Summary

OpenPraxis transforms local knowledge bases and notes into structured practice for applied mastery. It targets users seeking to actively use their stored information, moving beyond mere archiving to enhance recall, decision-making, and on-the-job application.

How It Works

The system ingests diverse local inputs, including text, Markdown, and images, leveraging LLM APIs (OpenAI, Doubao, Kimi, DeepSeek) for processing. For images, vision-capable models extract text before generating retrieval practice exercises during import. It supports an OpenClaw-oriented workflow for command-line-driven knowledge mastery and active recall.

Quick Start & Requirements

• Installation: pip install openpraxis or from source (git clone https://github.com/Sibo-Zhao/OpenPraxis.git, cd OpenPraxis, pip install -e ".[dev]").
• Prerequisites: Python 3.11+, LLM API Key (OpenAI, Doubao, Kimi, DeepSeek).
• Configuration: Run praxis llm setup or manually edit ~/.openpraxis/config.toml. API keys can also be set via environment variables (OPENAI_API_KEY, ARK_API_KEY, MOONSHOT_API_KEY, DEEPSEEK_API_KEY).
• Usage: Core commands include praxis add, praxis practice, praxis answer, praxis insight, praxis show, praxis export, and praxis list.
• Documentation: Configuration commands (praxis llm setup, praxis llm show) and CLI usage examples are provided.

Highlighted Details

• Supports multiple LLM providers (OpenAI, Doubao, Kimi, DeepSeek) with native structured output parsing or JSON mode validation.
• Processes image inputs by extracting text using vision-capable models.
• Integrates with OpenClaw for a CLI-first workflow focused on retrieval practice and command chaining.
• Enables global LLM overrides for single commands (e.g., praxis --provider doubao ... add note.md).

Maintenance & Community

No specific details on contributors, sponsorships, or community channels were provided in the README.

Licensing & Compatibility

The license is stated as "See the project repository." Specific license type and compatibility for commercial use or closed-source linking require further investigation of the repository.

Limitations & Caveats

The project relies on external LLM API keys, incurring potential costs and requiring careful provider selection for data privacy. The "OpenClaw-oriented" workflow may indicate a specific ecosystem focus. Image processing is limited to text extraction.