Comparison
harbor vs llm
Verdict
Pick harbor if harbor is a rapid deployment tool for AI stacks using Docker and docker-compose; pick llm if decision-critical facts for 'llm'.
Markdown twin · harbor alternatives · llm alternatives
GraphCanon updated Sep 20, 2026
7views this month
Trust & integrity
| Signal | harbor | llm |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 | Very active (4d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 7, 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 Jul 11, 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 |
Tagline
- harbor
- Complete pre-wired LLM stack via one command
- llm
- Access large language models from the command-line
Stars
- harbor
- 3.2k
- llm
- 12k
Forks
- harbor
- 227
- llm
- 978
Open issues
- harbor
- 67
- llm
- 689
Language
- harbor
- Python
- llm
- Python
Adopt for
- harbor
- Harbor is a rapid deployment tool for AI stacks using Docker and docker-compose.
- llm
- Decision-critical facts for 'llm'
Persona
- harbor
- -
- llm
- -
Runtime
- harbor
- -
- llm
- -
License
- harbor
- Apache-2.0
- llm
- Apache-2.0
Last pushed
- harbor
- Sep 19, 2026
- llm
- Sep 2, 2026
Categories
- harbor
- Inference & Serving, LLM Frameworks, Model Training
- llm
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- harbor
- 0d
- llm
- 4d
Open issues (now)
- harbor
- 67
- llm
- 689
Stars delta
- harbor
- +55 (30d)
- llm
- +149 (30d)
Open issues delta
- harbor
- +3 (30d)
- llm
- +25 (30d)
Full report
- harbor
- Trust report
- llm
- Trust report
Choose harbor if…
- Tags unique to harbor: automation, bash, cli, container.
- Also covers Model Training.
- - 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
Choose llm if…
- Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities..
- Tags unique to llm: llms, openai.
- - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods.
When NOT to use llm
- - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based.
- - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (av/harbor) · observed Sep 20, 2026
- GitHub forks (av/harbor) · observed Sep 20, 2026
- Last push (av/harbor) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (simonw/llm) · observed Sep 20, 2026
- GitHub forks (simonw/llm) · observed Sep 20, 2026
- Last push (simonw/llm) · observed Sep 2, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: harbor 3.2k · llm 12k (synced Sep 20, 2026).
Common questions
- What is the difference between harbor and llm?
- harbor: Complete pre-wired LLM stack via one command. llm: Access large language models from the command-line. See the comparison table for live GitHub stats and shared categories.
- When should I choose harbor over llm?
- Choose harbor over llm when Tags unique to harbor: automation, bash, cli, container; Also covers Model Training; - When you need to deploy an AI stack quickly with minimal configuration.
- When should I choose llm over harbor?
- Choose llm over harbor when Requirements: - Installation supports multiple methods including
pip, Homebrew (with caveats noted),pipx, anduv.; - Requires an OpenAI API key for certain functionalities.; Tags unique to llm: llms, openai; - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. - 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?
- - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models.
- Is harbor or llm more popular on GitHub?
- llm has more GitHub stars (12,473 vs 3,217). Stars measure visibility, not whether either tool fits your constraints.
- Are harbor and llm open source?
- Yes - both are open-source projects on GitHub (harbor: Apache-2.0, llm: Apache-2.0).
- Where can I find alternatives to harbor or llm?
- GraphCanon lists graph-backed alternatives at harbor alternatives and llm alternatives (harbor markdown twin, llm 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?
- harbor: Very active. llm: 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 harbor and llm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: harbor trust report; llm trust report.