🌐 Language

Contact 📫

The main contributor to this repository is a Master's student graduating in 2026; feel free to reach out for collaboration or opportunities.

The main contributor of this repository is a 2026 Master's graduate, welcome to contact for cooperation or exchange opportunities.

News 📅

[2026/03]: Now supports CLI and has released our fine-tuned models on Hugging Face 🤗!
[2026/01]: Supports freeform and template generation, PPTX export, and offline mode! Context management added to prevent context overflow.
[2025/12]: 🔥 V2 released with major improvements - Deep Research Integration, Free-Form Visual Design, Autonomous Asset Creation, Text-to-Image Generation, and Agent Environment with sandbox & 20+ tools.
[2025/09]: 🛠️ Added MCP server support - see MCP Server for configuration details
[2025/09]: 🚀 Released v2 with major improvements - see release notes for details
[2025/08]: 🎉 Paper accepted to EMNLP 2025!
[2025/05]: ✨ Released v1 with core functionality and 🌟 milestone: reached 1,000 stars on GitHub! - see release notes for details
[2025/01]: 🔓 Open-sourced the codebase, with experimental code archived at experiment release

Usage 📖

[!IMPORTANT]

Windows is not supported. If you are using Windows, please use WSL.

We highly recommend starting with the CLI and minimum task to verify dependencies and environment are correctly configured.

Configuration

If you use the CLI, pptagent onboard can assist in creating and updating these configurations interactively. If you use Docker Compose or build from source, you should prepare them manually:

cp deeppresenter/config.yaml.example deeppresenter/config.yaml
cp deeppresenter/mcp.json.example deeppresenter/mcp.json

#### Optional Services That Improve Quality

The following services can noticeably improve generation quality, especially for research depth, PDF parsing, and visual asset creation:

Tavily: improves web search quality. Apply for an API key at tavily.com, then set TAVILY_API_KEY in deeppresenter/mcp.json.
MinerU: improves PDF parsing quality. You can either apply for an API key at mineru.net and set MINERU_API_KEY in deeppresenter/mcp.json, or deploy MinerU locally and set MINERU_API_URL instead.
Text-to-image model: improves image generation quality. Configure t2i_model in deeppresenter/config.yaml.

If you want a fully offline setup, deploy MinerU locally and set offline_mode: true in deeppresenter/config.yaml to avoid loading network-dependent tools such as web search.

More configurable variables can be found in constants.py.

1. Personal Use / OpenClaw Integration: CLI

[!NOTE]

On macOS, the CLI may automatically install several local dependencies, including Homebrew, Node.js, Docker, poppler, Playwright, and llama.cpp.

On Linux, you should prepare the environment by yourself.

Use this mode if you want the fastest local setup or want to plug DeepPresenter into OpenClaw through the CLI.

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
First-time interactive setup
uvx pptagent onboard
Generate a presentation
uvx pptagent generate "Single Page with Title: Hello World" -o hello.pptx
Generate with attachments
uvx pptagent generate "Q4 Report" \
  -f data.xlsx \
  -f charts.pdf \
  -p "10-12" \
  -o report.pptx

| Command | Description | | ------------------- | ------------------------------------------------- | | pptagent onboard | Interactive configuration wizard | | pptagent generate | Generate presentations | | pptagent config | View current configuration | | pptagent reset | Reset configuration | | pptagent serve | Start the local inference service used by the CLI |

2. Minimal Setup / Development: Build From Source

Use this mode if you want the smallest abstraction layer and full control over dependencies during development.

uv pip install -e .
playwright install-deps
playwright install chromium
npm install --prefix deeppresenter/html2pptx
modelscope download forceless/fasttext-language-id
docker pull forceless/deeppresenter-sandbox
docker pull forceless/deeppresenter-host
docker tag forceless/deeppresenter-sandbox deeppresenter-sandbox
or build from dockerfile
docker build -t deeppresenter-sandbox -f deeppresenter/docker/SandBox.Dockerfile .

Start the app:

python webui.py

3. Server Deployment: Docker Compose

Use this mode for a stable server environment with explicit dependencies.

# Pull the public images to avoid build from source
docker pull forceless/deeppresenter-sandbox
docker tag forceless/deeppresenter-sandbox deeppresenter-sandbox
Or build from source
docker build -t deeppresenter-sandbox -f deeppresenter/docker/SandBox.Dockerfile .
Start the host service
docker compose up -d

The service exposes the web UI on http://localhost:7861.

Case Study 💡

#### Prompt: Please present the given document to me.

#### Prompt: 请介绍小米 SU7 的外观和价格

#### Prompt: 请制作一份高中课堂展示课件，主题为“解码立法过程：理解其对国际关系的影响”

Contributors 🌟

_Force1ess	_Puelloc	_hongyan	_Dnoob	_Sadahlu
_{KurisuMakiseSame}	_Angelen	_BrandonHu	_{Eliot White}	_EvolvedGhost
_ISCAS-zwl	_{James Brown}	_JunZhang	_{Open AI Tx}	_{Sense_wang}
_SuYao	_{Zakir Jiwani}	_Zhenyu	_lnennnn

Citation 🙏

If you find this project helpful, please use the following to cite it:

@inproceedings{zheng-etal-2025-pptagent, title = "{PPTA}gent: Generating and Evaluating Presentations Beyond Text-to-Slides", author = "Zheng, Hao and Guan, Xinyan and Kong, Hao and Zhang, Wenkai and Zheng, Jia and Zhou, Weixiang and Lin, Hongyu and Lu, Yaojie and Han, Xianpei and Sun, Le", editor = "Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet", booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2025", address = "Suzhou, China", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.emnlp-main.728/", doi = "10.18653/v1/2025.emnlp-main.728", pages = "14413--14429", ISBN = "979-8-89176-332-6", abstract = "Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to extract slide-level functional types and content schemas, then drafts an outline and iteratively generates editing actions based on selected reference slides to create new slides. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Results demonstrate that PPTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions." }

@misc{zheng2026deeppresenterenvironmentgroundedreflectionagentic, title={DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation}, author={Hao Zheng and Guozhao Mo and Xinru Yan and Qianhao Yuan and Wenkai Zhang and Xuanang Chen and Yaojie Lu and Hongyu Lin and Xianpei Han and Le Sun}, year={2026}, eprint={2602.22839}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2602.22839}, }

--- Tranlated By Open Ai Tx | Last indexed: 2026-04-09 ---