🚀 Production-Ready DSL for LLM Interaction

Token-Efficient
LLM Communication

Reduce your prompt sizes by 70% with CompText Codex - a powerful domain-specific language designed for efficient AI interaction.

70%
Prompt Reduction
500+
Commands
100%
MCP Compatible

Why CompText Codex?

Built for developers who want efficient, structured AI communication

Lightning Fast

Structured commands reduce token count dramatically, saving costs and improving response times.

🧩

Composable

Build complex workflows by combining simple, reusable commands. Perfect for automation.

🔧

MCP Integration

Native Model Context Protocol support for seamless integration with AI systems.

📦

Production Ready

Battle-tested DSL with comprehensive error handling and validation built-in.

🎯

Type Safe

Strong typing ensures your commands are correct before execution.

🌐

Cross-Platform

Works everywhere Python runs. CLI, web, serverless - you name it.

See It In Action

Clean, readable syntax that humans and LLMs understand

🔥 Example: Data Analysis Pipeline

# Traditional Prompt (verbose, 200+ tokens)
"Please analyze the sales data from Q4 2024, calculate the total revenue,
find the top 5 products by units sold, and create a summary report..."

# CompText Codex (concise, ~60 tokens)
@analyze sales_q4_2024
  calc total_revenue
  top 5 products by units_sold
  report summary
@end

# Result: Same output, 70% less tokens! 🎉

💡 Example: Multi-Step Workflow

# Chain multiple operations efficiently
@pipeline customer_insights
  load customers from "db"
  filter active = true
  group by region
  aggregate avg(revenue)
  visualize bar_chart
@end

Getting Started

Up and running in minutes

1

Install via pip

Get CompText Codex from PyPI with a single command

$ pip install comptext-codex
2

Import and Initialize

Start using CompText in your Python code immediately

from comptext import Codex
codex = Codex()
3

Write Your First Command

Create efficient, structured prompts in seconds

result = codex.execute("@analyze data.csv")

Ready to optimize your LLM workflows?