openpraxis 0.1.0

AI Bandwidth Amplifier - turn notes into structured practice and actionable learning insights

OpenPraxis

Built for applied mastery of your local knowledge bases, notes, and other multi-source materials: turn raw inputs into structured practice so you can use what you know, not just store it.

Now supports an OpenClaw-oriented knowledge assistant workflow via skills: ingest personal local knowledge into a local KB and generate retrieval practice during import, so users can actively master stored knowledge instead of only archiving it.

Requirements

• Python 3.11+
• LLM API Key (OpenAI / Doubao / Kimi / DeepSeek)

Installation

Install from PyPI:

pip install openpraxis

Or install from source (for development):

git clone https://github.com/Sibo-Zhao/OpenPraxis.git
cd OpenPraxis
pip install -e ".[dev]"

Configuration

Recommended first-time setup:

praxis llm setup
praxis llm show

This saves your default provider/model/api_key into ~/.openpraxis/config.toml. You can also edit config.example.toml manually and copy it to ~/.openpraxis/config.toml.

• openai (default): native structured output parse
• doubao: native structured output parse
• kimi / deepseek: JSON mode + JSON string -> Pydantic validation

API key env vars (higher priority than llm.api_key):

• OPENAI_API_KEY for openai
• ARK_API_KEY for doubao
• MOONSHOT_API_KEY for kimi
• DEEPSEEK_API_KEY for deepseek

Usage

praxis add <file> [--type report|interview|reflection|idea]
praxis practice <input_id>
praxis answer <scene_id> [--editor] [--file <path>]
praxis insight [<input_id>] [--type <insight_type>] [--min-intensity <n>]
praxis show <id>
praxis export [--format md|json] [--output <path>]
praxis list [--type report|interview|reflection|idea] [--limit N]

OpenClaw + Skills

Use the bundled skill at openclaw-knowledge-coach/ to run a CLI-first workflow for local-knowledge mastery:

• Clone and install OpenPraxis
• Configure provider/model/API key
• Import local files with praxis add
• Generate/re-run practice with praxis practice
• Submit answers with praxis answer
• Review/export insights with praxis insight, praxis show, and praxis export

The skill documents command chaining, output contracts, and exercise-generation patterns for retrieval practice on personal local knowledge bases.

praxis add accepts both text/markdown files and common image formats (.png, .jpg, .webp, ...). For images, OpenPraxis uses a vision-capable model to extract readable text first (providers: openai or doubao).

Global runtime LLM overrides (for a single command):

praxis --provider doubao --model doubao-seed-1-6-251015 add note.md
praxis --provider kimi --model kimi-k2-turbo-preview practice <input_id>
praxis --provider deepseek --model deepseek-chat answer <scene_id> --file answer.md

Development

pytest
ruff check src tests

Vision

Increase your "AI bandwidth" by converting fragmented inputs into reusable practice loops that build real transfer: faster recall, clearer decisions, better on-the-job application.

License

See the project repository.