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

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.

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

My picks

1. SciSpace — best overall starting point

   • Strong semantic literature search and deep-review workflows.
   • Particularly good when your question crosses disciplines or uses concepts that aren't obvious keywords.
   • A 2026 benchmark of 200 complex queries found its Deep Review mode retrieved substantially more highly relevant papers than Elicit or Consensus.
   • Best for: “What has actually been done, and where are the gaps?”

2. Elicit — best for structured evidence synthesis

   • Excellent for turning a research question into a table of papers, methods, findings, limitations, etc.
   • I'd use it when you want to systematically compare experiments rather than simply find papers.

3. Consensus — best for quickly testing scientific claims

   • Good for questions like “Does X improve Y?” and getting a fast view of what published studies say.
   • Less compelling for genuinely open-ended experiment ideation.

4. Google Scholar / Scholar Labs — best as a verification/discovery layer

   • Google's newer Scholar Labs is designed around complex semantic research questions and full-text relevance rather than conventional keyword matching.
   • I'd still use conventional Scholar/PubMed/etc. to make sure the AI search hasn't missed important literature.

But for your specific goal…

I'd actually use a two-stage workflow:

SciSpace/Elicit → reasoning model → experiment design

Give the model the actual papers or extracted evidence, then ask:

“Across these papers, identify contradictions, unexplained findings, methodological weaknesses, and unexplored combinations of variables. Generate 10 experiments that could distinguish between competing explanations. For each, state the hypothesis, independent/dependent variables, controls, expected outcomes under each hypothesis, feasibility, and which existing papers motivate it.”

That's substantially better than asking an AI “give me novel experiments” from scratch. Current research-agent studies suggest that AI-generated ideas tend to stay relatively close to their starting literature and often recombine existing methods rather than opening genuinely new research directions.

If you want one product: start with SciSpace.

If you want the strongest research workflow: SciSpace + Elicit + a reasoning model, with the original papers used as the ground truth.

If you tell me your field (e.g. biology, chemistry, materials, neuroscience, physics, ML), I can narrow this down considerably—there are some much better domain-specific options.

To search across scientific literature, reason through complex biological or chemical landscapes, and systematically propose or design experiments, you are looking for AI-powered scientific research agents rather than traditional academic search engines (which only find or summarize papers).

State-of-the-art multi-agent systems designed specifically for literature-grounded hypothesis generation and experimental design include:

• Google AI Co-Scientist: A compound, multi-agent reasoning engine built on top of Gemini. Given a broad research goal, it searches and reasons across vast corpuses of literature, has specialized agents that engage in a "tournament of ideas" to critique and evolve hypotheses, and outputs prioritized experimental protocols. It has been successfully deployed via Google Cloud and integrated with institutional research initiatives.
• Robin (by FutureHouse): An open-architecture, multi-agent system (utilizing underlying frontier models via FutureHouse ) designed to automate the scientific discovery loop. It coordinates distinct agents like Crow (for literature retrieval), Falcon (for deep scientific evaluation and experimental strategy), and Finch (for data analysis). You can explore or deploy its architecture directly via the FutureHouse Robin GitHub Repository.
• PaperQA2 (by FutureHouse / Structured Literature Agents): An autonomous literature-review and QA agent built to read thousands of pages of text, synthesize atomic facts, and accurately answer complex mechanistic questions with rigorous citations—serving as the foundational retrieval brain before experimental design.
• Open-Coscientist: For an open-source alternative mirroring Google's framework, the community-driven Jataware Open-Coscientist GitHub Repository orchestrates multiple specialized agents using LangGraph and LiteLLM to perform literature-backed hypothesis generation locally.

Note: While tools like Consensus or Elicit are brilliant for fast literature reviews, extraction, and finding what science already knows, they act more like search-and-summarize assistants rather than autonomous experimental design partners.

If you want to narrow this down, let me know:

• What specific scientific domain (e.g., drug discovery, synthetic biology, materials science) are you working in?
• Do you prefer a hosted enterprise platform or an open-source codebase you can run via API?

To reason across the deep scientific literature and actively propose testable hypotheses or experiment designs, you are looking at a new class of AI co-scientists and multi-agent research platforms rather than traditional keyword or semantic search engines.

The state-of-the-art options designed specifically for this workflow include:

• Google Co-Scientist : Developed by Google DeepMind, this multi-agent system (built with Gemini) doesn't just retrieve papers—it actively reasons across the entire published literature. It features specialized agents that generate hypotheses, run simulated peer-review critiques, and conduct Elo-style tournaments to rank the most promising experimental directions. It has been successfully validated in real-world biomedical and drug-discovery applications.
• FutureHouse (Robin / PaperQA2) : FutureHouse is a non-profit research institute focused entirely on building AI systems for automated scientific discovery. Their multi-agent workflow, Robin , integrates literature-searching agents (Crow and Falcon ) with data-analysis agents (Finch ) to read vast corpuses of biology literature, formulate therapeutic hypotheses, design assays, and interpret experimental results in an iterative loop.
• Undermind AI : If your priority is deep, interactive literature exploration before jumping into protocol design, Undermind functions as an exploratory AI agent. It engages in a back-and-forth clarifying dialogue, reads thousands of papers to map the research landscape, identifies critical research gaps, and even suggests a logical reading order and convergence score for your topic.
• Elicit : A more traditional yet powerful AI research assistant. While it leans heavily toward structured literature review, data extraction, and synthesis across millions of papers rather than autonomous experimental tournament design, it remains a gold standard for scoping what science already knows before you plan a study.

To help narrow down which tool fits your specific workflow, what is your primary scientific domain (e.g., molecular biology, materials science, organic chemistry), and are you looking to generate new drug/material targets or design physical lab assays?

If your goal is not just finding papers, but reasoning across the literature and proposing new experiments, there are a few standout tools depending on how autonomous you want the system to be.

Here's how I'd rank them:

1. FutureHouse — Best fit for your description.

   • Designed specifically for scientific discovery rather than general chat.
   • Includes specialized agents for:
     • literature synthesis,
     • checking whether an idea has already been tried,
     • chemistry experiment planning,
     • hypothesis generation.
   • It aims to move beyond summarization into suggesting experiments and research directions, although researchers should still critically evaluate every recommendation.

2. Elicit — Best for evidence synthesis.

   • Excellent semantic search over a very large corpus of papers.
   • Produces structured literature reviews with extracted evidence.
   • Helps identify research gaps, making it useful before designing experiments, though it is less focused on autonomous hypothesis generation than FutureHouse.

3. • Strong for evaluating whether findings have been supported or challenged.
   • Great for avoiding building experiments on weak or disputed results.
   • Complements Elicit well rather than replacing it.

4. • Best when your question is "What does the literature currently conclude?"
   • Less oriented toward generating novel experiments than the tools above.

If I were starting a research project

A practical workflow would be:

• Use FutureHouse to explore hypotheses, identify unexplored ideas, and draft potential experiments.
• Use Elicit to perform a comprehensive literature review and extract evidence.
• Use Scite to verify that the key supporting papers are robust and understand how later work has cited them.

That combination gives you hypothesis generation, comprehensive retrieval, and evidence validation.

If you're working in a specific area (for example, molecular biology, neuroscience, chemistry, materials science, or machine learning), I can also recommend domain-specific AI research tools that outperform general-purpose literature search in those fields.