Langsmith Dataset

<oneliner> Create, manage, and upload evaluation datasets to LangSmith for testing and validation. </oneliner>

<setup> Environment Variables

LANGSMITH_API_KEY=lsv2_pt_your_api_key_here          # REQUIRED
LANGSMITH_PROJECT=your-project-name                   # Check this to know which project has traces
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 dataset list --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.

Python Dependencies

pip install langsmith

JavaScript Dependencies

npm install langsmith

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 dataset quality problems unrelated to the CLI.

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, dataset contents, 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 dataset/run evaluation feedback. Example shape only—do not submit unless actually encountered:

langsmith feedback --category documentation --format json "The dataset upload help did not explain the required JSON structure."

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>

<usage> Use the langsmith CLI to manage datasets and examples.

Dataset Commands

• langsmith dataset list - List datasets in LangSmith
• langsmith dataset get <name-or-id> - View dataset details
• langsmith dataset create --name <name> - Create a new empty dataset
• langsmith dataset delete <name-or-id> - Delete a dataset
• langsmith dataset export <name-or-id> <output-file> - Export dataset to local JSON file
• langsmith dataset upload <file> --name <name> - Upload a local JSON file as a dataset

Example Commands

• langsmith example list --dataset <name> - List examples in a dataset
• langsmith example create --dataset <name> --inputs <json> - Add an example to a dataset
• langsmith example delete <example-id> - Delete an example

Experiment Commands

• langsmith experiment list --dataset <name> - List experiments for a dataset
• langsmith experiment get <name> - View experiment results

Common Flags

• --limit N - Limit number of results
• --yes - Skip confirmation prompts (use with caution)

IMPORTANT - Safety Prompts:

• The CLI prompts for confirmation before destructive operations (delete, overwrite)
• If you are running with user input: ALWAYS wait for user input; NEVER use --yes unless the user explicitly requests it
• If you are running non-interactively: Use --yes to skip confirmation prompts

</usage>

<dataset_types_overview> Common evaluation dataset types:

• final_response - Full conversation with expected output. Tests complete agent behavior.
• single_step - Single node inputs/outputs. Tests specific node behavior (e.g., one LLM call or tool).
• trajectory - Tool call sequence. Tests execution path (ordered list of tool names).
• rag - Question/chunks/answer/citations. Tests retrieval quality.

</dataset_types_overview>

<creating_datasets>

Creating Datasets

Datasets are JSON files with an array of examples. Each example has inputs and outputs.

From Exported Traces (Programmatic)

Export traces first, then process them into dataset format using code:

# 1. Export traces to JSONL files
langsmith trace export ./traces --project my-project --limit 20 --full --api-key $LANGSMITH_API_KEY

<python>

import json
from pathlib import Path
from langsmith import Client

client = Client()

# 2. Process traces into dataset examples
examples = []
for jsonl_file in Path("./traces").glob("*.jsonl"):
    runs = [json.loads(line) for line in jsonl_file.read_text().strip().split("\n")]
    root = next((r for r in runs if r.get("parent_run_id") is None), None)
    if root and root.get("inputs") and root.get("outputs"):
        examples.append({
            "trace_id": root.get("trace_id"),
            "inputs": root["inputs"],
            "outputs": root["outputs"]
        })

# 3. Save locally
with open("/tmp/dataset.json", "w") as f:
    json.dump(examples, f, indent=2)

</python>

<typescript>

import { Client } from "langsmith";
import { readFileSync, writeFileSync, readdirSync } from "fs";
import { join } from "path";

const client = new Client();

// 2. Process traces into dataset examples
const examples: Array<{trace_id?: string, inputs: Record<string, any>, outputs: Record<string, any>}> = [];
const files = readdirSync("./traces").filter(f => f.endsWith(".jsonl"));

for (const file of files) {
  const lines = readFileSync(join("./traces", file), "utf-8").trim().split("\n");
  const runs = lines.map(line => JSON.parse(line));
  const root = runs.find(r => r.parent_run_id == null);
  if (root?.inputs && root?.outputs) {
    examples.push({ trace_id: root.trace_id, inputs: root.inputs, outputs: root.outputs });
  }
}

// 3. Save locally
writeFileSync("/tmp/dataset.json", JSON.stringify(examples, null, 2));

</typescript>

Upload to LangSmith

# Upload local JSON file as a dataset
langsmith dataset upload /tmp/dataset.json --name "My Evaluation Dataset" --api-key $LANGSMITH_API_KEY

Using the SDK Directly

<python>

from langsmith import Client

client = Client()

# Create dataset and add examples in one step
dataset = client.create_dataset("My Dataset", description="Evaluation dataset")

# Pass examples as a list of dicts. The parallel `inputs=`/`outputs=` lists
# are still accepted but only as legacy keyword arguments.
client.create_examples(
    dataset_id=dataset.id,
    examples=[
        {"inputs": {"query": "What is AI?"}, "outputs": {"answer": "AI is..."}},
        {"inputs": {"query": "Explain RAG"}, "outputs": {"answer": "RAG is..."}},
    ],
)

</python>

<typescript>

import { Client } from "langsmith";

const client = new Client();

// Create dataset and add examples
const dataset = await client.createDataset("My Dataset", {
  description: "Evaluation dataset",
});

// Pass an array of examples. The `{ inputs, outputs, datasetName }` form is
// deprecated in the JS SDK; each example carries its own dataset_id.
await client.createExamples([
  {