kereva-scanner vs llm-app

Common questions

What is the difference between kereva-scanner and llm-app?

kereva-scanner: Code scanner to check for issues in prompts and LLM calls. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories.

When should I choose kereva-scanner over llm-app?

Choose kereva-scanner over llm-app when kereva-scanner is primarily Python; llm-app is Jupyter Notebook; License: kereva-scanner is Apache-2.0, llm-app is MIT; Tags unique to kereva-scanner: ai, ai-code-review, ai-evaluation, ai-performance; Also covers Developer Tools.

When should I choose llm-app over kereva-scanner?

Choose llm-app over kereva-scanner when llm-app is primarily Jupyter Notebook; kereva-scanner is Python; License: llm-app is MIT, kereva-scanner is Apache-2.0; 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: chatbot, hugging-face, llm, retrieval-augmented-generation; Also covers Data & Retrieval; - 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 avoid kereva-scanner?

Last GitHub push was 464 days ago (dormant maintenance, Apr 6, 2025). Validate activity before betting a new project on kereva-scanner. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. 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.

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.

Is kereva-scanner or llm-app more popular on GitHub?

llm-app has more GitHub stars (59,068 vs 78). Stars measure visibility, not whether either tool fits your constraints.

Are kereva-scanner and llm-app open source?

Yes - both are open-source projects on GitHub (kereva-scanner: Apache-2.0, llm-app: MIT).

Where can I find alternatives to kereva-scanner or llm-app?

GraphCanon lists graph-backed alternatives at kereva-scanner alternatives and llm-app alternatives (kereva-scanner markdown twin, llm-app 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, kereva-scanner or llm-app?

kereva-scanner: Dormant. llm-app: 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 kereva-scanner and llm-app?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kereva-scanner trust report; llm-app trust report.