You’re experiencing SSL certificate errors when connecting to the LangSmith service, resulting in a langsmith.utils.LangSmithConnectionError and preventing you from using the API. The error message indicates that the certificate has expired. This issue is affecting multiple browsers, suggesting the problem lies with the LangSmith server and not your local setup.
Understanding the “Why” (The Root Cause):
The error “certificate verify failed: certificate has expired” means that the SSL certificate LangSmith uses to secure its connection has expired. This is a server-side issue, meaning the problem is not with your client’s configuration (e.g., browser settings, operating system). Expired certificates prevent secure communication, thus causing connection failures. The LangSmith API relies on a valid SSL certificate for secure data transfer. The recent onset of the problem suggests that the certificate expired unexpectedly.
Step-by-Step Guide:
Check LangSmith’s Status: Before making any changes to your local configuration, visit the official LangSmith status page or check their social media channels for any announcements regarding service outages or certificate renewal issues. If a known issue exists, the team is likely already working to resolve it.
Verify Internet Connectivity: Though the error message suggests a connection problem, rule out basic issues by ensuring your internet connection is working correctly. Try accessing other websites to confirm network connectivity. If you encounter connection issues beyond LangSmith, address your internet connection problems first.
Wait for Resolution (If Applicable): If LangSmith acknowledges the certificate issue on their status page, it’s best to wait for their fix. Their engineers are the ones able to resolve the expired certificate problem. Checking periodically for updates is the recommended approach.
Contact LangSmith Support (If the Problem Persists): If no announcements are made about the issue, contact LangSmith support directly. Provide them with the detailed error message you received. They will likely have access to monitoring systems that can confirm the certificate’s status and address the problem.
Common Pitfalls & What to Check Next:
Incorrect Time: While unlikely, ensure your system clock is accurately synchronized. An incorrect system time could cause certificate verification issues.
Proxy Settings: If you use a proxy server, verify its configuration. Incorrect proxy settings can interfere with SSL certificate validation.
Firewall: Ensure that your firewall isn’t blocking access to LangSmith’s API endpoint.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
LangSmith doesn’t support comments in prompts like that. The Mustache syntax you’re using will probably throw errors or get passed straight to the model.
I’ve hit this same problem on several projects. Here’s what works:
Use LangSmith’s prompt description field for documentation. Keep your actual prompt clean and put all comments/explanations in the metadata.
For inline docs, I prefix comment lines with something the model ignores:
<!-- Generate a response about {{topic}} -->
<!-- Focus on technical accuracy -->
Generate a response about {{topic}}
Most models handle HTML comments fine, or just use hash symbols at line starts.
Yeah, you need to render prompts before sending them to models. LangSmith does variable substitution, but comment syntax has to be stripped out first.
PromptLayer and Weights & Biases have better prompt versioning with built-in annotations. But honestly, the description field approach works fine for most cases.
One trick: I keep a separate “template” version with heavy commenting, then maintain a clean production version in LangSmith. Version control handles the rest.
yeah, langsmith’s comment handling sucks. i just throw # comment here at the start of lines and strip them with regex before hitting the model. works great and won’t break stuff like mustache syntax does.
Had this exact problem six months ago on a client project. I keep two versions of each prompt template: one in my code repo with full comments using standard syntax, and the stripped version for LangSmith. Treat prompts like code; that’s the key insight. A quick preprocessing script removes comment blocks before uploading to LangSmith. This gives you detailed documentation during development and a clean execution in production. For rendering, yes, you must process them before sending to the model. LangSmith does variable substitution but won’t strip comment syntax. I learned this the hard way when I accidentally sent commented prompts to GPT-4 and got gibberish back. The tooling for prompt management is still pretty rough compared to regular software development, as most platforms treat prompts as text rather than structured code needing proper documentation.
Been using LangSmith for about a year and hit this issue right away. The platform just treats prompts as plain text, so any comment syntax either breaks the display or gets sent straight to the model. Here’s what works for me: I write prompts in separate .md files with all my comments and explanations, then copy the clean version into LangSmith. Markdown lets me document why I made each choice while keeping the actual prompt separate. For team stuff, LangSmith’s tagging and versioning beats trying to cram comments into prompts. Tag them with context like “customer-support” or “technical-docs” and use commit messages to explain your changes. One heads up: if you’re doing complex variable substitution, test your rendered prompts hard. I’ve seen leftover comment bits cause weird issues that only popped up in production. Always validate your final prompt string before it hits the model.
I went down this rabbit hole last year and tried most of these approaches. The automation route sounds nice but honestly felt like overkill.
What actually saved me time was treating prompt comments like database migrations. I keep a simple JSON file mapping each prompt version to its purpose and changes:
Then my prompts stay clean in LangSmith but I’ve got full context without juggling files or building pipelines. Works great for rollbacks too since I know exactly what each version does.
For inline stuff during development, I use Python triple quotes around comment blocks. Easy to spot and strip out with a one-liner before deployment.
Here’s the key insight everyone’s dancing around: LangSmith treats prompts like configuration, not code. Once you accept that and build your workflow around it, the comment problem becomes way simpler.
Understanding the “Why” (The Root Cause):
Step-by-Step Guide:
Common Pitfalls & What to Check Next:
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!