langsmith-code-eval

Imported from langchain-ai/lca-skills (skills/langsmith-code-eval/SKILL.md). Install upstream with npx skills add langchain-ai/lca-skills --skill langsmith-code-eval. Copyright stays with the author.

LangSmith Code Evaluator Creation

Creates evaluators for LangSmith experiments through structured inspection and implementation.

Prerequisites

• langsmith Python package installed
• LANGSMITH_API_KEY environment variable set (check project's .env file)

Workflow

Copy this checklist and track progress:

Evaluator Creation Progress:
- [ ] Step 1: Gather info from user
- [ ] Step 2: Inspect trace and dataset structure
- [ ] Step 3: Read agent code
- [ ] Step 4: Write evaluator
- [ ] Step 5: Write experiment runner
- [ ] Step 6: Run and iterate

Step 1: Gather Info from User

IMPORTANT: Do NOT search or explore the codebase. Ask the user all of these questions upfront using AskUserQuestion before doing anything else.

Ask the user the following in a single AskUserQuestion call:

1. Python command: How do you run Python in this project? (e.g., python, python3, uv run python, poetry run python)
2. Agent file path: What is the path to your agent file?
3. LangSmith project name: What is your LangSmith project name (where traces are logged)?
4. LangSmith dataset name: What is the name of the dataset to evaluate against?
5. Evaluation goal: What behavior should pass vs fail? Common types:
   • Tool usage: Did the agent call the correct tool?
   • Output correctness: Does output match expected format/content?
   • Policy compliance: Did it follow specific rules?
   • Classification: Did it categorize correctly?

Step 2: Inspect Trace and Dataset Structure

Using the info from Step 1, run the inspection scripts located in this skill's directory:

{python_cmd} {skill_dir}/scripts/inspect_trace.py PROJECT_NAME [RUN_ID]
{python_cmd} {skill_dir}/scripts/inspect_dataset.py DATASET_NAME

Replace {python_cmd} with the command from Step 1, and {skill_dir} with this skill's directory path.

Verify the trace matches the agent:

• Does the trace type match? (e.g., OpenAI trace for OpenAI agent)
• Does it contain the data needed for evaluation?
• If mismatched, clarify before proceeding.

From the dataset inspection, note:

• Input schema (what gets passed to the agent)
• Output schema (reference/expected outputs)
• Metadata fields (e.g., expected_tool, difficulty, labels)

The dataset metadata often contains ground truth for evaluation (e.g., which tool should be called, expected classification).

Step 3: Read Agent Code

Read the agent file provided in Step 1 to identify:

• Entry point function (look for @traceable decorator)
• Available tools
• Output format (what the function returns)

Step 4: Write the Evaluator

Create evaluator functions based on trace and dataset structure. See EVALUATOR_REFERENCE.md for function signatures and return formats.

Step 5: Write Experiment Runner

Create a script that:

1. Imports the agent's entry function
2. Wraps it as a target function
3. Runs evaluate() or aevaluate() against the dataset

See EVALUATOR_REFERENCE.md for evaluate() usage.

Step 6: Run and Iterate

Execute the experiment, review results in LangSmith, refine evaluators as needed.