--- title: "llm-app vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-rlancemartin-auto-evaluator" tools: ["pathwaycom-llm-app", "rlancemartin-auto-evaluator"] --- # llm-app vs auto-evaluator *GraphCanon updated Jul 11, 2026* ## 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. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. [auto-evaluator](https://autoevaluator.langchain.com/) has 1.1k stars, 92 forks, and 3 open issues, last pushed May 10, 2023. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [llm-app](/tools/pathwaycom-llm-app.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Evaluation tool for LLM QA chains | | Stars | 59,068 | 1,102 | | Forks | 1,432 | 92 | | Open issues | 10 | 3 | | Language | Jupyter Notebook | Python | | Adopt for | 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 | - | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | LLM Frameworks, Data & Retrieval, Vector Databases | LLM Frameworks, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 5d | 1158d | | Open issues (now) | 10 | 3 | | Owner type | Organization | User | | Security scan | No lockfile | 118 low (118 low) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** 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 ## Choose when ### 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. ### 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 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 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. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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](/tools/pathwaycom-llm-app/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/alternatives.md)), 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](/compare/pathwaycom-llm-app-vs-rlancemartin-auto-evaluator.md) 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](/tools/pathwaycom-llm-app/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_