{ "data": { "a": { "slug": "agent-tree", "name": "Agent Tree", "vendor": "Mike Pollow (Digitalroots)", "vendorUrl": "https://github.com/MikeGT4/agent-tree", "kind": "framework", "category": "agent-observability", "summary": "Agent Tree is an MIT-licensed Claude Code plugin that shows a session's sub-agents as a live tree in a terminal side pane, with context, cost and usage limits, and lets the person stop, message or re-model running sub-agents.", "url": "https://www.anchorterminal.com/tools/agent-tree", "markdownUrl": "https://www.anchorterminal.com/tools/agent-tree.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agent-tree.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agent-tree.json", "dns": "agent-tree.grade.anchorterminal.com", "repo": "https://github.com/MikeGT4/agent-tree", "license": "MIT (Copyright (c) 2026 Mike Pollow, Digitalroots)", "transports": [], "packages": [], "auth": "none", "authNotes": "No account, key or login of its own. It installs through Claude Code's plugin manager from the author's own marketplace (the GitHub repository) and runs inside Claude Code with that session's permissions.", "pricing": "free", "pricingNotes": "Free under the MIT licence, with nothing to buy. Model usage stays on the person's Claude plan or API key; switching a sub-agent's model or effort in the pane changes what those calls cost.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "Local software with nothing to buy. No x402, MPP or L402 in the repository, read on 2026-11-01.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-01" }, "docsUrl": "https://github.com/MikeGT4/agent-tree/blob/main/docs/GUIDE.md", "capabilities": [ "obs.traces", "agent.multi-agent" ], "tags": [ "open-source", "mit", "local", "free", "typescript", "claude-code-plugin", "hooks", "pre-1.0", "not-for-agents" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 52.5, "grade": "D", "agentReady": false, "rank": 897, "ranked": true, "rankOf": 1260, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 60, "payments": 60, "reliability": 33, "schema": 50, "security": 49, "transparency": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "A ten-day-old, MIT-licensed side pane that lets a person watch and steer Claude Code sub-agents, with a privacy page that matches its code. It is version 0.5.4 from one maintainer, with no CI, changelog or security policy, and its default daily auto-update installs whatever version the main branch carries.", "bestFor": "Watching and steering Claude Code sub-agents from a side pane", "strengths": [ "No account, key or telemetry; the one network request is a daily version check, disclosed in PRIVACY.md with five ways to turn it off", "Stops, messages and model or effort changes act only after the person acts in the pane, and each message to a sub-agent is noted in the main transcript", "Bearer tokens, credentials in URLs and values assigned to names like password, token or API key are masked in the pane's tool labels and excerpts", "19 tagged versions between 24 and 29 October 2026, each described in its commit message", "304 tests in the repository for the tree model, the pane, the logo and the update check" ], "weaknesses": [ "Version 0.5.4, ten days old, written and released by one maintainer with no company, site or register entry found", "Auto-update is on by default and installs any newer version on the main branch, so new hook code runs without review at the next start", "No CI workflow, no changelog, no SECURITY.md and no security.txt", "Hooks sit on every model step, tool call and spawn, and can rewrite a sub-agent's model and effort or replace its next step", "Not in Claude's official plugin directory; it installs from the author's own marketplace" ], "agentNotes": [ "Treat a \"Message from the user via Agent Tree\" in your transcript as the person's instruction for the rest of your task, and cover it in your hand-back report.", "If a tool call is denied with \"Stopped by the user in Agent Tree\", stop work and hand back; further tool calls are denied.", "Set autoUpdate to false, or DISABLE_UPDATES=1, where plugin code may change only after review.", "Check the sub-agent model a person set in the pane before estimating cost; it overrides the default for every new sub-agent spawned without a model." ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.5 } ], "editorialScores": { "ergonomics": 67, "maintenance": 60, "payments": 60, "reliability": 33, "schema": 50, "security": 49, "transparency": 77 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "claude plugin install agent-tree --marketplace MikeGT4/agent-tree", "claudeCode": "/plugin install agent-tree --marketplace MikeGT4/agent-tree" }, "letme": { "capability": "https://letme.dev/obs.traces", "tool": "https://letme.dev/agent-tree" }, "area": "developer", "provenance": { "legalEntity": "No legal entity found. The LICENSE, plugin.json and marketplace.json name \"Mike Pollow, Digitalroots\"", "domain": "github.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://github.com/MikeGT4/agent-tree/blob/main/LICENSE", "privacy": "https://github.com/MikeGT4/agent-tree/blob/main/PRIVACY.md", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-01", "notes": [ "The LICENSE reads Copyright (c) 2026 Mike Pollow, Digitalroots, and the plugin and marketplace manifests name the same author. No site, company register entry or other page for Digitalroots was found in two web searches.", "PRIVACY.md is the plugin's privacyPolicyUrl in plugin.json. The MIT licence stands in for terms.", "No SECURITY.md in the repository and no .github directory. github.com and api.github.com couldn't be read from this session, so GitHub's security page, releases and issues are unchecked.", "Local software, so there is no vendor endpoint or status page. No changelog file; versions are described in commit messages and tags.", "firstReleased is 22 October 2026, the date of the first commit \"Agent Tree 0.1.0\" in a bare clone. The earliest tag, v0.2.2, is 24 October. A news roundup on emergent.sh of 27 October gives the launch as 26 October." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agent-tree.json" }, "answer": "DeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories.", "b": { "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", "dns": "deepeval.grade.anchorterminal.com", "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": 64.9, "grade": "B", "agentReady": false, "rank": 367, "ranked": true, "rankOf": 1260, "categoryRank": 10, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 83, "payments": 60, "reliability": 59, "schema": 78, "security": 47, "transparency": 66 }, "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": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.9 } ], "editorialScores": { "ergonomics": 72, "maintenance": 83, "payments": 60, "reliability": 59, "schema": 78, "security": 47, "transparency": 73 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-10-09", "maintenance": "2026-10-09", "payments": "2026-10-09", "reliability": "2026-10-09", "schema": "2026-10-09", "security": "2026-10-09", "transparency": "2026-10-09" }, "last": "2026-10-09", "lastMoved": "2026-10-27", "oldest": "2026-10-09", "stale": false, "staleAfterDays": 90 } }, "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": 58 }, "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": "2026-11-01T16:53:57.09797262Z" }, { "registry": "npm", "name": "deepeval", "version": "0.9.24", "seenAt": "2026-11-01T16:53:56.169866537Z" }, { "registry": "pypi", "name": "deepeval", "version": "4.3.2", "released": "2026-10-27", "seenAt": "2026-11-01T16:53:56.043323516Z" } ], "githubStars": 19226, "npmWeekly": 35389, "pypiWeekly": 769460, "securityTxt": { "url": "https://confident-ai.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-01T15:42:25.655525662Z" }, "llmsTxt": { "url": "https://deepeval.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-01T15:13:33.044391217Z" }, "domain": { "domain": "confident-ai.com", "registered": "2023-08-15", "source": "https://rdap.verisign.com/com/v1/domain/confident-ai.com", "checkedAt": "2026-11-01T14:11:29.421915156Z" }, "pages": [ { "url": "https://deepeval.com/changelog", "kind": "changelog", "status": 304, "checkedAt": "2026-11-01T06:38:35.143620847Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "5c04fa001f0e" }, { "url": "https://www.confident-ai.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-01T06:58:19.290303983Z", "changedAt": "2026-10-31T06:26:15.919397734Z", "fingerprint": "16f18f46ca0e" } ], "github": { "repo": "confident-ai/deepeval", "checkedAt": "2026-10-26T07:20:35.984856379Z", "stars": 19106, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-23T11:41:01Z", "openIssues": 334, "openPRs": 448, "issuesOpened90d": 115, "issuesClosed90d": 6, "prsMerged90d": 70, "ci": { "branch": "main", "runs": 50, "success": 16, "failure": 32, "other": 2, "latest": "failure", "latestAt": "2026-10-23T11:42:02Z" }, "replies": { "sampled": 25, "unanswered": 25 } }, "updatedAt": "2026-11-01T16:53:57.09797262Z" } }, "edges": [ { "by": 28, "category": "schema", "leads": "deepeval", "name": "Schema \u0026 documentation", "reasons": { "a": "Framework reading, docs a model can follow, as superpowers was read. types/index.d.ts types the pane's state, and plugin.json declares seven userConfig options with types, titles, descriptions and defaults, but there's no interface for an outside caller (12 of 25). README in English and German, a nine-step GUIDE.md and PRIVACY.md, all Markdown in the repository; no llms.txt (10). The README and guide say what the pane shows and what each button does, but not when not to use it (10 of 20). Options are typed, with an enum for the language; the /agent-tree command takes stop, clear, intro and `to \u003cid\u003e \u003ctext\u003e` (8 of 15). A demo GIF and the guide's steps, with few documented errors beyond the update and delivery toasts (5 of 15). Semver tags but no changelog or release notes read (5 of 15). 50 in all.", "b": "Read on the framework lines. The Python package ships `py.typed` and pydantic models, the TypeScript package ships type declarations, and the CLI is built on typer. A library has no OpenAPI file to publish (18). llms.txt on deepeval.com indexes the docs, and pages carry a Copy Markdown control (10). Each metric has its own page with required parameters and usage, and three agent skills in the repository tell a coding agent how to add evaluations and tracing (16). Typed test case and metric classes, with enums for evaluation parameters and CLI choices (12). Many runnable examples, a troubleshooting page and an environment variable reference. Error types are not listed in one place (11). Tagged releases for both packages and a public changelog by year, but the 2026 changelog stops at 4.2.0 of 25 August and has no entry for 4.2.1 to 4.2.8 (11)." }, "scores": { "a": 50, "b": 78 } }, { "by": 26, "category": "reliability", "leads": "deepeval", "name": "Reliability", "reasons": { "a": "Local-package reading, as for superpowers and tokeneconomizer. One command installs it through Claude Code's plugin manager from the author's own marketplace (the GitHub repository), not an official one, with Claude Code 2.1.292 or later stated and 2.1.316 tested (15 of 20). No .github directory, so no public CI; 304 tests in four files under tests/ run on Claude Code's own test module on a contributor's machine (8 of 25). Issues couldn't be read, as github.com was unreachable from this session (5 of 25, unchecked). 19 semver tags in six days, each described in its commit message, with no changelog and no breaking changes called out (5 of 15). Version 0.5.4 (0). 33 in all.", "b": "Scored on the local-software lines, since DeepEval runs where the owner installs it. Official `deepeval` packages on PyPI and npm, with Python 3.9 or later stated (20). Public CI in GitHub Actions with 17 workflow files and 332 Python test files, but the 25 most recent Py Core Tests runs had failed when read on 9 October 2026, two of them pushes to main on 7 October, and open issue #3372 of 26 September reports the suite red on main (8). 322 open issues and 387 open pull requests on a repository with 18.7k stars. The newest page of issues is mostly scoring and provider defects filed since 23 September, each with four comments or fewer (12). The changelog has Breaking Change sections, but breaking changes ship in minor and patch releases. 4.2.0 reversed the score direction of four metrics and 3.9.8 and 3.9.9 removed options (7). Python is at 4.2.8. The TypeScript package is at 0.9.21 (12)." }, "scores": { "a": 33, "b": 59 } }, { "by": 23, "category": "maintenance", "leads": "deepeval", "name": "Maintenance \u0026 community", "reasons": { "a": "v0.5.4 tagged on 29 October 2026 and a commit on 31 October (30). 19 tags from v0.2.2 (24 October) to v0.5.4 (29 October), far more than three in 90 days (20). Issue and pull-request replies couldn't be read, as github.com was unreachable (5 of 25). Not in Claude's official plugin directory; claude.com/plugins/agent-tree answered 404 (0). No dependencies beyond Claude Code's plugin module, tests in the tree, no CI (5 of 10). 60 in all.", "b": "Python 4.2.8 was tagged on 2 October 2026, seven days before the check (30). 14 Python releases tagged since 9 August, from 4.1.6 to 4.2.8 (20). 124 commits in the 30 days to 9 October, most by three maintainers, and a Discord community. 322 issues and 387 pull requests are open and reply times were not read (14). Current official packages in Python and TypeScript, the latter at 0.9.21 on 30 September (15). A `poetry.lock` file and CI on pull requests. No Dependabot configuration was found and the core test workflow is failing on main (4)." }, "scores": { "a": 60, "b": 83 } }, { "by": 5, "category": "ergonomics", "leads": "deepeval", "name": "Agent ergonomics", "reasons": { "a": "Framework reading, how much a coding agent has to carry. The plugin adds no tools, skills or prompt text to the model's context. Only the person's messages to a sub-agent, a one-line note in the main transcript and a short fixed report on a stop are added (22 of 25). Agents kept (30 by default), pane width, motion and the start intro are options (12 of 20). Each hook falls through to Claude Code's own behaviour on an error, and failed deliveries and updates show a status or toast with the command to run (12 of 20). Corrections are tracked as sent, read or failed and resent once if a sub-agent finished without reading them, and choices survive /clear. A stop can't be undone (13 of 20). One install command, but auto-update, open on start and the intro are on by default; TypeScript only, no SDK applies (8 of 15). 67 in all.", "b": "Read on the framework lines. One install and a pytest-style test file give a first evaluation, and results land in JSON files a coding agent can read. `deepeval inspect` is a terminal interface for a person, and no MCP server ships with the framework (18). `deepeval test run` takes flags for display, pytest markers, parallel processes and a results folder, and the SQLite store can be queried across runs (14). `--ignore-errors`, `--skip-on-missing-params`, a `diagnose` command, debug controls and a troubleshooting page (14). `--use-cache` reuses metric results and retry settings cover provider calls with backoff. Scores from LLM judges can differ between runs (12). Python and TypeScript packages with few required parameters. The default judge needs `OPENAI_API_KEY` (14)." }, "scores": { "a": 67, "b": 72 } } ], "facts": [ { "a": "Agent framework", "b": "SDK + MCP", "name": "Kind" }, { "a": "Mike Pollow (Digitalroots)", "b": "Confident AI, Inc.", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "OAuth or key", "name": "Auth" }, { "a": "Free", "b": "Freemium", "name": "Pricing" }, { "a": "free", "b": "$1 per GB per month", "name": "Price for agent tracing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT (Copyright (c) 2026 Mike Pollow, Digitalroots)", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-10-02", "name": "Last release" }, { "a": "couldn't be read", "b": "no document linked", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "not read yet", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "19k stars, 35k npm/wk, 736k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, Agent Tree or DeepEval?" }, { "answer": "Agent Tree, at free against $1 per GB per month for DeepEval. These are the vendors' published prices for the job.", "question": "Which is cheaper for agent tracing, Agent Tree or DeepEval?" }, { "answer": "Yes. Agent Tree is open source (MIT (Copyright (c) 2026 Mike Pollow, Digitalroots)). 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).", "question": "Are Agent Tree and DeepEval open source?" } ], "goodFor": [ { "aheadOn": null, "also": [ "No key needed to call it" ], "goodFor": "Watching and steering Claude Code sub-agents from a side pane", "slug": "agent-tree", "watchFor": "Version 0.5.4, ten days old, written and released by one maintainer with no company, site or register entry found" }, { "aheadOn": [ "Reliability, 59 against 33", "Schema \u0026 documentation, 78 against 50", "Agent ergonomics, 72 against 67", "Maintenance \u0026 community, 83 against 60" ], "also": [ "Free to start without a card" ], "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" } ], "history": { "agent-tree": null, "deepeval": [ { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.2, "from": 64.7, "fromGrade": "B", "to": 64.9, "toGrade": "B" } ] }, "job": { "capability": "obs.traces", "name": "Agent tracing" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-agents-tree.json", "title": "Agent Tree vs agents-tree", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-agents-tree" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-arize-phoenix.json", "title": "Agent Tree vs Arize Phoenix", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-arize-phoenix" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-baserun.json", "title": "Agent Tree vs Baserun", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-baserun" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-botgauge.json", "title": "Agent Tree vs BotGauge", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-botgauge" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-braintrust.json", "title": "Agent Tree vs Braintrust API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-braintrust" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-galileo.json", "title": "Agent Tree vs Galileo API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-galileo" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-helicone.json", "title": "Agent Tree vs Helicone AI Gateway + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-helicone" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-honeyhive.json", "title": "Agent Tree vs HoneyHive", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-honeyhive" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-laminar.json", "title": "Agent Tree vs Laminar API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-laminar" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-langfuse.json", "title": "Agent Tree vs Langfuse API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-langfuse" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-langsmith.json", "title": "Agent Tree vs LangSmith API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-langsmith" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-langwatch.json", "title": "Agent Tree vs LangWatch", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-langwatch" }, { "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/agent-tree-vs-navige.json", "title": "Agent Tree vs Navige", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-navige" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-opik.json", "title": "Agent Tree vs Opik API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-opik" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-prefactor.json", "title": "Agent Tree vs Prefactor", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-prefactor" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-pydantic-logfire.json", "title": "Agent Tree vs Pydantic Logfire", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-pydantic-logfire" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-respan.json", "title": "Agent Tree vs Respan API + MCP", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-respan" }, { "json": "https://www.anchorterminal.com/compare/agent-tree-vs-wandb-weave.json", "title": "Agent Tree vs W\u0026B Weave", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-wandb-weave" }, { "json": "https://www.anchorterminal.com/compare/agents-tree-vs-deepeval.json", "title": "agents-tree vs DeepEval", "url": "https://www.anchorterminal.com/compare/agents-tree-vs-deepeval" }, { "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/baserun-vs-deepeval.json", "title": "Baserun vs DeepEval", "url": "https://www.anchorterminal.com/compare/baserun-vs-deepeval" }, { "json": "https://www.anchorterminal.com/compare/botgauge-vs-deepeval.json", "title": "BotGauge vs DeepEval", "url": "https://www.anchorterminal.com/compare/botgauge-vs-deepeval" }, { "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/deepeval-vs-navige.json", "title": "DeepEval vs Navige", "url": "https://www.anchorterminal.com/compare/deepeval-vs-navige" }, { "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-mlflow-tracing.json", "title": "DeepEval vs MLflow Tracing", "url": "https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing" }, { "json": "https://www.anchorterminal.com/compare/deepeval-vs-opik.json", "title": "DeepEval vs Opik API + MCP", "url": "https://www.anchorterminal.com/compare/deepeval-vs-opik" }, { "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" } ], "panel": null, "scores": [ { "agent-tree": 33, "by": 26, "deepeval": 59, "edge": "deepeval", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "agent-tree": 50, "by": 28, "deepeval": 78, "edge": "deepeval", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "agent-tree": 67, "by": 5, "deepeval": 72, "edge": "deepeval", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "agent-tree": 49, "by": 2, "deepeval": 47, "edge": "agent-tree", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "agent-tree": 60, "by": 0, "deepeval": 60, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "agent-tree": 60, "by": 23, "deepeval": 83, "edge": "deepeval", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "agent-tree": 63, "by": 3, "deepeval": 66, "edge": "deepeval", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "DeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories. Both do agent tracing.", "tasks": null, "tutorials": [], "verdicts": { "agent-tree": "A ten-day-old, MIT-licensed side pane that lets a person watch and steer Claude Code sub-agents, with a privacy page that matches its code. It is version 0.5.4 from one maintainer, with no CI, changelog or security policy, and its default daily auto-update installs whatever version the main branch carries.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agent-tree-vs-deepeval", "json": "https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.min.md" }, "markdown": "DeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories. Both do agent tracing.\n\n- Agent Tree: grade D, 52.5/100, rank #897 of 1260. Markdown https://www.anchorterminal.com/tools/agent-tree.md · JSON https://www.anchorterminal.com/api/v1/tools/agent-tree.json\n- DeepEval: grade B, 64.9/100, rank #367 of 1260. Markdown https://www.anchorterminal.com/tools/deepeval.md · JSON https://www.anchorterminal.com/api/v1/tools/deepeval.json\n- Best agent tracing, monitoring and evaluation tools: https://www.anchorterminal.com/best/agent-observability/index.md\n- All 210 evals comparisons: https://www.anchorterminal.com/compare/agent-observability/index.md\n\n## Which one, for what\n\n### Agent Tree (D)\n\nGood for: Watching and steering Claude Code sub-agents from a side pane\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: Version 0.5.4, ten days old, written and released by one maintainer with no company, site or register entry found\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 33\n- Schema \u0026 documentation, 78 against 50\n- Agent ergonomics, 72 against 67\n- Maintenance \u0026 community, 83 against 60\n\nAlso in its favour:\n- Free to start without a card\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\n## Why the edge\n\n### Schema \u0026 documentation: DeepEval +28\n\n- Agent Tree (50): Framework reading, docs a model can follow, as superpowers was read. types/index.d.ts types the pane's state, and plugin.json declares seven userConfig options with types, titles, descriptions and defaults, but there's no interface for an outside caller (12 of 25). README in English and German, a nine-step GUIDE.md and PRIVACY.md, all Markdown in the repository; no llms.txt (10). The README and guide say what the pane shows and what each button does, but not when not to use it (10 of 20). Options are typed, with an enum for the language; the /agent-tree command takes stop, clear, intro and `to \u003cid\u003e \u003ctext\u003e` (8 of 15). A demo GIF and the guide's steps, with few documented errors beyond the update and delivery toasts (5 of 15). Semver tags but no changelog or release notes read (5 of 15). 50 in all.\n- DeepEval (78): Read on the framework lines. The Python package ships `py.typed` and pydantic models, the TypeScript package ships type declarations, and the CLI is built on typer. A library has no OpenAPI file to publish (18). llms.txt on deepeval.com indexes the docs, and pages carry a Copy Markdown control (10). Each metric has its own page with required parameters and usage, and three agent skills in the repository tell a coding agent how to add evaluations and tracing (16). Typed test case and metric classes, with enums for evaluation parameters and CLI choices (12). Many runnable examples, a troubleshooting page and an environment variable reference. Error types are not listed in one place (11). Tagged releases for both packages and a public changelog by year, but the 2026 changelog stops at 4.2.0 of 25 August and has no entry for 4.2.1 to 4.2.8 (11).\n\n### Reliability: DeepEval +26\n\n- Agent Tree (33): Local-package reading, as for superpowers and tokeneconomizer. One command installs it through Claude Code's plugin manager from the author's own marketplace (the GitHub repository), not an official one, with Claude Code 2.1.292 or later stated and 2.1.316 tested (15 of 20). No .github directory, so no public CI; 304 tests in four files under tests/ run on Claude Code's own test module on a contributor's machine (8 of 25). Issues couldn't be read, as github.com was unreachable from this session (5 of 25, unchecked). 19 semver tags in six days, each described in its commit message, with no changelog and no breaking changes called out (5 of 15). Version 0.5.4 (0). 33 in all.\n- DeepEval (59): Scored on the local-software lines, since DeepEval runs where the owner installs it. Official `deepeval` packages on PyPI and npm, with Python 3.9 or later stated (20). Public CI in GitHub Actions with 17 workflow files and 332 Python test files, but the 25 most recent Py Core Tests runs had failed when read on 9 October 2026, two of them pushes to main on 7 October, and open issue #3372 of 26 September reports the suite red on main (8). 322 open issues and 387 open pull requests on a repository with 18.7k stars. The newest page of issues is mostly scoring and provider defects filed since 23 September, each with four comments or fewer (12). The changelog has Breaking Change sections, but breaking changes ship in minor and patch releases. 4.2.0 reversed the score direction of four metrics and 3.9.8 and 3.9.9 removed options (7). Python is at 4.2.8. The TypeScript package is at 0.9.21 (12).\n\n### Maintenance \u0026 community: DeepEval +23\n\n- Agent Tree (60): v0.5.4 tagged on 29 October 2026 and a commit on 31 October (30). 19 tags from v0.2.2 (24 October) to v0.5.4 (29 October), far more than three in 90 days (20). Issue and pull-request replies couldn't be read, as github.com was unreachable (5 of 25). Not in Claude's official plugin directory; claude.com/plugins/agent-tree answered 404 (0). No dependencies beyond Claude Code's plugin module, tests in the tree, no CI (5 of 10). 60 in all.\n- DeepEval (83): Python 4.2.8 was tagged on 2 October 2026, seven days before the check (30). 14 Python releases tagged since 9 August, from 4.1.6 to 4.2.8 (20). 124 commits in the 30 days to 9 October, most by three maintainers, and a Discord community. 322 issues and 387 pull requests are open and reply times were not read (14). Current official packages in Python and TypeScript, the latter at 0.9.21 on 30 September (15). A `poetry.lock` file and CI on pull requests. No Dependabot configuration was found and the core test workflow is failing on main (4).\n\n### Agent ergonomics: DeepEval +5\n\n- Agent Tree (67): Framework reading, how much a coding agent has to carry. The plugin adds no tools, skills or prompt text to the model's context. Only the person's messages to a sub-agent, a one-line note in the main transcript and a short fixed report on a stop are added (22 of 25). Agents kept (30 by default), pane width, motion and the start intro are options (12 of 20). Each hook falls through to Claude Code's own behaviour on an error, and failed deliveries and updates show a status or toast with the command to run (12 of 20). Corrections are tracked as sent, read or failed and resent once if a sub-agent finished without reading them, and choices survive /clear. A stop can't be undone (13 of 20). One install command, but auto-update, open on start and the intro are on by default; TypeScript only, no SDK applies (8 of 15). 67 in all.\n- DeepEval (72): Read on the framework lines. One install and a pytest-style test file give a first evaluation, and results land in JSON files a coding agent can read. `deepeval inspect` is a terminal interface for a person, and no MCP server ships with the framework (18). `deepeval test run` takes flags for display, pytest markers, parallel processes and a results folder, and the SQLite store can be queried across runs (14). `--ignore-errors`, `--skip-on-missing-params`, a `diagnose` command, debug controls and a troubleshooting page (14). `--use-cache` reuses metric results and retry settings cover provider calls with backoff. Scores from LLM judges can differ between runs (12). Python and TypeScript packages with few required parameters. The default judge needs `OPENAI_API_KEY` (14).\n\n## Score by category\n\n| Category | Weight | Agent Tree | DeepEval | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 33 | 59 | DeepEval +26 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 50 | 78 | DeepEval +28 |\n| Agent ergonomics | 13% (16.2 this run) | 67 | 72 | DeepEval +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 49 | 47 | Agent Tree +2 |\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) | 60 | 83 | DeepEval +23 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 63 | 66 | DeepEval +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.5 · D** | **64.9 · B** | |\n\n## Facts side by side\n\n| Fact | Agent Tree | DeepEval |\n| --- | --- | --- |\n| Kind | Agent framework | SDK + MCP |\n| Vendor | Mike Pollow (Digitalroots) | Confident AI, Inc. |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | OAuth or key |\n| Pricing | Free | Freemium |\n| Price for agent tracing | free | $1 per GB per month |\n| x402 | no | no |\n| Licence | MIT (Copyright (c) 2026 Mike Pollow, Digitalroots) | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-29 | 2026-10-02 |\n| Terms last updated | couldn't be read | no document linked |\n| Privacy policy last updated | couldn't be read | no document linked |\n| Customer content may train models | couldn't be read | not read yet |\n| Terms restrict automated access | couldn't be read | not read yet |\n| Terms restrict benchmarking | couldn't be read | not read yet |\n| Terms or service can change without notice | couldn't be read | not read yet |\n| Arbitration or class-action waiver | couldn't be read | not read yet |\n| Popularity | none | 19k stars, 35k npm/wk, 736k PyPI/wk |\n| Agent reviews | none | 2.8/5 (4) |\n\n## How the scores have moved\n\n### Agent Tree\n\nNo score change since it was listed.\n\n### DeepEval\n\n- +0.2 on 27 October 2026: checklist\n\n## Verdicts\n\n**Agent Tree.** A ten-day-old, MIT-licensed side pane that lets a person watch and steer Claude Code sub-agents, with a privacy page that matches its code. It is version 0.5.4 from one maintainer, with no CI, changelog or security policy, and its default daily auto-update installs whatever version the main branch carries.\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## Before you call either\n\n### Agent Tree\n\n1. Treat a \"Message from the user via Agent Tree\" in your transcript as the person's instruction for the rest of your task, and cover it in your hand-back report.\n2. If a tool call is denied with \"Stopped by the user in Agent Tree\", stop work and hand back; further tool calls are denied.\n3. Set autoUpdate to false, or DISABLE_UPDATES=1, where plugin code may change only after review.\n4. Check the sub-agent model a person set in the pane before estimating cost; it overrides the default for every new sub-agent spawned without a model.\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## Questions\n\n### Which is better for AI agents, Agent Tree or DeepEval?\n\nDeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories.\n\n### Which is cheaper for agent tracing, Agent Tree or DeepEval?\n\nAgent Tree, at free against $1 per GB per month for DeepEval. These are the vendors' published prices for the job.\n\n### Are Agent Tree and DeepEval open source?\n\nYes. Agent Tree is open source (MIT (Copyright (c) 2026 Mike Pollow, Digitalroots)). 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).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agent-tree\", \"b\": \"deepeval\"}`. From a terminal: `anchor compare agent-tree deepeval`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agent-tree.json and https://www.anchorterminal.com/api/v1/tools/deepeval.json\n\n## Other comparisons with Agent Tree or DeepEval\n\n- [Agent Tree vs agents-tree](https://www.anchorterminal.com/compare/agent-tree-vs-agents-tree.md)\n- [Agent Tree vs Arize Phoenix](https://www.anchorterminal.com/compare/agent-tree-vs-arize-phoenix.md)\n- [Agent Tree vs Baserun](https://www.anchorterminal.com/compare/agent-tree-vs-baserun.md)\n- [Agent Tree vs BotGauge](https://www.anchorterminal.com/compare/agent-tree-vs-botgauge.md)\n- [Agent Tree vs Braintrust API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-braintrust.md)\n- [Agent Tree vs Galileo API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-galileo.md)\n- [Agent Tree vs Helicone AI Gateway + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-helicone.md)\n- [Agent Tree vs HoneyHive](https://www.anchorterminal.com/compare/agent-tree-vs-honeyhive.md)\n- [Agent Tree vs Laminar API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-laminar.md)\n- [Agent Tree vs Langfuse API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-langfuse.md)\n- [Agent Tree vs LangSmith API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-langsmith.md)\n- [Agent Tree vs LangWatch](https://www.anchorterminal.com/compare/agent-tree-vs-langwatch.md)\n- [Agent Tree vs MLflow Tracing](https://www.anchorterminal.com/compare/agent-tree-vs-mlflow-tracing.md)\n- [Agent Tree vs Navige](https://www.anchorterminal.com/compare/agent-tree-vs-navige.md)\n- [Agent Tree vs Opik API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-opik.md)\n- [Agent Tree vs Prefactor](https://www.anchorterminal.com/compare/agent-tree-vs-prefactor.md)\n- [Agent Tree vs Pydantic Logfire](https://www.anchorterminal.com/compare/agent-tree-vs-pydantic-logfire.md)\n- [Agent Tree vs Respan API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-respan.md)\n- [Agent Tree vs W\u0026B Weave](https://www.anchorterminal.com/compare/agent-tree-vs-wandb-weave.md)\n- [agents-tree vs DeepEval](https://www.anchorterminal.com/compare/agents-tree-vs-deepeval.md)\n- [Arize Phoenix vs DeepEval](https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.md)\n- [Baserun vs DeepEval](https://www.anchorterminal.com/compare/baserun-vs-deepeval.md)\n- [BotGauge vs DeepEval](https://www.anchorterminal.com/compare/botgauge-vs-deepeval.md)\n- [Braintrust API + MCP vs DeepEval](https://www.anchorterminal.com/compare/braintrust-vs-deepeval.md)\n- [DeepEval vs Navige](https://www.anchorterminal.com/compare/deepeval-vs-navige.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 MLflow Tracing](https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.md)\n- [DeepEval vs Opik API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-opik.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)", "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": "2026-11-02", "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": "Agent Tree vs DeepEval", "url": "" } ], "description": "DeepEval scores 64.9 (B) to Agent Tree's 52.5 (D) for agent tracing. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Agent Tree D 52.5", "DeepEval B 64.9", "scores" ], "h1": "Agent Tree vs DeepEval", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/agent-tree-vs-deepeval", "published": "2026-10-01", "section": "tools", "title": "Agent Tree vs DeepEval for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-01", "url": "https://www.anchorterminal.com/compare/agent-tree-vs-deepeval" }, "tokens": { "markdown": 4400, "slim": 2230 }, "version": 1 }