Meta Debugger

AI-powered self-debugging system that identifies, analyzes, and fixes errors automatically. Learns from past errors, builds error patterns, generates fix suggestions, and can apply fixes autonomously. Essential capability for self-healing AI systems.

Self-diagnosing and self-healing AI capability.

Features

1. Error Detection

• Runtime Monitoring: Detect errors in real-time
• Pattern Recognition: Identify error patterns
• Anomaly Detection: Find unusual behaviors
• Log Analysis: Parse and analyze logs

2. Root Cause Analysis

• Stack Trace Analysis: Understand error origins
• Context Tracking: Track what led to error
• Similar Errors: Find related past errors
• Impact Assessment: Evaluate error severity

3. Fix Generation

• Solution Suggestions: Generate fix candidates
• Code Patches: Create actual code changes
• Configuration Fixes: Fix config issues
• Workarounds: Suggest alternative approaches

4. Automatic Fix

• Safe Application: Apply fixes safely
• Rollback Support: Undo if needed
• Test Validation: Verify fix works
• Learning Loop: Learn from results

5. Prevention

• Pattern Building: Build error patterns
• Pre-flight Checks: Validate before execution
• Guard Rails: Add safety checks
• Monitoring: Ongoing error watch

Installation

bash

pip install json traceback ast

Usage

Initialize Debugger

python

from meta_debugger import MetaDebugger

debugger = MetaDebugger(
    name="my_assistant",
    auto_fix=True,
    safe_mode=True
)

Register Error Handlers

python

@debugger.error_handler
def handle_api_error(error, context):
    """Custom error handler"""
    return {
        'action': 'retry',
        'max_retries': 3,
        'backoff': 'exponential'
    }

@debugger.error_handler  
def handle_timeout(error, context):
    """Handle timeout errors"""
    return {
        'action': 'increase_timeout',
        'new_timeout': 60
    }

Wrap Functions

python

@debugger.wrap
def call_api(url, params):
    """Function that might fail"""
    return requests.get(url, params=params)

Manual Debug

python

# Analyze an error
analysis = debugger.analyze(
    error=ValueError("Invalid input"),
    context={'input': user_input, 'function': 'process_data'}
)

print(analysis)
# {
#     'root_cause': 'Type mismatch',
#     'severity': 'medium',
#     'suggestions': [
#         'Convert input to correct type',
#         'Add input validation'
#     ]
# }

# Apply fix
result = debugger.apply_fix(analysis)

Error History

python

# Get error patterns
patterns = debugger.get_error_patterns()

# Get common fixes
fixes = debugger.get_common_fixes()

# Get prevention suggestions
prevention = debugger.get_prevention_tips()

API Reference

Error Handling

Method	Description
@error_handler	Decorator for error handlers
register_handler(type, handler)	Register custom handler
handle(error, context)	Handle an error

Analysis

Method	Description
analyze(error, context)	Analyze error root cause
get_stack_trace(error)	Parse stack trace
find_similar(error)	Find similar past errors

Fix Generation

Method	Description
generate_fixes(error)	Generate fix candidates
rank_fixes(fixes)	Rank fixes by probability
apply_fix(fix)	Apply a fix

Prevention

Method	Description
add_guardrail(check)	Add pre-execution check
validate_input(input, rules)	Validate inputs
build_pattern(error)	Build error pattern

Learning

Method	Description
record_error(error, context)	Record error for learning
record_fix(error, fix, success)	Record fix result
get_insights()	Get learned insights

Error Patterns

python

ERROR_PATTERNS = {
    "timeout": {
        "causes": ["network", "server_load", "query_complexity"],
        "fixes": ["increase_timeout", "retry", "cache"],
        "prevention": ["timeout_guards", "circuit_breaker"]
    },
    "value_error": {
        "causes": ["type_mismatch", "invalid_format", "out_of_range"],
        "fixes": ["type_conversion", "validation", "default_value"],
        "prevention": ["input_validation", "schema_check"]
    },
    "connection_error": {
        "causes": ["network_down", "server_unavailable", "auth_failed"],
        "fixes": ["retry", "reconnect", "fallback"],
        "prevention": ["health_check", "load_balancing"]
    }
}

Fix Strategies

Retry Strategy

python

{
    'strategy': 'retry',
    'max_attempts': 3,
    'backoff': 'exponential',
    'backoff_base': 2,
    'max_delay': 60
}

Fallback Strategy

python

{
    'strategy': 'fallback',
    'primary': 'api_v1',
    'fallback': 'api_v2',
    'condition': 'primary_unavailable'
}

Circuit Breaker

python

{
    'strategy': 'circuit_breaker',
    'failure_threshold': 5,
    'timeout': 60,
    'half_open_requests': 3
}

Default Value

python

{
    'strategy': 'default',
    'field': 'result',
    'default': {'status': 'unknown'}
}

Example: Full Usage

python

from meta_debugger import MetaDebugger

# Initialize
debugger = MetaDebugger("production_assistant")

# Register handlers
@debugger.error_handler
def handle_api_error(error, context):
    if "timeout" in str(error).lower():
        return {'action': 'retry', 'max_retries': 3}
    elif "auth" in str(error).lower():
        return {'action': 'refresh_token'}
    return {'action': 'log_and_continue'}

# Wrap risky function
@debugger.wrap
def fetch_stock_data(symbol):
    # This might fail
    return api.get(f"/stock/{symbol}")

# Use it
try:
    data = fetch_stock_data("600519")
except Exception as e:
    # Debugger automatically handles
    debugger.handle(e, {'function': 'fetch_stock_data', 'symbol': '600519'})

Integration

With Skills

python

class MySkill:
    def __init__(self):
        self.debugger = MetaDebugger()
    
    def execute(self, input):
        try:
            return self._execute(input)
        except Exception as e:
            return self.debugger.handle(e, {'skill': 'MySkill', 'input': input})

With OpenClaw

python

@hookimpl
def on_error(error, context):
    debugger = MetaDebugger()
    return debugger.handle(error, context)

Metrics

Metric	Description
error_rate	Errors per 1000 calls
fix_success_rate	Successful fixes
avg_recovery_time	Time to recover
prevented_errors	Errors caught by guards

Best Practices

1. Start with Safe Mode: Always review before auto-fixing
2. Log Everything: Build learning data
3. Test Fixes: Validate before production
4. Iterate: Improve patterns over time
5. Balance: Don't over-catch or under-catch

Future Capabilities

• Cross-system error correlation
• AI-generated fixes with LLMs
• Self-healing infrastructure
• Predictive error prevention