---
title: "Grade agent trajectories and tool-use decisions with AgentEvals"
description: "Score whether an agent took a sensible intermediate path, called tools correctly, and reached the outcome without relying only on final-answer checks."
verification: "listed"
source: "https://github.com/langchain-ai/agentevals"
author: "LangChain"
publisher_type: "open_source_project"
category:
  - "Code Quality & Review"
framework:
  - "Custom Agents"
tool_ecosystem:
  github_repo: "langchain-ai/agentevals"
  github_stars: 550
  npm_package: "agentevals"
  npm_weekly_downloads: 251033
---

# Grade agent trajectories and tool-use decisions with AgentEvals

Score whether an agent took a sensible intermediate path, called tools correctly, and reached the outcome without relying only on final-answer checks.

## Prerequisites

Python or TypeScript runtime, agent run outputs or trajectories, optional LLM judge provider

## Installation

Choose whichever fits your setup:

1. Copy this skill folder into your local skills directory.
2. Clone the repo and symlink or copy the skill into your agent workspace.
3. Add the repo as a git submodule if you manage shared skills centrally.
4. Install it through your internal provisioning or packaging workflow.
5. Download the folder directly from GitHub and place it in your skills collection.

Install command or upstream instructions:

```
pip install agentevals or npm install agentevals @langchain/core, then pass captured agent trajectories into the provided evaluators.
```

## Documentation

- https://github.com/langchain-ai/agentevals

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/grade-agent-trajectories-and-tool-use-decisions-with-agentevals/)