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Learning Path: Developer

For: Software engineers, technical leads, and developers building with ReasonKit

This learning path guides you through ReasonKit from a technical implementation perspective.

🎯 Goal

Build applications that integrate ReasonKit’s structured reasoning capabilities into your software.

πŸ“š Path Overview

Phase 1: Foundation (30 minutes)

  1. Quick Start - Get ReasonKit running locally
  2. Installation - Install via Cargo, npm, or Python
  3. Your First Analysis - Run your first ThinkTool

Outcome: You can execute ThinkTools from the CLI.

Phase 2: Integration (1-2 hours)

  1. Rust API - Use ReasonKit as a Rust library
  2. Python Bindings - Integrate with Python applications
  3. Output Formats - Parse and process results programmatically
  4. Integration Patterns - Common integration patterns

Outcome: You can integrate ReasonKit into your application.

Phase 3: Advanced Usage (2-3 hours)

  1. Architecture - Understand the system design
  2. Custom ThinkTools - Create your own reasoning protocols
  3. LLM Providers - Configure different LLM backends
  4. Performance Tuning - Optimize for your use case

Outcome: You can customize and optimize ReasonKit for production.

Phase 4: Production (1-2 hours)

  1. CLI Reference - Complete command reference
  2. Configuration - Production configuration
  3. Troubleshooting - Debug common issues

Outcome: You can deploy ReasonKit in production environments.

πŸ› οΈ Quick Reference

Common Tasks

Integrate in Rust:

#![allow(unused)]
fn main() {
use reasonkit_core::thinktool::{ProtocolExecutor, ProtocolInput};

let executor = ProtocolExecutor::new()?;
let result = executor.execute("gigathink", ProtocolInput::query("Your question")).await?;
}

Integrate in Python:

import reasonkit

executor = reasonkit.ProtocolExecutor()
result = executor.execute("gigathink", query="Your question")

CLI Usage:

rk think "Your question" --profile balanced

πŸŽ“ Next Steps

After completing this path:

  • Build a custom ThinkTool for your domain
  • Integrate ReasonKit into your production application
  • Contribute improvements back to the project

Estimated Time: 4-7 hours
Difficulty: Intermediate to Advanced
Prerequisites: Familiarity with Rust or Python