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Comparison

harbor vs llm-app

Verdict

Pick harbor if harbor is a rapid deployment tool for AI stacks using Docker and docker-compose; pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.

Markdown twin · harbor alternatives · llm-app alternatives

GraphCanon updated Sep 20, 2026

harbor logo

harbor

av/harbor

★ 3.2kpushed Sep 19, 2026
vs
llm-app logo

llm-app

pathwaycom/llm-app

★ 59kpushed Jul 5, 2026

Trust & integrity

Signalharborllm-app
Maintenance
Very active (0d since push)
As of Sep 20, 2026 · github_public_v1
Steady (74d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 15, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · 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
harbor trust reportllm-app trust reportTrust methodology

Tagline

harbor
Complete pre-wired LLM stack via one command
llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data

Stars

harbor
3.2k
llm-app
59k

Forks

harbor
227
llm-app
1.5k

Open issues

harbor
67
llm-app
8

Language

harbor
Python
llm-app
Jupyter Notebook

Adopt for

harbor
Harbor is a rapid deployment tool for AI stacks using Docker and docker-compose.
llm-app
llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.

Persona

harbor
-
llm-app
-

Runtime

harbor
-
llm-app
-

License

harbor
Apache-2.0
llm-app
MIT License

Last pushed

harbor
Sep 19, 2026
llm-app
Jul 5, 2026

Categories

harbor
Inference & Serving, LLM Frameworks, Model Training
llm-app
Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

harbor
Very active (96%)
llm-app
Steady (60%)

Days since push

harbor
0d
llm-app
74d

Open issues (now)

harbor
67
llm-app
8

Stars delta

harbor
+55 (30d)
llm-app
-117 (30d)

Open issues delta

harbor
+3 (30d)
llm-app
0 (30d)

Owner type

harbor
User
llm-app
Organization

Full report

harbor
Trust report
llm-app
Trust report

Choose harbor if…

  • harbor is primarily Python; llm-app is Jupyter Notebook.
  • License: harbor is Apache-2.0, llm-app is MIT.
  • Tags unique to harbor: ai, automation, bash, cli.
  • Also covers LLM Frameworks.
  • - When you need to deploy an AI stack quickly with minimal configuration

When NOT to use harbor

  • - If detailed customization at a service level is required beyond what the default setup offers
  • - In cases where the project does not align well with the pre-wired services and configurations harbor provides by default
GitHubDetails

Choose llm-app if…

  • llm-app is primarily Jupyter Notebook; harbor is Python.
  • License: llm-app is MIT, harbor is Apache-2.0.
  • Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs..
  • Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs..
  • Tags unique to llm-app: chatbot, hugging-face, llm, llm-local.
  • Also covers Data & Retrieval, Evaluation & Observability.
  • When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti

When NOT to use llm-app

  • Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support.
  • Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities.
GitHubDetails

Explore

harbor trust report →llm-app trust report →Inference & Serving category →LLM Frameworks category →Model Training category →Data & Retrieval category →Evaluation & Observability 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: harbor 3.2k · llm-app 59k (synced Sep 20, 2026).

Common questions

What is the difference between harbor and llm-app?
harbor: Complete pre-wired LLM stack via one command. 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 harbor over llm-app?
Choose harbor over llm-app when harbor is primarily Python; llm-app is Jupyter Notebook; License: harbor is Apache-2.0, llm-app is MIT; Tags unique to harbor: ai, automation, bash, cli; Also covers LLM Frameworks; - When you need to deploy an AI stack quickly with minimal configuration.
When should I choose llm-app over harbor?
Choose llm-app over harbor when llm-app is primarily Jupyter Notebook; harbor is Python; License: llm-app is MIT, harbor is Apache-2.0; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Data & Retrieval, Evaluation & Observability; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti.
When should I avoid harbor?
- If detailed customization at a service level is required beyond what the default setup offers - In cases where the project does not align well with the pre-wired services and configurations harbor provides by default
When should I avoid llm-app?
Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities.
Is harbor or llm-app more popular on GitHub?
llm-app has more GitHub stars (58,920 vs 3,217). Stars measure visibility, not whether either tool fits your constraints.
Are harbor and llm-app open source?
Yes - both are open-source projects on GitHub (harbor: Apache-2.0, llm-app: MIT).
Where can I find alternatives to harbor or llm-app?
GraphCanon lists graph-backed alternatives at harbor alternatives and llm-app alternatives (harbor 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, harbor or llm-app?
harbor: Very active. llm-app: Steady. 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 harbor and llm-app?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: harbor trust report; llm-app trust report.

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