Why LangSmith is the Backbone of our LLM Evaluation Stack

Introduction: From Black Box to Evaluation-First AI

Building LLM workflows without evaluation is like flying blind. Inputs go in. Responses come out. But what happened in between? Was it helpful? Was it right?

At Framework Friday, we don’t guess. We trace, score, and improve everything we build. That’s why LangSmith is the core observability layer in every agent system we ship. It’s not just for devs – it’s how BI, product, and operators track what matters: output quality, latency, cost, and continuous performance.

This post explains why we chose LangSmith, what it enables, and how it fits into a real, repeatable evaluation system.

Why Evaluation Can’t Be an Afterthought

Modern LLM workflows are complex. Agents make decisions. Tools run logic. Memory changes outcomes.

Without observability:
• You can’t trust performance
• You can’t explain failure cases
• You can’t optimize prompts or tools
• You lose time debugging what you can’t see

LangSmith changes that. It turns every agent run into a traceable, testable, reviewable event with metrics to prove what’s working.

The Framework Friday LLM Evaluation Stack

LangSmith is the backbone of our observability loop. It connects inputs to outputs with full traceability, exposing the hidden logic, edge cases, and failure points inside even the most complex LLM workflows.

While it excels with agentic systems, LangSmith’s core strengths – tracing, evaluation, and performance monitoring – apply to any LLM use case: from basic prompt chains to advanced RAG systems or multi-step agents.

We pair LangSmith with:
• n8n – Orchestration for agent workflows
• GPT-4 and Claude – For generation
• Supabase – As our vector store for high-precision retrieval

Every run, from tool invocation to LLM output, is logged and scored in real time. LangSmith’s dashboards give our entire team a shared, transparent view of system behavior across engineering, BI, and product.

What LangSmith Unlocks for Operators

Before LangSmith:
• We relied on logs and hunches
• Prompt changes weren’t tracked
• Evaluation was inconsistent and slow
• Stakeholders had no visibility

Now:
• Every agent run is logged with full context:

What was asked
How it responded
Which tools it used
How long it took
How much it cost
How helpful the answer was

We track every prompt version, score every output, and tag every run. Debugging takes minutes instead of hours. Token burn is under control. Edge cases get flagged before they hit production.

LangSmith gives us observability that scales.

How We Evaluate LLM Output

We use a mix of automatic and human-in-the-loop evaluation:
• LLM-as-Judge – LangSmith uses GPT or Claude to score responses against rubrics like correctness, helpfulness, and tone
• Pairwise comparison – Test new prompts against old ones
• Human review queues – For tone or clarity, with built-in annotation tools

This evaluation system helps us improve prompts, catch regressions, and ship better agents week after week.

Why LangSmith, Not a Custom Stack

We tested custom scoring in n8n and standalone scripts. It worked for small tests – but didn’t scale.

LangSmith gave us:
• Full trace of every run, including tools and memory
• Built-in scoring – no bolt-on hacks
• Prompt version control and rollback
• Human review queues for edge cases
• Real-time dashboards for cost, latency, and success rates

Unlike other observability platforms, LangSmith is built specifically for LLMs – not just generic ML.

👉 Join the Framework Friday community
Get our full LangSmith Template Pack: evaluation configs, scoring prompts, and workflow exports.

Let’s build agentic systems you can measure, trace, and trust.