--- title: "EasyEdit vs KnowledgeEditingPapers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/zjunlp-easyedit-vs-zjunlp-knowledgeeditingpapers" tools: ["zjunlp-easyedit", "zjunlp-knowledgeeditingpapers"] --- # EasyEdit vs KnowledgeEditingPapers *GraphCanon updated Aug 8, 2026* ## Verdict Pick EasyEdit if easyEdit is an easy-to-use knowledge editing framework for LLMs tailored to model customization and optimization; pick KnowledgeEditingPapers if a specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧. [EasyEdit](https://zjunlp.github.io/project/KnowEdit) reports 2.9k GitHub stars, 371 forks, and 0 open issues, last pushed Jul 14, 2026. [KnowledgeEditingPapers](https://github.com/zjunlp/KnowledgeEditingPapers) has 1.2k stars, 78 forks, and 0 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [EasyEdit's repository](https://github.com/zjunlp/EasyEdit) and [KnowledgeEditingPapers's repository](https://github.com/zjunlp/KnowledgeEditingPapers). | | [EasyEdit](/tools/zjunlp-easyedit.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Tagline | Easy-to-use knowledge editing framework for LLMs | Must-read Papers on Knowledge Editing for Large Language Models | | Stars | 2,895 | 1,245 | | Forks | 371 | 78 | | Open issues | 0 | 0 | | Language | Jupyter Notebook | - | | Adopt for | EasyEdit is an easy-to-use knowledge editing framework for LLMs tailored to model customization and optimization. | A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [EasyEdit](/tools/zjunlp-easyedit.md) | [KnowledgeEditingPapers](/tools/zjunlp-knowledgeeditingpapers.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 24d | 42d | | Full report | [trust report](/tools/zjunlp-easyedit/trust.md) | [trust report](/tools/zjunlp-knowledgeeditingpapers/trust.md) | ## 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. ## Decision facts: KnowledgeEditingPapers - **Hosting:** unknown - **Adopt for:** A specialized collection of foundational papers and reports that delve into the editing and manipulation of knowledge within large language models, making it a valuable resource for researchers looking to understand and斧 - **License detail:** MIT ## Choose when ### Choose EasyEdit if… - Requirements: Installation requires Python 3.9+ and Conda.. - Tags unique to EasyEdit: artificial-intelligence, tool, trustworthy-ai. - EasyEdit ships Docker support for self-hosted deployment. - In situations where you need a user-friendly tool for customizing and optimizing large language models. ### Choose KnowledgeEditingPapers if… - Tags unique to KnowledgeEditingPapers: pre-trained-language-models. - You are specifically interested in recent advancements in knowledge editing techniques for large language models. ## 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. ## When NOT to use KnowledgeEditingPapers - You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. - If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. - Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ## Common questions ### What is the difference between EasyEdit and KnowledgeEditingPapers? EasyEdit: Easy-to-use knowledge editing framework for LLMs. KnowledgeEditingPapers: Must-read Papers on Knowledge Editing for Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose EasyEdit over KnowledgeEditingPapers? Choose EasyEdit over KnowledgeEditingPapers when Requirements: Installation requires Python 3.9+ and Conda.; Tags unique to EasyEdit: artificial-intelligence, tool, trustworthy-ai; 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 choose KnowledgeEditingPapers over EasyEdit? Choose KnowledgeEditingPapers over EasyEdit when Tags unique to KnowledgeEditingPapers: pre-trained-language-models; You are specifically interested in recent advancements in knowledge editing techniques for large language models. ### 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. ### When should I avoid KnowledgeEditingPapers? You are looking for a broad overview of machine learning or AI in general, as this repository focuses narrowly on knowledge editing within large language models. If you seek practical tooling or implementation guidance rather than theoretical insights and review papers. Your focus is more on data preprocessing or model training techniques unrelated to the specific modification of knowledge mechanisms in LLMs. ### Is EasyEdit or KnowledgeEditingPapers more popular on GitHub? EasyEdit has more GitHub stars (2,895 vs 1,245). Stars measure visibility, not whether either tool fits your constraints. ### Are EasyEdit and KnowledgeEditingPapers open source? Yes - both are open-source projects on GitHub (EasyEdit: MIT, KnowledgeEditingPapers: MIT). ### Where can I find alternatives to EasyEdit or KnowledgeEditingPapers? GraphCanon lists graph-backed alternatives at [EasyEdit alternatives](/tools/zjunlp-easyedit/alternatives) and [KnowledgeEditingPapers alternatives](/tools/zjunlp-knowledgeeditingpapers/alternatives) ([EasyEdit markdown twin](/tools/zjunlp-easyedit/alternatives.md), [KnowledgeEditingPapers markdown twin](/tools/zjunlp-knowledgeeditingpapers/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/zjunlp-easyedit-vs-zjunlp-knowledgeeditingpapers.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, EasyEdit or KnowledgeEditingPapers? EasyEdit: Active. KnowledgeEditingPapers: 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 EasyEdit and KnowledgeEditingPapers? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [EasyEdit trust report](/tools/zjunlp-easyedit/trust); [KnowledgeEditingPapers trust report](/tools/zjunlp-knowledgeeditingpapers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=zjunlp-easyedit`](/api/graphcanon/graph?tool=zjunlp-easyedit) - 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/_