EvalView
Frameworks & Eval · tested 2026-08-27 · by the Hlido desk, not the vendor
In short: An Apache-2.0 regression-testing framework for multi-step agents — "pytest for AI agents" — that gates changes instead of only charting them, but is still pre-1.0 with a small community.
5 PASS · 0 FAIL of 5 public-surface claims
Quick answer
EvalView scores 70/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-08-27). STEADY (70), at the floor of the band. Pricing: Open source (free entry point documented).
EvalView's framing is the most useful thing about it: the category is full of tracing dashboards that tell you what happened after a regression shipped, and EvalView positions itself as the gate that stops the change instead. Its own comparison pages make that argument explicitly against LangSmith, Langfuse, Braintrust and DeepEval — regression gating rather than monitoring, trajectory and tool-path diffs, golden baselines for tool-calling agents. The feature list is substantive for a testing framework: YAML test definitions, weighted scoring across tool use, output and sequence, LLM-as-judge with custom prompts, sequence matching in exact/subsequence/unordered modes, JSON-schema output validation, and explicit hallucination and safety detection. It claims 9+ framework integrations (LangGraph, CrewAI, OpenAI, Anthropic, AutoGen, Dify, LangServe, Ollama, Claude Code), runs locally, and is 100% open source under Apache 2.0 — which for a security-sensitive testing tool that sees your prompts and traces is the right default. The honest caveat is maturity, and the surface states it plainly rather than hiding it: v0.8.1 Public Beta, 130 GitHub stars, and the cloud product is a waitlist, not a product. A testing framework's value depends on the community that finds its bugs, and 130 stars is a small one. The auto-generation claims ('generate a suite from a URL or logs', '100+ test variations') are the kind that sound strong and are hardest to verify from a landing page — nothing here demonstrates the quality of a generated suite.
Why STEADY
STEADY (70), at the floor of the band. The positioning is genuinely differentiated — regression gating rather than post-hoc dashboards — the evaluator surface is detailed and specific, and Apache 2.0 with local execution is the correct posture for a tool that reads your prompts and traces. Held exactly at the boundary because it is self-declared v0.8.1 Public Beta with 130 stars and a waitlist-only cloud: the design is credible, the adoption that would prove it is not there yet, and the auto-generation claims are undemonstrated on the public surface.
Public-surface checklist
- PASS Homepage loads (required)
- PASS Primary value prop (required) — 'pytest for AI agents' — 'The complete open-source testing framework for multi-step agents.'
- PASS Cta present (required) — 'pip install evalview' / 'Join Cloud Waitlist'
- PASS Pricing or access (required) — Open source and free to install (Apache 2.0, 'pip install evalview'); the Cloud tier is an unpriced waitlist
- PASS Maturity disclosed (required) — Vendor states 'v0.8.1 Public Beta' on the landing page
What we saw
1 screenshot captured by the Hlido engine during the reviewed run (run-cbd02731404034a9-evalview-com). Our own captures — not vendor marketing material.
What it does well
- Positions as a regression gate in CI rather than another post-hoc tracing dashboard
- Detailed, specific evaluator surface: weighted tool/output/sequence scoring, LLM-as-judge, sequence matching modes, JSON-schema validation
- Explicit hallucination and safety detection rather than generic quality scoring
- 100% open source under Apache 2.0 and runs locally — the right default for a tool that sees your prompts and traces
- Claims 9+ framework integrations spanning LangGraph, CrewAI, AutoGen, Dify, LangServe, Ollama and Claude Code
- States its own maturity honestly on the landing page (v0.8.1 Public Beta) rather than implying stability
What it fails at
- Pre-1.0 (v0.8.1 Public Beta) — API and behaviour should be assumed unstable
- 130 GitHub stars is a small community for a testing framework, where community size is what surfaces the bugs
- The cloud product is a waitlist, not a shipped tier — no managed option today
- Auto-generation claims ("suite from a URL or logs", "100+ test variations") are asserted with no demonstrated output quality
- Its competitor comparisons are vendor-authored, as all such pages are
Best for
- Teams that want agent regressions blocked in CI rather than discovered in a dashboard afterwards
- Security-sensitive environments that need evaluation to run locally on their own infrastructure
- Tool-calling agents where trajectory and tool-order correctness matter as much as final output
- Early adopters comfortable with a pre-1.0 dependency
Not recommended for
- Teams needing a managed, supported evaluation platform today — the cloud tier is a waitlist
- Production-critical pipelines that cannot absorb pre-1.0 API churn
- Buyers who weight ecosystem maturity and community size heavily
Pricing & access
- ModelOpen source
- Free entry pointYes — a free tier or open-source edition is documented
- Pricing findable on the public surfacePASS Open source and free to install (Apache 2.0, 'pip install evalview'); the Cloud tier is an unpriced waitlist (tested 2026-08-27)
Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-08-27.
Compared to
-
Langfuse
ci-regression-gating-vs-production-observability
Langfuse is a mature, widely adopted observability and evaluation platform with hosted and self-hosted options. EvalView is a pre-1.0 local-first framework that gates changes in CI. Langfuse for production observability at scale; EvalView if the specific need is a regression gate and you accept beta maturity.
-
Braintrust
open-source-local-vs-commercial-managed
Braintrust is a commercial eval platform with managed infrastructure. EvalView is Apache-2.0 and runs locally with no managed tier available yet. Braintrust for a supported product; EvalView when local execution and open licensing are hard requirements.
Agent relevance
CLI SDK Behavioral-testable
Agentic-Commerce Readiness 57/100 · INTEGRABLE
Independent readiness for agent delegation & transaction. How it’s scored · check live
Installed with pip and driven by a CLI (`evalview run`) over YAML test definitions, wired into CI to gate agent changes. It integrates against 9+ agent frameworks as the system under test rather than exposing itself as a tool an agent calls. The consumer is your pipeline, not your agent.
Agent-friendly score: 7/10
Score over time
The longitudinal record — every point is the score as published on that date. Raw series.
Evidence
- Open-source testing framework for multi-step agents, positioned to catch hallucinations, regressions and cost spikes before production — source (2026-08-27) verified
- Self-declared v0.8.1 Public Beta with 130 GitHub stars and a cloud waitlist — source (2026-08-27) verified
- Apache 2.0, security-first local execution, positioned against tracing-only tools — source (2026-08-27) verified
- Evaluator surface: YAML tests, weighted scoring, LLM-as-judge, sequence matching, schema validation, hallucination and safety detection — source (2026-08-27) verified
scorecard.json · transparency passport · registry · methodology
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