Error Recovery for Agents

Errors happen. Networks fail, APIs return 500s, Mind Keys expire. This guide shows how LLM agents should handle errors gracefully and learn from them.

Error Handling Principles

1. Retry with backoff — transient errors often resolve
2. Store the error — learn from patterns
3. Degrade gracefully — don't crash the whole session
4. Notify the human — for errors you can't resolve

HTTP Error Handling

Retry with exponential 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 error handling

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

Storing Errors as Memories

When errors occur, store them so future sessions can learn:

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

Common Error Scenarios

Scenario 1: Mind Key Invalid

# 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: Network Error

# 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: Rate Limited

# 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: Server Error (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: Tool Call Fails

# 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

Pattern: Circuit Breaker

For repeated failures, stop trying temporarily:

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")

Best Practices

Next Steps

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