How Ragas Supports Evaluating AI Agents and Tool Use

Updated

Ragas includes a dedicated set of agent and tool-use evaluation capabilities: agent-specific metrics (Topic Adherence, Tool Call Accuracy, Tool Call F1, Agent Goal Accuracy), a guide for evaluating a new LLM agent, and testset generation tailored to agent and tool-use scenarios. These are relevant if your application routes requests to tools or pursues multi-step goals, rather than only answering from retrieved context.

What Ragas Covers for Agents

The documentation groups agent evaluation under "Agents or Tool Use Cases," alongside the RAG and general-purpose metric families. The listed agent metrics are:

Metric What it targets (per Ragas docs)
Topic Adherence Whether the agent stays within permitted topics
Tool Call Accuracy Correctness of tool calls the agent makes
Tool Call F1 F1-style balance of tool call precision and recall
Agent Goal Accuracy Whether the agent actually achieves the user's goal

These sit next to the RAG metrics (Context Precision, Context Recall, Faithfulness, Response Relevancy, Noise Sensitivity, Context Entities Recall) and general metrics (Aspect Critic, rubric-based scoring), so an agent that also retrieves documents can be assessed on both retrieval quality and tool behavior.

When This Fits Your Case

Use the agent metrics when your system's output includes actions, not just text — for example, an assistant that must call a search tool, a database query tool, or an internal API before answering. If your application only produces answers from retrieved passages, the RAG metrics are the better starting point; the agent metrics add little there.

Ragas also documents an "Evaluate a Text-to-SQL Agent" guide and an "Align an LLM as a Judge" guide, which are useful when tool calls produce structured output that needs its own correctness check.

Testset Generation for Agents and Tool Use

Beyond metrics, Ragas lists "Testset Generation for Agents or Tool use cases" under Test Data Generation. This matters because agent evaluation needs inputs that exercise tool selection and multi-step behavior, not just question–answer pairs. The same section includes RAG testset generation, KG building, and scenario generation, so you can build evaluation data for a system that mixes retrieval and tool calls.

Getting Started

The documented path is: install Ragas, work through the Quick Start, then follow the tutorial "Evaluate an AI Agent." From there, the how-to guides cover customizing models, adapting metrics to languages, and training/aligning metrics. The docs do not state pricing or login requirements on this page, so check the main site before assuming access terms.

A practical sequence for a new agent:

  1. Define the goal and the tools the agent may call.
  2. Generate a testset for agent/tool-use scenarios.
  3. Score runs with Tool Call Accuracy, Tool Call F1, Agent Goal Accuracy, and Topic Adherence.
  4. Use the "Evaluate a New LLM Agent" guide to structure the comparison.

If you need a quick decision: choose Ragas' agent metrics when tool-call correctness and goal completion are the failure modes you care about; stay with RAG metrics when answer grounding is the primary concern.