एजेंट्स के लिए त्रुटि रिकवरी

त्रुटियाँ होती रहती हैं। नेटवर्क विफल होते हैं, APIs 500 लौटाते हैं, Mind Keys समाप्त हो जाते हैं। यह गाइड दिखाती है कि LLM एजेंट्स को त्रुटियों को सुंदर ढंग से कैसे संभालना चाहिए और उनसे कैसे सीखना चाहिए।

त्रुटि हैंडलिंग सिद्धांत

1. बैकऑफ़ के साथ पुनः प्रयास — क्षणिक त्रुटियाँ अक्सर हल हो जाती हैं
2. त्रुटि स्टोर करें — पैटर्न से सीखें
3. सुंदरता से डिग्रेड करें — पूरा सत्र क्रैश न करें
4. मानव को सूचित करें — उन त्रुटियों के लिए जिन्हें आप हल नहीं कर सकते

HTTP त्रुटि हैंडलिंग

एक्सपोनेंशियल बैकऑफ़ के साथ पुनः प्रयास

import time
import requests

def call_with_retry(url, max_retries=3, backoff_base=2):
    """Call URL with exponential backoff."""
    for attempt in range(max_retries):
        try:
            r = requests.get(url, headers={"Authorization": f"Bearer {KEY}"})
            
            # Success
            if r.status_code < 400:
                return r
            
            # Rate limited — wait and retry
            if r.status_code == 429:
                wait = int(r.headers.get("Retry-After", 60))
                print(f"Rate limited. Waiting {wait}s...")
                time.sleep(wait)
                continue
            
            # Server error — retry with backoff
            if r.status_code >= 500:
                wait = backoff_base ** attempt
                print(f"Server error {r.status_code}. Retrying in {wait}s...")
                time.sleep(wait)
                continue
            
            # Client error — don't retry
            if 400 <= r.status_code < 500:
                raise ClientError(f"{r.status_code}: {r.text}")
        
        except requests.RequestException as e:
            # Network error — retry
            wait = backoff_base ** attempt
            print(f"Network error: {e}. Retrying in {wait}s...")
            time.sleep(wait)
    
    raise MaxRetriesError(f"Failed after {max_retries} retries")

Auth त्रुटि हैंडलिंग

def safe_call(url):
    """Call with auth error handling."""
    try:
        return call_with_retry(url)
    except ClientError as e:
        if "401" in str(e):
            # Mind Key invalid — critical, can't recover
            store_error("auth_invalid", str(e), "Check Mind Key")
            notify_human("My Mind Key is invalid. Please update.")
            raise AuthError("Cannot continue without valid Mind Key")
        elif "403" in str(e):
            store_error("forbidden", str(e), "Wrong token type?")
            raise ForbiddenError(str(e))
        elif "404" in str(e):
            # Path doesn't exist — don't retry
            store_error("not_found", str(e), "Check endpoint path")
            raise NotFoundError(str(e))
        else:
            raise

त्रुटियों को मेमोरी के रूप में स्टोर करना

जब त्रुटियाँ होती हैं, तो उन्हें स्टोर करें ताकि भविष्य के सत्र सीख सकें:

def store_error(error_type, error_message, recovery_hint=""):
    """Store an error as a memory for future reference."""
    requests.post(f"{URL}/memory",
        headers={"Authorization": f"Bearer {KEY}",
                 "Content-Type": "application/json"},
        json={
            "category": "mistake",
            "key": f"error_{error_type}_{int(time.time())}",
            "content": f"Error: {error_type}\nMessage: {error_message}\nRecovery: {recovery_hint}",
            "tags": ["error", error_type],
            "priority": "high"
        })

# Example
try:
    deploy()
except DeployError as e:
    store_error("deploy_failed", str(e), 
                "Check CI logs, verify Docker image exists")
    raise

सामान्य त्रुटि परिदृश्य

परिदृश्य 1: Mind Key अमान्य

# Detection: 401 on every call
# Recovery: Cannot recover — need human intervention

def handle_invalid_mind_key():
    store_error("mind_key_invalid", 
                "All API calls returning 401",
                "Mind Key may be revoked. Need new key.")
    
    # Notify human via chat (if possible)
    try:
        reply("⚠️ My Mind Key is invalid. I cannot access memories. "
              "Please check and update SYNAPSE_MIND_KEY.")
    except:
        pass  # Chat may also fail with bad key
    
    # Exit gracefully
    raise CriticalError("Cannot continue without valid Mind Key")

परिदृश्य 2: नेटवर्क त्रुटि

# Detection: ConnectionError, Timeout
# Recovery: Retry with backoff, then degrade

def handle_network_error(url, retry=3):
    for attempt in range(retry):
        try:
            return requests.get(url, timeout=10)
        except (requests.ConnectionError, requests.Timeout) as e:
            wait = 2 ** attempt
            print(f"Network error, retrying in {wait}s: {e}")
            time.sleep(wait)
    
    # All retries failed — degrade
    store_error("network_failure", 
                f"Cannot reach {url}",
                "Check internet connection, Synapse may be down")
    
    # Work offline if possible
    return work_offline()

परिदृश्य 3: रेट लिमिट

# Detection: 429 with Retry-After header
# Recovery: Wait and retry, or switch to header auth

def handle_rate_limit(response):
    retry_after = int(response.headers.get("Retry-After", 60))
    print(f"Rate limited. Waiting {retry_after}s...")
    time.sleep(retry_after)
    
    # If this keeps happening, suggest switching to header auth
    if has_query_param_auth(url):
        store_error("rate_limited", 
                    "Frequent 429s with ?key= auth",
                    "Switch to Authorization: Bearer header (no rate limit)")

परिदृश्य 4: सर्वर त्रुटि (5xx)

# Detection: 500, 502, 503
# Recovery: Retry with backoff, check /health

def handle_server_error(url):
    # Check if server is up
    health = requests.get(f"{URL}/health")
    if health.status_code != 200:
        store_error("server_down", 
                    "Synapse health check failing",
                    "Wait for server recovery")
        raise ServerDownError()
    
    # Retry with backoff
    return call_with_retry(url, max_retries=5)

परिदृश्य 5: टूल कॉल विफल

# Detection: Tool returns error content
# Recovery: Try alternative approach, store failure

def call_tool_safely(tool_name, args, alternatives=None):
    try:
        result = call_tool(tool_name, args)
        if result.get("isError"):
            raise ToolError(result["content"])
        return result
    except ToolError as e:
        store_error(f"tool_{tool_name}_failed",
                    f"Args: {args}\nError: {e}",
                    f"Try: {alternatives or 'no alternatives'}")
        
        # Try alternatives
        if alternatives:
            for alt in alternatives:
                try:
                    return call_tool(alt, args)
                except:
                    continue
        
        raise

पैटर्न: सर्किट ब्रेकर

बार-बार विफलताओं के लिए, कुछ समय के लिए प्रयास बंद करें:

class CircuitBreaker:
    def __init__(self, threshold=5, reset_time=300):
        self.failures = 0
        self.threshold = threshold
        self.reset_time = reset_time
        self.last_failure = 0
    
    def call(self, fn, *args, **kwargs):
        if self.failures >= self.threshold:
            if time.time() - self.last_failure < self.reset_time:
                raise CircuitOpenError("Circuit breaker open")
            else:
                # Reset
                self.failures = 0
        
        try:
            result = fn(*args, **kwargs)
            self.failures = 0  # Reset on success
            return result
        except:
            self.failures += 1
            self.last_failure = time.time()
            raise

# Usage
breaker = CircuitBreaker(threshold=5)
try:
    result = breaker.call(api_call, url)
except CircuitOpenError:
    print("Too many failures, waiting before retry")

सर्वोत्तम अभ्यास

अगले कदम

• Errors API Reference
• Session Start Pattern
• Self-Healing Tests