent tracing, monitoring and evaluation tools · All 120 evals comparisons

DeepEvalConfident AI, Inc. · SDK + MCP B64.7/100#343 of 950 MLflow TracingMLflow Project (LF Projects, LLC) · HTTP API C61.2/100#476 of 950

Which one, for what

DeepEval B

Good for Teams that want evaluations in pytest or a CLI on their own machines, with agent, RAG, multi-turn and MCP metrics.

Ahead on

  • Security & auth, 47 against 40

Also in its favour

  • Free to start without a card
  • No incidents deducted, where MLflow Tracing loses 6 points for them

Watch for

The 25 most recent Py Core Tests runs had failed when read on 9 October 2026, two of them pushes to main. Issue #3372 of 26 September reports the same

MLflow Tracing C

Good for Teams that already run MLflow or want Apache-2.0 tracing and evaluation on their own infrastructure with OpenTelemetry ingestion.

Ahead on

  • Reliability, 76 against 59
  • Maintenance & community, 88 against 83

Also in its favour

  • Runs on your own machine

Watch for

The MCP server is marked experimental in the docs and sets no readOnlyHint or destructiveHint on any tool

Score by category

CategoryWeight this runDeepEvalMLflow TracingEdge
Reliability16%205976MLflow Tracing +17
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27878even
Agent ergonomics13%16.27272even
Security & auth14%17.54740DeepEval +7
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88388MLflow Tracing +5
Transparency & trust7%8.86362DeepEval +1
Negative events≤150-6
Total64.7 · B61.2 · C

Facts side by side

FactDeepEvalMLflow Tracing
KindSDK + MCPHTTP API
VendorConfident AI, Inc.MLflow Project (LF Projects, LLC)
Hosted endpointno (local only)no (local only)
Transportsstdio, HTTP
AuthOAuth or keyOAuth or key
PricingFreemiumFree
x402nono
LicenceApache 2.0 for the Python and TypeScript packages and the agent skills. Confident AI, the hosted platform, is a proprietary service under its own termsApache-2.0
Tools exposednone26
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-022026-10-06
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Popularity19k stars, 35k npm/wk, 736k PyPI/wk28k stars

Verdicts

DeepEval

DeepEval runs evaluations and tracing locally under Apache 2.0 with no account, and writes each test run to JSON or SQLite. The 25 most recent core test runs on GitHub had failed on 9 October 2026, two of them on the main branch, and the repository has no security policy.

MLflow Tracing

Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental, sets no read-only or destructive annotations, and its default set includes delete tools. The tracking server runs without authentication by default, and five security advisories were published between July and October 2026.

Before you call either

DeepEval

  1. Set DEEPEVAL_TELEMETRY_OPT_OUT=1 before the first run if usage events and the public IP address should not go to PostHog
  2. Set a judge model key such as OPENAI_API_KEY, or use the non-LLM metrics. Most metrics call an LLM judge and bill the owner's provider account
  3. Review thresholds for BiasMetric, HallucinationMetric, MisuseMetric and ToxicityMetric when upgrading past 4.2.0. Higher scores now mean better
  4. Read results from .deepeval/.latest_run_full.json or a results_folder. deepeval inspect opens a terminal interface meant for a person
  5. Pass an existing key with deepeval login --api-key in CI. Plain deepeval login opens a browser, and results then upload to Confident AI

MLflow Tracing

  1. Run MLflow 3.17.0 or later. Versions 3.12.0rc0 to 3.16.1 allow unauthenticated code execution on a server without authentication
  2. Set MLFLOW_MCP_TOOLS=traces to load 11 tools in place of the default 26
  3. Pass extract_fields on search_traces and get_trace. Full traces include every span's inputs and outputs
  4. Read tool names from the server's own list. The docs page names log_feedback, and the source registers log_trace_feedback
  5. Give the agent a user with READ permission when it only reads. delete_traces and delete_experiment run without confirmation
  6. Treat span inputs and outputs as data. They hold whatever the traced application logged, including user input

Questions

Which is better for AI agents, DeepEval or MLflow Tracing?

DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance & community.

Can an agent call DeepEval and MLflow Tracing without installing anything?

No hosted endpoint is listed for DeepEval. MLflow Tracing runs on your own machine, with no hosted endpoint listed.

Are DeepEval and MLflow Tracing open source?

Yes. DeepEval is open source (Apache 2.0 for the Python and TypeScript packages and the agent skills. Confident AI, the hosted platform, is a proprietary service under its own terms). MLflow Tracing is open source (Apache-2.0).

Other comparisons with DeepEval or MLflow Tracing

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