{ "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 MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance \u0026 community.", "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", "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": "2026-10-06", "graded": true, "anchor": { "graded": true, "score": 61.2, "grade": "C", "agentReady": false, "rank": 476, "ranked": true, "rankOf": 950, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 88, "payments": 60, "reliability": 76, "schema": 78, "security": 40, "transparency": 62 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-09" }, "negative": -6, "negativeNotes": [ "2026-10-09: GHSA-26p8-2jq9-3vq9, critical, remote code execution on MLflow servers without authentication through third-party scorer deserialisation, affecting 3.12.0rc0 up to 3.17.0 and patched in 3.17.0 of 6 October. Four more advisories were published between 27 July and 4 August 2026, among them GHSA-7gwp-5pfp-969j, a high-severity unauthenticated SSRF in webhook delivery patched in 3.15.0. All are published with fixes, so the deduction is reduced (https://github.com/mlflow/mlflow/security/advisories)" ], "verdict": "Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental, sets no read-only or destructive annotations, and its default set includes delete tools. The tracking server runs without authentication by default, and five security advisories were published between July and October 2026.", "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`", "Five security advisories published between 27 July and 9 October 2026, one a critical unauthenticated remote code execution fixed in 3.17.0", "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 3.17.0 or later. Versions 3.12.0rc0 to 3.16.1 allow unauthenticated code execution on a server without authentication", "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": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.2 } ], "editorialScores": { "ergonomics": 72, "maintenance": 88, "payments": 60, "reliability": 76, "schema": 78, "security": 40, "transparency": 83 }, "provenanceScore": 41 }, "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": 41 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mlflow-tracing.json", "live": { "slug": "mlflow-tracing", "versions": [ { "registry": "github", "name": "mlflow/mlflow", "version": "v3.17.0", "released": "2026-10-07", "seenAt": "2026-10-09T17:06:56.716473096Z" }, { "registry": "npm", "name": "@mlflow/core", "version": "0.4.0", "seenAt": "2026-10-09T17:06:55.82052791Z" }, { "registry": "pypi", "name": "mlflow", "version": "3.17.0", "released": "2026-10-07", "seenAt": "2026-10-09T17:06:53.813494162Z" }, { "registry": "pypi", "name": "mlflow-tracing", "version": "3.17.0", "released": "2026-10-07", "seenAt": "2026-10-09T17:06:53.928988136Z" } ], "githubStars": 28331, "npmWeekly": 15392, "pypiWeekly": 4590348, "pages": [ { "url": "https://raw.githubusercontent.com/mlflow/mlflow/master/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-10-09T18:45:35.157905643Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "81678c02de88" } ], "updatedAt": "2026-10-09T18:45:35.157905643Z" } }, "facts": [ { "a": "SDK + MCP", "b": "HTTP API", "name": "Kind" }, { "a": "Confident AI, Inc.", "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": "OAuth or key", "b": "OAuth or key", "name": "Auth" }, { "a": "Freemium", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "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": "26", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-02", "b": "2026-10-06", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "19k stars, 35k npm/wk, 736k PyPI/wk", "b": "28k stars", "name": "Popularity" } ], "faq": [ { "answer": "DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance \u0026 community.", "question": "Which is better for AI agents, DeepEval or MLflow Tracing?" }, { "answer": "No hosted endpoint is listed for DeepEval. MLflow Tracing runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call DeepEval and MLflow Tracing 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). MLflow Tracing is open source (Apache-2.0).", "question": "Are DeepEval and MLflow Tracing open source?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 47 against 40" ], "also": [ "Free to start without a card", "No incidents deducted, where MLflow Tracing loses 6 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" }, { "aheadOn": [ "Reliability, 76 against 59", "Maintenance \u0026 community, 88 against 83" ], "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" } ], "job": { "capability": "obs.evals", "name": "Evaluations" }, "others": [ { "json": "https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.json", "title": "Arize Phoenix vs DeepEval", "url": "https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval" }, { "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-deepeval.json", "title": "Baserun vs DeepEval", "url": "https://www.anchorterminal.com/compare/baserun-vs-deepeval" }, { "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/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-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-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/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-laminar.json", "title": "DeepEval vs Laminar API + MCP", "url": "https://www.anchorterminal.com/compare/deepeval-vs-laminar" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-langfuse.json", "title": "DeepEval vs Langfuse API + MCP", "url": "https://www.anchorterminal.com/compare/deepeval-vs-langfuse" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-langsmith.json", "title": "DeepEval vs LangSmith API + MCP", "url": "https://www.anchorterminal.com/compare/deepeval-vs-langsmith" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-langwatch.json", "title": "DeepEval vs LangWatch", "url": "https://www.anchorterminal.com/compare/deepeval-vs-langwatch" }, { "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": 17, "deepeval": 59, "edge": "mlflow-tracing", "key": "reliability", "mlflow-tracing": 76, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 0, "deepeval": 78, "edge": "", "key": "schema", "mlflow-tracing": 78, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 0, "deepeval": 72, "edge": "", "key": "ergonomics", "mlflow-tracing": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 7, "deepeval": 47, "edge": "deepeval", "key": "security", "mlflow-tracing": 40, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "deepeval": 60, "edge": "", "key": "payments", "mlflow-tracing": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "deepeval": 83, "edge": "mlflow-tracing", "key": "maintenance", "mlflow-tracing": 88, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 1, "deepeval": 63, "edge": "deepeval", "key": "transparency", "mlflow-tracing": 62, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance \u0026 community. 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.", "mlflow-tracing": "Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental, sets no read-only or destructive annotations, and its default set includes delete tools. The tracking server runs without authentication by default, and five security advisories were published between July and October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing", "json": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.md", "slim": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.min.md" }, "markdown": "DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance \u0026 community. 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- MLflow Tracing: grade C, 61.2/100, rank #476 of 950. 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 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- Security \u0026 auth, 47 against 40\n\nAlso in its favour:\n- Free to start without a card\n- No incidents deducted, where MLflow Tracing loses 6 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### MLflow Tracing (C)\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, 76 against 59\n- Maintenance \u0026 community, 88 against 83\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## Score by category\n\n| Category | Weight | DeepEval | MLflow Tracing | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 59 | 76 | MLflow Tracing +17 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 78 | even |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 72 | even |\n| Security \u0026 auth | 14% (17.5 this run) | 47 | 40 | DeepEval +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 88 | MLflow Tracing +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 63 | 62 | DeepEval +1 |\n| Negative events | ≤15 | 0 | -6 | |\n| **Total** | | **64.7 · B** | **61.2 · C** | |\n\n## Facts side by side\n\n| Fact | DeepEval | MLflow Tracing |\n| --- | --- | --- |\n| Kind | SDK + MCP | HTTP API |\n| Vendor | Confident AI, Inc. | MLflow Project (LF Projects, LLC) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | stdio, HTTP |\n| Auth | OAuth or key | OAuth or key |\n| Pricing | Freemium | Free |\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 | 26 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-02 | 2026-10-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 19k stars, 35k npm/wk, 736k PyPI/wk | 28k stars |\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**MLflow Tracing.** Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental, sets no read-only or destructive annotations, and its default set includes delete tools. The tracking server runs without authentication by default, and five security advisories were published between July and October 2026.\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### MLflow Tracing\n\n1. Run MLflow 3.17.0 or later. Versions 3.12.0rc0 to 3.16.1 allow unauthenticated code execution on a server without authentication\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, DeepEval or MLflow Tracing?\n\nDeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance \u0026 community.\n\n### Can an agent call DeepEval and MLflow Tracing without installing anything?\n\nNo hosted endpoint is listed for DeepEval. MLflow Tracing runs on your own machine, with no hosted endpoint listed.\n\n### Are DeepEval and MLflow Tracing 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). MLflow Tracing is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepeval\", \"b\": \"mlflow-tracing\"}`. From a terminal: `anchor compare deepeval mlflow-tracing`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepeval.json and https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json\n\n## Other comparisons with DeepEval or MLflow Tracing\n\n- [Arize Phoenix vs DeepEval](https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.md)\n- [Arize Phoenix vs MLflow Tracing](https://www.anchorterminal.com/compare/arize-phoenix-vs-mlflow-tracing.md)\n- [Baserun vs DeepEval](https://www.anchorterminal.com/compare/baserun-vs-deepeval.md)\n- [Baserun vs MLflow Tracing](https://www.anchorterminal.com/compare/baserun-vs-mlflow-tracing.md)\n- [Braintrust API + MCP vs DeepEval](https://www.anchorterminal.com/compare/braintrust-vs-deepeval.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 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- [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 Laminar API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-laminar.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 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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-10", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepEval vs MLflow Tracing", "url": "" } ], "description": "DeepEval scores 64.7 (B) to MLflow Tracing's 61.2 (C) for evaluations. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepEval B 64.7", "MLflow Tracing C 61.2", "scores" ], "h1": "DeepEval vs MLflow Tracing", "image": "https://www.anchorterminal.com/assets/og/compare-deepeval-vs-mlflow-tracing.png", "path": "/compare/deepeval-vs-mlflow-tracing", "published": "2026-10-01", "section": "tools", "title": "DeepEval vs MLflow Tracing for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing" }, "tokens": { "markdown": 2700, "slim": 580 }, "version": 1 }