--- title: "FastEdit vs EasyEdit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-fastedit-vs-zjunlp-easyedit" tools: ["hiyouga-fastedit", "zjunlp-easyedit"] --- # FastEdit vs EasyEdit *GraphCanon updated Aug 8, 2026* ## Verdict Pick FastEdit if fastEdit is a Python library for quick edits to large language models using PyTorch; pick EasyEdit if easyEdit is an easy-to-use knowledge editing framework for LLMs tailored to model customization and optimization. [FastEdit](https://github.com/hiyouga/FastEdit) reports 1.4k GitHub stars, 103 forks, and 21 open issues, last pushed Aug 13, 2023. [EasyEdit](https://zjunlp.github.io/project/KnowEdit) has 2.9k stars, 371 forks, and 0 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [FastEdit's repository](https://github.com/hiyouga/FastEdit) and [EasyEdit's repository](https://github.com/zjunlp/EasyEdit). | | [FastEdit](/tools/hiyouga-fastedit.md) | [EasyEdit](/tools/zjunlp-easyedit.md) | | --- | --- | --- | | Tagline | Editing large language models within 10 seconds | Easy-to-use knowledge editing framework for LLMs | | Stars | 1,370 | 2,895 | | Forks | 103 | 371 | | Open issues | 21 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | FastEdit is a Python library for quick edits to large language models using PyTorch. | EasyEdit is an easy-to-use knowledge editing framework for LLMs tailored to model customization and optimization. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastEdit](/tools/hiyouga-fastedit.md) | [EasyEdit](/tools/zjunlp-easyedit.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1086d | 24d | | Open issues (now) | 21 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-fastedit/trust.md) | [trust report](/tools/zjunlp-easyedit/trust.md) | ## Shared compatibility - **Python**: [FastEdit](/tools/hiyouga-fastedit.md) - Python runtime; [EasyEdit](/tools/zjunlp-easyedit.md) - Python runtime ## Decision facts: FastEdit - **Requirements:** Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models. - **Adopt for:** FastEdit is a Python library for quick edits to large language models using PyTorch. ## Decision facts: EasyEdit - **Requirements:** Installation requires Python 3.9+ and Conda. - **Adopt for:** EasyEdit is an easy-to-use knowledge editing framework for LLMs tailored to model customization and optimization. ## Choose when ### Choose FastEdit if… - FastEdit is primarily Python; EasyEdit is Jupyter Notebook. - License: FastEdit is Apache-2.0, EasyEdit is MIT. - Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.. - Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon. - When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### Choose EasyEdit if… - EasyEdit is primarily Jupyter Notebook; FastEdit is Python. - License: EasyEdit is MIT, FastEdit is Apache-2.0. - Requirements: Installation requires Python 3.9+ and Conda.. - Tags unique to EasyEdit: artificial-intelligence, knowledge-editing, model-editing, natural-language-processing. - Also covers Model Training. - EasyEdit ships Docker support for self-hosted deployment. - In situations where you need a user-friendly tool for customizing and optimizing large language models. ## When NOT to use FastEdit - If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. - For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. - If rapid edits within seconds are not a priority and longer processing times can be tolerated. ## When NOT to use EasyEdit - If your project requires minimal GPU memory usage; tools like AdaLoRA can operate with about 29GB on llama-2-7B, whereas EasyEdit may demand more resources. - You require a solution that works exclusively within CPU-only environments without the option to adjust for specific backends via tools such as uv. ## Common questions ### What is the difference between FastEdit and EasyEdit? FastEdit: Editing large language models within 10 seconds. EasyEdit: Easy-to-use knowledge editing framework for LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastEdit over EasyEdit? Choose FastEdit over EasyEdit when FastEdit is primarily Python; EasyEdit is Jupyter Notebook; License: FastEdit is Apache-2.0, EasyEdit is MIT; Requirements: Min -1 GB RAM; Requires Python 3.8+ and PyTorch 1.13.1+. Must also install 🤗Transformers, Datasets, Accelerate, sentencepiece, and fire.; Hardware requirements for a specific model can vary; refer to the provided table for minimum RAM sizes for different models.; Tags unique to FastEdit: bloom, chatbots, chatgpt, falcon; When rapid iterations on language model edits are necessary, such as testing and tuning with tight feedback loops. ### When should I choose EasyEdit over FastEdit? Choose EasyEdit over FastEdit when EasyEdit is primarily Jupyter Notebook; FastEdit is Python; License: EasyEdit is MIT, FastEdit is Apache-2.0; Requirements: Installation requires Python 3.9+ and Conda.; Tags unique to EasyEdit: artificial-intelligence, knowledge-editing, model-editing, natural-language-processing; Also covers Model Training; EasyEdit ships Docker support for self-hosted deployment; In situations where you need a user-friendly tool for customizing and optimizing large language models. ### When should I avoid FastEdit? If your workflow requires integration with TensorFlow instead of PyTorch, since FastEdit is built on top of PyTorch. For hardware configurations that cannot meet the fast editing mode's requirements; for instance, if you have less than 24GB RAM available. If rapid edits within seconds are not a priority and longer processing times can be tolerated. ### When should I avoid EasyEdit? If your project requires minimal GPU memory usage; tools like AdaLoRA can operate with about 29GB on llama-2-7B, whereas EasyEdit may demand more resources. You require a solution that works exclusively within CPU-only environments without the option to adjust for specific backends via tools such as uv. ### Is FastEdit or EasyEdit more popular on GitHub? EasyEdit has more GitHub stars (2,895 vs 1,370). Stars measure visibility, not whether either tool fits your constraints. ### Are FastEdit and EasyEdit open source? Yes - both are open-source projects on GitHub (FastEdit: Apache-2.0, EasyEdit: MIT). ### Where can I find alternatives to FastEdit or EasyEdit? GraphCanon lists graph-backed alternatives at [FastEdit alternatives](/tools/hiyouga-fastedit/alternatives) and [EasyEdit alternatives](/tools/zjunlp-easyedit/alternatives) ([FastEdit markdown twin](/tools/hiyouga-fastedit/alternatives.md), [EasyEdit markdown twin](/tools/zjunlp-easyedit/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/hiyouga-fastedit-vs-zjunlp-easyedit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastEdit or EasyEdit? FastEdit: Dormant. EasyEdit: 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 FastEdit and EasyEdit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastEdit trust report](/tools/hiyouga-fastedit/trust); [EasyEdit trust report](/tools/zjunlp-easyedit/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-fastedit`](/api/graphcanon/graph?tool=hiyouga-fastedit) - 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/_