Home/Compare/deepeval vs eval-view

Comparison

deepeval vs eval-view

Verdict

Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and.

Markdown twin · deepeval alternatives · eval-view alternatives

GraphCanon updated Sep 20, 2026

6views this month

deepeval logo

deepeval

confident-ai/deepeval

★ 18kpushed Sep 18, 2026
vs
eval-view logo

eval-view

hidai25/eval-view

★ 134pushed Sep 5, 2026

Trust & integrity

Signaldeepevaleval-view
Maintenance
Very active (1d since push)
As of Sep 20, 2026 · github_public_v1
Active (13d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 20, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

deepeval
LLM Evaluation Framework.
eval-view
Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI

Stars

deepeval
18k
eval-view
134

Forks

deepeval
2.0k
eval-view
24

Open issues

deepeval
624
eval-view
2

Language

deepeval
Python
eval-view
Python

Adopt for

deepeval
Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies.
eval-view
Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and

Persona

deepeval
-
eval-view
-

Runtime

deepeval
-
eval-view
-

License

deepeval
Apache-2.0 License
eval-view
Apache-2.0

Last pushed

deepeval
Sep 18, 2026
eval-view
Sep 5, 2026

Categories

deepeval
Evaluation & Observability
eval-view
AI Agents, Evaluation & Observability

Trust and health

Maintenance

deepeval
Very active (96%)
eval-view
Active (82%)

Days since push

deepeval
1d
eval-view
13d

Open issues (now)

deepeval
624
eval-view
2

Stars delta

deepeval
+1.1k (30d)
eval-view
+8 (30d)

Open issues delta

deepeval
+220 (30d)
eval-view
-1 (30d)

Owner type

deepeval
Organization
eval-view
User

Full report

deepeval
Trust report
eval-view
Trust report

Shared compatibility

  • Python · deepeval: Python runtime · eval-view: Python runtime

Choose deepeval if…

  • Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities..
  • Tags unique to deepeval: evaluation, llm-evaluation, metrics.
  • When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.

When NOT to use deepeval

  • For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill.
  • In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.

Choose eval-view if…

  • Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents.
  • Also covers AI Agents.
  • eval-view ships Docker support for self-hosted deployment.
  • When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.

When NOT to use eval-view

  • If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic.
  • When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes.
  • If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag.
  • If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: deepeval 18k · eval-view 134 (synced Sep 20, 2026).

Common questions

What is the difference between deepeval and eval-view?
deepeval: LLM Evaluation Framework.. eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. See the comparison table for live GitHub stats and shared categories.
When should I choose deepeval over eval-view?
Choose deepeval over eval-view when Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, llm-evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics.
When should I choose eval-view over deepeval?
Choose eval-view over deepeval when Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; Also covers AI Agents; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.
When should I avoid deepeval?
For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis.
When should I avoid eval-view?
If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.
Is deepeval or eval-view more popular on GitHub?
deepeval has more GitHub stars (18,342 vs 134). Stars measure visibility, not whether either tool fits your constraints.
Are deepeval and eval-view open source?
Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, eval-view: Apache-2.0).
Where can I find alternatives to deepeval or eval-view?
GraphCanon lists graph-backed alternatives at deepeval alternatives and eval-view alternatives (deepeval markdown twin, eval-view markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, deepeval or eval-view?
deepeval: Very active. eval-view: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for deepeval and eval-view?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deepeval trust report; eval-view trust report.

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