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SPECIFICTION
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PosterGen

github.com

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Spec score 7.5 #16 of 24 in AI Poster Generators

What it is

PosterGen generates academic posters from research paper PDFs, using a multi-agent language-model workflow to extract content, plan a narrative, arrange the layout, and apply styling. Its Parser Agent structures paper content, while the Curator Agent plans organization and visual placement. Layout and Balancer agents calculate spacing and column use, with the Balancer addressing overflow. Styling includes colors drawn from an affiliation logo, professional font choices, and keyword highlighting. Users can adjust layout, typography, color generation, visual asset sizing, and content optimization through configuration. A local web interface accepts a paper PDF and logos, lets users configure models and poster dimensions, and generates a PNG plus an editable PowerPoint file. Documented width-to-height ratios run from 1.4 to 2, and custom dimensions are supported. The project lists Windows, Linux, and macOS, but setup requires Python 3.11 and LibreOffice; the web interface also needs Node.js and configured API keys. Setup documents OpenAI, Anthropic, and Zhipu keys, with configurable service base URLs including Google. PosterGen is free and MIT-licensed; the license provides the software without warranty.

Spec sheet

Purpose
PosterGen generates academic posters from research papers using a multi-agent large language model framework guided by design principles.Read 4 Oct 2026 · github.com
Intended users
The README describes the system as generating academic posters and says it follows poster design practices for conferences.Read 4 Oct 2026 · github.com
License
The repository provides the software under the MIT License, which grants permission to use, copy, modify, distribute, sublicense and sell copies subject to its conditions.Read 4 Oct 2026 · github.com
Cost logging
The output directory includes a JSON log for runtime and API cost.Read 4 Oct 2026 · github.com
Customization
Users can customize layout, typography, color generation, visual asset sizing, and content optimization through the configuration file.Read 4 Oct 2026 · github.com
Dependencies
The project acknowledges LangGraph, Marker, and python-pptx as underlying open-source projects.Read 4 Oct 2026 · github.com
Intended audience
The README describes conference poster design and identifies the output as professional academic posters.Read 4 Oct 2026 · github.com
Interface
A local web interface lets users upload a PDF and logos, configure models and dimensions, then generate and download poster files.Read 4 Oct 2026 · github.com
Layout
Its Layout Agent calculates positioning and spacing, while a Balancer Sub-Agent optimizes column use and prevents overflow.Read 4 Oct 2026 · github.com
License warranty
The MIT license states that the software is provided as is without warranty.Read 4 Oct 2026 · github.com
Model options
The documented text and vision model options include GPT-4.1, GPT-4o, GPT-4.1 mini and Claude Sonnet 4.Read 4 Oct 2026 · github.com
Model providers
The README shows API key configuration for OpenAI, Anthropic and Zhipu and configurable base URLs for OpenAI, Anthropic, Google and Zhipu compatible services.Read 4 Oct 2026 · github.com
Models and APIs
The README lists OpenAI, Anthropic, and Zhipu API keys and documents configurable API base URLs, including a Google base URL.Read 4 Oct 2026 · github.com
Operating systems
The README lists Windows, Linux and macOS as supported operating systems and Python 3.11 as a system requirement.Read 4 Oct 2026 · github.com
Output
Generated files include a PNG poster and an editable PowerPoint file.Read 4 Oct 2026 · github.com
Outputs
The system produces PNG poster images and editable PowerPoint files.Read 4 Oct 2026 · github.com
Paper parsing
Its Parser Agent extracts and structures content from a paper PDF.Read 4 Oct 2026 · github.com
Poster dimensions
The documented width-to-height aspect ratio range is 1.4 to 2.Read 4 Oct 2026 · github.com
Required inputs
The setup instructions require a research paper PDF, an affiliation logo for color extraction and a conference logo for the poster.Read 4 Oct 2026 · github.com
Requirements
The README specifies Python 3.11 and requires LibreOffice; its web interface also requires Node.js and configured API keys.Read 4 Oct 2026 · github.com
Storyboarding
Its Curator Agent plans content organization and visual placement.Read 4 Oct 2026 · github.com
Styling
Color generation draws on an affiliation logo, and typography features include professional font choices and keyword highlighting.Read 4 Oct 2026 · github.com
Workflow
Its workflow uses parser, curator, layout and styling agents to extract paper content, plan a narrative storyboard, arrange a three-column layout and apply colors and typography.Read 4 Oct 2026 · github.com

Strong points and limits

Strong points

  • Creates PNG and editable PowerPoint outputs
  • Offers configurable layout, typography, and colors
  • Supports Windows, Linux, and macOS
  • Free and MIT-licensed

Limits

  • Requires Python 3.11 and LibreOffice
  • Web interface also requires Node.js and API keys
  • Documented aspect ratios range from 1.4 to 2
  • License provides the software without warranty

Who it suits

PosterGen is aimed at people preparing academic conference posters from research papers. It suits users comfortable configuring model API keys and installing the stated runtime requirements.

Verdict

PosterGen automates several stages of turning a paper into a poster and provides editable output. Setup depends on local software requirements and configured model access.

Where Specifiction places it

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