DeepEval vs Yoke Agent: honest comparison.
Both are Apache-2.0, both speak G-Eval and RAGAS, both run locally. So why pick one over the other — and why do so many teams end up running both?
DeepEval and Yoke Agent look superficially similar on feature lists. Both are open-source, both implement G-Eval, RAGAS-compatible metrics, and tool-use evaluation, and both run on a laptop. The difference is in shape: DeepEval is a pytest-style testing library; Yoke Agent is a grid-search studio with a dashboard. That one sentence is the decision you are trying to make.
What DeepEval is, precisely
DeepEval is a Python library. You import it, you write
assert-style test cases against your LLM output,
and you run them with pytest. Each assertion
calls an underlying metric — G-Eval, answer relevancy,
faithfulness, hallucination detection, and about fifty
others — which under the hood is an LLM-as-judge call
or a deterministic check.
The library is free and fully local. Confident AI is the hosted SaaS on top — dashboards, tracing, dataset management, team collaboration, monitoring — with a generous free tier and paid plans above. You can use DeepEval entirely without Confident AI and never leave your machine.
The canonical DeepEval experience looks like this:
from deepeval import assert_test
from deepeval.test_case import LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric
case = LLMTestCase(
input="What is poka-yoke?",
actual_output=my_rag_pipeline("What is poka-yoke?"),
retrieval_context=[...],
)
assert_test(case, [AnswerRelevancyMetric(threshold=0.8)])
That runs in your CI exactly like any other pytest. PR fails if the threshold is missed. That is the loop it is built for.
What Yoke Agent is, precisely
Yoke Agent is a studio — FastAPI backend, Next.js dashboard, workers, vector store — that orchestrates grid-searches across RAG and agent configurations. You don’t write assertions; you pick axes (chunking, embeddings, retrievers, rerankers, advanced strategies), define a dataset, and hit run. It scores every combination with RAGAS, produces a ranked leaderboard, and writes an improvement report recommending which configuration to ship.
For agents, the equivalent surface is the agent workbench:
personas, scenarios, parallel simulation, G-Eval scoring
across 14 rubric metrics, plus deterministic tool-call
accuracy parsed from TOOL_CALL invocations.
Transcripts are first-class objects you can drill into.
The canonical Yoke experience is not code. It is a UI step: “pick grid axes → approve the dataset → run → read the report.”
Side by side, category by category
| DeepEval | Yoke Agent | |
|---|---|---|
| License | Apache 2.0 | Apache 2.0 |
| Interface | Python library + pytest | Dashboard + REST API |
| Best for | Regression testing in CI | Picking a configuration to ship |
| Metrics shipped | 50+ | RAGAS 8 + G-Eval 14 + custom hook |
| Grid-search | Manual (write loops) | First-class axes |
| Agent simulation | Limited | Personas × scenarios in parallel |
| Tool-call scoring | Judge-based | Deterministic from TOOL_CALL |
| Cost tracking | Via Confident AI | Built-in, with flex-tier detection |
| OpenTelemetry | Not native | Built-in, OTLP export |
| Hosted option | Confident AI | Self-hosted only |
Pick DeepEval when…
- Your loop is pytest. You want eval to run in CI on every pull request, blocking the merge when a threshold is missed. DeepEval is purpose-built for this.
- You want a hosted option. Confident AI gives you dashboards and team collaboration without self-hosting.
- You need the widest metric library. Fifty-plus research-backed metrics covers edge cases (toxicity, bias, summarization quality) that most other tools do not.
- You only need to evaluate one configuration. If your pipeline is fixed and you are regression-testing, a grid-search is overkill.
Pick Yoke Agent when…
- You haven’t picked a configuration yet. If the open question is “which chunking / embedding / retriever / strategy combination should we ship,” grid-search is the whole point.
- You evaluate RAG and agents. One dashboard, shared providers, shared cost tracking, shared improvement-report format.
-
Agent tool-use accuracy matters. Yoke
parses
TOOL_CALLinvocations directly, so accuracy is not the judge’s reading of prose. - You need self-hosted end-to-end. Yoke has no hosted option. Your documents and transcripts stay on your infra; only LLM calls leave.
- Cost governance is a real constraint. Token and USD logging is the default path, flex-tier discount detection is automatic.
The case for running both
These tools are more complementary than competing. The pattern we see at teams that ship quickly:
- Yoke Agent picks the configuration. A project-launch grid-search across 50-200 combinations surfaces the winner.
- DeepEval locks the configuration. A handful of pytest assertions — faithfulness above 0.85, hallucination below 0.1, answer relevancy above 0.9 — run in CI on every pull request so regressions block the merge.
- Once a quarter, another Yoke sweep with fresh data to check whether the winning configuration still wins. DeepEval keeps the daily bar steady; Yoke adjusts the bar.
If you have to pick one, start with the tool that matches your current bottleneck. Can’t ship because you don’t know which configuration to ship? Yoke. Can ship but keep regressing? DeepEval.
Migration notes
If you are on DeepEval and want to add grid-search, your
existing test cases translate to a Yoke dataset directly:
inputs become questions, expected outputs become ground
truth, and any retrieval_context you tracked is
gold for context-recall metrics. Point a new Yoke experiment
at the same corpus and your first sweep runs in minutes.
If you are on Yoke and want to add CI regression tests, export the winning configuration’s score thresholds and drop them into a DeepEval test file. It is about fifty lines of glue for most projects.