I want a search product that reasons across scientific literature and suggests experiments. What should I use?

To find a system that reasons across scientific literature and actively suggests or designs experiments, you are looking at the bleeding edge of AI for scientific discovery and agentic research pipelines.

Depending on whether you want a ready-to-use application, an advanced reasoning model, or an open-source autonomous agent framework, here are the best options to consider:

• The AI Scientist (by Sakana AI) : An end-to-end open-source agentic system specifically built to autonomously brainstorm hypotheses, search literature via Semantic Scholar , write and run code to execute experiments, analyze results, and draft full papers. Version 2 uses progressive agentic tree search to explore open-ended research directions. (Best for computational/ML domains where experiments run entirely in code). You can inspect the project on The AI Scientist GitHub.nature.com+2
• Advanced Inference-Time Reasoning LLMs (e.g., OpenAI o1/o3 or Google Gemini with deep reasoning) : Modern reasoning models excel at "slow thinking," working through complex multi-step problems, digging into the long-tail of scientific literature, and connecting cross-disciplinary mechanisms to suggest logical next-step experimental parameters. Feeding these models structured sets of papers or using them via platforms with deep academic integrations allows you to prompt them iteratively for experimental design.
• Robin (Multi-Agent Systems for Biology/Wet-Lab) : Emerging multi-agent architectures (such as Robin, recently highlighted in literature for automated experimental biology) integrate literature-search agents directly with data-analysis and hypothesis-generation loops. They read papers, propose targeted therapeutic or experimental candidates, interpret outcomes, and refine the next iteration of tests.nature.com
• Semantic Search & Synthesis Tools (like Elicit, Consensus, or Undermind) : While these tools don't autonomously code or run wet-lab experiments, they specialize in reasoning across thousands of papers to extract methodologies, evaluate evidence grids, and highlight gaps in the literature. They serve as a powerful first step to see what hasn't been tested yet before you manually design an experiment.infoguides.gmu.edu+1

Are you looking to run these experiments digitally (in code/simulation) or in a wet-lab/physical setting , and what is your target scientific domain (e.g., machine learning, biology, chemistry)? I can help point you toward the most appropriate tool or prompt strategy.

If you mean “search the literature, synthesize across papers, identify gaps, and turn those gaps into plausible experiments,” I’d look beyond ordinary academic search engines.

My shortlist

• scite.ai — strongest general-purpose option for reasoning over the existing literature. It searches full text, not just abstracts, and its Smart Citations distinguish papers that support, contradict, or merely mention a finding. Its current index is 317M+ articles, including 49M full-text sources. scite.ai scite.ai Use it when: you want to know “What does the literature actually establish, and where is it contested?”
• elicit.com — particularly good for systematic literature work: semantic search, screening, extracting structured information across papers, and comparing studies. It currently searches 138M+ academic papers and can incorporate your own documents. Elicit Use it when: you want “Find everything relevant, extract the variables/results/methods, and help me see the gaps.”
• NousLab — one of the more directly relevant options if your desired output is hypotheses → experimental designs. It explicitly generates testable hypotheses from literature and can turn selected hypotheses into experimental designs/protocols. NousLab Use it when: experiment suggestions are more important than conventional literature-review workflow.
• Benchling AI — especially interesting for biological/biomedical research, because its 2026 hypothesis-generation system combines published literature with an organization's internal experimental data and program history. Benchling Use it when: your lab already works in Benchling and you want literature reasoning combined with your own experimental record.

If I were setting up the workflow

I'd use:

Scite/Elicit → identify evidence + gaps → an experiment-oriented reasoning system → human scientist validates the proposed experiment.

That's because the newest research systems are moving toward exactly this pipeline: literature search → hypothesis generation → experimental planning → experimental validation. Google's AI Co-Scientist, for example, is explicitly designed around generating, critiquing, and refining hypotheses for experimental verification, while the Nature-published AI Scientist work combines literature search, ideation, experiment planning, execution, analysis, and paper writing—although its demonstrated domain is primarily computational/ML research.doi.org+1

If you tell me your field (e.g. molecular biology, neuroscience, chemistry, materials science, physics, social science, ML), I can narrow this to the 2–3 tools that are actually appropriate for that field, including which ones can propose concrete experimental protocols rather than just summarize papers.

If your goal is “search across the literature → identify gaps → propose plausible experiments”, I’d look beyond ordinary literature-review tools. There are now a few different tiers:

• deepmind.google — closest match to what you described. Co-Scientist is designed to search literature, reason over evidence, generate novel hypotheses, and propose experimental protocols, with citations and iterative critique.Nature+1
• elicit.com — probably the most practical starting point if you primarily want literature synthesis and structured comparison. It searches papers, extracts study characteristics, and synthesizes evidence, but is less oriented toward autonomous experiment design.Sage Journals
• researchrabbit.ai — useful when the important thing is discovering connections between papers, authors, and research communities. Its database draws from Crossref, Semantic Scholar, and OpenAlex and currently contains 310M+ articles.ResearchRabbit
• orchestra-research.com — aimed at an end-to-end workflow: literature review → experiment setup → coding → analysis → scientific writing. This is interesting if you want agents actually carrying work forward rather than merely answering questions.Orchestra Research
• The AI Scientist — particularly relevant if your experiments are computational. The research system can generate ideas, search literature for novelty, write/run experiments, analyze results, and iterate. It is much more of a research agent than a search engine.Nature+1

What I'd choose

If you mean wet-lab / experimental science, I'd investigate Co-Scientist/Gemini for Science first, then use Elicit or ResearchRabbit alongside it for literature coverage and verification.

If you mean ML, computational biology, quantitative science, or other code-executable experiments, I'd look particularly closely at The AI Scientist, ERA/Computational Discovery, and Orchestra. Google's Computational Discovery prototype, for example, is explicitly designed to generate and test large numbers of computational hypotheses.Google Research+1

One important caveat: these systems can generate interesting-looking hypotheses without those hypotheses being genuinely novel, feasible, or experimentally sound. The strongest systems are therefore better viewed as research collaborators for hypothesis generation, with the literature and proposed protocols still requiring human verification.

If you tell me your field (e.g. biology, chemistry, neuroscience, materials, ML) and whether you want literature search only vs. actual experiment design/execution, I can narrow this to 2–3 tools and explain exactly how I'd use them.

If by “search product” you mean something that can synthesize a body of scientific literature and then help you identify plausible experiments, I’d shortlist these:

• scite.ai — my first choice for your use case. It searches full-text scholarly literature, tracks whether later papers support or contradict findings, and its AI assistant grounds answers in papers. It now also offers AI-generated research suggestions.scite.ai+1
• scispace.com — best for broad literature synthesis. Its Deep Review mode performed particularly well in a 2026 benchmark of complex cross-disciplinary queries, surfacing more highly relevant papers than Elicit or Consensus at most ranking depths. Caveat: the benchmark was conducted by SciSpace itself.SciSpace+1
• elicit.com — best for structured research questions. I'd use it when you want to turn a question into a systematic literature search, compare studies, and extract variables/methods across papers.
• consensus.app — best for “what does the literature say?” It searches 220M+ peer-reviewed papers and is particularly oriented toward synthesizing evidence around research questions.Consensus Help Center

But for experiment generation, I'd use a two-step workflow

Scite → reasoning model.

Ask Scite something like:

“Find studies investigating X under conditions A, B, and C. Identify disagreements, unexplained findings, methodological limitations, and variables that have not been systematically tested. Then propose 10 experiments that could distinguish between the competing explanations. For each experiment, cite the evidence motivating it and state the predicted outcomes under each hypothesis.” Then have the reasoning model turn that into a testable experimental matrix: hypothesis → manipulation → controls → measurements → expected outcomes → confounders → statistical test.

That's preferable to asking an AI to simply “invent experiments,” because the literature search and citation layer stays inspectable. Scite explicitly supports full-text search and citation-level support/contradiction analysis.scite.ai+1

If you're building this as a product rather than choosing one: Scite is especially interesting because it exposes an API for full-text search, citation classifications, papers, patents, clinical trials, grants, and other research data.scite.ai

If you tell me your field (e.g. molecular biology, materials science, ML, neuroscience) and whether you want a consumer tool or an API you can build into your own product, I can narrow this to the best 2–3 options.