v0.1.0 - Chain-of-Thought Loop

🧠 Chain-of-Thought Loop

This release introduces a new agentic loop pattern focused on deep reasoning through iterative self-reflection.

What's New

Chain-of-Thought Loop

• New trait: ChainOfThoughtLoop - Enables progressive reasoning with confidence evaluation
• Streaming support: Real-time streaming of reasoning iterations via chainOfThoughtStream()
• Self-reflection: Automatic confidence checks between reasoning cycles
• Full transparency: Shows step-by-step thinking process to users

Supporting Classes

• CoTResult - Enhanced result object with reasoning metadata
• CoTStep - Value object for each reasoning iteration
• CoTPrompts - Reusable prompt templates for CoT reasoning

Lifecycle Callbacks (6 new callbacks)

• onIterationStart - When reasoning iteration begins
• onBeforeReasoning - Before LLM reasons through problem
• onAfterReasoning - After reasoning step completes
• onReflection - When confidence evaluation occurs
• onLoopComplete - When agent reaches confident answer
• onMaxIterationsReached - When iteration limit is hit

Plus all standard action callbacks (onBeforeAction, onAfterAction)

Configuration

• max_reasoning_iterations - Control iteration limits (default: 5)
• throw_on_max_iterations - Configure error handling
• Environment variable support via AGENTIC_COT_MAX_ITERATIONS

Documentation

• New tutorial: Streaming Chain-of-Thought Loop
• Complete code examples with backend routes and React frontend
• Comparison tables: CoT vs ReAct vs Plan-Execute
• Best practices and customization guide
• Screenshots demonstrating the UI

Tests

• Comprehensive unit tests with 20+ test cases
• Integration tests with real API calls
• 100% coverage of CoT loop logic

When to Use Chain-of-Thought

Use CoT when:

• ✅ The reasoning process needs to be visible/auditable
• ✅ You need to demonstrate understanding, not just results
• ✅ The problem requires careful, step-by-step analysis
• ✅ Users want to see "how" the agent thinks
• ✅ Confidence in the answer is critical

Quick Example

```php
use Laragentic\Loops\ChainOfThoughtLoop;

class MathAgent implements Agent, HasTools
{
use Promptable, ChainOfThoughtLoop;

public function instructions(): string
{
    return 'Think step-by-step. Evaluate your confidence. '
         . 'Only answer when you're certain.';
}

}

$result = (new MathAgent)->chainOfThought(
'Which train is faster and by how much?'
);

echo $result->text();
echo "Reasoning iterations: {$result->reasoningIterations}";
```

Breaking Changes

None - fully backward compatible with v0.0.2

Updated Documentation

• README updated with Chain-of-Thought section
• Tutorial links updated
• Configuration documented
• All existing tutorials updated with CoT references

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Full Changelog: v0.0.2...v0.1.0