> ## Documentation Index
> Fetch the complete documentation index at: https://agent-compass.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepSearchQA

> Search QA benchmark.

DeepSearchQA evaluates agents on multi-domain search questions with single-answer and set-answer scoring.

## Runtime Status

| Field               | Value                                        |
| ------------------- | -------------------------------------------- |
| Benchmark id        | `deepsearchqa`                               |
| Tags                | `Deep Research`, `Search QA`, `Judge-Scored` |
| Execution type      | local with judge                             |
| Typical harness     | `researchharness or naive_search_agent`      |
| Typical environment | `host_process`                               |
| Current status      | registered in the direct runtime             |

## When to Use

Use DeepSearchQA when you need to measure deep research behavior with the task assumptions described by this benchmark. For large or remote benchmarks, prefer benchmark recipes so images, workspaces, and provider-specific defaults come from task metadata instead of manual CLI flags.

## Parameters

Common parameters for this benchmark include:

* `category`
* `answer_type`
* `judge_model`
* `sample_ids`

Shared runtime controls such as `k`, `avgk`, `sample_ids`, `resume`, and `category` follow the conventions in [Benchmark Parameters](/reference/benchmarks/overview).

## Run Example

```bash theme={null}
agentcompass run \
  deepsearchqa \
  researchharness \
  your-model \
  --env <env-provider> \
  --benchmark-params '{"sample_ids":["<task-id>"]}' \
  --model-base-url "$MODEL_BASE_URL" \
  --model-api-key "$MODEL_API_KEY" \
  --model-api-protocol openai-chat
```

Adjust the harness and environment to the supported combination for your branch and deployment.

## Outputs

Per-task details are written to `results/deepsearchqa/<model>/<run>/details/`. Aggregate metrics are written to `summary.md` in the same run directory.

## Notes

Set `answer_type` to narrow evaluation when comparing retrieval strategies.