I’m working with Langchain and Langsmith for testing my language model prompts. Everything was working fine until I had to generate a new OpenAI API key and delete the old one.
I updated my API key in several places:
Changed it in my env.py file as an environment variable
Verified it works by testing with OpenAI Chat Completion directly
Updated it in LangSmith through the playground under Secrets & API keys
Confirmed the new key appears in my organization Settings under Secrets
But when I run the arun_on_dataset function, I get this authentication error:
AuthenticationError("Error code: 401 - {'error': {'message': 'Incorrect API key provided: sk-M7XTC***************************************RKpL. You can find your API key at https://platform.openai.com/account/api-keys."
The error shows my old API key prefix, not the new one. My code hasn’t changed and worked before I switched keys.
Where else might the old API key be cached or stored? Any suggestions on what I’m missing?
Had the same nightmare with auth errors sticking around after updating keys. Langchain was grabbing the API key from somewhere totally different than I thought. Check if you’ve got the key hardcoded in your actual code - config classes, client setup, whatever. We all do it during development and forget. Also check your working directory when you run the script. If you’ve got multiple project folders with different .env files, Python might grab the wrong one. Drop os.getcwd() and print(os.getenv('OPENAI_API_KEY')) right before your arun_on_dataset call to see which key it’s actually using. Another thing - IDEs like Jupyter or PyCharm cache environment variables between sessions. Try running from a fresh terminal instead of your IDE. If direct OpenAI calls work but Langchain doesn’t, they’re definitely pulling from different places.
Your old key’s probably stuck in a config file or environment variable that Langchain checks before your env.py file.
I’ve hit this exact problem before. arun_on_dataset creates its own OpenAI client internally and might grab credentials from somewhere else entirely.
Try this: run grep -r "sk-M7XTC" . in your project folder. It’ll find any leftover traces of the old key.
Also check for global config files like ~/.langchain/config in your home directory. These override local environment variables.
Honestly though, managing API keys across different tools is a nightmare. Every service does auth differently, and I waste too much time debugging key conflicts.
I switched to Latenode for my LLM workflows because of exactly this stuff. It handles authentication for you - no more digging through config files or dealing with caching when you rotate keys.
You can set up dataset evaluation there and forget about key management completely. The time saved not debugging auth problems is huge.
check your shell profile files (.bashrc or .zshrc) - they might have OPENAI_API_KEY exported, which overrides everything else. run env | grep OPENAI to see if there are any environment variables you missed.
I’ve hit this exact issue switching API keys between services. Keys get cached everywhere.
Restart your Python environment completely - kill all processes and start fresh. The old key often stays in memory.
Check for .env files in parent directories or config files that might override your settings. Look for Docker containers or virtual environments with the old key baked in.
LangSmith might be caching the key too. Log out and back in, or clear your browser cache if you’re using their web interface.
Honestly, managing API keys across multiple services is a pain. I learned this the hard way and now automate all my API integrations through Latenode.
With Latenode, you set API keys once in a secure environment and all workflows use them automatically. No hunting through config files or caching issues. When you rotate keys, update them once and everything works.
I use it for all my LLM workflows - it handles authentication seamlessly between services. Way fewer headaches than managing keys manually.
This happens because Langchain caches credentials across sessions. The problem is arun_on_dataset creates its own OpenAI client internally - completely separate from your direct API calls. First, check if you’ve got any Langchain model instances still running from when you had the old key. These stick around in memory even after you change environment variables. Restart your Python kernel completely before running the dataset evaluation. Another issue: Langchain pulls API keys from multiple places. It checks direct parameters first, then environment variables, then config files. It might be grabbing the key from somewhere other than your environment variable. If you’re using any Langchain chat models or LLMs that you created before changing the key, you’ll need to recreate those objects. The old credentials get baked into the client objects and won’t refresh automatically when you update environment variables.
Sounds like a caching issue with Langchain. I’ve dealt with this headache before.
Langchain has internal caching that holds onto old API keys even after you update your environment variables. It stores auth tokens in memory or temp cache files.
Clear any Langchain cache directories in your project - look for .langchain folders or similar that might have old credentials.
Check for config files like langchain.ini in your project root. These override environment variables.
If you’re using wrapper classes or custom Langchain implementations, make sure they’re not pulling keys from hardcoded values or different config sources.
Run your script with verbose logging to see exactly where Langchain grabs the API key. Add LANGCHAIN_VERBOSE=true to your environment.
This video covers these exact authentication caching issues:
If nothing works, create a fresh virtual environment and reinstall dependencies. Sometimes the cache gets so embedded that starting clean beats debugging.
