langsmith-trace

LANGSMITH_API_KEY=lsv2_pt_your_api_key_here          # Required
LANGSMITH_PROJECT=your-project-name                   # Optional: default project
LANGSMITH_WORKSPACE_ID=your-workspace-id              # Optional: for org-scoped keys

IMPORTANT: Always check the environment variables or .env file for LANGSMITH_PROJECT before querying or interacting with LangSmith. This tells you which project contains the relevant traces and data. If the LangSmith project is not available, use your best judgement to identify the right one.

CLI Tool

curl -sSL https://raw.githubusercontent.com/langchain-ai/langsmith-cli/main/scripts/install.sh | sh

<trace_langchain_oss> For LangChain/LangGraph apps, tracing is automatic. Just set environment variables:

export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your-api-key>
export OPENAI_API_KEY=<your-openai-api-key>  # or your LLM provider's key

Optional variables:

• LANGSMITH_PROJECT - specify project name (defaults to "default")
• LANGCHAIN_CALLBACKS_BACKGROUND=false - use for serverless to ensure traces complete before function exit (Python) </trace_langchain_oss>

<trace_other_frameworks> For non-LangChain apps, if the framework has native OpenTelemetry support, use LangSmith's OpenTelemetry integration.

If the app is NOT using a framework, or using one without automatic OTel support, use the traceable decorator/wrapper and wrap your LLM client.

client = wrap_openai(OpenAI())

@traceable def my_llm_pipeline(question: str) -> str: resp = client.chat.completions.create( model="gpt-4o-mini", messages=[{"role": "user", "content": question}], ) return resp.choices[0].message.content

Nested tracing example

@traceable def rag_pipeline(question: str) -> str: docs = retrieve_docs(question) return generate_answer(question, docs)

@traceable(name="retrieve_docs") def retrieve_docs(query: str) -> list[str]: return docs

@traceable(name="generate_answer") def generate_answer(question: str, docs: list[str]) -> str: return client.chat.completions.create(...)

</python>

<typescript>
Use traceable() wrapper and wrapOpenAI() for automatic tracing.
```typescript
import { traceable } from "langsmith/traceable";
import { wrapOpenAI } from "langsmith/wrappers";
import OpenAI from "openai";

const client = wrapOpenAI(new OpenAI());

const myLlmPipeline = traceable(async (question: string): Promise<string> => {
  const resp = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: question }],
  });
  return resp.choices[0].message.content || "";
}, { name: "my_llm_pipeline" });

// Nested tracing example
const retrieveDocs = traceable(async (query: string): Promise<string[]> => {
  return docs;
}, { name: "retrieve_docs" });

const generateAnswer = traceable(async (question: string, docs: string[]): Promise<string> => {
  const resp = await client.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: `${question}\nContext: ${docs.join("\n")}` }],
  });
  return resp.choices[0].message.content || "";
}, { name: "generate_answer" });

const ragPipeline = traceable(async (question: string): Promise<string> => {
  const docs = await retrieveDocs(question);
  return await generateAnswer(question, docs);
}, { name: "rag_pipeline" });

Best Practices:

• Apply traceable to all nested functions you want visible in LangSmith
• Wrapped clients auto-trace all calls — wrap_openai()/wrapOpenAI() records every LLM call
• Name your traces for easier filtering
• Add metadata for searchability </trace_other_frameworks>