WorkflowsApr 2, 2026·2 min read

Taipy — Build Data & AI Web Apps in Python

Full-stack Python framework for data dashboards and AI pipelines. GUI builder + workflow orchestration in one package. 15K+ stars.

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Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

```bash pip install taipy ``` ```python import taipy.gui.builder as tgb from taipy.gui import Gui value = "Hello World" with tgb.Page() as page: tgb.text("# My AI App", mode="md") tgb.input("{value}", label="Enter text") tgb.text("You typed: {value}") Gui(page=page).run() ``` This launches a web app at `http://localhost:5000`. For AI pipeline orchestration: ```python from taipy import Config # Define data nodes and tasks input_cfg = Config.configure_data_node("raw_data", path="data.csv") clean_cfg = Config.configure_data_node("clean_data") model_cfg = Config.configure_data_node("predictions") clean_task = Config.configure_task("clean", clean_function, [input_cfg], [clean_cfg]) predict_task = Config.configure_task("predict", predict_function, [clean_cfg], [model_cfg]) scenario_cfg = Config.configure_scenario("ml_pipeline", [clean_task, predict_task]) ``` ---
Intro
Taipy is a full-stack Python framework with 15,000+ GitHub stars for building production data and AI web applications. It combines a **GUI builder** (create interactive dashboards without JavaScript) with a **workflow orchestrator** (manage data pipelines, ML training, scenario comparison). With Taipy, a single Python developer can build what normally requires a frontend engineer + backend engineer + DevOps. Used in production by enterprises for AI dashboards, what-if scenario analysis, and ML pipeline management. Works with: Python, Pandas, scikit-learn, TensorFlow, PyTorch, any Python ML library. Best for data scientists and ML engineers who want to build full web apps without learning React/Vue. Setup time: under 3 minutes. ---
## Taipy Two-in-One Architecture ### Taipy GUI — Interactive Dashboards Build rich web UIs with pure Python — no HTML, CSS, or JavaScript: ```python import taipy.gui.builder as tgb from taipy.gui import Gui import pandas as pd data = pd.DataFrame({"Month": ["Jan","Feb","Mar"], "Revenue": [100, 150, 200]}) selected_month = "All" def filter_data(state): if state.selected_month == "All": state.filtered = data else: state.filtered = data[data["Month"] == state.selected_month] with tgb.Page() as dashboard: tgb.text("# Revenue Dashboard", mode="md") tgb.selector("{selected_month}", lov=["All","Jan","Feb","Mar"], on_change=filter_data) tgb.chart("{filtered}", x="Month", y="Revenue", type="bar") tgb.table("{filtered}") Gui(page=dashboard).run() ``` Available components: - **Charts** — Line, bar, scatter, pie, heatmap (Plotly-based) - **Tables** — Sortable, filterable, editable data tables - **Forms** — Input, slider, toggle, date picker, file upload - **Layout** — Columns, expandable sections, tabs, dialog - **Media** — Images, videos, maps ### Taipy Core — Pipeline Orchestration Manage data pipelines with versioning and scenario comparison: ```python from taipy import Config import taipy as tp # Define the pipeline raw = Config.configure_data_node("raw_data", path="sales.csv") forecast = Config.configure_data_node("forecast") train_task = Config.configure_task("train_model", train_fn, [raw], [forecast]) scenario_cfg = Config.configure_scenario("sales_forecast", [train_task]) # Run scenarios tp.Core().run() scenario_1 = tp.create_scenario(scenario_cfg) tp.submit(scenario_1) # Compare scenarios (what-if analysis) scenario_2 = tp.create_scenario(scenario_cfg) scenario_2.raw_data.write(alternative_data) tp.submit(scenario_2) # Both scenarios tracked with full lineage ``` ### What-If Scenario Analysis Taipy's killer feature — compare multiple pipeline runs: ``` Scenario A: "Base forecast with 2025 data" └─ Revenue prediction: $2.1M Scenario B: "Optimistic — 10% growth assumption" └─ Revenue prediction: $2.3M Scenario C: "Conservative — recession adjustment" └─ Revenue prediction: $1.8M → Side-by-side comparison in the dashboard ``` ### REST API (Built-in) Every Taipy app automatically exposes a REST API: ```bash # Get scenario results GET /api/v1/scenarios/{id} # Submit a new scenario POST /api/v1/scenarios/{id}/submit # Read data node GET /api/v1/datanodes/{id}/read ``` ### Deployment ```bash # Development taipy run app.py # Production with Gunicorn gunicorn -w 4 -b 0.0.0.0:5000 app:gui # Docker docker build -t my-taipy-app . docker run -p 5000:5000 my-taipy-app ``` --- ## FAQ **Q: What is Taipy?** A: Taipy is a full-stack Python framework with 15,000+ GitHub stars that combines a GUI builder (create dashboards without JavaScript) and a workflow orchestrator (manage ML pipelines and scenarios) in one package. **Q: How is Taipy different from Streamlit or Gradio?** A: Streamlit/Gradio are great for quick demos but struggle with production needs. Taipy adds pipeline orchestration, scenario management, multi-page apps, built-in REST API, and enterprise deployment — it's designed for production, not just prototyping. **Q: Is Taipy free?** A: The Community Edition is free and open-source under Apache-2.0. Taipy also offers an Enterprise Edition with additional features. ---
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Source & Thanks

> Created by [Avaiga](https://github.com/Avaiga). Licensed under Apache-2.0. > > [taipy](https://github.com/Avaiga/taipy) — ⭐ 15,000+ Thanks to the Avaiga team for making full-stack AI app development accessible to Python developers.

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