> ## Documentation Index
> Fetch the complete documentation index at: https://docs.valyu.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Strain-to-fermentation software evaluation

> Compare research software such as openBIS and eLabFTW on sample lineage, experiment records and data integration in a cited capability matrix and CSV.

**Example question:** Can openBIS or eLabFTW connect our strain records, screening experiments and fermentation measurements?

**You get:** A capability matrix and CSV showing documented support, edition and license boundaries, integration gaps and questions to test.

**Start with:** Candidate systems; required sample identifiers; instrument export formats; deployment requirements; license constraints.

## Run with DeepResearch

Set `VALYU_API_KEY` in your environment, then run this illustrative request. Change the question and CSV fields for your own task. The CSV fields below describe the requested output, not a saved result.

<Accordion title="Run the research request">
  ```bash cURL theme={null}
  curl --fail-with-body https://api.valyu.ai/v1/deepresearch/tasks \
    -H "x-api-key: $VALYU_API_KEY" \
    -H "Content-Type: application/json" \
    --data-binary @- <<'JSON'
  {
    "query": "Evaluate openBIS and eLabFTW as components in a research workflow connecting strain identifiers, screening experiment records and fermentation measurement files. For each stage, distinguish documented native capability, documented integration and unsupported or unknown capability. Use official documentation, repositories and license files. Record edition and version when stated. Identify integration questions without inventing implemented features. Do not claim either candidate is a complete fermentation-control system or has industrial validation without specific evidence.",
    "mode": "standard",
    "research_strategy": "Prioritize project documentation, release notes, license files and documented APIs. Treat vendor deployment claims separately from independently published validation. Cite every row; mark missing or inaccessible evidence.",
    "report_format": "A concise evidence table, comparison limits and unresolved questions.",
    "deliverables": [
      {
        "type": "csv",
        "description": "One row per evidence item with the requested fields and supporting source URL.",
        "columns": [
          "candidate",
          "version_or_commit",
          "workflow_stage",
          "requirement",
          "support_type",
          "evidence_url",
          "license",
          "edition",
          "integration_gap",
          "verification_date"
        ]
      }
    ]
  }
  JSON
  ```
</Accordion>

Save the returned `deepresearch_id`. Poll [Get status](/api-reference/endpoint/deepresearch-status) until the task completes, then read the report and download the CSV from `deliverables`.

## How it works

1. Write the required chain: strain identifier → screening experiment → fermentation run → measurement file. Assign explicit requirements to each step.

2. Read official versioned documentation, repository license files and interface specifications for each candidate. Separate community and hosted editions.

3. Record native support, documented integrations and unverified gaps per requirement. Hand the resulting matrix to engineers for a representative integration test.

To build the workflow inside your own agent, use [Search](/search/quickstart) to find sources and [Contents](/guides/content-extraction) to read selected pages. Keep the CSV fields above as your extraction schema.

## Check the result

Check the exact component and edition behind each license or feature. A file attachment capability does not establish typed bioprocess data support; a chart does not establish instrument integration.


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