AI Native with python ecosystem-LangChain/LangGraph/LangSmith

1. What LangSmith actually does

2. How it integrates with LangChain & LangGraph

3. Exact configuration (env + code)

4. What gets traced automatically

5. How this works in AKS / production

6. How this maps to Spring / Java world

1️⃣ What LangSmith Actually Is (Architect View)

LangSmith is a managed telemetry + evaluation backend for:

• Prompt execution

• Tool calls

• Agent steps

• RAG retrieval

• LLM latency, cost, errors

• Automatic trace trees (like OpenTelemetry for LLMs)

Think of it as:

“Application Performance Monitoring (APM) for LLM workflows.”

It does NOT:

• Host your models

• Expose APIs

• Execute agents

It only collects traces + logs + metrics.

2️⃣ How LangSmith Integrates with LangChain & LangGraph

Very important point:

✅ LangSmith does NOT require explicit logging calls in most cases.

LangChain & LangGraph already emit internal events.LangSmith just subscribes to them automatically when enabled.

So the flow is:

LangChain / LangGraph
   ↓ (auto trace hooks)
LangSmith SDK
   ↓ (HTTPS export)
LangSmith Cloud

No manual logger.info() needed for LLM steps.

3️⃣ Exact Configuration Needed (Minimal & Production)

✅ Step 1: Create LangSmith API Key

From LangSmith UI:

• Create Workspace

• Create API Key

✅ Step 2: Environment Variables (Mandatory)

These 3 are enough to turn on tracing:

export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="lsm_xxxxxxxxxxxxx"
export LANGCHAIN_PROJECT="rag-prod"

Optional but recommended:

export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
export LANGCHAIN_SESSION="tenant-abc-session-1"

That’s it.No SDK init required in most cases.

✅ Step 3: Your Existing LangChain Code (No Change!)

Your normal LangChain code:

from langchain_openai import AzureChatOpenAI
from langchain.chains import RetrievalQA

llm = AzureChatOpenAI(deployment_name="gpt-4o")

qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever
)

response = qa.invoke("What is the lending policy?")

👉 This will now automatically appear in LangSmith UI as a trace tree.

4️⃣ LangGraph + LangSmith (Production Agent Tracing)

LangGraph is even more powerful with LangSmith.

Example:

from langgraph.graph import StateGraph

graph = StateGraph(State)
graph.add_node("agent", agent_node)
graph.add_node("tool", tool_node)

compiled = graph.compile()

compiled.invoke({"input": "Approve this loan"})

LangSmith will show:

• Agent reasoning

• Tool calls

• State transitions

• Failures & retries

• Token usage per node

With zero extra code.

5️⃣ What Exactly Gets Traced Automatically

Once enabled, LangSmith captures:

✅ Prompt templates

✅ Final resolved prompt

✅ LLM request + response

✅ Token count + cost

✅ Tool inputs / outputs

✅ Vector search queries

✅ Retrieved chunks

✅ Latency per hop

✅ Agent step graph

✅ Errors + stack traces

This is LLM observability at enterprise depth.

6️⃣ AKS / Kubernetes Production Setup

In AKS you never hardcode keys. You use:

✅ Kubernetes Secret

apiVersion: v1
kind: Secret
metadata:
  name: langsmith-secret
type: Opaque
data:
  LANGCHAIN_API_KEY: <base64-encoded-key>

✅ Deployment YAML

env:
- name: LANGCHAIN_TRACING_V2
  value: "true"
- name: LANGCHAIN_PROJECT
  value: "rag-prod"
- name: LANGCHAIN_API_KEY
  valueFrom:
    secretKeyRef:
      name: langsmith-secret
      key: LANGCHAIN_API_KEY

Now every pod automatically streams traces to LangSmith.

7️⃣ How This Works with Azure APIM in Front

Your real prod path:

Client
  ↓
Azure APIM (JWT, Rate Limit, IP Filter)
  ↓
AKS Service (FastAPI)
  ↓
LangChain / LangGraph
  ↓
Azure OpenAI + Vector DB
  ↓
LangSmith (Out-of-band HTTPS telemetry)

Important:

• LangSmith traffic does NOT go through APIM

• It is outbound HTTPS telemetry

• Same as:

  • AppInsights

  • Datadog

  • Prometheus Remote Write

8️⃣ How This Maps to Spring / Java World (Critical for You)

LangSmith today is native-first for Python.

In Spring AI / Java, we use:

Python World	Java / Spring World
LangSmith	OpenTelemetry + AppInsights
Auto LLM tracing	Manual Span instrumentation
Agent step tree	Custom Span nesting
Prompt registry	Git + Config Server

Equivalent Java setup:

management:
  tracing:
    enabled: true

azure:
  application-insights:
    enabled: true

And you manually wrap:

• Prompt execution

• Tool calls

• RAG calls

With OpenTelemetry spans.

So:

✅ Python = LangSmith (native)✅ Java = OpenTelemetry + AppInsights (enterprise)

“LangSmith is not required for RAG or agents to function. It is strictly an observability, debugging, evaluation, and governance layer for LLM workloads.”

✅

“To use LangSmith with LangChain or LangGraph, we only need to enable tracing via environment variables like LANGCHAIN_TRACING_V2 and provide the API key. LangChain internally emits execution events, which LangSmith captures automatically without code changes. In AKS, the API key is managed through Kubernetes Secrets and injected into pods as environment variables. LangSmith receives telemetry through outbound HTTPS. This is equivalent to how OpenTelemetry and Application Insights work for Spring AI in Java.”

“If we do not use LangSmith, then all LLM observability must be implemented using OpenTelemetry. FastAPI is auto-instrumented for HTTP traces, but LangChain, RAG pipelines, and agent steps must be manually wrapped using custom spans for vector search, prompt construction, and LLM inference. These spans are exported using the OTLP exporter to Azure Monitor or Grafana via an OpenTelemetry Collector deployed on AKS. Unlike LangSmith, OpenTelemetry provides infrastructure-level observability but does not give native prompt, agent, token, or RAG explainability out of the box.”

Java Ecosysem

====

✅ Observability (Correct for Spring AI)

“For observability we use OpenTelemetry with Micrometer in Spring Boot, and export traces and metrics to Azure Application Insights and Grafana. We instrument LLM calls, RAG retrieval, and tool executions using custom spans so we get full end-to-end traceability across microservices.”

✅ Evaluation (Correct for Spring AI)

Spring AI + Java does not have LangSmith-like native eval, so you should say:

“For evaluation we use a combination of offline golden datasets, JUnit-based prompt regression tests, and automated quality metrics like answer correctness, context recall, and hallucination rate. These are integrated into our CI/CD pipeline using Azure DevOps.”

Optionally add:

• Custom Python eval jobs in MLOps pipeline (if used)

• Human-in-the-loop review for BFSI

🎯 Using Spring AI (Java)

“We are using Spring AI on Spring Boot deployed on AKS. For observability we rely on Micrometer with OpenTelemetry and export telemetry to Azure Application Insights and Grafana. For evaluation, we use curated golden datasets, automated regression tests in CI/CD, and domain expert review to measure correctness, hallucination rate, and compliance.”

🎯 “What Tool Do You Use for LLM Observability?”

“It depends on the runtime. For our Java-based Spring AI production workloads, we use OpenTelemetry with Azure Monitor for observability and CI-based evaluation. For Python-based LangChain and LangGraph workloads used in experimentation, we use LangSmith for deep LLM tracing and evaluation.”