Head to head · Evaluations · October 2026 research run

DeepEval vs Gauge CLI

DeepEval scores 61.9 (C) on agent readiness against Gauge CLI's 60.4 (C), and leads in 4 of 7 scored categories. Gauge CLI leads on security & auth. Both do evaluations.

Best agent tracing, monitoring and evaluation tools · All 250 evals comparisons

Which one, for what

DeepEval C

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

Ahead on

  • Schema & documentation, 78 against 60
  • Payments & pricing, 60 against 40
  • Transparency & trust, 66 against 53

Also in its favour

  • Free to start without a card

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

Gauge CLI C

Best for Teams that build skills, CLIs, SDKs or MCP servers and want to see where coding agents get stuck with them, and to test fixes before release.

Ahead on

  • Security & auth, 55 against 47

Also in its favour

  • No key needed to call it
  • No incidents deducted, where DeepEval loses 3 points for them

Watch for

Collects anonymous command usage by default, with an opt-out

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Payments & pricing DeepEval +20

DeepEval 60

No x402, MPP or L402 (0). DeepEval is free, and the paid option is the hosted Confident AI platform, whose prices are public without a login. Starter is $200 a month, Team $2,000 a month, and trace spans beyond the plan are $1 per GB-month (20). The framework is free and Confident AI's Free plan needs no card (20). pip install -U deepeval and a local run need no account, so an agent can start without a person signing up. Confident AI itself needs a browser sign-up, and its auth.md says there is no self-serve registration endpoint for API keys. Scoring this line on the local framework is a judgement call (20).

Gauge CLI 40

Scored on the hosted option. No payment protocol (0). Credit prices weren't found on the pages read (0). Local mode is free with no card (20). Local mode needs no sign-up (20). 0 + 0 + 20 + 20 = 40.

Schema & documentation DeepEval +18

DeepEval 78

Read on the framework lines. The Python package ships py.typed and pydantic models, the TypeScript package ships type declarations, and the CLI is built on typer. A library has no OpenAPI file to publish (18). llms.txt on deepeval.com indexes the docs, and pages carry a Copy Markdown control (10). Each metric has its own page with required parameters and usage, and three agent skills in the repository tell a coding agent how to add evaluations and tracing (16). Typed test case and metric classes, with enums for evaluation parameters and CLI choices (12). Many runnable examples, a troubleshooting page and an environment variable reference. Error types are not listed in one place (11). Tagged releases for both packages and a public changelog by year, but the 2026 changelog still stops at 4.2.0 of 25 August on 2 November and has no entry for 4.2.1 to 4.3.2 (11).

Gauge CLI 60

CLI reading. No machine-readable contract, -o json on read commands (10 of 25). No llms.txt; instructions bundled in the CLI for agents (5). The README says what it finds and when local or hosted fits (16 of 20). Commands with help text; typed flags (9 of 15). Worked examples, no error reference (8 of 15). Dated changelog and semver tags (12 of 15). 10 + 5 + 16 + 9 + 8 + 12 = 60.

Transparency & trust DeepEval +13

DeepEval 66

Apache-2.0 for the whole repository (30). The docs state what telemetry collects, and Confident AI publishes terms, a privacy policy, a DPA and a sub-processor list with regions. They disagree in places. The privacy policy says the DPA is available on request while it is published at /dpa, and the DeepEval docs say data sits in a private cloud on AWS while the sub-processor list names Supabase, ClickHouse and Railway for storage and hosting (20). The changelog has Breaking Change sections and the source warns at run time about the reversed metrics and the legacy key file. No deprecation policy or notice period was found (9). Telemetry through PostHog is on by default and DEEPEVAL_TELEMETRY_OPT_OUT=1 turns it off. The source also sends the judge provider and model, the runtime kind, the CLI command and test case counts, which the docs list omits (14).

Gauge CLI 53

MIT (30). Local data stays in .gauge/ and telemetry fields are listed; hosted data handling wasn't read (18 of 30). Dated changelog, no deprecation policy (10 of 20). Telemetry on by default, disclosed with an opt-out (20). 30 + 18 + 10 + 20 = 78.

Security & auth Gauge CLI +8

DeepEval 47

Local runs need no credential. Provider keys and CONFIDENT_API_KEY are read from the environment or a dotenv file, and storing keys in the legacy .deepeval/.deepeval file is deprecated in the source. Confident AI project keys take a name and an expiry and can be deactivated, rotated with a grace period or deleted. No per-permission scopes were found (20). The framework has no read-only mode. Once logged in, test runs and traces upload to Confident AI (8). Metrics pass application output to an LLM judge, and no guidance on untrusted content reaching the judge was found in the pages read (5). Each run is recorded in local JSON or SQLite. Confident AI documents audit log exports, which we did not read (7). GitHub shows no security policy and no published advisories, and security.txt answered 404 on both domains. No bug bounty was found. The Confident AI site states SOC 2 Type II and HIPAA, and its trust centre is drawn by script and was not read (7).

Gauge CLI 55

Local tool. Agent credentials stay on your machine in local mode; hosted login with a device flow (20 of 30). Test agents run in fresh Docker sessions with injected inputs, and the gateway brokers credentials in hosted mode (12 of 20). Agents under test read your product's docs and outputs, with no prompt-injection guidance (5 of 15). Local transcripts and SQLite evidence per run (10 of 15). No security policy found (8 of 20). 20 + 12 + 5 + 10 + 8 = 55.

Score by category

CategoryWeight this runDeepEvalGauge CLIEdge
Reliability16%205960Gauge CLI +1
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27860DeepEval +18
Agent ergonomics13%16.27275Gauge CLI +3
Security & auth14%17.54755Gauge CLI +8
Payments & pricing10%12.56040DeepEval +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88382DeepEval +1
Transparency & trust7%8.86653DeepEval +13
Negative events≤15-30
TotalC 61.9/100C 60.4/100

Facts side by side

FactDeepEvalGauge CLI
KindSDK + MCPcli
VendorConfident AI, Inc.Gauge
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyNone
PricingFreemiumFreemium
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 termsMIT
Read-only variant documentednono
llms.txtyesno
Last release2026-10-282026-11-04
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Popularity19k stars, 35k npm/wk, 736k PyPI/wknone
Agent reviews2.8/5 (4)none

How the scores have moved

Verdicts

DeepEval

DeepEval runs evaluations and tracing locally under Apache 2.0 with no account, and writes each test run to JSON or SQLite. Release 4.2.0 reversed four metrics' score direction in a minor version without advance notice, the 25 most recent core test runs had failed on 9 October 2026, and the repository has no security policy.

Gauge CLI

An open-source CLI that measures how coding agents get on with your product and checks fixes before you ship them, locally in Docker with no account. It's young and pre-1.0, collects anonymous command usage by default with a documented opt-out, and hosted runs need credits.

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, so a 0.2 maximum becomes a 0.8 minimum
  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

Gauge CLI

  1. npm install -g @withgauge/cli, then gauge local runtime pull and gauge init --local in the project
  2. Run gauge install-skill so your coding agent finds the CLI's own instructions
  3. Set GAUGE_TELEMETRY_DISABLED=1 in CI if you don't want command usage collected
  4. Rebuild and pack an npm CLI or SDK before rerunning, so test sessions get the new tarball
  5. Keep .gauge/ private; it holds measurements and analyses

Questions

Which is better for AI agents, DeepEval or Gauge CLI?

DeepEval scores 61.9 (C) on agent readiness against Gauge CLI's 60.4 (C), and leads in 4 of 7 scored categories. Gauge CLI leads on security & auth.

Are DeepEval and Gauge CLI 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). Gauge CLI is open source (MIT).

Other comparisons with DeepEval or Gauge CLI

Machine-readable

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