How do you benchmark a web search API when the thing being tested can read the answer key? A search agent has a fetch tool. If the gold labels sit in a public dataset, the agent can download them mid-evaluation and skip retrieval entirely. A similar problem arises when the answers are already encoded in the model’s parametric memory: a correct response no longer demonstrates that web search worked. Keenable’s answer is NEEDLE, a live open-source benchmark that rebuilds its query set from fresh public sources rather than freezing one. News queries are regenerated hourly from RSS feeds and Google Trends; finance, scholar, legal, and rare-entity queries are regenerated daily from SEC XBRL, arXiv, Europe PMC, CourtListener, and public agent logs. Fifteen search APIs run against the same query text under one protocol, and every score is read against ultimate, a pooled oracle engine that marks what the whole field managed to find.
Is it reproducible?
Yes, as an open source evaluation harness rather than a product. needle is a Python CLI installed with uv sync and driven by two subcommands per benchmark, generate and run. It needs an OpenRouter key for judging and one API key per engine tested, and runs on a laptop or in CI. It allows recreated all query streams that are being used in addition to the ranking quality judgements.
What NEEDLE measures
NEEDLE stands for News, Everyday, Expert, Deep-tail, and Legal Evaluation. Each vertical models a different agent intent. News projects the newest item from ~124 curated RSS feeds and Google Trends into a keyword query. Finance asks registry facts from Wikidata and GLEIF plus single-quarter 10-Q figures from SEC XBRL. Scholar turns one paper into four query styles: a degraded title, a full-text-only detail, a natural-language clue, and a hedged tip-of-the-tongue description. Deep-tail samples rare-word queries from public agent-trajectory releases including DeepResearchGym, OpenResearcher and LRAT. Legal pulls recent CourtListener opinions across 14 federal courts and eCFR sections.
Scoring splits along the same line. News and deep-tail have no single correct result, so an LLM judge rates each result 0 to 4 and the harness reports nDCG@5 with a duplicate-URL penalty. Finance reports answer-recall@5: does the fact reach the agent inside a top-5 snippet. Scholar and legal are known-item tasks scored by identifier match.
The ceiling is the interesting part
Every engine receives the same query text. The runner issues one call at a time, so latency percentiles are comparable and no engine takes concurrent load. Judging happens on the engine’s own ranking, titles and snippets. Pages are never fetched and results are never re-ranked. Evidence is clipped to 2,000 characters for everyone, and the judge does not see the engine name.
The more interesting number is the ultimate ceiling. For each query, NEEDLE pools the results returned by every engine into a synthetic oracle engine, then orders that combined set by relevance. That creates an empirical ceiling based on what the entire field was able to retrieve.
The gap to ultimate is therefore an upper bound on agentic search quality as it stands today. A large gap means better results existed but every engine failed to surface or rank them well. A weak ultimate score means something different: even after pooling every provider, the benchmark found little strong evidence. In other words, NEEDLE can distinguish a ranking problem from a retrieval problem shared by the whole market.
Where the field actually stands
Numbers below are published 7-day means for the window ending 2026-08-28.
Finance is close to solved: Exa 0.910, Keenable 0.872, Perplexity 0.871, Google 0.847, against an ultimate of 0.965. Scholar spreads out, Keenable 0.774 to Tavily 0.310 against a 0.869 ceiling, because title queries are answerable from metadata and body queries are not. Deep-tail is hardest and closest to real agent traffic: Exa leads at 0.557 of ultimate, Keenable follows at 0.470, Bing sits at 0.199. The gap between delivered and achievable quality widens as queries approach how agents actually search.
Latency is another important metric here, because agents call search dozens of times per task. Same window: Keenable-realtime 193 ms p50 / 284 ms p95, Exa 1,876 / 2,955, Bing 2,767 / 9,381.
Key Takeaways
- NEEDLE regenerates queries hourly for news and daily for the other four verticals, so there is no fixed set to overfit.
- Five verticals, 15 search APIs, one protocol: same query text, same 2,000-character evidence cap, one request at a time.
- Every leaderboard is read against ultimate, a pooled oracle engine marking the ceiling the whole field reached.
- On rare-entity queries from real agent logs the top engine reaches 0.557 of that ceiling; on finance most engines cluster between 0.77 and 0.91.
- Code is MIT, runs execute in public GitHub Actions, and per-run artifacts ship to a Hugging Face dataset.
Check out the live dashboard, the GitHub repo, the technical write-up, and the archived artifacts. All credit goes to the researchers of this project.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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