---
title: "onyx vs llm-app"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/onyx-dot-app-onyx-vs-pathwaycom-llm-app"
tools: ["onyx-dot-app-onyx", "pathwaycom-llm-app"]
---

# onyx vs llm-app

Neutral, constraint-first comparison with live GitHub stats.

| | [onyx](/tools/onyx-dot-app-onyx.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Tagline | Open Source AI Platform - AI Chat with advanced features that works with every LLM | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. |
| Stars | 30,773 | 59,098 |
| Forks | 4,223 | 1,431 |
| Open issues | 453 | 10 |
| Language | Python | Jupyter Notebook |
| Adopt for | - | Offers ready-to-deploy LLM app templates for RAG and enterprise search with support for various data sources including real-time APIs. Comes with built-in vector indexing. |
| Persona | - | - |
| Runtime | - | - |
| License | Other | Offered under a MIT license allowing broad use and adaptation. |
| Categories | Data & Retrieval, Inference & Serving, AI Agents | Data & Retrieval, Model Training, Inference & Serving |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [onyx](/tools/onyx-dot-app-onyx.md) | [llm-app](/tools/pathwaycom-llm-app.md) |
| --- | --- | --- |
| Days since push | 0d | 2d |
| Open issues (now) | 453 | 10 |
| Security scan | 2 low (2 low) | No lockfile |
| Full report | [trust report](/tools/onyx-dot-app-onyx/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) |

**Typed relationship:** onyx _(alternative)_ llm-app

Both Onyx and llm-app are platforms that use LLMs for creating applications with features like RAG (Retrieval-Augmented Generation) and enterprise search. They offer similar functionality but are different implementations.

## Decision facts: onyx

- **Pricing:** unknown - The repository mentions an MIT license, implying open-source availability but does not specify additional licensing costs beyond the source code.
- **Requirements:** Requires Docker; Requires setup for major deployment modes including Docker, Kubernetes, and cloud provider setups as specified in its documentation.

## Decision facts: llm-app

- **Requirements:** Min 8 GB RAM; Requires Docker; Support for deployment on GCP, AWS, Azure, Render, or on-premises systems.
- **Adopt for:** Offers ready-to-deploy LLM app templates for RAG and enterprise search with support for various data sources including real-time APIs. Comes with built-in vector indexing.
- **License detail:** Offered under a MIT license allowing broad use and adaptation.

## Choose when

### Choose onyx if…

- onyx is primarily Python; llm-app is Jupyter Notebook.
- License: onyx is Other, llm-app is MIT.
- Pricing: The repository mentions an MIT license, implying open-source availability but does not specify additional licensing costs beyond the source code..
- Requirements: Requires Docker; Requires setup for major deployment modes including Docker, Kubernetes, and cloud provider setups as specified in its documentation..
- Both Onyx and llm-app are platforms that use LLMs for creating applications with features like RAG (Retrieval-Augmented Generation) and enterprise search. They offer similar functionality but are different implementations.
- Tags unique to onyx: web-search, ai-chat, code-execution, file-creation.
- Also covers AI Agents.
- You need a feature-rich interface with capabilities like RAG (Retrieval-Augmented Generation), web search, and code execution.

### Choose llm-app if…

- llm-app is primarily Jupyter Notebook; onyx is Python.
- License: llm-app is MIT, onyx is Other.
- Requirements: Min 8 GB RAM; Requires Docker; Support for deployment on GCP, AWS, Azure, Render, or on-premises systems..
- Both Onyx and llm-app are platforms that use LLMs for creating applications with features like RAG (Retrieval-Augmented Generation) and enterprise search. They offer similar functionality but are different implementations.
- Tags unique to llm-app: llm-prompting, open-ai, llm-local, hugging-face.
- Also covers Model Training.
- - When you need high-accuracy retrieval-augmented generation (RAG) or enterprise search applications that stay in sync with live data from multiple sources such as Sharepoint, Google Drive, and S3.

## When NOT to use onyx

- Your project does not require advanced capabilities like agentic RAG or deep research, preferring simplicity over feature richness.
- If you do not need extensive integration with external applications through the provided connectors or actions framework.
- When using Onyx for deployment would require features or integrations such as specific security protocols that are not inherently supported by its design.

## When NOT to use llm-app

- - If your project requires custom integration that goes beyond simple one-line changes in the provided templates. The 'llm-app' focuses on out-of-the-box solutions with limited depth into specialized,

## Common questions

### What is the difference between onyx and llm-app?

onyx: Open Source AI Platform - AI Chat with advanced features that works with every LLM. 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 onyx over llm-app?

Choose onyx over llm-app when onyx is primarily Python; llm-app is Jupyter Notebook; License: onyx is Other, llm-app is MIT; Pricing: The repository mentions an MIT license, implying open-source availability but does not specify additional licensing costs beyond the source code.; Requirements: Requires Docker; Requires setup for major deployment modes including Docker, Kubernetes, and cloud provider setups as specified in its documentation.; Both Onyx and llm-app are platforms that use LLMs for creating applications with features like RAG (Retrieval-Augmented Generation) and enterprise search. They offer similar functionality but are different implementations; Tags unique to onyx: web-search, ai-chat, code-execution, file-creation; Also covers AI Agents; You need a feature-rich interface with capabilities like RAG (Retrieval-Augmented Generation), web search, and code execution.

### When should I choose llm-app over onyx?

Choose llm-app over onyx when llm-app is primarily Jupyter Notebook; onyx is Python; License: llm-app is MIT, onyx is Other; Requirements: Min 8 GB RAM; Requires Docker; Support for deployment on GCP, AWS, Azure, Render, or on-premises systems.; Both Onyx and llm-app are platforms that use LLMs for creating applications with features like RAG (Retrieval-Augmented Generation) and enterprise search. They offer similar functionality but are different implementations; Tags unique to llm-app: llm-prompting, open-ai, llm-local, hugging-face; Also covers Model Training; - When you need high-accuracy retrieval-augmented generation (RAG) or enterprise search applications that stay in sync with live data from multiple sources such as Sharepoint, Google Drive, and S3.

### When should I avoid onyx?

Your project does not require advanced capabilities like agentic RAG or deep research, preferring simplicity over feature richness. If you do not need extensive integration with external applications through the provided connectors or actions framework. When using Onyx for deployment would require features or integrations such as specific security protocols that are not inherently supported by its design.

### When should I avoid llm-app?

- If your project requires custom integration that goes beyond simple one-line changes in the provided templates. The 'llm-app' focuses on out-of-the-box solutions with limited depth into specialized,

### Is onyx or llm-app more popular on GitHub?

llm-app has more GitHub stars (59,098 vs 30,773). Stars measure visibility, not whether either tool fits your constraints.

### Are onyx and llm-app open source?

Yes - both are open-source projects on GitHub (onyx: Other, llm-app: MIT).

### Where can I find alternatives to onyx or llm-app?

GraphCanon lists graph-backed alternatives at /tools/onyx-dot-app-onyx/alternatives and /tools/pathwaycom-llm-app/alternatives (/tools/onyx-dot-app-onyx/alternatives.md, /tools/pathwaycom-llm-app/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 /compare/onyx-dot-app-onyx-vs-pathwaycom-llm-app.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, onyx or llm-app?

onyx: Very active. 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 onyx and llm-app?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: onyx: /tools/onyx-dot-app-onyx/trust; llm-app: /tools/pathwaycom-llm-app/trust.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=onyx-dot-app-onyx`](/api/graphcanon/graph?tool=onyx-dot-app-onyx)
- 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/_