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PPTAgent

Added Jan 27, 2026
Agent & Tooling
Open Source
PythonTypeScriptWorkflow AutomationLarge Language ModelsAI AgentsAgent FrameworkAgent & ToolingAutomation, Workflow & RPAEnterprise Applications & Office

An agentic framework for reflective PowerPoint generation that automatically transforms documents into visually appealing and structurally coherent presentations.

One-Minute Overview#

PPTAgent is a revolutionary AI tool that can automatically transform documents, research materials, or any topic content into professional and visually appealing PowerPoint presentations. It's especially suitable for researchers, students, and professionals who need to quickly create high-quality presentation materials. Unlike traditional slide creation tools, PPTAgent can understand content structures, automatically generate appropriate visual designs, and maintain information coherence, significantly saving time on manual slide design.

Core Value: Automate the transformation from documents to professional presentations through AI agents, achieving high-quality results without manual design

Quick Start#

Installation Difficulty: Medium - Requires setup of multiple API services and dependencies, but Docker deployment is available

# Using Docker (recommended)
docker compose build
docker compose up -d

# Or running locally
pip install -e deeppresenter
playwright install-deps
npm install
npx playwright install chromium
python webui.py

Is this suitable for me?

  • ✅ Academic Research: Convert papers to presentations while preserving key content and structure
  • ✅ Business Reports: Quickly transform research reports or analysis results into professional presentations
  • ✅ Educational Materials: Automatically generate classroom slides based on teaching content
  • ❌ Simple Image Slides: Not suitable for scenarios requiring only a few image displays
  • ❌ Highly Customized Designs: Cannot meet very specific visual design requirements

Core Capabilities#

1. Intelligent Document Understanding#

  • Extracts and parses content from various sources (PDFs, web pages, etc.)
  • Automatically identifies document structures and key information points User Value: No manual organization needed - AI directly extracts core content from raw materials

2. Reflective Slide Generation#

  • Analyzes reference presentations to extract functional types and content schemas
  • Creates slides using a two-stage editing method inspired by human workflows User Value: Generated slides align better with human cognitive patterns, with more logical information organization

3. Autonomous Visual Design#

  • Supports free-form visual design without relying on templates
  • Automatically generates images and graphics that match the content User Value: Each slide has unique design, avoiding templated uniformity

4. Multimodal Content Generation#

  • Text-to-image generation capabilities
  • Automatically creates multimedia resources needed for presentations User Value: One-click generation of complete visual content, no need to find additional materials

5. Offline Mode Support#

  • Can run without internet (with limited capabilities)
  • Processes documents using locally deployed MinerU service User Value: Protects data privacy, suitable for handling sensitive content

Technology Stack & Integration#

Development Languages: Python (72.3%), JavaScript (16.9%), TypeScript (8.9%) Key Dependencies:

  • MinerU (document parsing service)
  • Tavily (search API)
  • Multiple LLM providers (Claude, Gemini, GLM-4.7)
  • Playwright (browser automation)
  • Node.js/npm (web components) Integration Method: API / SDK / Web Interface

Maintenance Status#

  • Development Activity: Very active with regular release cycles and updates
  • Recent Updates: New version released in January 2026, adding free-form generation, template support, and offline mode
  • Community Response: Project accepted by EMNLP 2025 with active development contributions

Commercial & Licensing#

License: MIT

  • ✅ Commercial Use: Permitted
  • ✅ Modification: Allowed
  • ⚠️ Restrictions: Attribution to original authors required

Documentation & Learning Resources#

  • Documentation Quality: Comprehensive
  • Official Documentation: https://github.com/icip-cas/PPTAgent
  • Example Code: Multiple case studies demonstrating usage in different scenarios
  • Academic Support: Theoretical foundation supported by EMNLP 2025 conference paper
  • Getting Started Guides: Detailed environment configuration and startup instructions

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