If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/Avaiga/taipy shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

GO — Healthy across the board

• Last commit 1d ago
• 15 active contributors
• Distributed ownership (top contributor 30% of recent commits)
• Apache-2.0 licensed
• CI configured
• Tests present

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live Avaiga/taipy repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/Avaiga/taipy.

What it runs against: a local clone of Avaiga/taipy — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in Avaiga/taipy | Confirms the artifact applies here, not a fork | | 2 | License is still Apache-2.0 | Catches relicense before you depend on it | | 3 | Default branch develop exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 31 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of Avaiga/taipy. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/Avaiga/taipy.git
#   cd taipy
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of Avaiga/taipy and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "Avaiga/taipy(\\.git)?\\b" \\
  && ok "origin remote is Avaiga/taipy" \\
  || miss "origin remote is not Avaiga/taipy (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(Apache-2\\.0)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"Apache-2\\.0\"" package.json 2>/dev/null) \\
  && ok "license is Apache-2.0" \\
  || miss "license drift — was Apache-2.0 at generation time"

# 3. Default branch
git rev-parse --verify develop >/dev/null 2>&1 \\
  && ok "default branch develop exists" \\
  || miss "default branch develop no longer exists"

# 4. Critical files exist
test -f ".github/workflows/build-and-release.yml" \\
  && ok ".github/workflows/build-and-release.yml" \\
  || miss "missing critical file: .github/workflows/build-and-release.yml"
test -f "package.json" \\
  && ok "package.json" \\
  || miss "missing critical file: package.json"
test -f ".github/actions/gui-test/action.yml" \\
  && ok ".github/actions/gui-test/action.yml" \\
  || miss "missing critical file: .github/actions/gui-test/action.yml"
test -f "README.md" \\
  && ok "README.md" \\
  || miss "missing critical file: README.md"
test -f "CONTRIBUTING.md" \\
  && ok "CONTRIBUTING.md" \\
  || miss "missing critical file: CONTRIBUTING.md"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 31 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~1d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/Avaiga/taipy"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

Taipy is a Python framework that converts data science and ML algorithms into production-ready web applications without requiring frontend expertise. It provides a declarative GUI system (written in Python with TypeScript/React rendering) paired with a data pipeline engine, allowing data scientists to build full-stack applications—dashboards, data apps, ML services—directly from Python code without writing HTML/CSS/JavaScript. Monorepo structure: Python packages in the root (likely src/taipy/ and variants based on typical layout), TypeScript/React GUI layer in a separate compiled layer (webpack config in package.json indicates frontend build pipeline). Multiple GitHub Actions workflows suggest modular testing: gui-test/, prefix/, pyi/, and e2e/ actions indicate separate test suites for GUI, type hints, and end-to-end scenarios. Examples in doc/gui/examples/ provide runnable templates.

Data scientists and ML engineers who need to deploy interactive web applications quickly. They want to avoid JavaScript/frontend complexity while maintaining production-grade performance, customization, and scalability. Also targets team leads building internal tools and data products at scale.

Actively developed and production-ready. The repo shows 4.6M lines of Python code, comprehensive CI/CD workflows (.github/workflows/ contains 15+ automated test and release pipelines), structured issue templates, and a mature mono-repo organization. Regular releases and active GitHub actions indicate ongoing maintenance, though the framework is younger than Streamlit or Dash so ecosystem maturity is still growing.

Moderate risk for large teams: the project is monolithic (single repo with GUI + core engine + examples), so breaking changes in core affect downstream users. Dependency surface is broad (webpack, React 19, multiple TypeScript tooling). Risk mitigated by comprehensive test coverage (.coveragerc present) and pre-commit hooks (.pre-commit-config.yaml), but smaller contributor base than Streamlit means slower issue resolution on edge cases.

Active development on frontend modernization (webpack config, TypeScript strict typing evident in devDependencies with @types/react ^19.2.0), type safety improvements (.github/workflows/update-config-pyi.yml suggests ongoing .pyi stub generation), and performance benchmarking (.github/workflows/trigger-benchmark.yml). Dependencies management workflow indicates active version maintenance.

git clone https://github.com/Avaiga/taipy.git && cd taipy && pip install -e . (Python) && cd frontend && npm install && npm run build:dev (TypeScript/React frontend). Start a demo with a Python script importing taipy.gui and taipy.core.

Daily commands: For development: npm run build:dev (builds TS→JS in dev mode) or npm run build (production build). Python side: run a script with python app.py where app.py imports taipy components. The postinstall script (scripts/install.js) likely scaffolds the environment.

• .github/workflows/build-and-release.yml — Primary CI/CD pipeline orchestrating builds, tests, and releases across the entire Taipy ecosystem; every contributor must understand deployment mechanics
• package.json — Declares build toolchain (Webpack, TypeScript, React) and runtime dependencies (Plotly, React); essential for frontend extension development
• .github/actions/gui-test/action.yml — Reusable GitHub Action for GUI testing across e2e, prefix, and pyi test suites; critical for validation workflows
• README.md — Project vision and quick-start guide; establishes scope that Taipy transforms data/AI algorithms into production web apps
• CONTRIBUTING.md — Defines contribution standards, code style, and PR expectations for 600-file monorepo with 200+ actions
• .github/workflows/frontend.yml — Dedicated frontend build, lint, and test pipeline; governs React/TypeScript extension quality gates

Add a New GUI Component Example

1. Create a new directory under doc/gui/examples/ (or a category subdirectory like blocks/) following the pattern: doc/gui/examples/{component_name}/ (doc/gui/examples/blocks/pane_simple/__init__.py)
2. Implement the example in __init__.py, exposing the main component builder or app logic (doc/gui/examples/blocks/pane_simple/__init__.py)
3. Create builder.py that programmatically constructs the UI using Taipy's fluent API (doc/gui/examples/blocks/pane_simple/builder.py)
4. Create markdown.py with narrative explanation and usage context (doc/gui/examples/blocks/pane_simple/markdown.py)
5. Update doc/gui/examples/README.md to index the new example with a link and description (doc/gui/examples/README.md)

Add a New GitHub Actions Workflow

1. Create a new .yml file in .github/workflows/ following the naming convention (e.g., feature-validation.yml) (.github/workflows/overall-tests.yml)
2. Define triggers (on: [push, pull_request]) and matrix strategy for multi-version Python/Node testing (.github/workflows/frontend.yml)
3. Reference reusable actions from .github/actions/ (e.g., gui-test, install) or inline shell steps (.github/actions/gui-test/action.yml)
4. If the workflow is called from other workflows, wrap it with workflow_call and document inputs/outputs (.github/workflows/build-and-release.yml)
5. Add the new workflow name to .github/workflows/README or relevant documentation for team visibility (CONTRIBUTING.md)

Configure Frontend Linting & Type Checking

1. Add or update ESLint rules in package.json under devDependencies (e.g., @typescript-eslint/eslint-plugin) (package.json)
2. Create or edit .eslintrc.json (not shown but inferred) to configure React, TypeScript plugin, and hook rules (.pre-commit-config.yaml)
3. Define pre-commit hooks in .pre-commit-config.yaml to run ESLint and type checks before commits (.pre-commit-config.yaml)
4. Ensure .github/workflows/frontend.yml runs npm run lint and type validation in CI (.github/workflows/frontend.yml)
5. Update CONTRIBUTING.md with local setup instructions: npm install and pre-commit install (CONTRIBUTING.md)

Release a New Version

1. Update version in package.json (for npm packages) and any setup.py or pyproject.toml (inferred from Taipy structure) (package.json)
2. Create a Git tag matching the version (e.g., v1.0.1) and push to develop branch (CONTRIBUTING.md)
3. Trigger .github/workflows/publish.yml manually or via tag push to build, test, and publish to PyPI/npm (.github/workflows/publish.yml)
4. Monitor .github/workflows/packaging.yml to ensure Docker images and distribution artifacts are generated (.github/workflows/packaging.yml)
5. Verify release appears on GitHub Releases page and package registries are updated (.github/workflows/build-and-release.yml)

• React 19 + TypeScript — Enables type-safe,

Frontend build is separate from Python package install — you must run npm install && npm run build or npm run build:dev in addition to Python setup, or GUI will not render. Python 3.9+ is required (see badge in README) but older versions in CI matrices may still be tested. The monorepo means a single breaking change in core can cascade across GUI and multiple packages — watch for large PRs touching multiple workflows. TypeScript strict mode (suggested by @typescript-eslint config) is enforced, so GUI contributions require no linting errors.

• Reactive data binding — Taipy's core feature is automatic re-execution of pipelines when data changes; understanding how state flows from GUI to backend data DAG is essential to writing efficient Taipy apps
• Declarative UI via Python DSL — Instead of HTML/JSX, Taipy uses Python function calls to define layouts (likely similar to React's JSX but in Python); this is the core differentiator from traditional web frameworks
• DAG-based data pipelines — Taipy.core implements a directed acyclic graph for data dependencies, allowing automatic caching, parallel execution, and dependency tracking — critical for performance in ML workloads
• Server-Sent Events (SSE) / WebSockets — The Python backend must push updates to the browser GUI in real-time; understanding the transport layer helps debug UI lag and real-time chart updates
• Webpack module federation — The TypeScript build uses webpack (version ^5.74.0); understanding its configuration is necessary for extending GUI with custom React components or plugins
• Type stub generation (.pyi files) — Taipy auto-generates .pyi stub files for type hints (see .github/workflows/update-config-pyi.yml); contributors must understand how type information flows from Python to static analyzers and IDEs
• Monorepo dependency management — Single repo with multiple publishable packages (gui, core, templates, etc.); CI must coordinate testing and versioning across all packages without breaking downstream consumers

• streamlit/streamlit — Direct competitor in the Python → web app space; Streamlit is more mature but less flexible; Taipy targets more complex, data-heavy pipelines
• plotly/dash — Alternative for building data dashboards in Python; Dash is lower-level (requires Callbacks, layout dicts) whereas Taipy aims for higher-level declarative DSL
• gradio-app/gradio — Lightweight alternative for exposing ML models as web UIs; Gradio is simpler but lacks Taipy's data pipeline and multi-page application support
• Avaiga/taipy-demo — Official example applications and benchmarks for Taipy; essential reference for architecture and best practices in real deployments
• python-visualization/plotly.py — Upstream dependency (listed in package.json as plotly.js); understanding Plotly's integration helps optimize charting in Taipy apps

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add comprehensive E2E tests for GUI extension examples in .github/workflows

The repo has a GUI test infrastructure (.github/actions/gui-test with e2e, prefix, and pyi actions) but no visible workflow that systematically tests the example applications in doc/gui/examples/. Given the complex nested structure of examples (binding_lov_is_enum, blocks/dialog_labels, blocks/dialog_show_hide, etc.), adding E2E tests would catch regressions in example functionality and ensure documentation examples remain functional across releases.

• [ ] Create a new workflow file .github/workflows/gui-examples-e2e-tests.yml that runs on PRs
• [ ] Add test scripts in doc/gui/examples/ that programmatically validate each example's builder.py and markdown.py files render correctly
• [ ] Integrate existing .github/actions/gui-test/e2e action to run against each example directory
• [ ] Document test execution in CONTRIBUTING.md with specific commands for local testing

Add pre-commit hooks validation for TypeScript/React GUI extensions in taipy-gui-extension-example

The repo has .pre-commit-config.yaml and a GUI extension example with TypeScript/React dependencies (@typescript-eslint, eslint-plugin-react), but no pre-commit hooks are configured for the frontend build validation. This creates a risk of merged code with linting or type errors in the extension example that serves as a template for users.

• [ ] Extend .pre-commit-config.yaml with hooks for eslint and typescript type-checking targeting the GUI extension example directory
• [ ] Add a pre-commit hook that runs 'npm run build:dev' to catch webpack compilation errors before commits
• [ ] Document the pre-commit setup in CONTRIBUTING.md with commands to install hooks locally
• [ ] Validate the configuration works by testing against the example's package.json scripts

Add missing integration test workflow for taipy-gui-extension-example package.json builds

The .github/workflows/ directory has overall-tests.yml, partial-tests.yml, and trigger-integration-tests.yml, but no dedicated workflow that validates the GUI extension example's npm build process (build:dev and build scripts). Given the complexity of webpack + TypeScript + React setup, a dedicated workflow would catch breaking changes to the extension template early.

• [ ] Create .github/workflows/gui-extension-example-build.yml that runs on PRs touching dependencies or webpack configs
• [ ] Add steps to install dependencies via npm and run both 'npm run build:dev' and 'npm run build' scripts
• [ ] Add linting validation using the eslint config already present in devDependencies
• [ ] Reference this workflow in the main prebuild.yml or packaging.yml to ensure GUI extensions build successfully during releases

• Add missing unit tests for GUI components in .tsx files: the repo has .github/actions/gui-test but coverage gaps likely exist in react-plotly.js wrapper or custom chart components
• Expand doc/gui/examples/ with a new example app (e.g., real-time data ingestion dashboard, multi-page layout demo) following the Alert.py pattern and add corresponding integration test to .github/workflows/frontend.yml
• Fix or document missing type stubs (.pyi files) for taipy.core or taipy.gui public APIs — the update-config-pyi.yml workflow suggests ongoing stub generation; audit exported symbols and generate missing stubs

The Taipy GUI Extension Example codebase has moderate security concerns, primarily related to outdated dependencies. Multiple dev dependencies are significantly out-of-date (TypeScript 4.7.4, Webpack 5.74.0, ESLint 8.20.0), which may harbor known vulnerabilities. The postinstall script execution poses a moderate risk that requires code review. React 19.2.0 is very recent and

• High · Outdated TypeScript Version — package.json - devDependencies.typescript. TypeScript version 4.7.4 is significantly outdated (released June 2022). Current versions are 5.x+. Outdated TypeScript may contain known security vulnerabilities and lacks security patches. Fix: Update TypeScript to the latest stable version (^5.0.0 or higher). Run 'npm update typescript' and test the build process.
• High · Outdated Webpack and Webpack CLI — package.json - devDependencies (webpack, webpack-cli). Webpack 5.74.0 and webpack-cli 4.10.0 are outdated versions. Current versions are 5.89.0+ and 5.x+. Known vulnerabilities may exist in these older releases. Fix: Update webpack to ^5.89.0 or higher and webpack-cli to ^5.1.0 or higher. Test build output thoroughly after upgrade.
• High · Outdated ESLint and Related Plugins — package.json - devDependencies (@typescript-eslint/*, eslint, eslint-plugin-*). ESLint 8.20.0 and related plugins (@typescript-eslint packages 5.30.7) are outdated. ESLint 9.x exists with improved security features and fixes. Fix: Update ESLint to version 9.x and associated packages. Review breaking changes in migration guides and update configuration accordingly.
• Medium · Postinstall Script Execution Risk — package.json - scripts.postinstall. The postinstall script 'node scripts/install.js' executes automatically on npm install. While not visible in the provided content, postinstall scripts pose potential risks if the script is compromised or if dependencies are tampered with. Fix: Review the scripts/install.js file for security issues. Consider using npm audit, and implement npm ci instead of npm install in production. Use npm package integrity checks.
• Medium · Outdated React Version — package.json - dependencies.react. React 19.2.0 is very recent and may have unresolved security issues or compatibility problems. Version pinning without thorough testing can be risky. Fix: Use caret versioning (^19.2.0) for flexibility. Maintain regular security audits with 'npm audit'. Monitor React security advisories.
• Medium · Deprecated Child_process Package — package.json - devDependencies.child_process. The package 'child_process' (^1.0.2) listed as a devDependency is a wrapper around Node's built-in child_process module and may not be necessary. The built-in module is better maintained. Fix: Remove the child_process package and use Node.js built-in 'child_process' module instead. This reduces dependency surface area.
• Low · Missing Security Headers Configuration — webpack configuration (not provided). No evidence of security headers configuration in webpack or build files provided. The application may be vulnerable to XSS, clickjacking, and other header-based attacks. Fix: Implement security headers via webpack plugin or web server configuration (Content-Security-Policy, X-Frame-Options, X-Content-Type-Options, etc.)
• Low · No npm Lockfile Information Provided — Repository root (package-lock.json / yarn.lock). The analysis lacks package-lock.json or yarn.lock content, making it impossible to verify exact dependency versions and transitive dependencies are secure. Fix: Ensure package-lock.json is committed and up-to-date. Use 'npm ci' in CI/CD pipelines. Regularly run 'npm audit' and address findings.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.