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Comparison

llm-app vs auto-evaluator

Verdict

Pick llm-app when llm-app is primarily Jupyter Notebook; auto-evaluator is Python; pick auto-evaluator when auto-evaluator is primarily Python; llm-app is Jupyter Notebook.

Markdown twin · llm-app alternatives · auto-evaluator alternatives

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llm-app logo

llm-app

pathwaycom/llm-app

★ 59kpushed Jul 5, 2026
vs
auto-evaluator logo

auto-evaluator

rlancemartin/auto-evaluator

★ 1.1kpushed May 10, 2023

Trust & integrity

Signalllm-appauto-evaluator
Maintenance
Very active (5d since push)
As of today · github_public_v1
Dormant (1158d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
118 low (118 low)
As of today · osv@v1
llm-app trust reportauto-evaluator trust reportTrust methodology

Tagline

llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
auto-evaluator
Evaluation tool for LLM QA chains

Stars

llm-app
59k
auto-evaluator
1.1k

Forks

llm-app
1.4k
auto-evaluator
92

Open issues

llm-app
10
auto-evaluator
3

Language

llm-app
Jupyter Notebook
auto-evaluator
Python

Adopt for

llm-app
llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz
auto-evaluator
-

Persona

llm-app
-
auto-evaluator
-

Runtime

llm-app
-
auto-evaluator
-

License

llm-app
MIT
auto-evaluator
-

Last pushed

llm-app
Jul 5, 2026
auto-evaluator
May 10, 2023

Categories

llm-app
LLM Frameworks, Vector Databases, Data & Retrieval
auto-evaluator
LLM Frameworks, Vector Databases, Data & Retrieval

Trust and health

Maintenance

llm-app
Very active (96%)
auto-evaluator
Dormant (18%)

Days since push

llm-app
5d
auto-evaluator
1158d

Open issues (now)

llm-app
10
auto-evaluator
3

Owner type

llm-app
Organization
auto-evaluator
User

Security scan

llm-app
No lockfile
auto-evaluator
118 low (118 low)

Full report

llm-app
Trust report
auto-evaluator
Trust report

Choose llm-app if…

  • llm-app is primarily Jupyter Notebook; auto-evaluator is Python.
  • Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others..
  • Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation.
  • - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.

When NOT to use llm-app

  • - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app.
  • - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
GitHubDetails

Choose auto-evaluator if…

  • auto-evaluator is primarily Python; llm-app is Jupyter Notebook.
  • Tags unique to auto-evaluator: python.
  • Leaner open-issue backlog (3).

When NOT to use auto-evaluator

  • Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
GitHubDetails

Explore

llm-app trust report →auto-evaluator trust report →LLM Frameworks category →Vector Databases category →Data & Retrieval category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: llm-app 59k · auto-evaluator 1.1k (synced Jul 11, 2026).

Common questions

What is the difference between llm-app and auto-evaluator?
llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. auto-evaluator: Evaluation tool for LLM QA chains. See the comparison table for live GitHub stats and shared categories.
When should I choose llm-app over auto-evaluator?
Choose llm-app over auto-evaluator when llm-app is primarily Jupyter Notebook; auto-evaluator is Python; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.
When should I choose auto-evaluator over llm-app?
Choose auto-evaluator over llm-app when auto-evaluator is primarily Python; llm-app is Jupyter Notebook; Tags unique to auto-evaluator: python; Leaner open-issue backlog (3).
When should I avoid llm-app?
- You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app.
When should I avoid auto-evaluator?
Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
Is llm-app or auto-evaluator more popular on GitHub?
llm-app has more GitHub stars (59,068 vs 1,102). Stars measure visibility, not whether either tool fits your constraints.
Are llm-app and auto-evaluator open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to llm-app or auto-evaluator?
GraphCanon lists graph-backed alternatives at llm-app alternatives and auto-evaluator alternatives (llm-app markdown twin, auto-evaluator 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, llm-app or auto-evaluator?
llm-app: Very active. auto-evaluator: Dormant. 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 llm-app and auto-evaluator?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-app trust report; auto-evaluator trust report.