How to debug OpenAI assistant runs
OpenAI assistant runs, enable detailed logging of API requests and responses, handle exceptions like RateLimitError and APIError, and verify your request payloads match the expected format for client.chat.completions.create. Use environment variables for API keys and check model names like gpt-4o to avoid configuration errors.api_error RateLimitError automatically.Why this happens
Common issues during OpenAI assistant runs include incorrect API usage, invalid model names, missing or invalid API keys, and exceeding rate limits. For example, calling the deprecated openai.ChatCompletion.create() method or using an outdated model name triggers errors. The API returns error messages like 401 Unauthorized, 429 Rate Limit Exceeded, or 400 Bad Request. Debugging requires inspecting these errors and the request payload.
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# Incorrect usage: deprecated method and wrong model
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)openai.error.InvalidRequestError: The model `gpt-3.5-turbo` is deprecated or invalid.
The fix
Use the current SDK v1 pattern with correct model names like gpt-4o and handle exceptions properly. This code logs the response and catches common API errors. It also uses environment variables for the API key to avoid config errors.
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
try:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
except Exception as e:
print(f"API call failed: {e}")Hello! How can I assist you today?
Preventing it in production
Implement exponential backoff retries for RateLimitError and APIError to handle transient failures gracefully. Validate your request payloads and model names before sending. Use structured logging to capture request and response details for troubleshooting. Monitor usage quotas and API key validity regularly to avoid unexpected failures.
import time
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
max_retries = 5
for attempt in range(max_retries):
try:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)
break
except Exception as e:
print(f"Attempt {attempt + 1} failed: {e}")
if attempt < max_retries - 1:
time.sleep(2 ** attempt) # exponential backoff
else:
print("Max retries reached. Exiting.")Hello! How can I assist you today?
Key Takeaways
- Always use the latest
OpenAISDK v1 client pattern with correct model names likegpt-4o. - Implement exponential backoff retries to handle transient
RateLimitErrorand network errors. - Use environment variables for API keys and validate request payloads before sending.
- Enable detailed logging of requests and responses to diagnose issues quickly.