--- title: "fastembed vs hazm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/qdrant-fastembed-vs-roshan-research-hazm" tools: ["qdrant-fastembed", "roshan-research-hazm"] --- # fastembed vs hazm *GraphCanon updated Aug 22, 2026* ## 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. [fastembed](https://qdrant.github.io/fastembed/) reports 3.2k GitHub stars, 231 forks, and 111 open issues, last pushed Aug 19, 2026. [hazm](https://www.roshan-ai.ir/hazm/) has 1.4k stars, 208 forks, and 8 open issues, last pushed Apr 1, 2026. Figures are from public GitHub metadata via [fastembed's repository](https://github.com/qdrant/fastembed) and [hazm's repository](https://github.com/roshan-research/hazm). | | [fastembed](/tools/qdrant-fastembed.md) | [hazm](/tools/roshan-research-hazm.md) | | --- | --- | --- | | Tagline | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization | | Stars | 3,158 | 1,417 | | Forks | 231 | 208 | | Open issues | 111 | 8 | | Language | Python | Python | | Adopt for | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | Hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastembed](/tools/qdrant-fastembed.md) | [hazm](/tools/roshan-research-hazm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 142d | | Open issues (now) | 111 | 8 | | Stars delta | +55 (30d) | +5 (30d) | | Open issues delta | -26 (30d) | +1 (30d) | | Full report | [trust report](/tools/qdrant-fastembed/trust.md) | [trust report](/tools/roshan-research-hazm/trust.md) | ## Shared compatibility - **Python**: [fastembed](/tools/qdrant-fastembed.md) - Python runtime; [hazm](/tools/roshan-research-hazm.md) - Python runtime ## Decision facts: fastembed - **Requirements:** Does not require Docker, making the setup straightforward for Python environments. - **Adopt for:** Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. - **License detail:** Apache-2.0 License ## Decision facts: hazm - **Adopt for:** Hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/qdrant-fastembed/alternatives) and [hazm alternatives](/tools/roshan-research-hazm/alternatives) ([fastembed markdown twin](/tools/qdrant-fastembed/alternatives.md), [hazm markdown twin](/tools/roshan-research-hazm/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 [this comparison](/compare/qdrant-fastembed-vs-roshan-research-hazm.md) 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](/tools/qdrant-fastembed/trust); [hazm trust report](/tools/roshan-research-hazm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=qdrant-fastembed`](/api/graphcanon/graph?tool=qdrant-fastembed) - 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/_