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AI Frameworks · BIT-DataLab

Edit-Banana

Edit-Banana is a Python framework that converts static diagrams (PNG, JPG, etc.) into editable DrawIO XML format. It uses SAM 3 for element segmentation, multimodal LLMs for content extraction, and OCR/Pix2Text for text and formula recognition, enabling users to modify previously non-editable diagrams.

Source: GitHub — github.com/BIT-DataLab/Edit-Banana
5.4k
GitHub stars
361
Forks
Python
Primary language
AGPL-3.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
RepositoryBIT-DataLab/Edit-Banana
OwnerBIT-DataLab
Primary languagePython
LicenseAGPL-3.0 — OSI-approved
Stars5.4k
Forks361
Open issues35
Latest releaseUnknown
Last updated2026-07-05
Sourcehttps://github.com/BIT-DataLab/Edit-Banana

What Edit-Banana is

The system combines fine-tuned SAM 3 segmentation, parallel text extraction (Tesseract OCR + Pix2Text for LaTeX), and DrawIO XML generation. It includes a credit-based user system, LRU caching for embeddings, and concurrent processing via global GPU locks. A web demo runs at editbanana.net; GitHub code lags behind the live service.

Quickstart

Get the Edit-Banana source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/BIT-DataLab/Edit-Banana.gitcd Edit-Banana# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Diagram Digitization & Modernization

Convert scanned/PDF diagrams, legacy flowcharts, and architecture drawings into editable, version-controllable DrawIO files for teams.

Technical Documentation Workflow

Extract diagrams from paper documents, books, or slides and edit them directly in DrawIO without manual redraw.

Mathematical & Scientific Formula Preservation

Recognize and convert handwritten or printed formulas to LaTeX, enabling downstream rendering and modification in documentation tools.

Implementation considerations

  • Requires CUDA-capable GPU and sufficient VRAM for SAM 3 + LLM inference; local deployment cost may be high.
  • AGPL-3.0 license necessitates legal review before integration into commercial or proprietary systems; consider GPL exception or commercial license inquiry.
  • Model weights (SAM 3) must be manually downloaded and placed in /models/; installation script provided but not verified for all environments.
  • GitHub version lags live service; production use should clarify feature parity and consider adopting the web API (editbanana.net) instead.
  • Credit-based system and user registration in place, but no pricing docs, quota limits, or SLA terms visible in README.

When to avoid it — and what to weigh

  • Requires Latest Features — GitHub repo is stated to trail the live web service significantly; self-hosted deployments may lack recent improvements.
  • Strict AGPL Compliance Burden — AGPL-3.0 requires distributing source code of derivative works; closed-source commercial products or SaaS without source disclosure face legal risk.
  • GPU Resource Constraints — SAM 3 and multimodal LLM inference require CUDA; no clear CPU fallback or quantization guidance in README.
  • Zero Production Stability Signal — No formal releases, minimal issue resolution rate (35 open issues, ~6 months active), and no documented SLAs or uptime guarantees.

License & commercial use

Licensed under AGPL-3.0. This is a strong copyleft license: any derivative work or service must disclose source code and provide it to users. Commercial use is possible but requires either open-sourcing derivatives or obtaining a separate commercial license from the authors (contact [email protected]).

AGPL-3.0 is incompatible with closed-source commercial products without explicit exception. SaaS deployment of Edit-Banana itself requires source code availability to end-users. Recommend obtaining written clarification or a commercial license from BIT-DataLab before building proprietary systems. README lists contact for 'commercial licensing' inquiry.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityHigh
DEV.co fitGood
Assessment confidenceMedium
Security considerations

No explicit security audit or hardening details provided. Multi-user system includes global GPU locks and LRU caching, but input validation, rate limiting, and prompt injection risks (LLM-based extraction) are not documented. File upload handling and data retention policies unknown. AGPL source availability aids security review but doesn't guarantee it has been done.

Alternatives to consider

Diagram.net (DrawIO) Native Reverse-Engineering

DrawIO offers manual import and export; no automation, but zero license/security friction and full control. Suitable for low-volume, high-accuracy needs.

Tesseract-based OCR + Custom XML Gen

Build a lightweight pipeline using open-source OCR (Tesseract) + custom logic; avoids LLM dependency, AGPL compliance, and GPU cost. Trade-off: no semantic understanding.

CloudConvert / Online Converters (Closed-Source SaaS)

Outsource diagram conversion; no deployment burden or AGPL concerns. Trade-off: privacy, cost per conversion, and vendor lock-in.

Software development agency

Build on Edit-Banana with DEV.co software developers

Test the live demo at editbanana.net. For integration, clarify AGPL licensing, GPU requirements, and feature parity with GitHub. Contact BIT-DataLab for commercial licensing.

Talk to DEV.co

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Edit-Banana FAQ

Can I use Edit-Banana in a closed-source product?
Not without a commercial license. AGPL-3.0 requires source disclosure of derivatives. Contact [email protected] to explore commercial licensing options.
Does the GitHub code match the online demo at editbanana.net?
No. The README explicitly states the GitHub repo 'currently trails' the web service in features and performance. Consider using the web API if available, or clarify feature gaps before self-hosting.
What hardware do I need to run this locally?
A CUDA-capable GPU (NVIDIA recommended). VRAM requirements for SAM 3 + LLM not documented; assume 8–16 GB+. CPU-only mode is not detailed.
How accurate is the formula recognition?
Uses Pix2Text + LaTeX conversion on cropped high-res regions. No accuracy metrics provided. Test with your document samples before production deployment.

Software development & web development with DEV.co

From first prototype to production, DEV.co delivers software development services around tools like Edit-Banana. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across ai frameworks and beyond.

Evaluate Edit-Banana for Your Diagram Workflow

Test the live demo at editbanana.net. For integration, clarify AGPL licensing, GPU requirements, and feature parity with GitHub. Contact BIT-DataLab for commercial licensing.