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Easyedit

Easyedit is an MIT-licensed framework for editing knowledge in large language models. The public repository includes installation and quick-start materials, evaluation resources, notebooks and links to related papers, demos and benchmarks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Easyedit — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Easyedit when the task involves experimentally changing, evaluating or steering what a language model knows or does, rather than providing a general-purpose chat interface.

Who it is for

  • LLM researchers studying knowledge editing and model editing
  • Developers building tools for testing factual updates to language models
  • Teams prototyping evaluation or domain-specific knowledge-maintenance workflows

Documented uses

  • Insert, update or erase selected knowledge in an LLM
  • Compare model-editing techniques through documented evaluation and benchmark resources
  • Create notebook-based experiments for factual model updates
  • Investigate inference-time steering with the EasyEdit 2 workflow
Before you choose

Requirements & limits

The repository is publicly visible and displays an MIT license. It is classified for cloud or self-hosted use.

  • Whether the project should study parameter-based editing or EasyEdit 2’s inference-time steering
  • Whether the workflow needs insertion, updating or erasure of knowledge
  • Whether benchmark, evaluation, notebook and KnowEdit resources are important to the project
  • Whether an MIT-licensed framework fits the intended development context

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Easyedit for knowledge-editing research

Prepare the documented framework materials for exploring LLM knowledge editing and evaluation.

Before you start

  • A development environment suitable for the repository’s installation instructions.
  • Access to the Easyedit repository and its linked documentation.

Steps

  1. Follow the repository’s Installation section.

  2. Review the quick-start material and the simple BaseEditor example.

  3. Choose the relevant knowledge-editing task: insertion, updating or erasure.

  4. Use the documented evaluation, tutorial notebook or KnowEdit materials for the selected workflow.

Expected outcome

A prepared starting point for investigating Easyedit’s documented model-editing and evaluation workflows.

Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
EasyeditInsert, update or erase selected knowledge in an LLMCloud, Self-hostedThe repository is publicly visible and displays an MIT license. It is classified for cloud or self-hosted use. No pricing or account requirement is documented in the supplied evidence.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
zjunlp/EasyEdit · Official homepage title · retained in its source language
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