Comparison
fastembed vs hazm
Verdict
Pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings; pick hazm if hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python.
Markdown twin · fastembed alternatives · hazm alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | fastembed | hazm |
|---|---|---|
| Maintenance | Very active (2d since push) As of 4d · github_public_v1 | Slowing (142d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- fastembed
- Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings
- hazm
- Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization
Stars
- fastembed
- 3.2k
- hazm
- 1.4k
Forks
- fastembed
- 231
- hazm
- 208
Open issues
- fastembed
- 111
- hazm
- 8
Language
- fastembed
- Python
- hazm
- Python
Adopt for
- fastembed
- Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings.
- hazm
- Hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python.
Persona
- fastembed
- -
- hazm
- -
Runtime
- fastembed
- -
- hazm
- -
License
- fastembed
- Apache-2.0 License
- hazm
- MIT
Last pushed
- fastembed
- Aug 19, 2026
- hazm
- Apr 1, 2026
Categories
- fastembed
- Data & Retrieval, Vector Databases
- hazm
- Data & Retrieval, Model Training
Trust and health
Maintenance
- fastembed
- Very active (96%)
- hazm
- Slowing (36%)
Days since push
- fastembed
- 2d
- hazm
- 142d
Open issues (now)
- fastembed
- 111
- hazm
- 8
Stars delta
- fastembed
- +55 (30d)
- hazm
- +5 (30d)
Open issues delta
- fastembed
- -26 (30d)
- hazm
- +1 (30d)
Full report
- fastembed
- Trust report
- hazm
- Trust report
Shared compatibility
- Python · fastembed: Python runtime · hazm: Python runtime
Choose fastembed if…
- License: fastembed is Apache-2.0, hazm is MIT.
- Requirements: Does not require Docker, making the setup straightforward for Python environments..
- Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search.
- Also covers Vector Databases.
- When you need to generate high-quality embeddings quickly in Python.
When NOT to use fastembed
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly.
- In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
Choose hazm if…
- License: hazm is MIT, fastembed is Apache-2.0.
- Tags unique to hazm: dependency-parser, lemmatization, natural-language-processing, nlp.
- Also covers Model Training.
- When working exclusively with Farsi language texts where comprehensive processing tools like dependency parsing or POS tagging are required.
When NOT to use hazm
- If the project involves languages other than Persian, as Hazm lacks capabilities for multilingual support beyond Farsi.
- When advanced machine learning models requiring extensive training data and resources are necessary; Hazm provides utilities but does not include state-of-the-art models.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (qdrant/fastembed) · observed Aug 22, 2026
- GitHub forks (qdrant/fastembed) · observed Aug 22, 2026
- Last push (qdrant/fastembed) · observed Aug 19, 2026
- License file (Apache-2.0) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (roshan-research/hazm) · observed Aug 22, 2026
- GitHub forks (roshan-research/hazm) · observed Aug 22, 2026
- Last push (roshan-research/hazm) · observed Apr 1, 2026
- License file (MIT) · observed Aug 22, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: fastembed 3.2k · hazm 1.4k (synced Aug 22, 2026).
Common questions
- What is the difference between fastembed and hazm?
- fastembed: Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings. hazm: Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization. See the comparison table for live GitHub stats and shared categories.
- When should I choose fastembed over hazm?
- Choose fastembed over hazm when License: fastembed is Apache-2.0, hazm is MIT; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search; Also covers Vector Databases; When you need to generate high-quality embeddings quickly in Python.
- When should I choose hazm over fastembed?
- Choose hazm over fastembed when License: hazm is MIT, fastembed is Apache-2.0; Tags unique to hazm: dependency-parser, lemmatization, natural-language-processing, nlp; Also covers Model Training; When working exclusively with Farsi language texts where comprehensive processing tools like dependency parsing or POS tagging are required.
- When should I avoid fastembed?
- If your project is not using Python, as Fastembed does not offer support for other programming languages directly. In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility.
- When should I avoid hazm?
- If the project involves languages other than Persian, as Hazm lacks capabilities for multilingual support beyond Farsi. When advanced machine learning models requiring extensive training data and resources are necessary; Hazm provides utilities but does not include state-of-the-art models.
- Is fastembed or hazm more popular on GitHub?
- fastembed has more GitHub stars (3,158 vs 1,417). Stars measure visibility, not whether either tool fits your constraints.
- Are fastembed and hazm open source?
- Yes - both are open-source projects on GitHub (fastembed: Apache-2.0, hazm: MIT).
- Where can I find alternatives to fastembed or hazm?
- GraphCanon lists graph-backed alternatives at fastembed alternatives and hazm alternatives (fastembed markdown twin, hazm 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, fastembed or hazm?
- fastembed: Very active. hazm: Slowing. 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 fastembed and hazm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: fastembed trust report; hazm trust report.