openbenchmarks/OB-Code-Websearch-Val

Web Search for Coding Agents: Validation Set

75 realistic product tickets for developing and tuning against the web search providers benchmark for coding agents. The model, task, budgets, and runner stay fixed; the search or fetch provider is the variable under test.

Dataset Description

75 examples: Each row comes from a distinct source URL. Each row has:

• task_id: stable identifier, prefixed by vendor.
• vendor: The vendor the question is about.
• ticket: the product request in natural language. It deliberately does not name the answer.
• starter_code: a real but wrong API call. It would run today; it does not satisfy the ticket.
• gold_code: reference patch, used for grading. The model never sees it.

The rows follow the same format as the public set (openbenchmarks/OB-Code-Websearch) and the held-out private set, so a harness built against them runs unchanged on the scored set. The vendor mix follows the private set, which leans toward ServiceNow and Workday. Harness, scoring and vendor runners are in openbenchmarks-labs/web-search-for-coding-agents.

How the tickets were built

1. Discover. Official documentation pages for each vendor: the ServiceNow REST API reference, Workday's published OpenAPI specs, and SAP pages found through web search on SAP's official sites. Pages off those sites are dropped.
2. Fetch. The full text of each page is saved locally.
3. Generate. An authoring model reads only those saved pages and proposes tickets: an opaque gold token copied from the page, capability English that does not name it, and a starter that calls a real but wrong API.
4. Verify. An automatic keep-gate rejects a ticket that names its gold token, whose gold does not compile, or whose starter already contains the answer. Every gold patch passes the grader and every starter fails it.
5. Memory check. Each ticket was run with no search tools. Tickets the model solved from memory were removed.

Dataset design note

Vendors with highly regular, developer-friendly documentation can be poor fits for this benchmark. In earlier Stripe tasks, the model often inferred the documentation slug or URL structure directly from the ticket and reached the correct implementation without performing meaningful retrieval. When documentation followed a predictable pattern such as /changelog/dahlia/{kebab-case-ticket-title}, the model could construct the likely documentation location from the ticket itself. Those tasks measured how well a model exploits documentation naming conventions, which is a different skill from search quality, so they were removed from the active benchmark.