# Trajectory accuracy

`TrajectoryAccuracy` checks how closely a ReAct-style agent followed a sensible sequence of thoughts, actions, and observations to achieve the stated goal. It is useful for auditing complex workflow agents and reinforcement-learning traces.

```python title="Auditing an agent run"
from opik.evaluation.metrics import TrajectoryAccuracy

metric = TrajectoryAccuracy()

score = metric.score(
    goal="Book travel to Paris",
    trajectory=[
        {
            "thought": "Check available flights",
            "action": "search_flights(destination='Paris')",
            "observation": "Found flights for next week",
        },
        {
            "thought": "Summarise the best option",
            "action": "summarise(options)",
            "observation": "Shared top three flights",
        },
    ],
    final_result="Here are the best flights to Paris next week.",
)

print(score.value)  # Already normalised between 0.0 and 1.0
print(score.reason)  # Explanation of the verdict
```

## Inputs

| Argument       | Type         | Required | Description                                                         |
| -------------- | ------------ | -------- | ------------------------------------------------------------------- |
| `goal`         | `str`        | **Yes**  | The agent’s objective or task description.                          |
| `trajectory`   | `list[dict]` | **Yes**  | Sequence of steps with `thought`, `action`, and `observation` keys. |
| `final_result` | `str`        | **Yes**  | Outcome that the agent reported after completing the trajectory.    |

## Configuration

| Parameter      | Default      | Notes                                                                                                               |
| -------------- | ------------ | ------------------------------------------------------------------------------------------------------------------- |
| `model`        | `gpt-5-nano` | Judge used to score the trajectory.                                                                                 |
| `temperature`  | `None`       | Forwarded to the underlying model when provided.                                                                    |
| `track`        | `True`       | Disable to skip logging to Opik. When `False`, disables tracing for both the metric and underlying LLM judge calls. |
| `project_name` | `None`       | Override the tracking project name.                                                                                 |

The metric returns a value in the 0.0–1.0 range together with a detailed explanation highlighting missing steps, misaligned actions, or other issues.

## Related pages

- [Overview](./evaluation-metrics-overview.md)
- [Heuristic metrics](./evaluation-metrics-heuristic-metrics.md)
- [Hallucination](./evaluation-metrics-hallucination.md)
- [LLM Juries](./evaluation-metrics-llm-juries.md)
- [G-Eval](./evaluation-metrics-g-eval.md)
- [Conversation-level GEval Metrics](./evaluation-metrics-g-eval-conversation-metrics.md)
- [Compliance risk](./evaluation-metrics-compliance-risk.md)
- [Prompt uncertainty](./evaluation-metrics-prompt-diagnostics.md)
- [Moderation](./evaluation-metrics-moderation.md)
- [Meaning Match](./evaluation-metrics-meaning-match.md)

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