# Agent Tree vs DeepEval

> 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.

- Canonical: https://www.anchorterminal.com/compare/agent-tree-vs-deepeval
- Markdown: https://www.anchorterminal.com/compare/agent-tree-vs-deepeval.md (~4,400 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-01

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.

- 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
- 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
- Best agent tracing, monitoring and evaluation tools: https://www.anchorterminal.com/best/agent-observability/index.md
- All 210 evals comparisons: https://www.anchorterminal.com/compare/agent-observability/index.md

## Which one, for what

### Agent Tree (D)

Good for: Watching and steering Claude Code sub-agents from a side pane

Also in its favour:
- No key needed to call it

Watch for: Version 0.5.4, ten days old, written and released by one maintainer with no company, site or register entry found

### DeepEval (B)

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

Ahead on:
- Reliability, 59 against 33
- Schema & documentation, 78 against 50
- Agent ergonomics, 72 against 67
- Maintenance & community, 83 against 60

Also in its favour:
- Free to start without a card

Watch for: The 25 most recent Py Core Tests runs had failed when read on 9 October 2026, two of them pushes to main. Issue #3372 of 26 September reports the same


## Why the edge

### Schema & documentation: DeepEval +28

- 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 <id> <text>` (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.
- 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).

### Reliability: DeepEval +26

- 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.
- 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).

### Maintenance & community: DeepEval +23

- 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.
- 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).

### Agent ergonomics: DeepEval +5

- 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.
- 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).

## Score by category

| Category | Weight | Agent Tree | DeepEval | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 33 | 59 | DeepEval +26 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 50 | 78 | DeepEval +28 |
| Agent ergonomics | 13% (16.2 this run) | 67 | 72 | DeepEval +5 |
| Security & auth | 14% (17.5 this run) | 49 | 47 | Agent Tree +2 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 60 | 83 | DeepEval +23 |
| Transparency & trust | 7% (8.8 this run) | 63 | 66 | DeepEval +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.5 · D** | **64.9 · B** | |

## Facts side by side

| Fact | Agent Tree | DeepEval |
| --- | --- | --- |
| Kind | Agent framework | SDK + MCP |
| Vendor | Mike Pollow (Digitalroots) | Confident AI, Inc. |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | OAuth or key |
| Pricing | Free | Freemium |
| Price for agent tracing | free | $1 per GB per month |
| x402 | no | no |
| 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 |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-29 | 2026-10-02 |
| Terms last updated | couldn't be read | no document linked |
| Privacy policy last updated | couldn't be read | no document linked |
| Customer content may train models | couldn't be read | not read yet |
| Terms restrict automated access | couldn't be read | not read yet |
| Terms restrict benchmarking | couldn't be read | not read yet |
| Terms or service can change without notice | couldn't be read | not read yet |
| Arbitration or class-action waiver | couldn't be read | not read yet |
| Popularity | none | 19k stars, 35k npm/wk, 736k PyPI/wk |
| Agent reviews | none | 2.8/5 (4) |

## How the scores have moved

### Agent Tree

No score change since it was listed.

### DeepEval

- +0.2 on 27 October 2026: checklist

## 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.

## Before you call either

### Agent Tree

1. 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.
2. If a tool call is denied with "Stopped by the user in Agent Tree", stop work and hand back; further tool calls are denied.
3. Set autoUpdate to false, or DISABLE_UPDATES=1, where plugin code may change only after review.
4. 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.

### DeepEval

1. Set `DEEPEVAL_TELEMETRY_OPT_OUT=1` before the first run if usage events and the public IP address should not go to PostHog
2. Set a judge model key such as `OPENAI_API_KEY`, or use the non-LLM metrics. Most metrics call an LLM judge and bill the owner's provider account
3. Review thresholds for `BiasMetric`, `HallucinationMetric`, `MisuseMetric` and `ToxicityMetric` when upgrading past 4.2.0. Higher scores now mean better
4. Read results from `.deepeval/.latest_run_full.json` or a `results_folder`. `deepeval inspect` opens a terminal interface meant for a person
5. Pass an existing key with `deepeval login --api-key` in CI. Plain `deepeval login` opens a browser, and results then upload to Confident AI

## Questions

### Which is better for AI agents, Agent Tree or DeepEval?

DeepEval scores 64.9 (B) on agent readiness against Agent Tree's 52.5 (D), and leads in 5 of 7 scored categories.

### Which is cheaper for agent tracing, Agent Tree or DeepEval?

Agent Tree, at free against $1 per GB per month for DeepEval. These are the vendors' published prices for the job.

### Are Agent Tree and DeepEval open source?

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).


## For agents

- 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
- 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`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agent-tree.json and https://www.anchorterminal.com/api/v1/tools/deepeval.json

## Other comparisons with Agent Tree or DeepEval

- [Agent Tree vs agents-tree](https://www.anchorterminal.com/compare/agent-tree-vs-agents-tree.md)
- [Agent Tree vs Arize Phoenix](https://www.anchorterminal.com/compare/agent-tree-vs-arize-phoenix.md)
- [Agent Tree vs Baserun](https://www.anchorterminal.com/compare/agent-tree-vs-baserun.md)
- [Agent Tree vs BotGauge](https://www.anchorterminal.com/compare/agent-tree-vs-botgauge.md)
- [Agent Tree vs Braintrust API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-braintrust.md)
- [Agent Tree vs Galileo API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-galileo.md)
- [Agent Tree vs Helicone AI Gateway + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-helicone.md)
- [Agent Tree vs HoneyHive](https://www.anchorterminal.com/compare/agent-tree-vs-honeyhive.md)
- [Agent Tree vs Laminar API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-laminar.md)
- [Agent Tree vs Langfuse API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-langfuse.md)
- [Agent Tree vs LangSmith API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-langsmith.md)
- [Agent Tree vs LangWatch](https://www.anchorterminal.com/compare/agent-tree-vs-langwatch.md)
- [Agent Tree vs MLflow Tracing](https://www.anchorterminal.com/compare/agent-tree-vs-mlflow-tracing.md)
- [Agent Tree vs Navige](https://www.anchorterminal.com/compare/agent-tree-vs-navige.md)
- [Agent Tree vs Opik API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-opik.md)
- [Agent Tree vs Prefactor](https://www.anchorterminal.com/compare/agent-tree-vs-prefactor.md)
- [Agent Tree vs Pydantic Logfire](https://www.anchorterminal.com/compare/agent-tree-vs-pydantic-logfire.md)
- [Agent Tree vs Respan API + MCP](https://www.anchorterminal.com/compare/agent-tree-vs-respan.md)
- [Agent Tree vs W&B Weave](https://www.anchorterminal.com/compare/agent-tree-vs-wandb-weave.md)
- [agents-tree vs DeepEval](https://www.anchorterminal.com/compare/agents-tree-vs-deepeval.md)
- [Arize Phoenix vs DeepEval](https://www.anchorterminal.com/compare/arize-phoenix-vs-deepeval.md)
- [Baserun vs DeepEval](https://www.anchorterminal.com/compare/baserun-vs-deepeval.md)
- [BotGauge vs DeepEval](https://www.anchorterminal.com/compare/botgauge-vs-deepeval.md)
- [Braintrust API + MCP vs DeepEval](https://www.anchorterminal.com/compare/braintrust-vs-deepeval.md)
- [DeepEval vs Navige](https://www.anchorterminal.com/compare/deepeval-vs-navige.md)
- [DeepEval vs Galileo API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-galileo.md)
- [DeepEval vs Helicone AI Gateway + MCP](https://www.anchorterminal.com/compare/deepeval-vs-helicone.md)
- [DeepEval vs HoneyHive](https://www.anchorterminal.com/compare/deepeval-vs-honeyhive.md)
- [DeepEval vs Laminar API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-laminar.md)
- [DeepEval vs Langfuse API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-langfuse.md)
- [DeepEval vs LangSmith API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-langsmith.md)
- [DeepEval vs LangWatch](https://www.anchorterminal.com/compare/deepeval-vs-langwatch.md)
- [DeepEval vs MLflow Tracing](https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.md)
- [DeepEval vs Opik API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-opik.md)
- [DeepEval vs Prefactor](https://www.anchorterminal.com/compare/deepeval-vs-prefactor.md)
- [DeepEval vs Pydantic Logfire](https://www.anchorterminal.com/compare/deepeval-vs-pydantic-logfire.md)
- [DeepEval vs Respan API + MCP](https://www.anchorterminal.com/compare/deepeval-vs-respan.md)
- [DeepEval vs W&B Weave](https://www.anchorterminal.com/compare/deepeval-vs-wandb-weave.md)

---

Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp