Langsmith Evaluators
Langsmith Evaluators assesses LLM outputs for quality and consistency, streamlining feedback loops and improving model performance.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add langsmith-evaluators npx -- -y @trustedskills/langsmith-evaluators
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"langsmith-evaluators": {
"command": "npx",
"args": [
"-y",
"@trustedskills/langsmith-evaluators"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill provides access to LangSmith evaluators, allowing you to evaluate and analyze the performance of your AI agents. It facilitates structured feedback collection and enables you to track agent behavior over time for improved reliability and quality. You can use these evaluators to assess various aspects of an agent's execution, such as accuracy, helpfulness, and safety.
When to use it
- Evaluating Agent Responses: After an agent completes a task, use the evaluator to score its response based on predefined criteria.
- Debugging Agent Behavior: Identify areas where an agent is struggling by analyzing evaluation data across multiple runs.
- Tracking Performance Over Time: Monitor changes in agent performance after updates or modifications.
- Improving Agent Training Data: Use evaluations to identify gaps and biases in training datasets, leading to better agent behavior.
Key capabilities
- Structured feedback collection
- Performance tracking over time
- Evaluation of agent accuracy, helpfulness, and safety
- Analysis of agent behavior
Example prompts
- "Evaluate the agent's response to this user query: 'What is the capital of France?'"
- "Score the agent’s summary of this document for clarity and conciseness."
- "Analyze the agent's reasoning process when answering this question."
Tips & gotchas
The effectiveness of LangSmith Evaluators relies on well-defined evaluation criteria. Ensure your evaluation prompts are clear, specific, and aligned with desired agent behavior to get meaningful results.
Tags
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🏢 Official
Published by the company or team that built the technology.