Comparison
evidently vs promptfoo
Verdict
Pick evidently if evidently provides comprehensive observability across a wide range of data types and metrics, particularly suited for integration with Jupyter Notebook environments; pick promptfoo if promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support.
Markdown twin · evidently alternatives · promptfoo alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | evidently | promptfoo |
|---|---|---|
| Maintenance | Very active (2d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- evidently
- An open-source ML and LLM observability framework.
- promptfoo
- Tool for evaluating prompts and AI agents by comparing performance across various models and red teaming.
Stars
- evidently
- 7.8k
- promptfoo
- 24k
Forks
- evidently
- 895
- promptfoo
- 2.1k
Open issues
- evidently
- 295
- promptfoo
- 481
Language
- evidently
- Jupyter Notebook
- promptfoo
- TypeScript
Adopt for
- evidently
- Evidently provides comprehensive observability across a wide range of data types and metrics, particularly suited for integration with Jupyter Notebook environments.
- promptfoo
- promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support.
Persona
- evidently
- -
- promptfoo
- -
Runtime
- evidently
- -
- promptfoo
- -
License
- evidently
- Apache-2.0
- promptfoo
- MIT
Last pushed
- evidently
- Aug 5, 2026
- promptfoo
- Aug 1, 2026
Categories
- evidently
- Evaluation & Observability
- promptfoo
- Evaluation & Observability, LLM Frameworks
Trust and health
Days since push
- evidently
- 2d
- promptfoo
- 0d
Open issues (now)
- evidently
- 295
- promptfoo
- 481
Stars delta
- evidently
- +117 (30d)
- promptfoo
- Unknown
Open issues delta
- evidently
- +10 (30d)
- promptfoo
- Unknown
Full report
- evidently
- Trust report
- promptfoo
- Trust report
Typed relationship
evidently alternative promptfooEvidently and Promptfoo both aim at observability for LLMs but differ in their methodologies, approach to evaluation, and the specific tools provided.
Shared compatibility
- Python · evidently: Python runtime · promptfoo: Python runtime
Choose evidently if…
- evidently is primarily Jupyter Notebook; promptfoo is TypeScript.
- License: evidently is Apache-2.0, promptfoo is MIT.
- Evidently and Promptfoo both aim at observability for LLMs but differ in their methodologies, approach to evaluation, and the specific tools provided.
- Tags unique to evidently: data-drift, data-quality, data-validation, gen-ai.
- Integrating into projects using Jupyter Notebooks where detailed observability is needed
When NOT to use evidently
- For developers preferring non-Jupyter based development environments
- Projects needing fewer, simpler monitoring tools without extensive metric support
Choose promptfoo if…
- promptfoo is primarily TypeScript; evidently is Jupyter Notebook.
- License: promptfoo is MIT, evidently is Apache-2.0.
- Evidently and Promptfoo both aim at observability for LLMs but differ in their methodologies, approach to evaluation, and the specific tools provided.
- Tags unique to promptfoo: ci-cd, evaluation-framework, llm-evaluation, pentesting.
- Also covers LLM Frameworks.
- promptfoo ships Docker support for self-hosted deployment.
- For comparing performance across GPT, Claude, Gemini, DeepSeek
When NOT to use promptfoo
- If you do not require comparative analysis among multiple LLM models
- If your project does not benefit from the specific red teaming capabilities offered by promptfoo
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (evidentlyai/evidently) · observed Aug 7, 2026
- GitHub forks (evidentlyai/evidently) · observed Aug 7, 2026
- Last push (evidentlyai/evidently) · observed Aug 5, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (promptfoo/promptfoo) · observed Aug 2, 2026
- GitHub forks (promptfoo/promptfoo) · observed Aug 2, 2026
- Last push (promptfoo/promptfoo) · observed Aug 1, 2026
- License file (MIT) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: evidently 7.8k · promptfoo 24k (synced Aug 7, 2026).
Common questions
- What is the difference between evidently and promptfoo?
- evidently: An open-source ML and LLM observability framework.. promptfoo: Tool for evaluating prompts and AI agents by comparing performance across various models and red teaming.. See the comparison table for live GitHub stats and shared categories.
- When should I choose evidently over promptfoo?
- Choose evidently over promptfoo when evidently is primarily Jupyter Notebook; promptfoo is TypeScript; License: evidently is Apache-2.0, promptfoo is MIT; Evidently and Promptfoo both aim at observability for LLMs but differ in their methodologies, approach to evaluation, and the specific tools provided; Tags unique to evidently: data-drift, data-quality, data-validation, gen-ai; Integrating into projects using Jupyter Notebooks where detailed observability is needed.
- When should I choose promptfoo over evidently?
- Choose promptfoo over evidently when promptfoo is primarily TypeScript; evidently is Jupyter Notebook; License: promptfoo is MIT, evidently is Apache-2.0; Evidently and Promptfoo both aim at observability for LLMs but differ in their methodologies, approach to evaluation, and the specific tools provided; Tags unique to promptfoo: ci-cd, evaluation-framework, llm-evaluation, pentesting; Also covers LLM Frameworks; promptfoo ships Docker support for self-hosted deployment; For comparing performance across GPT, Claude, Gemini, DeepSeek.
- When should I avoid evidently?
- For developers preferring non-Jupyter based development environments Projects needing fewer, simpler monitoring tools without extensive metric support
- When should I avoid promptfoo?
- If you do not require comparative analysis among multiple LLM models If your project does not benefit from the specific red teaming capabilities offered by promptfoo
- Is evidently or promptfoo more popular on GitHub?
- promptfoo has more GitHub stars (23,838 vs 7,790). Stars measure visibility, not whether either tool fits your constraints.
- Are evidently and promptfoo open source?
- Yes - both are open-source projects on GitHub (evidently: Apache-2.0, promptfoo: MIT).
- Where can I find alternatives to evidently or promptfoo?
- GraphCanon lists graph-backed alternatives at evidently alternatives and promptfoo alternatives (evidently markdown twin, promptfoo 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, evidently or promptfoo?
- evidently: Very active. promptfoo: Very 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 evidently and promptfoo?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evidently trust report; promptfoo trust report.