README

Type-safe structured logging with JSON schema validation for hierarchical application workflows.

AI-Native Analysis with MCP Server

Realizing Tim Berners-Lee's Semantic Web Vision - structured data that AI can understand and reason about autonomously.

# Install and run MCP server for Claude Code integration
composer require koriym/semantic-logger
php vendor/koriym/semantic-logger/bin/server.php /tmp

AI-Powered Performance Analysis

The included MCP Server provides two powerful tools for AI-native analysis:

getSemanticProfile - Retrieve latest semantic performance profile with AI-optimized prompts
semanticAnalyze - Execute PHP script with profiling + automatic AI analysis in one command

MCP Server Configuration

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "semantic-profiler": {
      "command": "php",
      "args": [
        "vendor/koriym/semantic-logger/bin/server.php",
        "/tmp"
      ]
    }
  }
}

See docs/mcp-setup.md for detailed configuration options.

Semantic Web Architecture

Everything is Linked for Machine Understanding:

JSON Schema URLs - Every context includes $schema for semantic validation
JSON Pointer Links - Schema properties include links arrays pointing to specifications
RFC 8288 Relations - Standard rel attributes (describedby, related, canonical) for semantic connections
AI Interview Process - Regular AI feedback to minimize non-semantic noise

Example Schema with Semantic Links:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "method": {
      "type": "string",
      "enum": ["GET", "POST", "PUT", "DELETE"]
    }
  },
  "links": [
    {
      "anchor": "#/properties/method",
      "rel": "describedby", 
      "href": "https://tools.ietf.org/html/rfc7231#section-4",
      "title": "HTTP Methods (RFC 7231)"
    }
  ]
}

Semantic Web Mindset:

Everything is machine-readable - No human-only documentation
Links over comments - Semantic relationships through URIs, not prose
AI as quality gatekeeper - Regular AI interviews to identify non-semantic patterns
Schema-first design - Structure meaning before implementation
Minimize semantic noise - Every field must have clear semantic purpose

This approach enables AI to autonomously understand system behavior, diagnose issues, and provide architectural insights beyond basic metrics.

DevLogger for Development

Development logging with AI-native analysis prompt generation:

use Koriym\SemanticLogger\DevLogger;
use Koriym\SemanticLogger\SemanticLogger;

// Initialize with log directory (defaults to system temp)
$devLogger = new DevLogger('/path/to/logs');

// Output semantic logs with AI analysis prompts
$semanticLogger = new SemanticLogger();
$devLogger->log($semanticLogger);

Creates two files:

semantic-dev-*.json - Structured semantic log data
semantic-dev-*-prompt.md - AI-optimized analysis prompt with embedded JSON

Perfect for development debugging and AI-assisted performance analysis.

Self-Proving Responses

Every response becomes self-proving by logging how it was generated. When your API returns "restaurant menu list", the semantic log proves why those specific items were returned through the complete chain of web API calls, database queries, and business logic.

Response (what): {"menu": ["pasta", "pizza", "salad"]}
Semantic Log (how/why): Complete proof of why these 3 items were returned

Provides equal context to both AI systems and human developers for comprehensive system understanding.

Beyond Traditional Logging: Semantic Structure

Traditional logging captures level and message for humans:

[INFO] User query executed: SELECT * FROM users WHERE id = 123
[DEBUG] Query took 12.5ms, returned 1 row

Semantic logging captures data structure AND meaning structure:

Open: "what we plan to do" (intent)
Event: "what happened during execution" (occurrences)
Close: "what actually occurred" (result)

This structure enables understanding not just what happened, but why it happened and how it relates to the intended operation.

Best Practice: Use alongside traditional logging

Traditional logs for quick debugging and stack traces
Semantic logs for complex workflows and system analysis

Use Cases

Development & Debugging - Complex workflow tracing with intent vs result analysis
Compliance & Auditing - GDPR, SOX, HIPAA compliance with complete audit trails
Security & Monitoring - Track data modifications and detect anomalous behavior
Business Intelligence - Analyze behavior patterns and optimize processes

Features

Type-safe context objects with const properties
Hierarchical logging with open/event/close patterns
JSON Schema validation for log entries

Structured, semantic, type-safe logging with web linking enables deep understanding for humans, AI, and data science.

Installation

composer require koriym/semantic-logger

Quick Start

1. Define Context Classes

Create type-safe context classes by extending AbstractContext:

use Koriym\SemanticLogger\AbstractContext;

final class ProcessContext extends AbstractContext
{
    public const TYPE = 'process';
    public const SCHEMA_URL = 'https://example.com/schemas/process.json';
    
    public function __construct(
        public readonly string $name,
    ) {}
}

final class EventContext extends AbstractContext
{
    public const TYPE = 'event';
    public const SCHEMA_URL = 'https://example.com/schemas/event.json';
    
    public function __construct(
        public readonly string $message,
    ) {}
}

final class ResultContext extends AbstractContext
{
    public const TYPE = 'result';
    public const SCHEMA_URL = 'https://example.com/schemas/result.json';
    
    public function __construct(
        public readonly string $status,
    ) {}
}

2. Log with Semantic Structure: Intent → Events → Result

use Koriym\SemanticLogger\SemanticLogger;

$logger = new SemanticLogger();

// OPEN: Declare intent - what we plan to do
$processId = $logger->open(new ProcessContext('data processing'));

// EVENT: What happened during execution
$logger->event(new EventContext('processing started'));

// CLOSE: What actually occurred - the result
$logger->close(new ResultContext('success'), $processId);

// Optional: Add relations for debugging context
$relations = [
    ['rel' => 'related', 'href' => 'https://github.com/example/my-app', 'title' => 'Source Code Repository'],
    ['rel' => 'describedby', 'href' => 'https://example.com/db/schema/processes.sql', 'title' => 'Database Schema']
];

// Get structured log with complete intent→result mapping
$logJson = $logger->flush($relations);
echo json_encode($logJson, JSON_PRETTY_PRINT);

3. Output Structure

The semantic structure captures the complete intent→result flow with hierarchical nesting:

{
  "$schema": "https://koriym.github.io/Koriym.SemanticLogger/schemas/semantic-log.json",
  "open": {
    "type": "process",
    "schemaUrl": "https://example.com/schemas/process.json",
    "context": {
      "name": "data processing"
    },
    "events": [
      {
        "type": "event",
        "schemaUrl": "https://example.com/schemas/event.json",
        "context": {
          "message": "processing started"
        }
      }
    ],
    "close": {
      "type": "result",
      "schemaUrl": "https://example.com/schemas/result.json",
      "context": {
        "status": "success"
      }
    }
  },
  "relations": [
    {
      "rel": "related",
      "href": "https://github.com/example/my-app",
      "title": "Source Code Repository"
    },
    {
      "rel": "describedby", 
      "href": "https://example.com/db/schema/processes.sql",
      "title": "Database Schema"
    }
  ]
}

Structure Meaning:

open: Intent and planned operations with nested events and close results (hierarchical structure)
events: Occurrences during execution (nested within their operation context)
close: Actual results and outcomes (nested within the corresponding open operation)
schemaUrl: JSON Schema URL for validation and documentation
relations: Optional RFC 8288 compliant links (related resources, schemas, etc.)

Hierarchical Nesting Benefits:

Request Tracing: Clear operation boundaries through nested structure in complex workflows
Debugging: Trace the flow from intent (open) → events → result (close) within each operation context
Monitoring: Track operation completion and identify unclosed operations
Compliance: Maintain audit trails with clear operation boundaries

Documentation

Schema Portal - AI-native semantic schema portal with comprehensive documentation

koriym / semantic-logger

Maintainers

Details