Langsmith Trace

<oneliner> Two main topics: adding tracing to your application, and querying traces for debugging and analysis. Python and Javascript implementations are both supported. </oneliner>

<setup> Environment Variables

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

Authentication is REQUIRED: either set the LANGSMITH_API_KEY environment variable, or pass the --api-key flag to CLI commands (preferred):

langsmith trace list --project my-project --api-key $LANGSMITH_API_KEY

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

</setup>

<cli_feedback> After an actual CLI failure, undocumented workaround, confusing output, or missing capability, submit one concise product-feedback note per distinct issue in the task. Do not report routine successes or failures in the traced application itself.

CLI requirement: langsmith feedback requires LangSmith CLI v0.2.58 or later. Check the installed version with langsmith --version.

Check langsmith feedback --help for feedback <note> and --category; if unavailable, skip feedback without raw HTTP or unreleased builds. Use the existing authenticated profile, endpoint, and workspace. Feedback goes to LangSmith Cloud, including through the BYOC relay; skip standalone self-hosted. Respect user/organization restrictions and ask first if permission to send feedback is unclear.

Summarize expected versus observed CLI behavior and any workaround in your own words. Never send secrets, customer data, trace payloads, prompts, full stack traces, copied command output, raw arguments, environment-variable values, local paths, or resource identifiers. The CLI adds version/OS/architecture, but does not redact your note; skip it if it cannot be safely redacted.

Choose bug, feature-request, usability, documentation, or other. This is CLI product feedback, not run evaluation feedback. Example shape only—do not submit unless actually encountered:

langsmith feedback --category usability --format json "The trace list output made it hard to distinguish root runs from child runs."

Do not retry a failed or rate-limited feedback submission, switch credentials/endpoints to bypass a failure, or block the original task on feedback. </cli_feedback>

<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.

<python> Use @traceable decorator and wrap_openai() for automatic tracing.

from langsmith import traceable
from langsmith.wrappers import wrap_openai
from openai import OpenAI

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.

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" });

</typescript>

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>

<traces_vs_runs> Use the langsmith CLI to query trace data.

Understanding the difference is critical:

• Trace = A complete execution tree (root run + all child runs). A trace represents one full agent invocation with all its LLM calls, tool calls, and nested operations.
• Run = A single node in the tree (one LLM call, one tool call, etc.)

Generally, query traces first — they provide complete context and preserve hierarchy needed for trajectory analysis and dataset generation. </traces_vs_runs>

<command_structure> Two command groups with consistent behavior:

langsmith
├── trace (operations on trace trees - USE THIS FIRST)
│   ├── list    - List traces (filters apply to root run)
│   ├── get     - Get single trace with full hierarchy
│   └── export  - Export traces to JSONL files (one file per trace)
│
├── run (operations on individual runs - for specific analysis)
│   ├── list    - List runs (flat, filters apply to any run)
│   ├── get     - Get single run
│   └── export  - Export runs to single JSONL file (flat)
│
├── dataset (dataset operations)
│   ├── list    - List datasets
│   ├── get     - Get dataset details
│   ├── create  - Create empty dataset
│   ├── delete  - Delete dataset
│   ├── export  - Export dataset to file
│   └── upload  - Upload local JSON as dataset
│
├── example (example operations)
│   ├── list    - List examples in a dataset
│   ├── create  - Add example to a dataset
│   └── delete  - Delete an example
│
├── evaluator (evaluator operations)
│   ├── list    - List evaluators
│   ├── upload  - Upload evaluator
│   └── delete  - Delete evaluator
│
├── experiment (experiment operations)
│   ├── list    - List experiments
│   └── get     - Get experiment results
│
├── thread (thread operations)
│   ├── list    - List conversation threads
│   └── get     - Get thread details
│