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
OPENAI_API_KEY=your_openai_key                        # For LLM as Judge

IMPORTANT: Always check the environment variables or

.env

LANGSMITH_PROJECT

Python Dependencies

pip install langsmith langchain-openai python-dotenv

CLI Tool (for uploading evaluators)

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

JavaScript Dependencies

npm install langsmith openai

<crucial_requirement>

Golden Rule: Inspect Before You Implement

CRITICAL: Before writing ANY evaluator or extraction logic, you MUST:

1. Run your agent on sample inputs and capture the actual output
2. Inspect the output - print it, query LangSmith traces, understand the exact structure
3. Only then write code that processes that output

Output structures vary significantly by framework, agent type, and configuration. Never assume the shape - always verify first. Query LangSmith traces to when outputs don't contain needed data to understand how to extract from execution. </crucial_requirement>

<evaluator_format>

Offline vs Online Evaluators

Offline Evaluators (attached to datasets):

• Function signature:

  (run, example)

  - receives both run outputs and dataset example
• Use case: Comparing agent outputs to expected values in a dataset
• Upload with:

  --dataset "Dataset Name"

Online Evaluators (attached to projects):

• Function signature:

  (run)

  - receives only run outputs, NO example parameter
• Use case: Real-time quality checks on production runs (no reference data)
• Upload with:

  --project "Project Name"

CRITICAL - Return Format:

• Each evaluator returns ONE metric only. For multiple metrics, create multiple evaluator functions.
• Do NOT return

  {"metric_name": value}

  or lists of metrics - this will error.

CRITICAL - Local vs Uploaded Differences:

evaluate()

key

key

run

RunTree

run.outputs

dict

run["outputs"]

run.outputs if hasattr(run, "outputs") else run.get("outputs", {})

run

run.outputs?.field

run.outputs?.field

{"score": value, "comment": "..."}

{"score": value, "comment": "..."}

{ key: "name", score: value, comment: "..." }

{ score: value, comment: "..." }

<evaluator_types>

• LLM as Judge - Uses an LLM to grade outputs. Best for subjective quality (accuracy, helpfulness, relevance).
• Custom Code - Deterministic logic. Best for objective checks (exact match, trajectory validation, format compliance). </evaluator_types>

<llm_judge>

LLM as Judge Evaluators

NOTE: LLM-as-Judge upload is currently not supported by the CLI — only code evaluators are supported. For evaluations against a dataset, STRONGLY PREFER defining local evaluators to use with

evaluate(evaluators=[...])

class Grade(TypedDict): reasoning: Annotated[str, ..., "Explain your reasoning"] is_accurate: Annotated[bool, ..., "True if response is accurate"]

judge = ChatOpenAI(model="gpt-4o-mini", temperature=0).with_structured_output(Grade, method="json_schema", strict=True)

async def accuracy_evaluator(run, example): run_outputs = run.outputs if hasattr(run, "outputs") else run.get("outputs", {}) or {} example_outputs = example.outputs if hasattr(example, "outputs") else example.get("outputs", {}) or {} grade = await judge.ainvoke([{"role": "user", "content": f"Expected: {example_outputs}\nActual: {run_outputs}\nIs this accurate?"}]) return {"score": 1 if grade["is_accurate"] else 0, "comment": grade["reasoning"]}

</python>

<typescript>
```javascript
import OpenAI from "openai";

const openai = new OpenAI();

async function accuracyEvaluator(run, example) {
    const runOutputs = run.outputs ?? {};
    const exampleOutputs = example.outputs ?? {};

    const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    temperature: 0,
    response_format: { type: "json_object" },
    messages: [
        { role: "system", content: 'Respond with JSON: {"is_accurate": boolean, "reasoning": string}' },
        { role: "user", content: `Expected: ${JSON.stringify(exampleOutputs)}\nActual: ${JSON.stringify(runOutputs)}\nIs this accurate?` }
    ]
    });

    const grade = JSON.parse(response.choices[0].message.content);
    return { score: grade.is_accurate ? 1 : 0, comment: grade.reasoning };
}

<code_evaluators>

Custom Code Evaluators

Before writing an evaluator:

1. Inspect your dataset to understand expected field names (see Golden Rule above)
2. Test your run function and verify its output structure matches the dataset schema
3. Query LangSmith traces to debug any mismatches

<run_functions>

Defining Run Functions

Run functions execute your agent and return outputs for evaluation.

CRITICAL - Test Your Run Function First: Before writing evaluators, you MUST test your run function and inspect the actual output structure. Output shapes vary by framework, agent type, and configuration.

Debugging workflow:

1. Run your agent once on sample input
2. Query the trace to see the execution structure
3. Print the raw output and verify against trace to output contains the right data
4. Adjust the run function as needed
5. Verify your output matches your dataset schema

Try your hardest to match your run function output to your dataset schema. This makes evaluators simple and reusable. If matching isn't possible, your evaluator must know how to extract and compare the right fields from each side.

Capturing Trajectories

For trajectory evaluation, your run function must capture tool calls during execution.

CRITICAL: Run output formats vary significantly by framework and agent type. You MUST inspect before implementing:

LangGraph agents (LangChain OSS): Use

stream_mode="debug"

subgraphs=True

import uuid

def run_agent_with_trajectory(agent, inputs: dict) -> dict:
    config = {"configurable": {"thread_id": f"eval-{uuid.uuid4()}"}}
    trajectory = []
    final_result = None

    for chunk in agent.stream(inputs, config=config, stream_mode="debug", subgraphs=True):
        # STEP 1: Print chunks to understand the structure
        print(f"DEBUG chunk: {chunk}")

        # STEP 2: Write extraction based on YOUR observed structure
        # ... your extraction logic here ...

    # IMPORTANT: After running, query the LangSmith trace to verify
    # your trajectory data is complete. Default output may be missing
    # tool calls that appear in the trace.
    return {"output": final_result, "trajectory": trajectory}

Custom / Non-LangChain Agents:

1. Inspect output first - Run your agent and inspect the result structure. Trajectory data may already be included in the output (e.g.,

   result.tool_calls

   ,

   result.steps

   , etc.)
2. Callbacks/Hooks - If your framework supports execution callbacks, register a hook that records tool names on each invocation
3. Parse execution logs - As a last resort, extract tool names from structured logs or trace data

The key is to capture the tool name at execution time, not at definition time. </run_functions>

IMPORTANT - Auto-Run Behavior: Evaluators uploaded to a dataset automatically run when you run experiments on that dataset. You do NOT need to pass them to

evaluate()

IMPORTANT - Local vs Uploaded: Uploaded evaluators run in a sandboxed environment with very limited package access. Only use built-in/standard library imports, and place all imports inside the evaluator function body. For dataset (offline) evaluators, prefer running locally with

evaluate(evaluators=[...])

IMPORTANT - Code vs Structured Evaluators:

• Code evaluators (what the CLI uploads): Run in a limited environment without external packages. Use for deterministic logic (exact match, trajectory validation).
• Structured evaluators (LLM-as-Judge): Configured via LangSmith UI, use a specific payload format with model/prompt/schema. The CLI does not support this format yet.

IMPORTANT - Choose the right target:

• --dataset

  : Offline evaluator with

  (run, example)

  signature - for comparing to expected values

• --project

  : Online evaluator with

  (run)

  signature - for real-time quality checks

You must specify one. Global evaluators are not supported.

# List all evaluators
langsmith evaluator list

# Upload offline evaluator (attached to dataset)
langsmith evaluator upload my_evaluators.py \
  --name "Trajectory Match" --function trajectory_evaluator \
  --dataset "My Dataset" --replace

# Upload online evaluator (attached to project)
langsmith evaluator upload my_evaluators.py \
  --name "Quality Check" --function quality_check \
  --project "Production Agent" --replace

# Delete
langsmith evaluator delete "Trajectory Match"

IMPORTANT - Safety Prompts:

• The CLI prompts for confirmation before destructive operations
• NEVER use

  --yes

  flag unless the user explicitly requests it </upload>

<best_practices>

1. Use structured output for LLM judges - More reliable than parsing free-text
2. Match evaluator to dataset type
   • Final Response → LLM as Judge for quality
   • Trajectory → Custom Code for sequence
3. Use async for LLM judges - Enables parallel evaluation
4. Test evaluators independently - Validate on known good/bad examples first
5. Choose the right language
   • Python: Use for Python agents, langchain integrations
   • JavaScript: Use for TypeScript/Node.js agents </best_practices>

<running_evaluations>

Running Evaluations

Uploaded evaluators auto-run when you run experiments - no code needed. Local evaluators are passed directly for development/testing.

Uploaded evaluators run automatically

results = evaluate(run_agent, data="My Dataset", experiment_prefix="eval-v1")

Or pass local evaluators for testing

results = evaluate(run_agent, data="My Dataset", evaluators=[my_evaluator], experiment_prefix="eval-v1")

</python>

<typescript>
```javascript
import { evaluate } from "langsmith/evaluation";

// Uploaded evaluators run automatically
const results = await evaluate(runAgent, {
  data: "My Dataset",
  experimentPrefix: "eval-v1",
});

// Or pass local evaluators for testing
const results = await evaluate(runAgent, {
  data: "My Dataset",
  evaluators: [myEvaluator],
  experimentPrefix: "eval-v1",
});

Output doesn't match what you expect: Query the LangSmith trace. It shows exact inputs/outputs at each step - compare what you find to what you're trying to extract.

One metric per evaluator: Return

{"score": value, "comment": "..."}

Field name mismatch: Your run function output must match dataset schema exactly. Inspect dataset first with

client.read_example(example_id)

RunTree vs dict (Python only): Local

evaluate()

RunTree

dict

run_outputs = run.outputs if hasattr(run, "outputs") else run.get("outputs", {}) or {}

TypeScript always uses attribute access:

run.outputs?.field