> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluate agent performance

To evaluate your agent's performance you can use `LangSmith` [evaluations](https://docs.smith.langchain.com/evaluation). You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output:

```python theme={null}
def evaluator(*, outputs: dict, reference_outputs: dict):
    # compare agent outputs against reference outputs
    output_messages = outputs["messages"]
    reference_messages = reference_outputs["messages"]
    score = compare_messages(output_messages, reference_messages)
    return {"key": "evaluator_score", "score": score}
```

To get started, you can use prebuilt evaluators from `AgentEvals` package:

<CodeGroup>
  ```bash pip theme={null}
  pip install -U agentevals
  ```

  ```bash uv theme={null}
  uv add agentevals
  ```
</CodeGroup>

## Create evaluator

A common way to evaluate agent performance is by comparing its trajectory (the order in which it calls its tools) against a reference trajectory:

```python theme={null}
import json
from agentevals.trajectory.match import create_trajectory_match_evaluator  # [!code highlight]

outputs = [
    {
        "role": "assistant",
        "tool_calls": [
            {
                "function": {
                    "name": "get_weather",
                    "arguments": json.dumps({"city": "san francisco"}),
                }
            },
            {
                "function": {
                    "name": "get_directions",
                    "arguments": json.dumps({"destination": "presidio"}),
                }
            }
        ],
    }
]
reference_outputs = [
    {
        "role": "assistant",
        "tool_calls": [
            {
                "function": {
                    "name": "get_weather",
                    "arguments": json.dumps({"city": "san francisco"}),
                }
            },
        ],
    }
]

# Create the evaluator
evaluator = create_trajectory_match_evaluator(
    trajectory_match_mode="superset",    # [!code highlight]
)

# Run the evaluator
result = evaluator(
    outputs=outputs, reference_outputs=reference_outputs
)
```

1. Specify how the trajectories will be compared. `superset` will accept output trajectory as valid if it's a superset of the reference one. Other options include: [strict](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#strict-match), [unordered](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#unordered-match) and [subset](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#subset-and-superset-match)

As a next step, learn more about how to [customize trajectory match evaluator](https://github.com/langchain-ai/agentevals?tab=readme-ov-file#agent-trajectory-match).

### LLM-as-a-judge

You can use LLM-as-a-judge evaluator that uses an LLM to compare the trajectory against the reference outputs and output a score:

```python theme={null}
import json
from agentevals.trajectory.llm import (
    create_trajectory_llm_as_judge,  # [!code highlight]
    TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE
)

evaluator = create_trajectory_llm_as_judge(
    prompt=TRAJECTORY_ACCURACY_PROMPT_WITH_REFERENCE,
    model="openai:o3-mini"
)
```

## Run evaluator

To run an evaluator, you will first need to create a [LangSmith dataset](https://docs.smith.langchain.com/evaluation/concepts#datasets). To use the prebuilt AgentEvals evaluators, you will need a dataset with the following schema:

* **input**: `{"messages": [...]}` input messages to call the agent with.
* **output**: `{"messages": [...]}` expected message history in the agent output. For trajectory evaluation, you can choose to keep only assistant messages.

```python theme={null}
from langsmith import Client
from langchain.agents import create_agent
from agentevals.trajectory.match import create_trajectory_match_evaluator


client = Client()
agent = create_agent(...)
evaluator = create_trajectory_match_evaluator(...)

experiment_results = client.evaluate(
    lambda inputs: agent.invoke(inputs),
    # replace with your dataset name
    data="<Name of your dataset>",
    evaluators=[evaluator]
)
```

***

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