{
  "data": {
    "a": {
      "slug": "deepeval",
      "name": "DeepEval",
      "vendor": "Confident AI, Inc.",
      "vendorUrl": "https://www.confident-ai.com",
      "kind": "sdk",
      "category": "agent-observability",
      "summary": "DeepEval is an open-source Python and TypeScript framework from Confident AI for evaluating LLM applications and agents. Evaluations run locally through a pytest plugin and the `deepeval` CLI, with optional reporting to the hosted Confident AI platform.",
      "url": "https://www.anchorterminal.com/tools/deepeval",
      "markdownUrl": "https://www.anchorterminal.com/tools/deepeval.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepeval.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepeval.json",
      "repo": "https://github.com/confident-ai/deepeval",
      "license": "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",
      "transports": [],
      "packages": [
        {
          "registry": "pypi",
          "name": "deepeval"
        },
        {
          "registry": "npm",
          "name": "deepeval"
        }
      ],
      "auth": "mixed",
      "authNotes": "No credential is needed to run evaluations locally. Most metrics call an LLM judge, so the owner supplies a model provider key such as `OPENAI_API_KEY` in the environment or a dotenv file. Reporting to Confident AI needs a project API key in `CONFIDENT_API_KEY`. `deepeval login` pairs with a browser through a device code and writes the key to `.env.local`, and `deepeval login --api-key` takes an existing key for CI. Confident AI project keys are named, take an expiry, and can be deactivated, rotated with a grace period or deleted. No per-permission scopes for project keys were found in the page read.",
      "pricing": "freemium",
      "pricingNotes": "DeepEval is free under Apache 2.0 and runs without an account. LLM judge calls are billed by the owner's model provider. The hosted Confident AI platform has a Free plan with no card (5 test runs a week, 2 seats, 1 project, 1 GB-month of trace spans), Starter at $200 a month, Team at $2,000 a month, both with $1 per GB-month beyond the included trace spans, and Enterprise by quote (https://www.confident-ai.com/pricing).",
      "priceSummary": "$200 / mo",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the repository docs, the Confident AI docs index or the pricing page (checked 2026-10-09).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 18700,
        "npmWeekly": 35092,
        "pypiWeekly": 735637,
        "asOf": "2026-10-09"
      },
      "docsUrl": "https://deepeval.com/docs/getting-started",
      "llmsTxt": "https://deepeval.com/llms.txt",
      "capabilities": [
        "obs.evals",
        "obs.traces",
        "obs.datasets",
        "obs.prompts"
      ],
      "tags": [
        "open-source",
        "apache-2.0",
        "local",
        "cli",
        "python",
        "typescript",
        "pytest",
        "llms-txt",
        "agent-skills",
        "opentelemetry",
        "freemium",
        "no-card"
      ],
      "lastRelease": "2026-10-02",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.7,
        "grade": "B",
        "agentReady": false,
        "rank": 343,
        "ranked": true,
        "rankOf": 950,
        "categoryRank": 9,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 72,
          "maintenance": 83,
          "payments": 60,
          "reliability": 59,
          "schema": 78,
          "security": 47,
          "transparency": 63
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-09"
        },
        "negative": 0,
        "verdict": "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.",
        "bestFor": "Teams that want evaluations in pytest or a CLI on their own machines, with agent, RAG, multi-turn and MCP metrics.",
        "strengths": [
          "Apache-2.0 framework that runs evaluations and tracing locally with no account. Test runs are written to JSON files or a SQLite database on the owner's disk",
          "Python 4.2.8 was tagged on 2 October 2026, with 14 Python releases tagged since 9 August and TypeScript 0.9.21 on 30 September",
          "`deepeval test run` takes flags for parallel processes, repeats, a result cache, ignoring errors and skipping cases with missing parameters",
          "llms.txt on deepeval.com, three agent skills in the repository and plugin manifests for Claude Code and Cursor",
          "Telemetry goes only to PostHog per the docs and the source, and `DEEPEVAL_TELEMETRY_OPT_OUT=1` turns it off"
        ],
        "weaknesses": [
          "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",
          "No SECURITY.md, no published advisories and no security.txt on deepeval.com or confident-ai.com. The trust centre is drawn by script and was not read",
          "Release 4.2.0 reversed the score direction of four safety metrics in a minor version. The changelog marks it as breaking and the code warns at run time",
          "The 2026 changelog stops at 4.2.0 of 25 August. Releases 4.2.1 to 4.2.8 have no entries in it",
          "Usage telemetry is on by default and sends the public IP address. The source also sends the judge model, runtime kind and CLI command, which the docs list omits",
          "Confident AI's terms forbid using its services for competitive analysis. This matters before any probe of the hosted platform is run"
        ],
        "agentNotes": [
          "Set `DEEPEVAL_TELEMETRY_OPT_OUT=1` before the first run if usage events and the public IP address should not go to PostHog",
          "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",
          "Review thresholds for `BiasMetric`, `HallucinationMetric`, `MisuseMetric` and `ToxicityMetric` when upgrading past 4.2.0. Higher scores now mean better",
          "Read results from `.deepeval/.latest_run_full.json` or a `results_folder`. `deepeval inspect` opens a terminal interface meant for a person",
          "Pass an existing key with `deepeval login --api-key` in CI. Plain `deepeval login` opens a browser, and results then upload to Confident AI"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.7
          }
        ],
        "editorialScores": {
          "ergonomics": 72,
          "maintenance": 83,
          "payments": 60,
          "reliability": 59,
          "schema": 78,
          "security": 47,
          "transparency": 73
        },
        "provenanceScore": 52
      },
      "connect": {
        "install": "pip install -U deepeval"
      },
      "letme": {
        "capability": "https://letme.dev/obs.evals",
        "tool": "https://letme.dev/deepeval"
      },
      "area": "developer",
      "unitPrices": [
        {
          "item": "Confident AI Starter plan",
          "unit": "month",
          "usd": 200,
          "note": "Hosted platform, optional. Unlimited seats, 5 projects, 5 GB-months of trace spans"
        },
        {
          "item": "Confident AI Team plan",
          "unit": "month",
          "usd": 2000,
          "note": "Hosted platform, optional. Unlimited projects, 75 GB-months of trace spans"
        },
        {
          "item": "Confident AI trace spans beyond the plan",
          "unit": "gb-month",
          "usd": 1,
          "note": "Per GB-month ingested or retained on Starter and Team"
        }
      ],
      "provenance": {
        "legalEntity": "Confident AI, Inc.",
        "domain": "confident-ai.com",
        "domainRegistered": "2023-08-15",
        "endpointOnVendorDomain": true,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://deepeval.com/changelog",
        "securityTxt": "none",
        "checked": "2026-10-09",
        "notes": [
          "DeepEval is software the owner runs under the Apache-2.0 licence, which stands in for terms here. Confident AI's Terms of Service (https://www.confident-ai.com/terms) and privacy policy (https://www.confident-ai.com/privacy-policy) govern only the optional hosted platform that `deepeval login`, `view` and `gate` use, so they are not linked as this listing's terms.",
          "The Terms of Service (last modified 2 March 2026) name Confident AI, Inc., a Delaware corporation, with offices at 33rd 8th St, San Francisco, CA 94103, as printed on the page.",
          "The privacy policy (last modified 26 May 2026) says it does not apply to Customer Data, which Confident AI processes under its data processing agreement. A DPA and a sub-processor list are published at /dpa and /subprocessors-list.",
          "When logged in, DeepEval sends results to api.confident-ai.com or eu.api.confident-ai.com and traces to otel.confident-ai.com, all on the vendor's domain. Telemetry goes to us.i.posthog.com.",
          "https://www.confident-ai.com/.well-known/security.txt and https://deepeval.com/.well-known/security.txt both answered 404.",
          "No status page link was found on the pages read. The trust centre at trust.oneleet.com/confident-ai is drawn by script and was not read.",
          "RDAP gives 2023-08-15 as the registration date of confident-ai.com and 2023-07-15 for deepeval.com, both through NameCheap, Inc."
        ],
        "score": 52
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/deepeval.json",
      "live": {
        "slug": "deepeval",
        "versions": [
          {
            "registry": "github",
            "name": "confident-ai/deepeval",
            "version": "python-v4.2.4",
            "released": "2026-09-22",
            "seenAt": "2026-10-09T16:48:57.205584437Z"
          },
          {
            "registry": "npm",
            "name": "deepeval",
            "version": "0.9.22",
            "seenAt": "2026-10-09T16:48:56.771405961Z"
          },
          {
            "registry": "pypi",
            "name": "deepeval",
            "version": "4.2.8",
            "released": "2026-10-02",
            "seenAt": "2026-10-09T16:48:56.555388028Z"
          }
        ],
        "githubStars": 18722,
        "npmWeekly": 35092,
        "pypiWeekly": 735637,
        "pages": [
          {
            "url": "https://deepeval.com/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-09T18:34:45.297656608Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "5c04fa001f0e"
          },
          {
            "url": "https://www.confident-ai.com/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-09T18:49:15.052218782Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "f37a25bd3065"
          }
        ],
        "updatedAt": "2026-10-09T18:49:15.052218782Z"
      }
    },
    "answer": "DeepEval scores 64.7 (B) on agent readiness against Laminar API + MCP's 56.6 (C), and leads in 4 of 7 scored categories. Laminar API + MCP leads on schema \u0026 documentation and agent ergonomics.",
    "b": {
      "slug": "laminar",
      "name": "Laminar API + MCP",
      "vendor": "Laminar (LMNR AI)",
      "vendorUrl": "https://laminar.sh",
      "kind": "http-api",
      "category": "agent-observability",
      "summary": "Open-source tracing, evaluations and an agent debugger built on OpenTelemetry, with browser session recordings synced to traces for browser agents.",
      "url": "https://www.anchorterminal.com/tools/laminar",
      "markdownUrl": "https://www.anchorterminal.com/tools/laminar.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laminar.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laminar.json",
      "repo": "https://github.com/lmnr-ai/lmnr",
      "license": "Apache-2.0",
      "transports": [
        "http",
        "streamable-http"
      ],
      "remoteUrl": "https://api.lmnr.ai/v1",
      "packages": [
        {
          "registry": "pypi",
          "name": "lmnr"
        },
        {
          "registry": "npm",
          "name": "@lmnr-ai/lmnr"
        }
      ],
      "auth": "api-key",
      "authNotes": "Project API key as a Bearer token, for ingestion, the REST API and the MCP server. One key maps to one project.",
      "pricing": "freemium",
      "pricingNotes": "Free $0 a month with 1 GB of data, 7 days of retention, 1 project and 1 seat, no overage. Starter $30 a month with 3 GB then $2 a GB, 30 days, unlimited projects and seats. Pro $150 a month with 10 GB then $1.50 a GB, 6 months. Enterprise custom with on-premise. Signals (LLM analysis of traces) comes with $2.50, $7.50 or $25 of credit, then $0.05 per million input tokens and $0.35 per million output tokens. Self-hosted open source is free, you run Postgres, ClickHouse and Quickwit yourself. Signals and Slack alerts on self-hosted need an enterprise licence key (https://laminar.sh/pricing).",
      "priceSummary": "$30 / mo",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402 support in docs or pricing (checked 2026-09-30).",
        "endpoints": []
      },
      "toolCount": 3,
      "popularity": {
        "githubStars": 3288,
        "npmWeekly": 79857,
        "pypiWeekly": 1698863,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://laminar.sh/docs",
      "llmsTxt": "https://laminar.sh/docs/llms.txt",
      "openapi": "https://laminar.sh/docs/openapi/openapi.yaml",
      "capabilities": [
        "obs.traces",
        "obs.evals",
        "obs.datasets"
      ],
      "tags": [
        "hosted",
        "freemium",
        "open-source",
        "self-hosted",
        "mcp",
        "llms-txt",
        "openapi",
        "python",
        "typescript"
      ],
      "lastRelease": "2026-09-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.6,
        "grade": "C",
        "agentReady": false,
        "rank": 629,
        "ranked": true,
        "rankOf": 950,
        "categoryRank": 12,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 80,
          "maintenance": 83,
          "payments": 30,
          "reliability": 50,
          "schema": 88,
          "security": 45,
          "transparency": 62
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": -5,
        "negativeNotes": [
          "2026-08-27. Laminar's own fix says a client-supplied `projectId` in the body of the SQL export-job route overrode the one the server had authorised, allowing cross-tenant SQL execution and bulk data export (CWE-862). The route dates from July 2025. Fixed with a one-line change, disclosed only in the commit message with no advisory or customer notice we could find. -5 after decay for the fix (https://github.com/lmnr-ai/lmnr/commit/5059c7d951fe47d4c91c8016a542a11e82090ade)"
        ],
        "verdict": "Browser session recordings can be matched to agent traces. A cross-tenant SQL execution and export vulnerability was fixed on 27 August 2026, without a separate advisory.",
        "bestFor": "Teams running browser agents who want session replay next to traces, and for agents that read traces through SQL.",
        "strengths": [
          "Browser session recordings synced to traces for Browser Use, Stagehand, Playwright and Puppeteer",
          "Three MCP tools with descriptions that say when to use each, and SQL that runs SELECT-only on a read-only client",
          "Apache-2.0 with Docker Compose and Helm self-hosting, and self-hosted telemetry that switches off with one variable",
          "Priced by GB stored, $2 a GB on Starter and $1.50 on Pro, published without login",
          "SDK releases most weeks, TypeScript 0.8.49 on 2026-09-23 and Python 0.7.64 on 2026-09-21"
        ],
        "weaknesses": [
          "A cross-tenant SQL execution and export hole was fixed on 27 August 2026 in a commit, with no advisory",
          "No SECURITY.md, security.txt or published advisories",
          "Rate limits exist per project but no numbers are published",
          "Status page covers only the homepage and an echo probe",
          "Signals, clusters and Slack or email alerts aren't in the open-source build"
        ],
        "agentNotes": [
          "Use `query_laminar_sql` to find trace IDs, then `get_trace_context` for a compact summary of one run",
          "Always add a time range and `LIMIT` to SQL. Server caps on time, memory and result size reject wide scans",
          "On 429 from the SQL API, back off. The per-project limit isn't published",
          "Set `flush_by_size` when spans carry whole conversation histories, or large batches get rejected",
          "Keep the project API key in a header, not in the URL"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.6
          }
        ],
        "editorialScores": {
          "ergonomics": 80,
          "maintenance": 83,
          "payments": 30,
          "reliability": 50,
          "schema": 88,
          "security": 45,
          "transparency": 57
        },
        "provenanceScore": 67
      },
      "connect": {
        "http": "curl https://api.lmnr.ai/v1/project -H \"Authorization: Bearer $LMNR_PROJECT_API_KEY\"",
        "claudeCode": "claude mcp add --transport http laminar https://api.lmnr.ai/v1/mcp \\\n  --header \"Authorization: Bearer $LMNR_PROJECT_API_KEY\""
      },
      "letme": {
        "capability": "https://letme.dev/obs.traces",
        "tool": "https://letme.dev/laminar"
      },
      "area": "developer",
      "unitPrices": [
        {
          "item": "Starter plan",
          "unit": "month",
          "usd": 30,
          "note": "3 GB of data, 30 days retention, unlimited seats"
        },
        {
          "item": "Pro plan",
          "unit": "month",
          "usd": 150,
          "note": "10 GB of data, 6 months retention"
        },
        {
          "item": "Extra data on Starter",
          "unit": "gb",
          "usd": 2
        },
        {
          "item": "Extra data on Pro",
          "unit": "gb",
          "usd": 1.5
        }
      ],
      "provenance": {
        "legalEntity": "LMNR AI, Inc.",
        "domain": "laminar.sh",
        "domainRegistered": "",
        "domainNote": "The .sh registry has no RDAP service. The former domain lmnr.ai, registered 2024-02-26, now redirects to laminar.sh and still serves the API.",
        "endpointOnVendorDomain": true,
        "terms": "https://laminar.sh/policies/terms",
        "privacy": "https://laminar.sh/policies/privacy",
        "statusPage": "https://status.laminar.sh",
        "changelog": "https://laminar.sh/docs/changelog",
        "securityTxt": "none",
        "checked": "2026-09-30",
        "score": 67
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/laminar.json",
      "live": {
        "slug": "laminar",
        "probe": {
          "target": "https://api.lmnr.ai/v1",
          "method": "get",
          "lastAt": "2026-10-10T04:40:53.604955946Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 255,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 262,
          "p95ms24h": 333,
          "samples24h": 248,
          "samples30d": 2484,
          "days": [
            {
              "date": "2026-09-30",
              "probes": 35,
              "ok": 35
            },
            {
              "date": "2026-10-01",
              "probes": 276,
              "ok": 276
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-05",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-06",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-07",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-08",
              "probes": 268,
              "ok": 268
            },
            {
              "date": "2026-10-09",
              "probes": 250,
              "ok": 250
            },
            {
              "date": "2026-10-10",
              "probes": 48,
              "ok": 48
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.laminar.sh",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-10T00:50:45.081355528Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "lmnr-ai/lmnr",
            "version": "v0.2.5",
            "released": "2026-09-13",
            "seenAt": "2026-10-09T17:01:10.921704374Z"
          },
          {
            "registry": "npm",
            "name": "@lmnr-ai/lmnr",
            "version": "0.8.50",
            "seenAt": "2026-10-09T17:01:10.034838694Z"
          },
          {
            "registry": "pypi",
            "name": "lmnr",
            "version": "0.7.66",
            "released": "2026-10-08",
            "seenAt": "2026-10-09T17:01:09.844654569Z"
          }
        ],
        "githubStars": 3366,
        "npmWeekly": 50433,
        "pypiWeekly": 1417058,
        "securityTxt": {
          "url": "https://laminar.sh/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-09T15:40:32.65538848Z"
        },
        "llmsTxt": {
          "url": "https://laminar.sh/docs/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-09T14:02:13.117730659Z"
        },
        "domain": {
          "domain": "laminar.sh",
          "checkedAt": "2026-10-04T13:08:33.087077121Z"
        },
        "pages": [
          {
            "url": "https://laminar.sh/docs/changelog",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-09T18:40:51.121892306Z",
            "changedAt": "2026-10-09T18:40:51.121892306Z",
            "fingerprint": "95f5f42e72a0"
          },
          {
            "url": "https://laminar.sh/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-09T18:40:57.361552768Z",
            "changedAt": "2026-10-06T16:09:56.88152339Z",
            "fingerprint": "01dfef303505"
          },
          {
            "url": "https://laminar.sh/policies/privacy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-10-09T18:40:53.442220685Z",
            "changedAt": "2026-10-06T16:09:52.898864083Z",
            "fingerprint": "8a5158639e90"
          },
          {
            "url": "https://laminar.sh/policies/terms",
            "kind": "terms",
            "status": 200,
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            "changedAt": "2026-10-06T16:09:54.933933309Z",
            "fingerprint": "fbe76e3f4607"
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        "updatedAt": "2026-10-10T04:40:53.604955946Z"
      }
    },
    "facts": [
      {
        "a": "SDK + MCP",
        "b": "HTTP API",
        "name": "Kind"
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      {
        "a": "Confident AI, Inc.",
        "b": "Laminar (LMNR AI)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "https://api.lmnr.ai/v1",
        "name": "Hosted endpoint"
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      {
        "a": "",
        "b": "HTTP, Streamable HTTP",
        "name": "Transports"
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        "a": "OAuth or key",
        "b": "API key",
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        "a": "Freemium",
        "b": "Freemium",
        "name": "Pricing"
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      {
        "a": "no",
        "b": "no",
        "name": "x402"
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      {
        "a": "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",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "none",
        "b": "3",
        "name": "Tools exposed"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-02",
        "b": "2026-09-23",
        "name": "Last release"
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      {
        "a": "no document linked",
        "b": "2026-08-11",
        "name": "Terms last updated"
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      {
        "a": "no document linked",
        "b": "2026-08-11",
        "name": "Privacy policy last updated"
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      {
        "a": "",
        "b": "yes, with an opt-out",
        "name": "Customer content may train models"
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      {
        "a": "",
        "b": "yes",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "yes",
        "name": "Terms restrict benchmarking"
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      {
        "a": "",
        "b": "yes",
        "name": "Terms or service can change without notice"
      },
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        "a": "",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
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      {
        "a": "19k stars, 35k npm/wk, 736k PyPI/wk",
        "b": "3.3k stars, 80k npm/wk, 1.7M PyPI/wk",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "3.5/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "DeepEval scores 64.7 (B) on agent readiness against Laminar API + MCP's 56.6 (C), and leads in 4 of 7 scored categories. Laminar API + MCP leads on schema \u0026 documentation and agent ergonomics.",
        "question": "Which is better for AI agents, DeepEval or Laminar API + MCP?"
      },
      {
        "answer": "No hosted endpoint is listed for DeepEval. Laminar API + MCP has a hosted endpoint at https://api.lmnr.ai/v1.",
        "question": "Can an agent call DeepEval and Laminar API + MCP without installing anything?"
      },
      {
        "answer": "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). Laminar API + MCP is open source (Apache-2.0).",
        "question": "Are DeepEval and Laminar API + MCP open source?"
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    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 59 against 50",
          "Payments \u0026 pricing, 60 against 30"
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          "Free to start without a card",
          "No incidents deducted, where Laminar API + MCP loses 5 points for them"
        ],
        "goodFor": "Teams that want evaluations in pytest or a CLI on their own machines, with agent, RAG, multi-turn and MCP metrics.",
        "slug": "deepeval",
        "watchFor": "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"
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          "Schema \u0026 documentation, 88 against 78",
          "Agent ergonomics, 80 against 72"
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          "A hosted endpoint, with nothing to install"
        ],
        "goodFor": "Teams running browser agents who want session replay next to traces, and for agents that read traces through SQL.",
        "slug": "laminar",
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        "json": "https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.json",
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        "url": "https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arize-phoenix-vs-laminar.json",
        "title": "Arize Phoenix vs Laminar API + MCP",
        "url": "https://www.anchorterminal.com/compare/arize-phoenix-vs-laminar"
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      {
        "json": "https://www.anchorterminal.com/compare/baserun-vs-deepeval.json",
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        "url": "https://www.anchorterminal.com/compare/baserun-vs-deepeval"
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      {
        "json": "https://www.anchorterminal.com/compare/baserun-vs-laminar.json",
        "title": "Baserun vs Laminar API + MCP",
        "url": "https://www.anchorterminal.com/compare/baserun-vs-laminar"
      },
      {
        "json": "https://www.anchorterminal.com/compare/braintrust-vs-deepeval.json",
        "title": "Braintrust API + MCP vs DeepEval",
        "url": "https://www.anchorterminal.com/compare/braintrust-vs-deepeval"
      },
      {
        "json": "https://www.anchorterminal.com/compare/braintrust-vs-laminar.json",
        "title": "Braintrust API + MCP vs Laminar API + MCP",
        "url": "https://www.anchorterminal.com/compare/braintrust-vs-laminar"
      },
      {
        "json": "https://www.anchorterminal.com/compare/galileo-vs-laminar.json",
        "title": "Galileo API + MCP vs Laminar API + MCP",
        "url": "https://www.anchorterminal.com/compare/galileo-vs-laminar"
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        "json": "https://www.anchorterminal.com/compare/helicone-vs-laminar.json",
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      {
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        "title": "HoneyHive vs Laminar API + MCP",
        "url": "https://www.anchorterminal.com/compare/honeyhive-vs-laminar"
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      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-langfuse.json",
        "title": "Laminar API + MCP vs Langfuse API + MCP",
        "url": "https://www.anchorterminal.com/compare/laminar-vs-langfuse"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-langsmith.json",
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        "url": "https://www.anchorterminal.com/compare/laminar-vs-langsmith"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-langwatch.json",
        "title": "Laminar API + MCP vs LangWatch",
        "url": "https://www.anchorterminal.com/compare/laminar-vs-langwatch"
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      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-mlflow-tracing.json",
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        "json": "https://www.anchorterminal.com/compare/laminar-vs-prefactor.json",
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        "url": "https://www.anchorterminal.com/compare/laminar-vs-prefactor"
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        "json": "https://www.anchorterminal.com/compare/laminar-vs-pydantic-logfire.json",
        "title": "Laminar API + MCP vs Pydantic Logfire",
        "url": "https://www.anchorterminal.com/compare/laminar-vs-pydantic-logfire"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-respan.json",
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        "url": "https://www.anchorterminal.com/compare/laminar-vs-respan"
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      {
        "json": "https://www.anchorterminal.com/compare/laminar-vs-wandb-weave.json",
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        "url": "https://www.anchorterminal.com/compare/laminar-vs-wandb-weave"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-galileo.json",
        "title": "DeepEval vs Galileo API + MCP",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-galileo"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-helicone.json",
        "title": "DeepEval vs Helicone AI Gateway + MCP",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-helicone"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-honeyhive.json",
        "title": "DeepEval vs HoneyHive",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-honeyhive"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-langfuse.json",
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        "url": "https://www.anchorterminal.com/compare/deepeval-vs-langfuse"
      },
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        "json": "https://www.anchorterminal.com/compare/deepeval-vs-langsmith.json",
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        "json": "https://www.anchorterminal.com/compare/deepeval-vs-langwatch.json",
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        "url": "https://www.anchorterminal.com/compare/deepeval-vs-langwatch"
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        "title": "DeepEval vs MLflow Tracing",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-prefactor.json",
        "title": "DeepEval vs Prefactor",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-prefactor"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-pydantic-logfire.json",
        "title": "DeepEval vs Pydantic Logfire",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-pydantic-logfire"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-respan.json",
        "title": "DeepEval vs Respan API + MCP",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-respan"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepeval-vs-wandb-weave.json",
        "title": "DeepEval vs W\u0026B Weave",
        "url": "https://www.anchorterminal.com/compare/deepeval-vs-wandb-weave"
      }
    ],
    "scores": [
      {
        "by": 9,
        "deepeval": 59,
        "edge": "deepeval",
        "key": "reliability",
        "laminar": 50,
        "name": "Reliability",
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 10,
        "deepeval": 78,
        "edge": "laminar",
        "key": "schema",
        "laminar": 88,
        "name": "Schema \u0026 documentation",
        "weight": 13
      },
      {
        "by": 8,
        "deepeval": 72,
        "edge": "laminar",
        "key": "ergonomics",
        "laminar": 80,
        "name": "Agent ergonomics",
        "weight": 13
      },
      {
        "by": 2,
        "deepeval": 47,
        "edge": "deepeval",
        "key": "security",
        "laminar": 45,
        "name": "Security \u0026 auth",
        "weight": 14
      },
      {
        "by": 30,
        "deepeval": 60,
        "edge": "deepeval",
        "key": "payments",
        "laminar": 30,
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 0,
        "deepeval": 83,
        "edge": "",
        "key": "maintenance",
        "laminar": 83,
        "name": "Maintenance \u0026 community",
        "weight": 7
      },
      {
        "by": 1,
        "deepeval": 63,
        "edge": "deepeval",
        "key": "transparency",
        "laminar": 62,
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
    ],
    "summary": "DeepEval scores 64.7 (B) on agent readiness against Laminar API + MCP's 56.6 (C), and leads in 4 of 7 scored categories. Laminar API + MCP leads on schema \u0026 documentation and agent ergonomics. Both do evaluations.",
    "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.",
      "laminar": "Browser session recordings can be matched to agent traces. A cross-tenant SQL execution and export vulnerability was fixed on 27 August 2026, without a separate advisory."
    }
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  "markdown": "DeepEval scores 64.7 (B) on agent readiness against Laminar API + MCP's 56.6 (C), and leads in 4 of 7 scored categories. Laminar API + MCP leads on schema \u0026 documentation and agent ergonomics. Both do evaluations.\n\n- DeepEval: grade B, 64.7/100, rank #343 of 950. Markdown https://www.anchorterminal.com/tools/deepeval.md · JSON https://www.anchorterminal.com/api/v1/tools/deepeval.json\n- Laminar API + MCP: grade C, 56.6/100, rank #629 of 950. Markdown https://www.anchorterminal.com/tools/laminar.md · JSON https://www.anchorterminal.com/api/v1/tools/laminar.json\n- Best agent tracing, monitoring and evaluation tools: https://www.anchorterminal.com/best/agent-observability/index.md\n- All 120 evals comparisons: https://www.anchorterminal.com/compare/agent-observability/index.md\n\n## Which one, for what\n\n### DeepEval (B)\n\nGood for: Teams that want evaluations in pytest or a CLI on their own machines, with agent, RAG, multi-turn and MCP metrics.\n\nAhead on:\n- Reliability, 59 against 50\n- Payments \u0026 pricing, 60 against 30\n\nAlso in its favour:\n- Free to start without a card\n- No incidents deducted, where Laminar API + MCP loses 5 points for them\n\nWatch 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\n\n### Laminar API + MCP (C)\n\nGood for: Teams running browser agents who want session replay next to traces, and for agents that read traces through SQL.\n\nAhead on:\n- Schema \u0026 documentation, 88 against 78\n- Agent ergonomics, 80 against 72\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: A cross-tenant SQL execution and export hole was fixed on 27 August 2026 in a commit, with no advisory\n\n\n## Score by category\n\n| Category | Weight | DeepEval | Laminar API + MCP | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 50 | DeepEval +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 88 | Laminar API + MCP +10 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 80 | Laminar API + MCP +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 47 | 45 | DeepEval +2 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 30 | DeepEval +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 83 | even |\n| Transparency \u0026 trust | 7% (8.8 this run) | 63 | 62 | DeepEval +1 |\n| Negative events | ≤15 | 0 | -5 | |\n| **Total** | | **64.7 · B** | **56.6 · C** | |\n\n## Facts side by side\n\n| Fact | DeepEval | Laminar API + MCP |\n| --- | --- | --- |\n| Kind | SDK + MCP | HTTP API |\n| Vendor | Confident AI, Inc. | Laminar (LMNR AI) |\n| Hosted endpoint | no (local only) | `https://api.lmnr.ai/v1` |\n| Transports |  | HTTP, Streamable HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | 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 | Apache-2.0 |\n| Tools exposed | none | 3 |\n| Read-only variant documented | no | yes |\n| llms.txt | yes | yes |\n| Last release | 2026-10-02 | 2026-09-23 |\n| Terms last updated | no document linked | 2026-08-11 |\n| Privacy policy last updated | no document linked | 2026-08-11 |\n| Customer content may train models |  | yes, with an opt-out |\n| Terms restrict automated access |  | yes |\n| Terms restrict benchmarking |  | yes |\n| Terms or service can change without notice |  | yes |\n| Arbitration or class-action waiver |  | not found in the text |\n| Popularity | 19k stars, 35k npm/wk, 736k PyPI/wk | 3.3k stars, 80k npm/wk, 1.7M PyPI/wk |\n| Agent reviews | none | 3.5/5 (2) |\n\n## Verdicts\n\n**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.\n\n**Laminar API + MCP.** Browser session recordings can be matched to agent traces. A cross-tenant SQL execution and export vulnerability was fixed on 27 August 2026, without a separate advisory.\n\n## Before you call either\n\n### DeepEval\n\n1. Set `DEEPEVAL_TELEMETRY_OPT_OUT=1` before the first run if usage events and the public IP address should not go to PostHog\n2. 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\n3. Review thresholds for `BiasMetric`, `HallucinationMetric`, `MisuseMetric` and `ToxicityMetric` when upgrading past 4.2.0. Higher scores now mean better\n4. Read results from `.deepeval/.latest_run_full.json` or a `results_folder`. `deepeval inspect` opens a terminal interface meant for a person\n5. Pass an existing key with `deepeval login --api-key` in CI. Plain `deepeval login` opens a browser, and results then upload to Confident AI\n\n### Laminar API + MCP\n\n1. Use `query_laminar_sql` to find trace IDs, then `get_trace_context` for a compact summary of one run\n2. Always add a time range and `LIMIT` to SQL. Server caps on time, memory and result size reject wide scans\n3. On 429 from the SQL API, back off. The per-project limit isn't published\n4. Set `flush_by_size` when spans carry whole conversation histories, or large batches get rejected\n5. Keep the project API key in a header, not in the URL\n\n## Questions\n\n### Which is better for AI agents, DeepEval or Laminar API + MCP?\n\nDeepEval scores 64.7 (B) on agent readiness against Laminar API + MCP's 56.6 (C), and leads in 4 of 7 scored categories. Laminar API + MCP leads on schema \u0026 documentation and agent ergonomics.\n\n### Can an agent call DeepEval and Laminar API + MCP without installing anything?\n\nNo hosted endpoint is listed for DeepEval. Laminar API + MCP has a hosted endpoint at https://api.lmnr.ai/v1.\n\n### Are DeepEval and Laminar API + MCP open source?\n\nYes. 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). Laminar API + MCP is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepeval-vs-laminar.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepeval-vs-laminar.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepeval\", \"b\": \"laminar\"}`. From a terminal: `anchor compare deepeval laminar`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepeval.json and https://www.anchorterminal.com/api/v1/tools/laminar.json\n\n## Other comparisons with DeepEval or Laminar API + MCP\n\n- [Arize Phoenix vs DeepEval](https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.md)\n- [Arize Phoenix vs Laminar API + MCP](https://www.anchorterminal.com/compare/arize-phoenix-vs-laminar.md)\n- [Baserun vs DeepEval](https://www.anchorterminal.com/compare/baserun-vs-deepeval.md)\n- [Baserun vs Laminar API + MCP](https://www.anchorterminal.com/compare/baserun-vs-laminar.md)\n- [Braintrust API + MCP vs DeepEval](https://www.anchorterminal.com/compare/braintrust-vs-deepeval.md)\n- [Braintrust API + MCP vs Laminar API + MCP](https://www.anchorterminal.com/compare/braintrust-vs-laminar.md)\n- [Galileo API + MCP vs Laminar API + MCP](https://www.anchorterminal.com/compare/galileo-vs-laminar.md)\n- [Helicone AI Gateway + MCP vs Laminar API + MCP](https://www.anchorterminal.com/compare/helicone-vs-laminar.md)\n- [HoneyHive vs Laminar API + MCP](https://www.anchorterminal.com/compare/honeyhive-vs-laminar.md)\n- [Laminar API + MCP vs Langfuse API + MCP](https://www.anchorterminal.com/compare/laminar-vs-langfuse.md)\n- [Laminar API + MCP vs LangSmith API + MCP](https://www.anchorterminal.com/compare/laminar-vs-langsmith.md)\n- [Laminar API + MCP vs LangWatch](https://www.anchorterminal.com/compare/laminar-vs-langwatch.md)\n- [Laminar API + MCP vs MLflow Tracing](https://www.anchorterminal.com/compare/laminar-vs-mlflow-tracing.md)\n- [Laminar API + MCP vs Prefactor](https://www.anchorterminal.com/compare/laminar-vs-prefactor.md)\n- [Laminar API + MCP vs Pydantic Logfire](https://www.anchorterminal.com/compare/laminar-vs-pydantic-logfire.md)\n- [Laminar API + MCP vs Respan API + MCP](https://www.anchorterminal.com/compare/laminar-vs-respan.md)\n- [Laminar API + MCP vs W\u0026B Weave](https://www.anchorterminal.com/compare/laminar-vs-wandb-weave.md)\n- [DeepEval vs Galileo API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-galileo.md)\n- [DeepEval vs Helicone AI Gateway + MCP](https://www.anchorterminal.com/compare/deepeval-vs-helicone.md)\n- [DeepEval vs HoneyHive](https://www.anchorterminal.com/compare/deepeval-vs-honeyhive.md)\n- [DeepEval vs Langfuse API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-langfuse.md)\n- [DeepEval vs LangSmith API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-langsmith.md)\n- [DeepEval vs LangWatch](https://www.anchorterminal.com/compare/deepeval-vs-langwatch.md)\n- [DeepEval vs MLflow Tracing](https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.md)\n- [DeepEval vs Prefactor](https://www.anchorterminal.com/compare/deepeval-vs-prefactor.md)\n- [DeepEval vs Pydantic Logfire](https://www.anchorterminal.com/compare/deepeval-vs-pydantic-logfire.md)\n- [DeepEval vs Respan API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-respan.md)\n- [DeepEval vs W\u0026B Weave](https://www.anchorterminal.com/compare/deepeval-vs-wandb-weave.md)\n",
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