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https://github.com/activeinferenceinstitute/start

https://github.com/activeinferenceinstitute/start

Science Score: 36.0%

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Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: ActiveInferenceInstitute
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 360 MB
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  • Watchers: 2
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  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License Code of conduct

README.md

START: Scalable, Tailored Active-inference Research & Training

Python 3.10+ License: CC BY-SA 4.0 Code style: black

An advanced AI-powered system for creating personalized Active Inference and Free Energy Principle curricula Quick links: activeinference.institute • activities.activeinference.institute • x.com/InferenceActive • discord.activeinference.institute • donate.activeinference.institute • youtube.com/c/ActiveInference • video.activeinference.institute

START combines real-time research capabilities with sophisticated content generation to produce professional-grade, personalized educational materials for Active Inference and the Free Energy Principle. The system integrates multiple APIs, comprehensive prompt engineering, and multilingual capabilities to create world-class curricula tailored to specific domains and individual learners.

📌 Start here: here.md — interactive landing for the full experience. Also see the Docs Hub and this README for GitHub-oriented navigation.

🌐 Documentation (Live)

🔗 Quick Links

▶️ Run the Experience

  • Matrix Terminal UI (end-to-end interactive):

bash ./run.sh

  • Documentation Website (serve, build, deploy):

```bash

Serve locally with live reload

./run_docs.sh --serve

Build static site to ./site and open it

./run_docs.sh --build

Deploy to GitHub Pages and open the URL

./run_docs.sh --deploy ```

📚 Documentation Overview

  • High-level docs live under docs/. Each page is modular and links to deeper guides.
  • Start with the Docs Hub, then dive into:

🚀 Key Features

```mermaid graph LR A[START] --> R[Research] A --> C[Content Generation] A --> V[Visualizations] A --> T[Translation] A --> Q[Quality & Testing] R --> R1[Perplexity API] C --> C1[OpenRouter LLMs] V --> V1[Charts & Mermaid] T --> T1[11+ Languages] Q --> Q1[pytest, ruff, black]

click R "docs/pipeline.md" "Pipeline" click Q "docs/TESTING.md" "Testing" click V "docs/pipeline.md" "Visualizations" click T "docs/pipeline.md" "Translation" ```

🔍 Intelligent Research

  • Real-time Domain Analysis: Live research using Perplexity API for current industry insights
  • Personalized Learner Profiling: In-depth analysis of individual learning needs and backgrounds
  • 16+ Professional Domains: Life sciences, technology, business, healthcare, education, and more
  • Configuration-Driven: YAML-based target management with priority and category filtering
  • Enhanced Error Handling: Robust validation and retry mechanisms for reliable operation

✍️ Advanced Content Generation

  • Professional-Grade Curricula: 40-60 hour structured learning programs
  • Comprehensive Modules: 3-5 hour learning units with integrated assessments
  • 5,000-8,000 Word Analyses: Deep personalization and domain integration
  • Enhanced Prompts: 6-9 section frameworks with validation and quality assurance
  • Content Quality Validation: Automatic checking for completeness and consistency

📊 Rich Visualizations

  • Data Charts: PNG visualizations of curriculum metrics and learning analytics
  • Process Diagrams: Mermaid flowcharts showing curriculum structure and pathways
  • Interactive Elements: Visual learning aids and conceptual frameworks
  • Metrics Dashboard: Comprehensive curriculum analysis and reporting

🌍 Multilingual Excellence

  • 11+ Languages: Chinese, Spanish, Arabic, Hindi, French, Japanese, Russian, Swahili, Tagalog, and custom languages
  • Cultural Adaptation: Full localization beyond literal translation
  • Professional Quality: Native-speaker level fluency with technical accuracy
  • Smart Language Handling: Flexible language support with custom language warnings

🧪 Comprehensive Testing & Quality Assurance

  • 375+ Test Cases: Extensive unit and integration test coverage
  • API Validation: Robust testing of all external API integrations
  • Error Scenario Coverage: Comprehensive testing of edge cases and error conditions
  • Continuous Integration: Automated testing pipeline ensuring reliability

📦 Core Pipeline Scripts

Configuration-Based Research

See docs/getting_started.md for full command lists and script paths.

Learn More

  • Docs Hub: docs/README.md
  • Getting Started: docs/getting_started.md
  • Configuration: docs/configuration.md
  • Examples & Outputs: docs/examples.md
  • Pipeline & Architecture: docs/pipeline.md
  • Environment & CI: docs/environment.md
  • Testing Policy: docs/TESTING.md
  • Clone Management: docs/clones.md

🛠️ Quick Installation

Prerequisites

  • Python 3.10+ (3.11+ recommended)
  • uv package manager
  • Perplexity API key for research
  • OpenRouter API key for content generation

Installation Steps

```bash

Install uv package manager

curl -LsSf https://astral.sh/uv/install.sh | sh

Clone and set up project

git clone https://github.com/ActiveInferenceInstitute/Start.git cd Start

Install dependencies

uv sync --all-extras --dev

Download language models

uv run python -m spacy download encoreweb_sm

Configure API keys

cp .env.example .env $EDITOR .env # Add PERPLEXITYAPIKEY and OPENROUTERAPIKEY

Verify installation

uv run pytest -q uv run ruff check . uv run black --check . ```

🎯 Getting Started

See docs/getting_started.md for first-run commands, generation steps, and exploring outputs.

⚙️ Configuration System

See docs/configuration.md for YAML examples and CLI usage.

📈 Generated Content Quality

Research Analysis (Enhanced with new prompts)

  • Domain Reports: 3,000-5,000 words of professional landscape analysis
  • Entity Profiles: 5,000-8,000 words of personalized learning strategies
  • Real-time Data: Current industry insights via Perplexity API
  • Evidence-Based: Grounded in current research and best practices

Curriculum Content (Professional-grade)

  • Structured Programs: 40-60 hour comprehensive learning experiences
  • Modular Design: 6-9 section frameworks with integrated assessments
  • Practical Applications: Real-world case studies and hands-on exercises
  • Professional Integration: Career development and workplace applications

Multilingual Adaptations (Cultural excellence)

  • Full Localization: Examples adapted to target cultures
  • Technical Accuracy: Precise translation of scientific terms
  • Educational Quality: Maintains pedagogical effectiveness across languages
  • Native Fluency: Professional-quality content for each target language

🏗️ System Architecture

```mermaid flowchart TD A[data/config/] --> B[Research] B --> C[Curriculum] C --> D[Visualizations] D --> E[Translations] E --> F[data/ outputs]

click B "docs/pipeline.md" "Pipeline" click F "docs/README.md" "Docs Hub" ```

See docs/pipeline.md for architecture, templates, and data flow.

Prompt Template System

data/prompts/ ├── research_domain_analysis.md # 6-section domain framework (3K-5K words) ├── research_domain_curriculum.md # 9-section curriculum generation (40-60 hours) ├── research_entity.md # 6-section personalization (5K-8K words) ├── curriculum_section.md # Comprehensive module creation (3-5 hours) └── translation.md # 7-section multilingual framework

Quality Assurance Framework

  • Comprehensive Testing: pytest with TDD approach
  • Code Quality: ruff linting and black formatting
  • API Integration: Real-time validation with Perplexity and OpenRouter
  • Content Standards: Professional-grade educational material validation

📊 Example Outputs

See docs/examples.md for example outputs and paths.

🎯 Use Cases & Applications

Educational Institutions

  • University Courses: Neuroscience, psychology, AI program curricula
  • Professional Development: Corporate training for data science, healthcare, management
  • Research Training: Graduate-level courses with theory and implementation

Individual Learning

  • Self-Directed Study: Personalized curricula based on background and goals
  • Career Transition: Bridge existing expertise to Active Inference applications
  • Academic Research: Foundation for thesis work and research projects

Organizational Training

  • Technology Companies: AI ethics, decision frameworks, intelligent systems
  • Healthcare Organizations: Evidence-based practice, clinical decision support
  • Consulting Firms: Advanced analytical frameworks, problem-solving methodologies

📚 Comprehensive Documentation

🚀 Getting Started Guides

  • See the Docs Hub for the complete documentation: docs/README.md
  • Environment Setup: docs/environment.md
  • Pipeline Overview: docs/pipeline.md
  • Usage Guide: learning/curriculum_creation/USAGE_GUIDE.md

🔧 Technical References

  • API Documentation: learning/curriculum_creation/README.md
  • Configuration Reference & Docs Hub: docs/README.md
  • Clone Management: docs/clones.md

📋 Advanced Topics

  • Prompt Engineering: Custom templates in data/prompts/
  • Extension Development: Adding new domains and entities
  • Integration Patterns: Incorporating START into existing workflows

🤝 Active Inference Ecosystem Integration

See also: implementation repos and knowledge resources in docs/clones.md.

🔄 Development Roadmap

Immediate Enhancements

  • [ ] Expanded domain coverage (20+ professional fields)
  • [ ] Enhanced visualization types (interactive diagrams, 3D models)
  • [ ] Integration with learning management systems (LMS)
  • [ ] Mobile-responsive curriculum formats

Future Vision

  • [ ] Real-time personalization based on learning progress
  • [ ] Community-contributed domain and entity profiles
  • [ ] AR/VR learning platform integration
  • [ ] Automated assessment and credentialing systems

🤝 Contributing

We welcome contributions! See our Contributing Guide for details on:

  • Code style and development process
  • Pull request procedure and review guidelines
  • Community guidelines and communication
  • Testing requirements and quality standards

Development Workflow

```bash

Set up development environment

uv sync --all-extras --dev

Run quality checks

uv run pytest -q # Test suite uv run ruff check . # Linting uv run black --check . # Formatting

Development with proper Python path

export PYTHONPATH=$(pwd):$PYTHONPATH ```

🧪 Development & Testing

Testing Framework

The project includes a comprehensive testing framework with 375+ test cases covering: - Unit Tests: Individual component testing with proper mocking - Integration Tests: End-to-end pipeline validation
- API Tests: External service integration validation - Error Handling: Edge cases and failure scenarios

Running Tests

```bash

Run full test suite

uv run pytest

Run specific test categories

uv run pytest -m "not integration" # Skip integration tests uv run pytest -m integration # Only integration tests uv run pytest tests/test_domain.py # Specific test file

Run with coverage

uv run pytest --cov=src --cov-report=html

Set environment for GUI-free testing

export MPLBACKEND=Agg uv run pytest ```

Development Guidelines

  • Code Quality: Black formatting, Ruff linting, comprehensive type hints
  • Testing: Write tests for all new functionality with proper mocking
  • Documentation: Include docstrings and update relevant docs
  • Error Handling: Implement graceful degradation and user-friendly messages

Project Structure

├── src/ # Core system modules │ ├── common/ # Shared utilities │ ├── perplexity/ # API integrations │ ├── system/ # System utilities │ └── terminal/ # CLI components ├── learning/ # Educational pipeline scripts ├── tests/ # Test suite (375+ tests) ├── docs/ # Documentation │ ├── TESTING.md # Testing guide │ └── environment.md # Setup instructions └── data/ # Generated content storage

For detailed testing information, see docs/TESTING.md.

📄 License & Citation

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License - see the LICENSE file for details.

DOI & Archive

DOI

This repository is archived and citable via Zenodo: 10.5281/zenodo.17047619

Citation

If you use START in academic work, please cite: ``` Daniel Ari Friedman, & Active Inference Institute. (2025). ActiveInferenceInstitute/Start: v1 (v1). Zenodo. https://doi.org/10.5281/zenodo.17047617

START: Scalable, Tailored Active-inference Research & Training Active Inference Institute (2024) https://github.com/ActiveInferenceInstitute/Start Licensed under Creative Commons Attribution-ShareAlike 4.0 International ```

🙏 Acknowledgments

  • Active Inference Institute for foundational research and community support
  • Contributors to pymdp, ActiveInference.jl, RxInfer.jl, and related implementation packages
  • Educational partners providing feedback and validation for curriculum effectiveness
  • Open source community for tools, libraries, and collaborative development

📬 Contact & Support

🌟 Join the Community

Together we're building tools to make Active Inference accessible and adaptable across domains, languages, and perspectives. START represents a new paradigm in AI-powered educational content creation - join us in making advanced neuroscience and cognitive science accessible to learners worldwide!

📚 Explore Documentation | 🚀 Get Started | 🤝 Join Community

Institute Shortlinks (email-friendly)

Owner

  • Name: Active Inference Institute
  • Login: ActiveInferenceInstitute
  • Kind: user
  • Location: Online
  • Company: Active Inference Institute

http://activeinference.org/

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