Compare Libraries
See which libraries have better AI support across different models
Format: owner/repo — max 5 repositories
Knowledge cutoff: 2025-08-31
bokeh
bokeh
Interactive Data Visualization in the browser, from Python
streamlit
streamlit
Streamlit — A faster way to build and share data apps.
dash
plotly
Data Apps & Dashboards for Python. No JavaScript Required.
voila
voila-dashboards
Voilà turns Jupyter notebooks into standalone web applications
gradio
gradio-app
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Summary for GPT-5.2-Codex
Score by LLM
See how each library scores across different AI models
AI Evaluation
Data AppsGenerated 1/29/2026
The Data Apps ecosystem offers a stark choice between rapid scripting and enterprise-grade architecture. While Streamlit and Gradio dominate momentum with their 'script-to-app' paradigms ideal for prototyping and ML demos, Plotly Dash remains the superior choice for complex production systems due to its React-based component model and exceptional documentation. Bokeh continues to excel in high-performance custom visualization, while Voila serves the specific niche of serving Jupyter Notebooks directly. The 2024-2025 period highlights a divergence where Streamlit aims for broader app capabilities (Chat elements) while Dash solidifies its enterprise footing.
Recommendations by Scenario
New Projects
Offers the fastest time-to-value with its imperative execution model and massive component ecosystem. Its perfect momentum score (100) ensures continuous feature drops like the recent chat elements and fragments, minimizing the 'build vs buy' decision overhead for most internal tools.
AI Coding
With an AI Readiness score of 70 and top-tier Documentation (90), Dash's declarative component structure is far easier for LLMs to reason about than Streamlit's rerun-on-interaction model. The strict separation of layout and callbacks helps AI tools generate bug-free state management code.
Migrations
Uniquely positioned for teams needing to migrate legacy Jupyter Notebooks to web apps without rewriting code. With the highest Coverage score (88), it benefits from extensive presence in training data, allowing seamless transition of existing analysis workflows to standalone deployments.
Library Rankings
Enterprise-grade analytical applications requiring complex state management, custom styling, and scalability beyond simple prototypes.
Strengths
- +Unrivaled Documentation (90/100) with interactive examples facilitates rapid onboarding for complex enterprise requirements
- +React-based architecture provides granular control over state and rendering, avoiding the performance pitfalls of full-script reruns
- +Strong AI Readiness (70/100) due to explicit callback structures that align well with LLM code generation patterns
Weaknesses
- -Steeper learning curve requiring understanding of HTML/CSS fundamentals and callback chains compared to Streamlit's pure Python abstraction
- -Lower Maintenance score (70) suggests a more mature, less explosive update cycle compared to newer rivals
Projects where custom, high-performance interactive visualization is the core value proposition, rather than standard form-based CRUD apps.
Strengths
- +High Coverage (87/100) in LLM training data ensures AI assistants handle its stable API with high accuracy
- +Low-level interface offers pixel-perfect control over interactive visualizations that higher-level wrappers cannot match
- +Strong AI Readiness (70/100) makes it a reliable target for generating complex, custom plotting logic
Weaknesses
- -Lower Adoption (58/100) and Momentum (55/100) indicate a smaller community ecosystem for pre-built widgets compared to Streamlit
- -Requires more boilerplate code to set up basic application scaffolding compared to full-stack frameworks
Rapid prototyping, data science portfolio projects, and internal tools where development speed is the primary metric.
Strengths
- +Perfect Momentum (100/100) driven by frequent feature releases (e.g., st.chat, st.fragment) and massive community engagement
- +Highest Adoption (75/100) ensures a vast library of third-party components and easy hiring
- +Zero-boilerplate 'script-as-app' model is unbeatable for speed, allowing single-file deployment of complex data flows
Weaknesses
- -Moderate AI Readiness (50/100) due to the 'rerun-on-interaction' execution model which often confuses LLMs regarding state persistence
- -Performance bottlenecks in complex apps as the entire script re-executes on every user interaction without careful caching
Data science teams needing to securely share existing Jupyter notebooks as read-only dashboards with non-technical stakeholders.
Strengths
- +Highest LLM Coverage (88/100) means AI models are extremely proficient at generating compatible notebook structures
- +Top-tier Documentation (90/100) ensures clarity in deployment and configuration options
- +Zero-rewrite deployment allows instant conversion of existing Jupyter notebooks into secure, standalone web applications
Weaknesses
- -Lowest Momentum (25/100) suggests a stagnant feature set and potential long-term viability risks
- -Lowest Adoption (49/100) means fewer community extensions and a smaller pool of troubleshooting resources
Machine Learning engineers needing to wrap models in web interfaces instantly for demos, Hugging Face Spaces, or simple APIs.
Strengths
- +Highest Maintenance score (85/100) reflects a highly responsive team and rapid security patching velocity
- +Perfect Momentum (100/100) shows aggressive development, particularly in features for serving ML models and LLM interfaces
- +Specialized high-level APIs for ML inputs/outputs (Audio, Image, Chat) drastically reduce code for AI demos
Weaknesses
- -Lowest LLM Coverage (55/100) due to rapid API evolution means AI assistants often generate outdated or incorrect code
- -Lowest AI Readiness (30/100) and Documentation (55/100) scores make it difficult to self-service complex customizations