MemSlides

Hierarchical Memory for Personalized Slide Generation
Multi-turn Local Revision with User-profile, Working, and Tool Memory

Official code repository for the MemSlides paper.

This repository contains the official implementation of MemSlides: A Hierarchical Memory-Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision.

MemSlides integrates user-profile memory, working memory, and tool memory to support persistent personalization, revision-context retention, and the reuse of tool-execution experience. The implementation includes slide-generation and revision pipelines, template induction, artifact export, and experiment utilities for studying these mechanisms.

Official website: memslides.com. The domain memslides.org is not operated by this project and is not affiliated with this repository.

Demo

The following demonstration illustrates slide generation, multi-turn revision, explicit preference saving, preference reuse, and export through the web interface.

https://github.com/user-attachments/assets/51abee82-14bf-4b34-adf8-6f630086bb87

_{60-second website walkthrough using a four-slide research example. Waiting time is shortened; previews and downloads use a previously generated public example. Saving a preference is an explicit choice.}

News

• Web interface: the updated online demonstration provides access to slide generation, revision, preference management, and export.
• Demonstration video: the new walkthrough illustrates the workflow and the reuse of saved preferences across presentations.
• Research materials: the paper and project page describe the framework and present qualitative examples.

Key Capabilities

Getting Started

Online Demonstration

The hosted demonstration is available at memslides.com. Sign-in or registration is required to access the interface.

The interface supports specifying the topic, audience, language, and slide count, followed by preview and natural-language revision. Preference persistence is explicit: users save selected preferences and activate them for subsequent presentations.

This repository provides the Python implementation and experiment environment associated with the paper. The Docker image supports CLI execution and experiment reproduction.

Local Installation and Inference

Recommended environment: Python 3.11, Node.js 20, LibreOffice, Poppler, CJK fonts, and Playwright Chromium. See the installation guide for Windows, private configuration, and export setup.

On Linux, with Conda installed:

git clone https://github.com/huohua325/Memslides.git
cd Memslides

sudo apt-get update
sudo apt-get install -y libreoffice fontconfig fonts-noto-cjk poppler-utils

conda env create -f environment.yml
conda activate memslides
pip install -e ".[research]"
python -m playwright install chromium ffmpeg

Configure your models before generating. Copy the public configuration to a local file:

cp src/memslides/memslides.yaml memslides.yaml

Configure the root-level memslides.yaml with your model endpoints, model names, and credentials for the agents and enabled services. This local file is ignored by git; the packaged public configuration contains environment-variable placeholders. Credentials can also be supplied through those environment variables. See configuration details.

Generate a deck, then revise it in the same workspace:

python -m memslides --config memslides.yaml --workspace .memslides/demo generate \
  --instruction "Create a four-slide research overview" --num-pages 4

python -m memslides --config memslides.yaml --workspace .memslides/demo revise \
  --feedback "Shorten the title on slide 2"

The commands print a JSON result with the workspace and available artifact paths. Global options such as --config, --workspace, and --language go before the subcommand.

docker run --rm huohua325/memslides:v2.0.1 python -m memslides.experiment --help

python -m memslides template induct --template-file template.pptx

python -m memslides.experiment run smoke_minimal \
  --output-base .memslides/experiments --parallel 1

python -m memslides.experiment report .memslides/experiments/smoke_minimal
python -m memslides.experiment personas

Method Overview

The framework organizes generation and revision around three complementary memory components:

• User-profile memory stores persistent preferences and retrieves them according to presentation intent for subsequent generation tasks.
• Working memory maintains task-specific constraints, temporary preferences, and editing state across revision turns.
• Tool memory stores and retrieves prior tool-execution experience to inform related editing operations.

During local revision, the runtime interprets user feedback to identify the affected slide region and constrain the scope of the update. See the paper for the method and experimental evaluation.

Research resources: Paper · Project page · Hugging Face Space.

src/memslides/              Python runtime, contracts, and public CLI
src/memslides/pipelines/    Generation, revision, and export
src/memslides/memory/       Profile, working, and tool memory
src/memslides/experiment/   Experiment suites and reports
src/memslides/tools/        MCP tools and services
tests/                     Regression and contract tests
docs/INSTALL.md            Installation and configuration
docker/README.md           Docker environment and image tags
.memslides/                Ignored local workspaces and outputs

pip install -e ".[dev,research]"
python -m compileall -q src tests
python scripts/check_source_hygiene.py
python -m pytest -q
ruff check src tests

npm ci
npm run check:pptx-export

Contributing

Implementation questions, reproducibility reports, and proposed extensions are welcome in Issues.

• Report a bug: open an issue with reproduction steps, your environment, and sanitized logs. Mention whether it concerns the website or the local runtime.
• Report an experimental issue: provide the suite or configuration used, relevant environment details, and the observed result.
• Propose an extension: describe the research or implementation motivation and the intended behavior.
• Contribute code or documentation: browse open issues, discuss substantial changes first, and include relevant checks in your pull request. Corrections and reproducible examples are welcome.

To follow implementation releases, choose Watch → Custom → Releases on GitHub.

Privacy and configuration

Keep API keys in environment variables, .env, or private YAML files. Do not commit credentials, generated workspaces, or memory databases. Remove private content from examples and logs before sharing them. External acquisition depends on the services you configure; review downloaded assets before presenting.

Citation

If you use MemSlides in your research, please cite:

@misc{jin2026memslides,
  title={MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision},
  author={Ye Jin and Yangyang Xu and Jun Zhu and Yibo Yang},
  year={2026},
  eprint={2606.17162},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  doi={10.48550/arXiv.2606.17162},
  url={https://arxiv.org/abs/2606.17162},
}

License

Released under the Apache License 2.0. See third-party notices for included components.