{ "data": { "a": { "slug": "gauge-cli", "name": "Gauge CLI", "vendor": "Gauge", "vendorUrl": "https://www.withgauge.com/agents/", "kind": "cli", "category": "agent-observability", "summary": "Gauge CLI runs real coding agents (Claude Code, Codex, Pi, OpenCode) against your skills, CLIs, SDKs, MCP servers and docs in Docker, records where they get stuck, and lets your agent test fixes before release.", "url": "https://www.anchorterminal.com/tools/gauge-cli", "markdownUrl": "https://www.anchorterminal.com/tools/gauge-cli.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gauge-cli.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gauge-cli.json", "dns": "gauge-cli.grade.anchorterminal.com", "repo": "https://github.com/gauge-sh/gauge-cli", "license": "MIT", "transports": [], "packages": [ { "registry": "npm", "name": "@withgauge/cli" } ], "auth": "none", "authNotes": "Local mode needs no Gauge account, only credentials for the agent under test (native Claude or Codex logins, or API keys). Hosted Gauge uses `gauge auth login`, with a device flow for remote terminals.", "pricing": "freemium", "pricingNotes": "The CLI and local mode are free and MIT-licensed; agent usage is billed by your provider or subscription. Hosted Gauge runs on workspace credits, whose prices weren't read (checked 2026-11-06).", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No payment protocol in the CLI or its README; hosted runs use workspace credits (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://docs.withgauge.com/agents", "capabilities": [ "obs.evals" ], "tags": [ "open-source", "mit", "cli", "node", "docker", "telemetry", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 60.4, "grade": "C", "agentReady": false, "rank": 590, "ranked": true, "rankOf": 1354, "categoryRank": 13, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 82, "payments": 40, "reliability": 60, "schema": 60, "security": 55, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-06" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "Runs Claude Code, Codex, Pi and OpenCode in fresh Docker sessions against working-tree or pre-release versions", "Local mode needs no Gauge account, only your existing agent credentials", "Dated changelog with six releases in about two weeks", "Instructions bundled in the CLI for coding agents, and -o json on read commands", "CI runs checks on every push and pull request" ], "weaknesses": [ "Collects anonymous command usage by default, with an opt-out", "Pre-1.0 and about two weeks old in this repository", "Docs, authenticated MCP servers and authenticated APIs need hosted Gauge and credits", "Hosted credit prices weren't found on the pages read" ], "agentNotes": [ "npm install -g @withgauge/cli, then gauge local runtime pull and gauge init --local in the project", "Run gauge install-skill so your coding agent finds the CLI's own instructions", "Set GAUGE_TELEMETRY_DISABLED=1 in CI if you don't want command usage collected", "Rebuild and pack an npm CLI or SDK before rerunning, so test sessions get the new tarball", "Keep .gauge/ private; it holds measurements and analyses" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.4 } ], "editorialScores": { "ergonomics": 75, "maintenance": 82, "payments": 40, "reliability": 60, "schema": 60, "security": 55, "transparency": 78 }, "provenanceScore": 27, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "npm install -g @withgauge/cli\ngauge local runtime pull\ngauge init --local\ngauge install-skill" }, "letme": { "capability": "https://letme.dev/obs.evals", "tool": "https://letme.dev/gauge-cli" }, "area": "developer", "provenance": { "legalEntity": "", "domain": "withgauge.com", "domainRegistered": "", "domainNote": "Not looked up: RDAP isn't requested under the 1 November 2026 rules.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/gauge-sh/gauge-cli/blob/main/cli/CHANGELOG.md", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The npm package's author is Gauge \u003csupport@withgauge.com\u003e, its homepage agents.withgauge.com, and its repository field points at gauge-sh/gauge-cli. The licence names Gauge.", "Hosted Gauge's terms and privacy weren't read, and security.txt wasn't checked." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gauge-cli.json" }, "answer": "Gauge CLI scores 60.4 (C) on agent readiness against MLflow Tracing's 51.5 (D), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability, schema \u0026 documentation, payments \u0026 pricing, maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "mlflow-tracing", "name": "MLflow Tracing", "vendor": "MLflow Project (LF Projects, LLC)", "vendorUrl": "https://mlflow.org", "kind": "http-api", "category": "agent-observability", "summary": "Open-source tracing, evaluation and prompt management for LLM applications and agents, part of MLflow, a Linux Foundation project. Owners run the server themselves, and agents read and annotate traces through an experimental MCP server or the `mlflow traces` CLI.", "url": "https://www.anchorterminal.com/tools/mlflow-tracing", "markdownUrl": "https://www.anchorterminal.com/tools/mlflow-tracing.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mlflow-tracing.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json", "dns": "mlflow-tracing.grade.anchorterminal.com", "repo": "https://github.com/mlflow/mlflow", "license": "Apache-2.0", "transports": [ "stdio", "http" ], "packages": [ { "registry": "pypi", "name": "mlflow" }, { "registry": "pypi", "name": "mlflow-tracing" }, { "registry": "npm", "name": "@mlflow/core" } ], "auth": "mixed", "authNotes": "No authentication on a default server. Starting it with `mlflow server --app-name basic-auth` requires a username and password on every request, with role-based access control and no default admin password. The MCP server is a local stdio process that reads `MLFLOW_TRACKING_URI` and the MLflow credential environment variables, such as `MLFLOW_TRACKING_USERNAME` and `MLFLOW_TRACKING_PASSWORD`. Single sign-on needs a community plugin or a reverse proxy.", "pricing": "free", "pricingNotes": "Free to self-host under Apache-2.0, with no account or contract. The owner pays for compute, a database and artifact storage. The MLflow project sells nothing. Its README links managed MLflow from Databricks, Amazon SageMaker, Azure ML and Nebius, whose prices were not read (checked 2026-10-09).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the repository docs or the MCP server source (checked 2026-10-09).", "endpoints": [] }, "toolCount": 26, "popularity": { "githubStars": 28320, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://mlflow.org/docs/latest/genai/", "llmsTxt": "https://mlflow.org/docs/latest/llms.txt", "capabilities": [ "obs.traces", "obs.evals", "obs.prompts", "obs.datasets", "obs.gateway" ], "tags": [ "open-source", "self-hosted", "local", "apache-2.0", "mcp", "cli", "opentelemetry", "llms-txt", "python", "typescript", "linux-foundation" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 51.5, "grade": "D", "agentReady": false, "rank": 919, "ranked": true, "rankOf": 1354, "categoryRank": 18, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 88, "payments": 60, "reliability": 71, "schema": 78, "security": 40, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": -15, "negativeNotes": [ "Advisories counted one by one at this run's values (a fixed Critical or High over two months old 1, under two months 2, a fixed Moderate MLflow published itself 1, one known only from an imported record 0, a fixed Low 0) reach the -15 floor. OSV lists 32 GitHub advisories for `mlflow` published between 2 November 2025 and 2 November 2026, all in MLflow's own code, 10 Critical, 15 High, 6 Moderate and 1 Low. 22 Criticals and Highs were published before 2 September 2026, among them GHSA-7gwp-5pfp-969j (Critical, 17 August, unauthenticated SSRF in webhook delivery, fixed in 3.15.0) and GHSA-gqvg-gmmx-x4hm (High, 1 September, a pickle-deserialisation guard bypassed through the statsmodels flavour, fixed in 3.15.0) (22). Three are under two months old, GHSA-26p8-2jq9-3vq9 (Critical, 27 October, remote code execution on unauthenticated servers through scorer deserialisation, fixed in 3.17.0), GHSA-m65p-9j7q-6w33 (High, 27 October, arbitrary file read, fixed in 3.17.0) and GHSA-wqmh-qccw-pqwf (CVE-2026-53995, High, 20 October, FileStore trace path traversal) (6). Two Moderates are MLflow's own, GHSA-2wqp-q3c6-ccj5 and GHSA-j7qq-vr8c-xvw3 (2) (https://api.osv.dev/v1/query).", "Separate kinds, inside the floor. -3 for security fixes in no release. OSV lists CVE-2026-53995 as fixed in 3.17.2, but the 3.17.2 tag's `FileStore.start_trace` doesn't validate the trace id, and the fixes are on master only, the one the advisory cites on 22 October (31ff9e5) and a stricter one on 29 October (d64cc8b), with authorisation fixes for `./` artifact proxy paths (849cb9c, 28 October) and for run and logged-model IDs (69fe346, 27 October) also unreleased. -3 for marked breaking changes in the 3.16.0 minor (4 September 2026, basic-auth fail-closed authorisation by default) with no release candidate or notice found (https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)." ], "verdict": "Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental and sets no read-only or destructive annotations. The tracking server runs without authentication by default, and 32 security advisories were published in the year to November 2026, one fix still unreleased.", "bestFor": "Teams that already run MLflow or want Apache-2.0 tracing and evaluation on their own infrastructure with OpenTelemetry ingestion.", "strengths": [ "Apache-2.0 licence, free to self-host, with nothing to buy from the project", "`extract_fields` on `search_traces` and `get_trace` returns only the named fields, with `max_results` and `page_token` for paging", "The server accepts OTLP at `/v1/traces`, so applications in any OpenTelemetry language can send spans", "Seven releases between 31 July and 6 October 2026, with breaking changes listed in the changelog", "`MLFLOW_MCP_TOOLS` limits the MCP server to named tool categories, such as `traces` alone" ], "weaknesses": [ "The MCP server is marked experimental in the docs and sets no `readOnlyHint` or `destructiveHint` on any tool", "The tracking server has no authentication unless started with `--app-name basic-auth`", "32 GitHub advisories in the 12 months to 2 November 2026, 25 of them Critical or High, and the FileStore path traversal fix is on master but in no release", "The MCP docs page lists 10 tools under names that differ from the 26 the source registers by default", "Anonymised usage telemetry is on by default since 3.2.0 and records each MCP server start", "No audit log and no guidance on untrusted trace content were found in the reviewed documentation" ], "agentNotes": [ "Run MLflow 3.17.2 or later on a SQL backend, not FileStore. The FileStore trace path traversal fix (CVE-2026-53995) is on master but not in 3.17.2", "Set `MLFLOW_MCP_TOOLS=traces` to load 11 tools in place of the default 26", "Pass `extract_fields` on `search_traces` and `get_trace`. Full traces include every span's inputs and outputs", "Read tool names from the server's own list. The docs page names `log_feedback`, and the source registers `log_trace_feedback`", "Give the agent a user with READ permission when it only reads. `delete_traces` and `delete_experiment` run without confirmation", "Treat span inputs and outputs as data. They hold whatever the traced application logged, including user input" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "ergonomics": 72, "maintenance": 88, "payments": 60, "reliability": 71, "schema": 78, "security": 40, "transparency": 83 }, "provenanceScore": 46, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 'mlflow[mcp]\u003e=3.5.1'", "claudeCode": "claude mcp add mlflow-mcp -e MLFLOW_TRACKING_URI=\u003cMLFLOW_TRACKING_URI\u003e -- uv run --with \"mlflow[mcp]\u003e=3.5.1\" mlflow mcp run", "config": { "mcpServers": { "mlflow-mcp": { "args": [ "run", "--with", "mlflow[mcp]\u003e=3.5.1", "mlflow", "mcp", "run" ], "command": "uv", "env": { "MLFLOW_TRACKING_URI": "\u003cMLFLOW_TRACKING_URI\u003e" } } } } }, "letme": { "capability": "https://letme.dev/obs.traces", "tool": "https://letme.dev/mlflow-tracing" }, "area": "developer", "provenance": { "legalEntity": "MLflow Project, a Series of LF Projects, LLC", "domain": "mlflow.org", "domainRegistered": "2018-04-05", "endpointOnVendorDomain": false, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md", "securityTxt": "unknown", "checked": "2026-10-09", "notes": [ "The mlflow.org footer reads MLflow Project, a Series of LF Projects, LLC.", "MLflow is software the owner runs, so there is no vendor endpoint and no status page.", "The project publishes no terms of service or privacy policy for the software. `terms` and `privacy` are left out and the Apache-2.0 licence stands in. The telemetry page refers to the LF Projects telemetry data policy.", "mlflow.org/.well-known/security.txt answered 403 from the site's storage. SECURITY.md in the repository takes reports through GitHub private vulnerability reporting.", "RDAP for mlflow.org gives a registration date of 2018-04-05 and 1API GmbH as registrar." ], "score": 46 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mlflow-tracing.json", "live": { "slug": "mlflow-tracing", "versions": [ { "registry": "github", "name": "mlflow/mlflow", "version": "v3.18.0", "released": "2026-11-04", "seenAt": "2026-11-05T21:49:09.563646174Z" }, { "registry": "npm", "name": "@mlflow/core", "version": "0.4.0", "seenAt": "2026-11-05T21:49:08.778863055Z" }, { "registry": "pypi", "name": "mlflow", "version": "3.18.0", "released": "2026-11-03", "seenAt": "2026-11-05T21:49:06.766516509Z" }, { "registry": "pypi", "name": "mlflow-tracing", "version": "3.18.0", "released": "2026-11-03", "seenAt": "2026-11-05T21:49:06.91205798Z" } ], "githubStars": 28694, "npmWeekly": 31629, "pypiWeekly": 5158845, "securityTxt": { "url": "https://mlflow.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-05T15:56:52.049804804Z" }, "llmsTxt": { "url": "https://mlflow.org/docs/latest/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-05T15:26:35.439626642Z" }, "domain": { "domain": "mlflow.org", "registered": "2018-04-05", "source": "https://rdap.publicinterestregistry.org/rdap/domain/mlflow.org", "checkedAt": "2026-11-01T14:06:59.208338279Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/mlflow/mlflow/master/CHANGELOG.md", "kind": "changelog", "status": 304, "checkedAt": "2026-11-05T13:05:56.030923207Z", "changedAt": "2026-11-03T08:00:13.592673172Z", "fingerprint": "06688b2fa5aa" } ], "github": { "repo": "mlflow/mlflow", "checkedAt": "2026-11-02T09:08:03.350096092Z", "stars": 28626, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-02T08:47:46Z", "openIssues": 1520, "openPRs": 698, "issuesOpened90d": 470, "issuesClosed90d": 419, "prsMerged90d": 1484, "ci": { "branch": "master", "runs": 50, "success": 29, "failure": 0, "other": 21, "latest": "skipped", "latestAt": "2026-11-02T08:46:21Z" }, "advisories": [ { "ghsa": "GHSA-4449-4cjp-ffp5", "severity": "medium", "published": "2026-10-28T14:33:14Z" }, { "ghsa": "GHSA-2wqp-q3c6-ccj5", "cve": "CVE-2026-115886", "severity": "medium", "published": "2026-10-22T05:12:18Z" }, { "ghsa": "GHSA-m65p-9j7q-6w33", "cve": "CVE-2026-116690", "severity": "high", "published": "2026-10-13T05:56:11Z" }, { "ghsa": "GHSA-j7qq-vr8c-xvw3", "cve": "CVE-2026-116691", "severity": "medium", "published": "2026-10-13T03:44:45Z" }, { "ghsa": "GHSA-26p8-2jq9-3vq9", "cve": "CVE-2026-116692", "severity": "critical", "published": "2026-10-09T08:20:37Z" }, { "ghsa": "GHSA-gqch-g4w5-7qcw", "cve": "CVE-2026-69148", "severity": "medium", "published": "2026-08-04T07:23:14Z" }, { "ghsa": "GHSA-3p64-6gvh-82v5", "cve": "CVE-2026-69146", "severity": "medium", "published": "2026-08-04T07:23:10Z" }, { "ghsa": "GHSA-7gwp-5pfp-969j", "cve": "CVE-2026-64849", "severity": "high", "published": "2026-08-02T08:12:41Z" }, { "ghsa": "GHSA-gqvg-gmmx-x4hm", "severity": "high", "published": "2026-07-27T23:17:36Z" }, { "ghsa": "GHSA-83fm-w79m-64r5", "severity": "critical", "published": "2023-04-28T09:12:46Z" }, { "ghsa": "GHSA-wp72-7hj9-5265", "cve": "CVE-2023-1176", "severity": "medium", "published": "2023-03-24T00:07:29Z" }, { "ghsa": "GHSA-xg73-94fp-g449", "cve": "CVE-2023-1177", "severity": "critical", "published": "2023-03-24T00:05:42Z" } ], "replies": { "sampled": 25, "unanswered": 6, "medianDays": 0.3 } }, "updatedAt": "2026-11-05T21:49:09.563646174Z" } }, "edges": [ { "by": 20, "category": "payments", "leads": "mlflow-tracing", "name": "Payments \u0026 pricing", "reasons": { "a": "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.", "b": "No x402, MPP or L402 (0). MLflow is free software with nothing to buy from the project, so the self-hosted rule applies (20 + 20 + 20). `pip install mlflow` and `mlflow server` need no account. Managed MLflow from Databricks, Amazon SageMaker, Azure ML and Nebius, which the README links, are separate services and are not scored here. That is a judgement call." }, "scores": { "a": 40, "b": 60 } }, { "by": 18, "category": "schema", "leads": "mlflow-tracing", "name": "Schema \u0026 documentation", "reasons": { "a": "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.", "b": "The MCP tools are generated from the Click commands of the MLflow CLI, so every tool has a JSON Schema input with types, required fields and enums for choice options. No OpenAPI file was found in the repository, and the REST API is documented as a reference page generated from protobuf (22). llms.txt at mlflow.org/docs/latest/llms.txt links 349 Markdown pages (10). Tool descriptions are the CLI help text. They state the purpose and carry examples in command-line syntax, and they do not say when not to use a tool. `trace_id` has no description. The docs page lists 10 tools under names such as `log_feedback`, while the source registers 26 by default, with `log_trace_feedback` (12). Lists travel as comma-separated strings and feedback values as strings (9). Many examples, and few documented error responses (10). Versioned releases with a public changelog (15)." }, "scores": { "a": 60, "b": 78 } }, { "by": 15, "category": "security", "leads": "gauge-cli", "name": "Security \u0026 auth", "reasons": { "a": "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.", "b": "The tracking server has no authentication by default. The `basic-auth` app adds usernames and passwords with role-based access control, ships no default admin password and requires 12 characters. Clients, the MCP server included, read a username and password or a token from environment variables. No scoped API keys were found (15). Roles can grant READ only, and 3.17.0 added grants on traces and explicit DENY. The MCP server has no read-only mode and no confirmation before `delete_traces` or `delete_experiment` (12). Traces hold whatever the application logged. No guidance on untrusted content was found in the MCP docs (2). No audit log found (0). SECURITY.md takes reports through GitHub private reporting and advisories are published with patched versions. The project stopped accepting bounty-platform reports, and no security.txt or certification was found (11)." }, "scores": { "a": 55, "b": 40 } }, { "by": 12, "category": "transparency", "leads": "mlflow-tracing", "name": "Transparency \u0026 trust", "reasons": { "a": "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.", "b": "Apache-2.0, an OSI licence, with the whole source public (30). Trace data stays on the owner's server and database. The docs cover trace archival and masking of span content. The project publishes no privacy policy or data processing terms of its own, and the telemetry page refers to the LF Projects telemetry policy (22). Semantic versioning rules say what needs a major version, and experimental APIs can change in a minor release. No notice period is stated (14). Usage telemetry has been on by default since 3.2.0. The docs list each field collected, and `MLFLOW_DISABLE_TELEMETRY=true` or `DO_NOT_TRACK=true` turns it off (17)." }, "scores": { "a": 53, "b": 65 } }, { "by": 11, "category": "reliability", "leads": "mlflow-tracing", "name": "Reliability", "reasons": { "a": "Local package. Installs from npm as @withgauge/cli with Node 22.13 or later and Docker stated (20). checks.yml runs npm run check on push and pull request across a matrix, and the repository has 76 test files; whether main passes wasn't read (20 of 25). Issues weren't read (5, unchecked). A dated changelog with Added sections per release (15). Version 0.28.0 (0). 20 + 20 + 5 + 15 + 0 = 60.", "b": "Scored on the local-software lines, since MLflow runs where the owner installs it, although the listing's kind is http-api. Official `mlflow` and `mlflow-tracing` packages on PyPI with Python 3.10 or later stated, and the MCP server behind the `mlflow[mcp]` extra (20). Public CI in GitHub Actions with cross-version test workflows. github.com refuses our requests in this run, so the pass state of the default branch is unread, and the 9 October reading of pull-request runs didn't isolate it either (15, the value this run gives an unread pass state). 1,504 open issues on 9 October, with a written issue policy and triage automation, not re-read on 2 November (13). Semantic versioning rules are documented and the changelog carries Breaking Changes sections (15). MLflow is at 3.17.2 and `mlflow-tracing` is classified Production/Stable, but the MCP server is still marked experimental (8). 20 + 15 + 13 + 15 + 8 = 71." }, "scores": { "a": 60, "b": 71 } }, { "by": 6, "category": "maintenance", "leads": "mlflow-tracing", "name": "Maintenance \u0026 community", "reasons": { "a": "CLI 0.28.0 on 4 November 2026 (30). Six releases in 90 days (20). Issues and pull requests weren't read; a support address and issue tracker are published (12 of 25). Current official package (15). CI on push and pull request (5 of 10, results not read). 30 + 20 + 12 + 15 + 5 = 82.", "b": "MLflow 3.17.2 was released on 20 October 2026, per the changelog (30). Six tagged releases since 4 August, 3.15.2, 3.16.0, 3.16.1, 3.17.0, 3.17.1 and 3.17.2 (20). A written issue policy, triage and stale-issue automation, and a community Slack. 1,504 issues were open on 9 October and reply times were not read (14). Current official SDKs, Python at 3.17.2 and TypeScript `@mlflow/core` 0.4.0 tagged on 27 August. Presence in the official MCP registry was not checked (15). Dependabot, a lock file and cross-version test workflows. The test workflow showed 5 failed runs among 28 recent ones on 9 October (9). 30 + 20 + 14 + 15 + 9 = 88." }, "scores": { "a": 82, "b": 88 } } ], "facts": [ { "a": "cli", "b": "HTTP API", "name": "Kind" }, { "a": "Gauge", "b": "MLflow Project (LF Projects, LLC)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "stdio, HTTP", "name": "Transports" }, { "a": "None", "b": "OAuth or key", "name": "Auth" }, { "a": "Freemium", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "none", "b": "26", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-04", "b": "2026-10-20", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "28k stars", "name": "Popularity" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gauge CLI scores 60.4 (C) on agent readiness against MLflow Tracing's 51.5 (D), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability, schema \u0026 documentation, payments \u0026 pricing, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, Gauge CLI or MLflow Tracing?" }, { "answer": "No hosted endpoint is listed for Gauge CLI. MLflow Tracing runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call Gauge CLI and MLflow Tracing without installing anything?" }, { "answer": "Yes. Gauge CLI is open source (MIT). MLflow Tracing is open source (Apache-2.0).", "question": "Are Gauge CLI and MLflow Tracing open source?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 55 against 40" ], "also": [ "No key needed to call it", "No incidents deducted, where MLflow Tracing loses 15 points for them" ], "goodFor": "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.", "slug": "gauge-cli", "watchFor": "Collects anonymous command usage by default, with an opt-out" }, { "aheadOn": [ "Reliability, 71 against 60", "Schema \u0026 documentation, 78 against 60", "Payments \u0026 pricing, 60 against 40", "Maintenance \u0026 community, 88 against 82", "Transparency \u0026 trust, 65 against 53" ], "also": [ "Runs on your own machine" ], "goodFor": "Teams that already run MLflow or want Apache-2.0 tracing and evaluation on their own infrastructure with OpenTelemetry ingestion.", "slug": "mlflow-tracing", "watchFor": "The MCP server is marked experimental in the docs and sets no `readOnlyHint` or `destructiveHint` on any tool" } ], "history": { "gauge-cli": null, "mlflow-tracing": [ { "causes": [ "security", "correction" ], "date": "2026-11-02", "delta": -10, "from": 61.5, "fromGrade": "C", "to": 51.5, "toGrade": "D" }, { "causes": [ "evidence" ], "date": "2026-10-27", "delta": 0.3, "from": 61.2, "fromGrade": "C", "to": 61.5, "toGrade": "C" } ] }, "job": { "capability": "obs.evals", "name": "Evaluations" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-mlflow-tracing.json", "title": "Agent Tree vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/agents-tree-vs-mlflow-tracing.json", "title": "agents-tree vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/agents-tree-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/arize-phoenix-vs-mlflow-tracing.json", "title": "Arize Phoenix vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/arize-phoenix-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/baserun-vs-mlflow-tracing.json", "title": "Baserun vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/baserun-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/botgauge-vs-mlflow-tracing.json", "title": "BotGauge vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/botgauge-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/braintrust-vs-mlflow-tracing.json", "title": "Braintrust API + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/braintrust-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/galileo-vs-mlflow-tracing.json", "title": "Galileo API + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/galileo-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/helicone-vs-mlflow-tracing.json", "title": "Helicone AI Gateway + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/helicone-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/honeyhive-vs-mlflow-tracing.json", "title": "HoneyHive vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/honeyhive-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/laminar-vs-mlflow-tracing.json", "title": "Laminar API + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/laminar-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/langfuse-vs-mlflow-tracing.json", "title": "Langfuse API + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/langfuse-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/langsmith-vs-mlflow-tracing.json", "title": "LangSmith API + MCP vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/langsmith-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/langwatch-vs-mlflow-tracing.json", "title": "LangWatch vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/langwatch-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-navige.json", "title": "MLflow Tracing vs Navige", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-navige" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-opik.json", "title": "MLflow Tracing vs Opik API + MCP", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-opik" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-overmind.json", "title": "MLflow Tracing vs Overmind", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-overmind" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-prefactor.json", "title": "MLflow Tracing vs Prefactor", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-prefactor" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-pydantic-logfire.json", "title": "MLflow Tracing vs Pydantic Logfire", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-pydantic-logfire" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-respan.json", "title": "MLflow Tracing vs Respan API + MCP", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-respan" }, { "json": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-wandb-weave.json", "title": "MLflow Tracing vs W\u0026B Weave", "url": "https://www.anchorterminal.com/compare/mlflow-tracing-vs-wandb-weave" }, { "json": "https://www.anchorterminal.com/compare/arize-phoenix-vs-gauge-cli.json", "title": "Arize Phoenix vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/arize-phoenix-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/baserun-vs-gauge-cli.json", "title": "Baserun vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/baserun-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/botgauge-vs-gauge-cli.json", "title": "BotGauge vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/botgauge-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/braintrust-vs-gauge-cli.json", "title": "Braintrust API + MCP vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/braintrust-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-gauge-cli.json", "title": "DeepEval vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/deepeval-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.json", "title": "DeepEval vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/galileo-vs-gauge-cli.json", "title": "Galileo API + MCP vs Gauge CLI", "url": "https://www.anchorterminal.com/compare/galileo-vs-gauge-cli" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-helicone.json", "title": "Gauge CLI vs Helicone AI Gateway + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-helicone" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-honeyhive.json", "title": "Gauge CLI vs HoneyHive", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-honeyhive" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-laminar.json", "title": "Gauge CLI vs Laminar API + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-laminar" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-langfuse.json", "title": "Gauge CLI vs Langfuse API + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-langfuse" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-langsmith.json", "title": "Gauge CLI vs LangSmith API + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-langsmith" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-langwatch.json", "title": "Gauge CLI vs LangWatch", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-langwatch" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-opik.json", "title": "Gauge CLI vs Opik API + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-opik" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-overmind.json", "title": "Gauge CLI vs Overmind", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-overmind" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-prefactor.json", "title": "Gauge CLI vs Prefactor", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-prefactor" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-pydantic-logfire.json", "title": "Gauge CLI vs Pydantic Logfire", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-pydantic-logfire" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-respan.json", "title": "Gauge CLI vs Respan API + MCP", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-respan" }, { "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-wandb-weave.json", "title": "Gauge CLI vs W\u0026B Weave", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-wandb-weave" } ], "panel": null, "scores": [ { "by": 11, "edge": "mlflow-tracing", "gauge-cli": 60, "key": "reliability", "mlflow-tracing": 71, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 18, "edge": "mlflow-tracing", "gauge-cli": 60, "key": "schema", "mlflow-tracing": 78, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 3, "edge": "gauge-cli", "gauge-cli": 75, "key": "ergonomics", "mlflow-tracing": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 15, "edge": "gauge-cli", "gauge-cli": 55, "key": "security", "mlflow-tracing": 40, "name": "Security \u0026 auth", "weight": 14 }, { "by": 20, "edge": "mlflow-tracing", "gauge-cli": 40, "key": "payments", "mlflow-tracing": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 6, "edge": "mlflow-tracing", "gauge-cli": 82, "key": "maintenance", "mlflow-tracing": 88, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 12, "edge": "mlflow-tracing", "gauge-cli": 53, "key": "transparency", "mlflow-tracing": 65, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gauge CLI scores 60.4 (C) on agent readiness against MLflow Tracing's 51.5 (D), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability, schema \u0026 documentation, payments \u0026 pricing, maintenance \u0026 community and transparency \u0026 trust. Both do evaluations.", "tasks": null, "tutorials": [], "verdicts": { "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.", "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 and sets no read-only or destructive annotations. The tracking server runs without authentication by default, and 32 security advisories were published in the year to November 2026, one fix still unreleased." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing", "json": "https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing.min.md" }, "markdown": "Gauge CLI scores 60.4 (C) on agent readiness against MLflow Tracing's 51.5 (D), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability, schema \u0026 documentation, payments \u0026 pricing, maintenance \u0026 community and transparency \u0026 trust. Both do evaluations.\n\n- Gauge CLI: grade C, 60.4/100, rank #590 of 1354. Markdown https://www.anchorterminal.com/tools/gauge-cli.md · JSON https://www.anchorterminal.com/api/v1/tools/gauge-cli.json\n- MLflow Tracing: grade D, 51.5/100, rank #919 of 1354. Markdown https://www.anchorterminal.com/tools/mlflow-tracing.md · JSON https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json\n- Best agent tracing, monitoring and evaluation tools: https://www.anchorterminal.com/best/agent-observability/index.md\n- All 250 evals comparisons: https://www.anchorterminal.com/compare/agent-observability/index.md\n\n## Which one, for what\n\n### Gauge CLI (C)\n\nGood 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.\n\nAhead on:\n- Security \u0026 auth, 55 against 40\n\nAlso in its favour:\n- No key needed to call it\n- No incidents deducted, where MLflow Tracing loses 15 points for them\n\nWatch for: Collects anonymous command usage by default, with an opt-out\n\n### MLflow Tracing (D)\n\nGood for: Teams that already run MLflow or want Apache-2.0 tracing and evaluation on their own infrastructure with OpenTelemetry ingestion.\n\nAhead on:\n- Reliability, 71 against 60\n- Schema \u0026 documentation, 78 against 60\n- Payments \u0026 pricing, 60 against 40\n- Maintenance \u0026 community, 88 against 82\n- Transparency \u0026 trust, 65 against 53\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: The MCP server is marked experimental in the docs and sets no `readOnlyHint` or `destructiveHint` on any tool\n\n\n## Why the edge\n\n### Payments \u0026 pricing: MLflow Tracing +20\n\n- 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.\n- MLflow Tracing (60): No x402, MPP or L402 (0). MLflow is free software with nothing to buy from the project, so the self-hosted rule applies (20 + 20 + 20). `pip install mlflow` and `mlflow server` need no account. Managed MLflow from Databricks, Amazon SageMaker, Azure ML and Nebius, which the README links, are separate services and are not scored here. That is a judgement call.\n\n### Schema \u0026 documentation: MLflow Tracing +18\n\n- 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.\n- MLflow Tracing (78): The MCP tools are generated from the Click commands of the MLflow CLI, so every tool has a JSON Schema input with types, required fields and enums for choice options. No OpenAPI file was found in the repository, and the REST API is documented as a reference page generated from protobuf (22). llms.txt at mlflow.org/docs/latest/llms.txt links 349 Markdown pages (10). Tool descriptions are the CLI help text. They state the purpose and carry examples in command-line syntax, and they do not say when not to use a tool. `trace_id` has no description. The docs page lists 10 tools under names such as `log_feedback`, while the source registers 26 by default, with `log_trace_feedback` (12). Lists travel as comma-separated strings and feedback values as strings (9). Many examples, and few documented error responses (10). Versioned releases with a public changelog (15).\n\n### Security \u0026 auth: Gauge CLI +15\n\n- 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.\n- MLflow Tracing (40): The tracking server has no authentication by default. The `basic-auth` app adds usernames and passwords with role-based access control, ships no default admin password and requires 12 characters. Clients, the MCP server included, read a username and password or a token from environment variables. No scoped API keys were found (15). Roles can grant READ only, and 3.17.0 added grants on traces and explicit DENY. The MCP server has no read-only mode and no confirmation before `delete_traces` or `delete_experiment` (12). Traces hold whatever the application logged. No guidance on untrusted content was found in the MCP docs (2). No audit log found (0). SECURITY.md takes reports through GitHub private reporting and advisories are published with patched versions. The project stopped accepting bounty-platform reports, and no security.txt or certification was found (11).\n\n### Transparency \u0026 trust: MLflow Tracing +12\n\n- 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.\n- MLflow Tracing (65): Apache-2.0, an OSI licence, with the whole source public (30). Trace data stays on the owner's server and database. The docs cover trace archival and masking of span content. The project publishes no privacy policy or data processing terms of its own, and the telemetry page refers to the LF Projects telemetry policy (22). Semantic versioning rules say what needs a major version, and experimental APIs can change in a minor release. No notice period is stated (14). Usage telemetry has been on by default since 3.2.0. The docs list each field collected, and `MLFLOW_DISABLE_TELEMETRY=true` or `DO_NOT_TRACK=true` turns it off (17).\n\n### Reliability: MLflow Tracing +11\n\n- Gauge CLI (60): Local package. Installs from npm as @withgauge/cli with Node 22.13 or later and Docker stated (20). checks.yml runs npm run check on push and pull request across a matrix, and the repository has 76 test files; whether main passes wasn't read (20 of 25). Issues weren't read (5, unchecked). A dated changelog with Added sections per release (15). Version 0.28.0 (0). 20 + 20 + 5 + 15 + 0 = 60.\n- MLflow Tracing (71): Scored on the local-software lines, since MLflow runs where the owner installs it, although the listing's kind is http-api. Official `mlflow` and `mlflow-tracing` packages on PyPI with Python 3.10 or later stated, and the MCP server behind the `mlflow[mcp]` extra (20). Public CI in GitHub Actions with cross-version test workflows. github.com refuses our requests in this run, so the pass state of the default branch is unread, and the 9 October reading of pull-request runs didn't isolate it either (15, the value this run gives an unread pass state). 1,504 open issues on 9 October, with a written issue policy and triage automation, not re-read on 2 November (13). Semantic versioning rules are documented and the changelog carries Breaking Changes sections (15). MLflow is at 3.17.2 and `mlflow-tracing` is classified Production/Stable, but the MCP server is still marked experimental (8). 20 + 15 + 13 + 15 + 8 = 71.\n\n### Maintenance \u0026 community: MLflow Tracing +6\n\n- Gauge CLI (82): CLI 0.28.0 on 4 November 2026 (30). Six releases in 90 days (20). Issues and pull requests weren't read; a support address and issue tracker are published (12 of 25). Current official package (15). CI on push and pull request (5 of 10, results not read). 30 + 20 + 12 + 15 + 5 = 82.\n- MLflow Tracing (88): MLflow 3.17.2 was released on 20 October 2026, per the changelog (30). Six tagged releases since 4 August, 3.15.2, 3.16.0, 3.16.1, 3.17.0, 3.17.1 and 3.17.2 (20). A written issue policy, triage and stale-issue automation, and a community Slack. 1,504 issues were open on 9 October and reply times were not read (14). Current official SDKs, Python at 3.17.2 and TypeScript `@mlflow/core` 0.4.0 tagged on 27 August. Presence in the official MCP registry was not checked (15). Dependabot, a lock file and cross-version test workflows. The test workflow showed 5 failed runs among 28 recent ones on 9 October (9). 30 + 20 + 14 + 15 + 9 = 88.\n\n## Score by category\n\n| Category | Weight | Gauge CLI | MLflow Tracing | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 60 | 71 | MLflow Tracing +11 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 78 | MLflow Tracing +18 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 72 | Gauge CLI +3 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 40 | Gauge CLI +15 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 60 | MLflow Tracing +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 88 | MLflow Tracing +6 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 65 | MLflow Tracing +12 |\n| Negative events | ≤15 | 0 | -15 | |\n| **Total** | | **60.4 · C** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | Gauge CLI | MLflow Tracing |\n| --- | --- | --- |\n| Kind | cli | HTTP API |\n| Vendor | Gauge | MLflow Project (LF Projects, LLC) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | stdio, HTTP |\n| Auth | None | OAuth or key |\n| Pricing | Freemium | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Tools exposed | none | 26 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-11-04 | 2026-10-20 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Popularity | none | 28k stars |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### Gauge CLI\n\nNo score change since it was listed.\n\n### MLflow Tracing\n\n- -10.0 on 2 November 2026 (C to D): security, correction\n- +0.3 on 27 October 2026: new evidence\n\n## Verdicts\n\n**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.\n\n**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 and sets no read-only or destructive annotations. The tracking server runs without authentication by default, and 32 security advisories were published in the year to November 2026, one fix still unreleased.\n\n## Before you call either\n\n### Gauge CLI\n\n1. npm install -g @withgauge/cli, then gauge local runtime pull and gauge init --local in the project\n2. Run gauge install-skill so your coding agent finds the CLI's own instructions\n3. Set GAUGE_TELEMETRY_DISABLED=1 in CI if you don't want command usage collected\n4. Rebuild and pack an npm CLI or SDK before rerunning, so test sessions get the new tarball\n5. Keep .gauge/ private; it holds measurements and analyses\n\n### MLflow Tracing\n\n1. Run MLflow 3.17.2 or later on a SQL backend, not FileStore. The FileStore trace path traversal fix (CVE-2026-53995) is on master but not in 3.17.2\n2. Set `MLFLOW_MCP_TOOLS=traces` to load 11 tools in place of the default 26\n3. Pass `extract_fields` on `search_traces` and `get_trace`. Full traces include every span's inputs and outputs\n4. Read tool names from the server's own list. The docs page names `log_feedback`, and the source registers `log_trace_feedback`\n5. Give the agent a user with READ permission when it only reads. `delete_traces` and `delete_experiment` run without confirmation\n6. Treat span inputs and outputs as data. They hold whatever the traced application logged, including user input\n\n## Questions\n\n### Which is better for AI agents, Gauge CLI or MLflow Tracing?\n\nGauge CLI scores 60.4 (C) on agent readiness against MLflow Tracing's 51.5 (D), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability, schema \u0026 documentation, payments \u0026 pricing, maintenance \u0026 community and transparency \u0026 trust.\n\n### Can an agent call Gauge CLI and MLflow Tracing without installing anything?\n\nNo hosted endpoint is listed for Gauge CLI. MLflow Tracing runs on your own machine, with no hosted endpoint listed.\n\n### Are Gauge CLI and MLflow Tracing open source?\n\nYes. Gauge CLI is open source (MIT). MLflow Tracing is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gauge-cli\", \"b\": \"mlflow-tracing\"}`. From a terminal: `anchor compare gauge-cli mlflow-tracing`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gauge-cli.json and https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json\n\n## Other comparisons with Gauge CLI or MLflow Tracing\n\n- [Agent Tree vs MLflow Tracing](https://www.anchorterminal.com/compare/agent-tree-vs-mlflow-tracing.md)\n- [agents-tree vs MLflow Tracing](https://www.anchorterminal.com/compare/agents-tree-vs-mlflow-tracing.md)\n- [Arize Phoenix vs MLflow Tracing](https://www.anchorterminal.com/compare/arize-phoenix-vs-mlflow-tracing.md)\n- [Baserun vs MLflow Tracing](https://www.anchorterminal.com/compare/baserun-vs-mlflow-tracing.md)\n- [BotGauge vs MLflow Tracing](https://www.anchorterminal.com/compare/botgauge-vs-mlflow-tracing.md)\n- [Braintrust API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/braintrust-vs-mlflow-tracing.md)\n- [Galileo API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/galileo-vs-mlflow-tracing.md)\n- [Helicone AI Gateway + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/helicone-vs-mlflow-tracing.md)\n- [HoneyHive vs MLflow Tracing](https://www.anchorterminal.com/compare/honeyhive-vs-mlflow-tracing.md)\n- [Laminar API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/laminar-vs-mlflow-tracing.md)\n- [Langfuse API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/langfuse-vs-mlflow-tracing.md)\n- [LangSmith API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/langsmith-vs-mlflow-tracing.md)\n- [LangWatch vs MLflow Tracing](https://www.anchorterminal.com/compare/langwatch-vs-mlflow-tracing.md)\n- [MLflow Tracing vs Navige](https://www.anchorterminal.com/compare/mlflow-tracing-vs-navige.md)\n- [MLflow Tracing vs Opik API + MCP](https://www.anchorterminal.com/compare/mlflow-tracing-vs-opik.md)\n- [MLflow Tracing vs Overmind](https://www.anchorterminal.com/compare/mlflow-tracing-vs-overmind.md)\n- [MLflow Tracing vs Prefactor](https://www.anchorterminal.com/compare/mlflow-tracing-vs-prefactor.md)\n- [MLflow Tracing vs Pydantic Logfire](https://www.anchorterminal.com/compare/mlflow-tracing-vs-pydantic-logfire.md)\n- [MLflow Tracing vs Respan API + MCP](https://www.anchorterminal.com/compare/mlflow-tracing-vs-respan.md)\n- [MLflow Tracing vs W\u0026B Weave](https://www.anchorterminal.com/compare/mlflow-tracing-vs-wandb-weave.md)\n- [Arize Phoenix vs Gauge CLI](https://www.anchorterminal.com/compare/arize-phoenix-vs-gauge-cli.md)\n- [Baserun vs Gauge CLI](https://www.anchorterminal.com/compare/baserun-vs-gauge-cli.md)\n- [BotGauge vs Gauge CLI](https://www.anchorterminal.com/compare/botgauge-vs-gauge-cli.md)\n- [Braintrust API + MCP vs Gauge CLI](https://www.anchorterminal.com/compare/braintrust-vs-gauge-cli.md)\n- [DeepEval vs Gauge CLI](https://www.anchorterminal.com/compare/deepeval-vs-gauge-cli.md)\n- [DeepEval vs MLflow Tracing](https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.md)\n- [Galileo API + MCP vs Gauge CLI](https://www.anchorterminal.com/compare/galileo-vs-gauge-cli.md)\n- [Gauge CLI vs Helicone AI Gateway + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-helicone.md)\n- [Gauge CLI vs HoneyHive](https://www.anchorterminal.com/compare/gauge-cli-vs-honeyhive.md)\n- [Gauge CLI vs Laminar API + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-laminar.md)\n- [Gauge CLI vs Langfuse API + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-langfuse.md)\n- [Gauge CLI vs LangSmith API + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-langsmith.md)\n- [Gauge CLI vs LangWatch](https://www.anchorterminal.com/compare/gauge-cli-vs-langwatch.md)\n- [Gauge CLI vs Opik API + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-opik.md)\n- [Gauge CLI vs Overmind](https://www.anchorterminal.com/compare/gauge-cli-vs-overmind.md)\n- [Gauge CLI vs Prefactor](https://www.anchorterminal.com/compare/gauge-cli-vs-prefactor.md)\n- [Gauge CLI vs Pydantic Logfire](https://www.anchorterminal.com/compare/gauge-cli-vs-pydantic-logfire.md)\n- [Gauge CLI vs Respan API + MCP](https://www.anchorterminal.com/compare/gauge-cli-vs-respan.md)\n- [Gauge CLI vs W\u0026B Weave](https://www.anchorterminal.com/compare/gauge-cli-vs-wandb-weave.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-06", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gauge CLI vs MLflow Tracing", "url": "" } ], "description": "Gauge CLI scores 60.4 (C) to MLflow Tracing's 51.5 (D) for evaluations. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gauge CLI C 60.4", "MLflow Tracing D 51.5", "scores" ], "h1": "Gauge CLI vs MLflow Tracing", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gauge-cli-vs-mlflow-tracing", "published": "2026-10-01", "section": "tools", "title": "Gauge CLI vs MLflow Tracing for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-06", "url": "https://www.anchorterminal.com/compare/gauge-cli-vs-mlflow-tracing" }, "tokens": { "markdown": 4550, "slim": 2280 }, "version": 1 }