<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

bash

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

bash

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

</setup>

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

bash

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. ```python 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.
```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" });

</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
│
└── project (project operations)
    └── list    - List tracing projects

Key differences:

	`traces *`	`runs *`
Filters apply to	Root run only	Any matching run
`--run-type`	Not available	Available
Returns	Full hierarchy	Flat list
Export output	Directory (one file/trace)	Single file
</command_structure>

<querying_traces> Query traces using the

langsmith

CLI. Commands are language-agnostic.

bash

# List recent traces (most common operation)
langsmith trace list --limit 10 --project my-project

# List traces with metadata (timing, tokens, costs)
langsmith trace list --limit 10 --include-metadata

# Filter traces by time
langsmith trace list --last-n-minutes 60
langsmith trace list --since 2025-01-20T10:00:00Z

# Get specific trace with full hierarchy
langsmith trace get <trace-id>

# List traces and show hierarchy inline
langsmith trace list --limit 5 --show-hierarchy

# Export traces to JSONL (one file per trace, includes all runs)
langsmith trace export ./traces --limit 20 --full

# Filter traces by performance
langsmith trace list --min-latency 5.0 --limit 10    # Slow traces (>= 5s)
langsmith trace list --error --last-n-minutes 60     # Failed traces

# List specific run types (flat list)
langsmith run list --run-type llm --limit 20

</querying_traces>

<filters> All commands support these filters (all AND together):

Basic filters:

```
--trace-ids abc,def
```
- Filter to specific traces
```
--limit N
```
- Max results
```
--project NAME
```
- Project name
```
--last-n-minutes N
```
- Time filter
```
--since TIMESTAMP
```
- Time filter (ISO format)
```
--error / --no-error
```
- Error status
```
--name PATTERN
```
- Name contains (case-insensitive)

Performance filters:

```
--min-latency SECONDS
```
- Minimum latency (e.g.,
```
5
```
for >= 5s)
```
--max-latency SECONDS
```
- Maximum latency
```
--min-tokens N
```
- Minimum total tokens
```
--tags tag1,tag2
```
- Has any of these tags

Advanced filter:

```
--filter QUERY
```
- Raw LangSmith filter query for complex cases (feedback, metadata, etc.)

bash

# Filter traces by feedback score using raw LangSmith query
langsmith trace list --filter 'and(eq(feedback_key, "correctness"), gte(feedback_score, 0.8))'

</filters>

<export_format> Export creates

.jsonl

files (one run per line) with these fields:

json

{"run_id": "...", "trace_id": "...", "name": "...", "run_type": "...", "parent_run_id": "...", "inputs": {...}, "outputs": {...}}

Use

--include-io

--full

to include inputs/outputs (required for dataset generation). </export_format>

<tips> - **Start with traces** — they provide complete context needed for trajectory and dataset generation - Use `traces export --full` for bulk data destined for datasets - Always specify `--project` to avoid mixing data from different projects - Use `/tmp` for temporary exports - Include `--include-metadata` for performance/cost analysis - Stitch files: `cat ./traces/*.jsonl > all.jsonl` </tips>

langsmith-trace

NPX Install

Tags

SKILL.md Content

Nested tracing example