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

If you mean “search the scientific literature, synthesize what’s known, identify gaps, and turn those gaps into plausible experiments,” I’d start with elicit.com.

My shortlist

• 🥇 Elicit — best overall fit. It combines semantic literature search with multi-paper synthesis, data extraction, and research reports. Its current corpus includes 138M+ academic papers and 545K+ clinical trials, and its reports provide sentence-level citations back to the evidence. Elicit Elicit Best for: “What do we know about X, where are the contradictions/gaps, and what should I investigate next?”
• 🥈 scite.ai — best for testing whether an idea survives scrutiny. It searches full text across 300M+ articles and, importantly, its Smart Citations distinguish research that supports, contradicts, or merely mentions a finding. scite.ai scite.ai Best for: validating the premises behind a proposed experiment and finding counterevidence.
• 🥉 ChatGPT + Scite/Elicit — best for actual experimental ideation. Use the literature tool to establish the evidence base, then have an LLM reason across the findings to propose hypotheses, controls, measurements, and discriminating experiments. Scite can now connect to ChatGPT/Claude through MCP, bringing its verified literature into those workflows.

If your goal is specifically experiment discovery

I'd use this workflow:

Elicit → identify gaps → Scite → stress-test the evidence → LLM → generate experiments → literature check

For example, instead of asking:

“What does the literature say about mitochondrial stress?” ask:

“Across the literature on mitochondrial stress and [your system], identify claims that are well supported, claims that conflict, and important unresolved causal relationships. For each unresolved relationship, propose 3 experiments that could distinguish the competing hypotheses. Cite the papers supporting each hypothesis and explain what result would falsify it.” That's much closer to the product you're describing than a conventional academic search engine.

One caveat: no current literature-search product should be treated as an autonomous scientific reasoner. The experiment suggestions are hypotheses to evaluate, not validated protocols or evidence that an experiment will work.

If you tell me your field (biology, chemistry, materials, physics, neuroscience, etc.), I can recommend the best tool specifically for that field and show you a prompt/workflow for generating genuinely novel experiments.

Finding an AI product that genuinely reasons across the global scientific corpus and transitions smoothly into suggesting or designing experiments bridges traditional literature search (like Google Scholar or PubMed) and autonomous lab assistants.

Depending on whether you want a tool to brainstorm and collaborate with you or an autonomous agent that attempts the cycle end-to-end, options fall into a few clear tiers:

1. Advanced Multimodal & Scientific Workbenches (Best for Human-in-the-Loop Reasoning & Design)

• Claude (with Artifacts / Projects): Frontier models like Claude handle massive contextual windows of scientific PDFs, synthesize cross-disciplinary mechanisms, write out rigorous step-by-step experimental protocols, and self-correct logic. Using Projects/Artifacts allows you to upload a targeted library of papers and prompt it to cross-examine them for methodological gaps and propose controlled experimental designs.
• Elicit: An AI research assistant built specifically to parse semantic meaning from scientific literature. While it doesn't automatically run physical lab work, it excels at searching over 125M papers, extracting specific variables, finding outcome measurements, and helping you identify where literature is thin—ideal for finding the precise "gap" your next experiment should target.
• Consensus: An AI search engine tailored for scientific research that reads through paper findings and extracts consensus. It helps you ask direct empirical questions (e.g., "Does X downregulate Y in cell line Z?" ) so you can ground your experimental premise in established data.

2. Autonomous AI Scientists (Best for End-to-End Hypothesis & Code/Experiment Generation)

• The AI Scientist (by Sakana AI): This is an open-source framework specifically built to perform automated, open-ended scientific discovery. Given a starting codebase or template, it brainstorms novel research ideas, writes code, executes the computational/machine learning experiments, analyzes its own failures, and writes a full academic paper draft. (Note: It is heavily geared toward computational/ML domains right now and requires careful human oversight regarding novelty checks).
• Specialized Lab-Integration Agents: Frontier developments from labs like Anthropic and specialized biotech platforms are introducing agentic frameworks capable of connecting LLM reasoning engines to laboratory hardware (such as liquid handlers or bioinformatics pipelines). These are typically custom enterprise implementations rather than off-the-shelf consumer search bars.

To help narrow down the ideal tool, could you share:

• What scientific domain are you working in (e.g., molecular biology, machine learning, materials science, chemistry)?
• Are you looking for a tool to brainstorm with you , or do you need something that integrates with code/wet-lab execution?

If you need an AI system that doesn't just surface papers , but actively reasons across scientific literature to identify gaps and suggest/design experiments , you are looking at the frontier between AI literature discovery engines and autonomous scientific co-pilots.

No single commercial consumer app magically outputs a flawless, ready-to-run lab protocol out of the box without human guidance, but the ecosystem offers specialized options depending on your exact workflow:

1. Dedicated AI Literature & Hypothesis Engines

• Elicit: Best for structured data extraction and finding research gaps. It searches over 125 million papers, extracts specific methodologies, outcome measures, and variables into data tables, and helps map out what has and hasn't been tested yet. Use it to build an evidence matrix that highlights where the literature falls short.
• SciSpace: Excellent for deep-diving into complex PDFs, parsing mathematical formulas or biomedical mechanisms, and breaking down a research gap into a structured research question (using frameworks like PECOT). It bridges the gap between reading the literature and formulating the next logical query.
• Consensus: An AI-powered academic search engine that reads through millions of papers to answer whether a scientific claim is supported or unsupported by existing literature, complete with a "consensus meter". Great for quickly validating whether an experimental premise is backed by consensus before you design a test for it.

2. Advanced / Specialized Scientific Co-Pilots

• Domain-Specific Knowledge Graphs (e.g., BioSkepsis): If your field is in the life sciences, biomedicine, or pharma, specialized engines built on biological knowledge graphs (such as Gene Ontology and MeSH) allow for deeper full-text reasoning and translating literature findings into tangible biological hypotheses.
• Custom LLM Agents with RAG (Retrieval-Augmented Generation): Many computational and wet-lab researchers currently bypass generic tools by setting up local or secure RAG pipelines (using tools like LangChain or LlamaIndex combined with vector databases of specific PubMed/ArXiv dumps) paired with reasoning models (like Claude 3.5 Sonnet or OpenAI o1-level reasoning models). You feed them a curated cluster of 50–200 relevant papers and prompt them explicitly: "Act as a critical peer reviewer. Identify methodological gaps across these texts and propose three high-novelty, falsifiable follow-up experiments with step-by-step control groups."

What scientific domain or field (e.g., molecular biology, materials science, machine learning) are you working in, and are you looking for a tool to help with wet-lab experimental design or computational/data-driven hypotheses?

If you mean a tool that goes beyond finding papers and actually connects findings, identifies gaps, proposes hypotheses, and suggests experiments, you’ll want an “AI scientist” or “AI research assistant” rather than a normal literature search engine.

A few options depending on what you need:

Best general-purpose research reasoning

• Elicit — Good for literature reviews, evidence synthesis, comparing papers, and turning a research question into structured reports with citations. It is strong for “what is known?” and “what evidence exists?” questions.
• Semantic Scholar — Excellent for finding influential papers and exploring citation networks; better as a discovery layer than as an experiment planner.

Closest to “suggest experiments”

• Google Gemini for Science experiments — Google has been developing tools aimed at literature synthesis and hypothesis generation. Its “Literature Insights” focuses on finding and synthesizing research, while “Co-Scientist” is designed to generate hypotheses and experimental plans grounded in literature.
• FutureHouse systems — Their research on multi-agent scientific discovery systems describes workflows that combine literature search, hypothesis generation, experimental planning, and data analysis, particularly in biology.

For computational science / ML experiments

• Empirical Research Assistance — Aimed at computational experiments: it can incorporate literature ideas, generate code, explore approaches, and optimize solutions against evaluation criteria.

For biology / life sciences specifically

• SCiNiTO — Positions itself as an end-to-end research assistant for life sciences, including literature analysis and experimental planning.
• BioSkepsis — Focuses on life-science literature synthesis, evidence weighting, and cited research briefs.

My practical recommendation

• If you are doing a literature-driven project: start with Elicit + Semantic Scholar.
• If you want “what experiment should I run next?”: try Gemini for Science / Co-Scientist-style tools if you have access.
• If you are doing computational research: look at ERA-like systems.
• If you are in wet-lab biology: use a biology-focused assistant, but treat generated experiments as hypotheses to validate—not as a substitute for domain expertise, safety review, or protocol optimization.

If you tell me your field (e.g., molecular biology, materials science, neuroscience, chemistry, ML, physics) and whether you need idea generation, protocol design, or paper discovery, I can narrow this down.

If you mean “search the literature, synthesize it, identify gaps, and then propose testable experiments”, I’d start with FutureHouse rather than a conventional literature-search product.

My picks

1. FutureHouse Platform — best match

   • Built specifically around AI agents for scientific discovery.
   • Its Falcon agent does deep literature synthesis, Owl investigates whether something has already been done, and Phoenix can help with chemistry experiment planning.
   • Their Robin system goes further: it can generate hypotheses, propose experiments, analyze results, and update hypotheses. The work was published in Nature in 2026.
   • Best for: “Given everything known about X, what experiment should I run next?”

2. Elicit — best for rigorous literature grounding

   • Excellent for finding and screening relevant papers semantically, extracting structured information, and producing cited research reports.
   • Best for: establishing what is known, what has been tried, and where the evidence is weak before designing experiments.

3. Consensus — best for fast evidence synthesis

   • Searches 220M+ peer-reviewed papers and its Research Agent can chain searches, compare studies, perform gap analysis, and return citation-backed answers.
   • Best for: quickly interrogating a large literature rather than building an end-to-end experimental workflow.

4. Google Labs Science experiments — worth watching

   • Google's current science experiments include Hypothesis Generation, explicitly aimed at finding knowledge gaps and proposing testable research plans through a multi-agent system.

Bottom line

For your exact description: FutureHouse.
For literature-first rigor: Elicit.
For quick literature Q&A: Consensus.

One important caveat: I would treat AI-generated experiment proposals as hypotheses to evaluate, not validated protocols. FutureHouse itself notes that its experimental Phoenix chemistry agent can make mistakes.

If you tell me your field—e.g. biology, chemistry, materials, neuroscience, medicine, or physics—I can narrow this down to the best 2–3 tools for that domain.

To reason across scientific literature and actively suggest or design experiments, you should use Google's AI Co-Scientist (available via Google Labs or integrated for institutional/enterprise workflows), which is explicitly architected as a multi-agent reasoning system to formulate testable hypotheses and experimental protocols. If you are looking for commercially accessible deep discovery engines that analyze thousands of papers to surface hidden research gaps and methodology structures, use Undermind AI or Elicit.

Specialized Reasoning & Hypothesis Generation Platforms

• AI Co-Scientist (Google Labs): Uses a multi-agent orchestration framework (generation, reflection, ranking, and evolution) modeled on human research teams to connect disparate findings, debate mechanisms, and output concrete experimental designs.
• Undermind: An advanced AI co-researcher that reads thousands of academic papers, traces deep citation trails, maps the broader scientific landscape, and explicitly identifies overlooked research gaps and reading orders.
• Elicit: An AI research assistant that searches massive corpuses of peer-reviewed papers and clinical trials, extracts specific variables or methodologies into structured comparison tables, and provides sentence-level attribution.

Literature Review & Evidence Synthesis Engines

• Consensus: An academic search engine that extracts direct, evidence-based answers from millions of papers and features a consensus meter to show how strongly literature supports a specific claim.
• Scite: Uses Smart Citations to show not just that a paper was referenced, but the exact context—whether subsequent studies supported, mentioned, or contrasted the experimental findings.
• ResearchRabbit: A dynamic, visual citation-mapping platform that connects seed literature to related works, authors, and conceptual topic evolution.

If you can share your specific scientific domain (e.g., genomics, materials science, drug discovery) and whether you need open-web academic search or closed enterprise data integration , I can help you narrow down the ideal tool stack.

To reason across scientific literature and actively suggest or design experiments, you should look at the emerging class of AI Co-Scientists and multi-agent discovery frameworks.

The leading systems built specifically for this workflow include:

• Google's Hypothesis Generation (powered by Co-Scientist) : Part of the Gemini for Science suite available via Google Labs , this multi-agent system simulates the scientific method. It runs an "idea tournament" where different agents generate, debate, critique, and refine research hypotheses while anchoring every claim with verified citations from millions of scientific papers.
• Robin by FutureHouse : A prominent multi-agent system for biological and chemical discovery. Robin coordinates specialized agents (such as Crow for literature synthesis and Falcon for experimental strategy) to ingest a disease target or broad scientific quest, scan the literature, propose actionable assays or drug-repurposing candidates, and analyze the resulting data in an iterative loop.
• Elicit : A widely used research agent that moves past simple keyword searching. Elicit searches a massive corpus of academic papers and clinical trials, extracts key variables and methodologies into structured matrices, and helps synthesize gaps to guide next steps.
• Consensus (Deep Search) : An evidence-based academic search engine that extracts direct answers from peer-reviewed studies. Its Deep Search and Scholar Agent features build comprehensive search strategies, evaluate conflicting theories, and surface research gaps.

To help narrow down which of these platforms fits your exact project, tell me:

• What is your primary scientific field (e.g., molecular biology, materials science, clinical medicine, computer science)?
• Are you looking to repurpose existing data/drugs , or design entirely novel lab assays?

If your goal is specifically “search the scientific literature → reason across it → propose plausible next experiments,” I’d use a small stack rather than one tool.

My picks

1. Best starting point: Elicit
Best for turning a research question into a structured body of literature. It can find papers, compare studies, extract variables/results, and synthesize across papers. That makes it a strong foundation for asking, “What has already been tried, what remains unexplained, and what experiments follow?”

2. Best for deeper literature reasoning: Scite
This is particularly useful when your experiment ideas depend on knowing whether an apparently established finding is actually robust. Scite searches full text and uses its Smart Citations to distinguish work that supports, contradicts, or merely mentions a finding. Its current index is 280M+ sources and 1.6B+ citation relationships.

It also has an MCP integration, so you can connect Scite's literature search and citation context to an AI assistant such as ChatGPT or Claude.

3. Best for discovering unexpected connections: ResearchRabbit
Use this when you have a few important seed papers and want to explore the surrounding research network—related papers, authors, citation trails, and research clusters.

If I were doing actual research

I'd do:

Elicit → ResearchRabbit → Scite → LLM

For example:

“Find literature explaining why X happens. Identify competing mechanisms and experiments that distinguish them. Find papers supporting and contradicting each mechanism. Based on the gaps, propose 10 experiments, ranked by how strongly each would discriminate between the hypotheses.”

Then verify the proposed experiments against the original papers rather than trusting the AI's synthesis.

One caveat: today's tools are considerably better at literature synthesis and hypothesis generation than at reliably inventing genuinely novel experiments. Evidence for the superiority of these AI search systems is still relatively limited, so I would treat experiment suggestions as hypotheses to investigate, not as validated scientific conclusions.

If you want one product only: I'd start with Elicit.
If you want the closest thing to a “scientific research copilot”: Scite + an LLM is the more interesting combination.

To reason across scientific literature and actively suggest or design experiments, you need tools that transcend basic keyword search or flat document summarization . Platforms designed for this level of deep scientific reasoning and hypothesis generation typically fall into a few categories:

• Claude Science / Anthropic Research Workspaces : Purpose-built AI workspaces designed specifically to handle complex scientific reasoning, cross-disciplinary literature analysis, code-driven data pulls, and experimental planning.
• Future House AI : An open-source and non-profit initiative focused on building AI "scientists" that parse biological and chemical literature, run deep conceptual searches, and suggest experimental precedents or new chemical pathways.
• Undermind : An advanced AI co-researcher that reads thousands of scientific papers, maps out citation trails, identifies structural research gaps, and helps conceptualize testable hypotheses.
• Elicit : An AI research assistant that extracts data matrices, variables, and methodologies side-by-side from peer-reviewed literature, helping you transition from broad findings to specific experimental parameters.
• Consensus : An AI-powered search engine that extracts direct, evidence-based answers and scientific consensus from peer-reviewed studies rather than standard web text.

If you share your specific scientific field (e.g., molecular biology, materials science, organic chemistry) and whether you need hypothesis generation or protocol optimization , I can recommend the exact platform best suited for your workflow.

If your goal is specifically “search across the literature → synthesize it → generate hypotheses → suggest experiments,” I’d start with Edison Scientific’s Kosmos rather than a conventional AI literature-search engine.

My picks

Why Kosmos: it is designed around a longer research loop rather than just search. Its published system combines literature search, data analysis, hypothesis generation, and synthesis, with runs reportedly reading ~1,500 papers and performing iterative analyses. The research report describes discoveries spanning areas including metabolomics, materials science, neuroscience, and statistical genetics.

More importantly for your question, the predecessor Robin explicitly integrated hypothesis generation, experimental strategy, data analysis, and follow-up insight generation; FutureHouse says that work led to an experimentally validated therapeutic hypothesis.

If you want something you can use immediately

I'd actually use two layers:

1. Kosmos → give it the broad research objective and ask it to identify gaps, competing explanations, and promising experiments.
2. PaperQA2 → independently interrogate the underlying literature and verify the claims/citations. PaperQA2 is specifically optimized for scientific-literature retrieval and synthesis and is open source.

I would not rely on any of these to autonomously decide that an experiment is scientifically sound. Recent work is finding that even citation-verification systems can disagree about whether an AI-generated claim is actually supported, so the proposed experiment and its evidentiary chain still need human checking.

Bottom line:

• Want the closest thing today to an AI research scientist → Kosmos.
• Want the strongest literature-reasoning/search component → PaperQA2.
• Want an easy literature-review interface → Elicit.
• Want quick evidence/consensus checks → Consensus.

If you tell me your field (e.g. biology, chemistry, materials, neuroscience, physics, ML), I can narrow this down substantially—some of these tools are much better in particular domains.