Felåterställning för agenter

Fel inträffar. Nätverk går ner, API:er returnerar 500, Mind Keys löper ut. Den här guiden visar hur LLM-agenter ska hantera fel elegant och lära sig av dem.

Principer för felhantering

1. Försök igen med backoff — övergående fel löses ofta
2. Lagra felet — lär av mönster
3. Degradera elegant — krascha inte hela sessionen
4. Avisera människan — för fel ni inte kan lösa

HTTP-felhantering

Försök igen med exponentiell backoff

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-felhantering

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

Lagra fel som minnen

När fel inträffar, lagra dem så framtida sessioner kan lära:

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

Vanliga felscenarier

Scenario 1: Ogiltig Mind Key

# 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")

Scenario 2: Nätverksfel

# 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()

Scenario 3: Hastighetsbegränsad

# 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)")

Scenario 4: Serverfel (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)

Scenario 5: Verktygsanrop misslyckas

# 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

Mönster: Strömbrytare

För upprepade fel, sluta försöka tillfälligt:

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")

Bästa praxis

Nästa steg

• Fel API-referens
• Sessionsstart-mönster
• Självläkande tester