LangSmith - Debug Your LLM Agents When They Go Sideways

Look, if you've built anything more complex than "ask GPT a question and print the answer," you've experienced the pain. Your agent works perfectly in testing, then in production it starts calling the database 200 times, gets stuck in a loop trying to format JSON, or suddenly decides every user query needs a 15-paragraph response about penguins.

Traditional logging is fucking useless for this. Your logs show "called OpenAI API" and "got response," but they don't show you that your agent got confused by ambiguous context and spent like $73 or maybe $89 in API calls trying to figure out if "apple" meant the fruit or the company. I stopped counting after the receipt hit triple digits.

The Real Problem Nobody Talks About

The issue isn't that LLMs are non-deterministic - that's just AI consultant buzzword bingo. The real problem is that you can't see what they're thinking. When your agent fails, you get an error message like "Unable to complete request" - which tells you absolutely nothing about whether it failed because:

• The prompt was malformed
• The model couldn't parse the tool schema
• It hit a rate limit on the 47th tool call
• Your vector search returned garbage
• The context window filled up with circular reasoning

I learned this debugging a customer service bot that started responding to every question with poetry. The logs showed successful API calls. LangSmith showed me the agent had somehow convinced itself that "professional tone" meant "iambic pentameter." Took me 4 hours and three cups of coffee to trace through the prompt chain and find where someone had added a fucking Shakespeare example to the few-shot prompts. The customer was not amused when their refund request got answered in sonnet form.

Unlike traditional software where you can predict execution paths, LLM debugging requires seeing the model's actual reasoning process - which is impossible with normal logging.

What LangSmith Actually Does

LangSmith traces every step your LLM takes - every API call, every tool execution, every reasoning step. When shit goes wrong, you can see exactly where and why.

Real debugging scenario: Our RAG system was giving wrong answers 30% of the time. Traditional logs showed successful document retrieval and OpenAI calls. LangSmith revealed the vector search was returning documents from the wrong knowledge base because someone fucked up the metadata filtering. Fix took 5 minutes once I could see the actual retrieved context.

Another example: Agent kept timing out on "simple" queries. Logs showed nothing. LangSmith trace revealed it was getting caught in a loop where Claude kept calling a search tool, getting no results, then calling it again with slightly different parameters. Added a retry limit and saved our API budget.

The Cost Reality Check

Free tier gives you 5,000 traces per month. Sounds generous until you realize a single conversation with tool usage can generate 20+ traces. In active development, you'll burn through that in a week.

Paid plan starts at $39/user/month for 100k traces. Worth it when one production bug costs more than a year of subscriptions, but painful for side projects.

Integration Isn't Magic

Works great with LangChain. For everything else, you'll need to instrument your code manually or use their OpenTelemetry integration, which is more setup work but gives you framework independence.

Warning: The auto-instrumentation sometimes misses custom tool calls or complex async operations. Had to add manual tracing for our document processing pipeline because the automatic stuff only caught the high-level agent calls.

Companies like Replit and GitLab use this in production, so it's proven at scale. But it's not magic - it's just the debugging tool you should have started with instead of trying to debug LLM behavior with print statements like some kind of caveman.

Setting Up Tracing: The Good and The Ugly

If you're using LangChain, setup is easy:

import os
from langchain_openai import ChatOpenAI

## This is literally all you need
os.environ["LANGCHAIN_TRACING_V2"] = "true"  
os.environ["LANGCHAIN_API_KEY"] = "your-key-here"

llm = ChatOpenAI()
## Every call is now automatically traced

For everything else, you're writing more code:

from langsmith import Client, traceable

client = Client()

@traceable
def my_agent_function(query):
    # Your agent logic here
    return response

Real gotcha: The auto-instrumentation misses async operations half the time. I spent 3 hours wondering why my concurrent document processing wasn't showing up in traces. Had to add manual @traceable decorators to every async function.

Tracing Overhead: The Numbers Nobody Mentions

LangSmith adds about 15-30ms per request in my testing. Not huge, but it adds up. More importantly, traces with 200+ steps make the UI unusable. I've had agent conversations that generated traces so large the browser tab crashed trying to render them.

The workaround is trace sampling, but then you miss the exact failure you're trying to debug. It's a catch-22.

Evaluation: Actually Useful vs Marketing Hype

The built-in evaluators are decent for basic stuff:

from langsmith.evaluation import evaluate

## This actually works well for basic quality
evaluate(
    dataset_name="customer_queries",
    experiment_prefix="gpt4_vs_claude",
    evaluators=["qa_correctness", "helpfulness"]
)

Reality check: Custom evaluators require writing Python functions and understanding their evaluation framework. The docs make it sound easy. It's not. Took our team 2 days to build a domain-specific evaluator that actually worked correctly.

Production horror story: We rolled out an "improved" prompt based on LangSmith evaluation scores. Evaluation said 15% better. Real users hated it - response quality tanked because our custom evaluator was measuring the wrong thing. Always validate automated metrics with real humans.

The Prompt Playground: Good for Demos, Limited for Real Work

The playground is great for quick tests and showing off to non-technical people. For serious prompt development, you'll still end up in your IDE because:

• No version control integration (despite what they claim)
• Can't test with dynamic variables easily
• No way to run evaluation suites from the UI
• Complex prompts with multiple tools don't work well

What Actually Breaks in Production

Memory leaks: Long-running applications with heavy tracing sometimes accumulate trace data in memory. We had to add explicit cleanup in our worker processes after finding 2GB memory usage from trace buffers.

Rate limiting: Hit their API limits during high-traffic periods. Error handling is graceful (app keeps working), but you lose visibility exactly when you need it most.

Data retention gotchas: Free tier deletes traces after 14 days. Found this out when trying to debug an issue that happened 3 weeks ago. Paid plans give you longer retention, but it's per-plan tier - can't just pay for longer storage.

Self-Hosting Reality Check

Enterprise self-hosting exists but requires:

• Kubernetes expertise
• Significant infrastructure (minimum 3-node cluster)
• Database management (PostgreSQL + Redis)
• SSL certificates and networking setup
• Regular updates and maintenance

Budget at least 40 hours for initial setup plus ongoing DevOps overhead. Most teams are better off paying for hosted unless they have strict data sovereignty requirements.

Integration Pain Points I've Hit

Azure OpenAI: Works but requires custom configuration. Their examples assume OpenAI API.

Custom models: Tracing works but cost calculation breaks. Shows $0.00 for all calls to self-hosted models.

Async frameworks: FastAPI integration is solid. Asyncio applications need manual instrumentation.

Docker: Trace data doesn't persist by default. Need volume mounts or external storage configured properly.

The Real Value Proposition

Despite the issues, LangSmith saves time when shit hits the fan. Last month our customer support agent started giving random responses. Without tracing, debugging would have taken days of log analysis. With LangSmith, I saw the exact conversation flow where the context window filled up and the model started hallucinating.

Cost analysis is also genuinely useful. Found out our RAG system was bleeding money on embeddings, something like $180 or maybe $240 a day - I stopped looking at the daily bills after they hit my credit card limit. Inefficient caching was the culprit. Fixed it in an hour once I could see the actual API usage patterns, but not before my boss asked if I was mining Bitcoin on the company dime.

It's not perfect, but it's the debugging tool I wish I had when I started building LLM applications. Just go in with realistic expectations and budget for the learning curve.