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Average price: 52 products listed
52 Listings in Research Agents Available
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What is Bohrium? Bohrium is an AI for Science platform that its site describes as a space station for scientists. It is developed by DP Technology, a China-based AI for Science company founded in 2018. The product pairs research tools with access to scientific computing and AI models. Key capabilities of Bohrium Science Q and A: Conversational question tasks for scientific queries. Literature search: Search and read research papers with AI support. AI writing: Tools to help draft scientific text. Workspace: A workspace for organizing research tasks and conversations. Scientific models: Access to DP Technology models such as DPA, Uni-Mol, Uni-RNA and Uni-Fold, per third-party coverage. Notebook platform: Notebook tools for running analysis in the cloud. How Bohrium works Researchers start a conversation or Q and A task, search literature, and organize results in a workspace. The platform connects those steps to cloud computing and to molecular and materials models, so a researcher can move from reading papers to running calculations in one place. Who uses Bohrium? Bohrium is built for researchers in chemistry, materials science, biology and drug discovery. Graduate students and R and D teams are typical users. The interface is available in Chinese and may be less familiar to English-only teams. Bohrium pricing Bohrium has a free tier, and the site does not list prices on its main page. Compute and premium features are charged by the vendor, so check the platform for current rates. Bohrium alternatives Afforai is an AI research assistant and reference manager focused on papers. Anara is an AI research workspace for reading and querying sources. R Discovery is a literature discovery app, and none of these provide cloud scientific computing with molecular models.
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What is Kompas AI? Kompas AI is an AI deep research product. It researches a topic across multiple sources and produces structured reports with source citations that can be edited. Key capabilities of Kompas AI Deep web research: multi-source research on a topic. Structured reports: organized, sectioned output. Report editing: refine the report after generation. Source citations: claims link to their sources. How Kompas AI works You enter a research question, Kompas AI gathers and synthesizes sources, and returns a structured report with citations that you can edit. Who uses Kompas AI? Analysts, students and professionals who need a sourced first draft of a research report, such as a market overview, a literature scan or a briefing on an unfamiliar topic. Because the output is a structured document rather than a chat reply, it suits work that will be shared, reviewed and refined by a person afterward. As with any AI-generated research, claims should be checked against the linked sources before they are relied on. Kompas AI pricing Kompas AI has a free version and paid plans. Current plan prices are on the vendor's site. Kompas AI alternatives Elicit and Consensus focus on answering questions from academic literature. Txyz.ai is another research reading and summarization tool. Emergent Mind surfaces trending arXiv papers.
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What is Scholarcy? Scholarcy is an AI summarizer for research articles that converts a paper into an interactive summary flashcard. A flashcard can include a snapshot, key findings, key concepts, important references and a structured summary. Key capabilities of Scholarcy Summary flashcards: snapshot, key findings and structured summary of each paper Key findings extraction: pulls out main results Reference extraction: lists important references Flashcard collections: organize saved flashcards on premium Library export: exports flashcards, up to 100 at once on premium Literature matrices: compare papers on premium How Scholarcy works You import a paper and Scholarcy generates a flashcard with background, methodology and findings sections plus key facts and references. Premium adds unlimited summaries, highlighting and editing, and collections. Third-party reviews say data is held on secure servers in the European Economic Area. The vendor pricing page was blocked, so prices come from reviews. Who uses Scholarcy? Researchers, students and academics skimming large reading lists use it to triage papers before reading in full. Scholarcy pricing Third-party reviews report a free Article Summarizer with 10 summaries and one-at-a-time flashcard exports, and a premium plan at $9.99 a month with unlimited summarization, a discounted yearly option and a one-week trial for new users. Verify on the vendor site. Scholarcy alternatives Alternatives include Elicit for literature review tables, Consensus for evidence-based answers, Causaly for biomedical research, Wokelo for company research, and Parallel for web research APIs.
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What is OpenRead? OpenRead is an AI-powered research platform that helps users discover, analyze and manage academic papers. It combines paper search, question answering, cross-paper comparison and note-taking in one workspace. Key capabilities of OpenRead AI paper search: papers from 20,000+ journals, updated every 5 minutes Paper Q&A: ask questions about a paper Paper comparison: compare findings across papers to spot contradictions Oat chat: OpenRead AI Technology chat with top-tier models and diagram drawing Trending papers: discover popular work in your field Notes: organize research with integrated note-taking How OpenRead works You search the paper index, open a paper and ask questions about it, or select several papers to compare findings and contradictions. Oat, the chat interface, supports homework help, diagram drawing and image editing with top-tier models. Notes live alongside the papers so reading and writing stay in one place. Who uses OpenRead? OpenRead is aimed at researchers and students who read many papers and want help summarizing and comparing them. It maintains a free tier and a Pro tier. OpenRead pricing OpenRead offers Free and Pro tiers. Pro pricing was not available from the pages reviewed, so see the vendor pricing page for the current monthly or annual rate. OpenRead alternatives Alternatives include Humata for document Q&A, Litmaps for citation mapping and literature discovery, and Explainpaper for explaining highlighted text.
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What is Scite? Scite is an AI research platform for finding and evaluating scientific literature. Its Smart Citations show whether later studies have supported, challenged or merely mentioned a paper's claims. The vendor says its full-text citation search spans more than 300 million articles, preprints, books, patents and datasets. Key capabilities of Scite Assistant: AI answers grounded in research with links to the exact sentence. Smart Citations: Shows how other papers cite a study. Search: Full-text citation search across 300M+ sources. Collections: Organize and monitor sets of papers. Reference Check: Checks a manuscript's references for retractions and contrasting citations. MCP and API: Connect Scite to other AI assistants and programs. How Scite works You ask a question or search for a topic. Scite retrieves relevant passages from its indexed literature, and the assistant writes an answer with citations that link to the source sentences. Smart Citation reports then show how each cited paper has been treated by later work. Who uses Scite? Researchers, students and industry experts use it, along with universities and organizations. The vendor cites over 2 million users and names Purdue, Novo Nordisk and Johns Hopkins among institutions. Scite pricing Scite lists Basic at $12 per month billed yearly, Pro at $40 per month billed yearly, and a custom Enterprise plan. Basic and Pro include a 7-day free trial, and Pro seats can be added at $40 each. Scite alternatives Elicit is built around extracting structured data from papers for literature reviews. Consensus returns evidence-based answers drawn from published studies with a consensus-style summary. SciSpace emphasizes reading and explaining papers, including PDF chat, where Scite emphasizes how papers cite one another.
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What is FutureHouse? FutureHouse is a nonprofit organization focused on automating scientific discovery with AI agents, mainly in biology. It built the Crow, Falcon, Owl and Phoenix research agents, and publishes systems such as Robin. Key capabilities of FutureHouse Literature search agents: Crow answers cited scholarly questions, Falcon runs deep literature reviews. Prior-work checks: Owl checks whether an experiment has been done before. Chemistry planning: Phoenix assists experimental chemistry planning. Robin: A multi-agent system for end-to-end scientific discovery. DISCO: Research project on enzyme design. OXtal: Research project on molecular crystal structure prediction. How FutureHouse works Agents were exposed through a web interface and a Python client API, so researchers could send a question or task and receive a cited, structured answer. Chaining agents lets one agent's output feed the next. The vendor says the systems originally developed under its name are now part of the Edison Scientific platform, where access and pricing are set by Edison. Who uses FutureHouse? Biologists, chemists and early-career researchers. FutureHouse also runs an AI-for-Science Independent Postdoctoral Fellowship that pairs early-career researchers with AI tools and academic advisors, plus training resources. FutureHouse pricing FutureHouse does not publish prices on its own site. The original platform was free to use, and current access to those agents and any pricing are determined by Edison Scientific. FutureHouse alternatives Related tools include Humata, ChatPDF, Petal, Explainpaper and Sourcely, which help read and query research papers rather than run discovery agents.
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What is R Discovery? R Discovery is an AI research assistant and academic reading app. It searches a database of 250M+ research papers and gives summaries, a personalized feed and tools to read, translate and cite papers. Key capabilities of R Discovery Ask R Discovery: AI summaries drawn from 300M+ verified sources. Chat PDF: Upload a paper and ask questions. Literature review: Generate reviews and synthesize insights. Audio papers: Listen to research in human-like voices. Paper translation: 30+ language options. Citation generator: Create citations for papers. How R Discovery works You follow topics to build a personalized feed, search the indexed literature, or ask a question and receive an AI summary drawn from verified sources. You can upload a PDF to chat with it, generate a literature review, translate a paper or listen to it. Apps cover web, iOS, Android, a Chrome extension and ChatGPT. Who uses R Discovery? Researchers, students and academics who keep up with journals. The index covers 64K+ journals and 120K+ institutions. R Discovery pricing The free tier covers basic search and the personalized feed. R Discovery Prime starts at $12 per month, and annual pricing was not specified in the content reviewed. R Discovery alternatives Related tools include OpenRead, Potato, Wokelo and Parallel for research assistance, and FutureHouse for AI science agents.
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What is AskYourPDF? AskYourPDF is an AI document assistant that lets you chat with PDFs and other files to pull out answers and summaries. The vendor says more than 5 million users rely on it. Key capabilities of AskYourPDF Document chat: Ask questions of any uploaded document. Summarization: Summaries of long files. Knowledge Base: Search across multiple documents on paid plans. OCR support: Reads scanned documents on paid plans. Multiple AI models: Claude and Gemini options on paid plans. Large files: Up to 877 MB per document on Pro. How AskYourPDF works You upload a document, then ask questions in a conversation and receive answers drawn from it. The Free plan uses GPT-5 Mini only, while paid plans add other models including Claude and Gemini. Limits scale with the plan, from 100 pages per document on Free to 6,000 on Pro. Who uses AskYourPDF? Students, researchers and professionals who read long documents. Enterprise offers unlimited pages and questions, 1,000 documents a day, custom credits and dedicated support. AskYourPDF pricing Free is $0 with 50 questions and 3 conversations a day. Premium is $11.99 per month billed yearly with 1,200 questions a day. Pro is $14.99 per month billed yearly with 100,000 questions a day. Enterprise is custom. Annual billing gives a 25% discount. AskYourPDF alternatives Sharly AI also chats with documents, while Semantic Scholar and Connected Papers focus on discovering papers. AskYourPDF centers on conversation over your own files.
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What is Tamarind Bio? Tamarind Bio is a platform that gives scientists access to over 300 AI molecular design tools through a web interface, an API and agents, with no setup required. It designs and optimizes proteins and molecules, including variants, de novo binders and novel compounds, and manages compute scaling and GPU orchestration. Key capabilities of Tamarind Bio Tool library: 300+ models including AlphaFold, RFdiffusion, MPNN, GROMACS and BindCraft. Protein and binder design: variants, de novo binders and antibodies or nanobodies. Small molecule and enzyme work: focus areas include peptides, enzymes and small molecules. Compute orchestration: handles scaling, parallelization and GPUs. API and MCP server: programmatic access and an MCP server. Fine-tuning: models can be tuned on internal data. How Tamarind Bio works You choose a tool in the web app, submit sequences or structures, and the platform runs the job on managed GPUs and returns results. Tools can be chained together, and the same functions are available through the API. The vendor says users keep ownership of inputs and outputs, hosted on a SOC 2 compliant cloud. Who uses Tamarind Bio? Scientists at biotechs and large biopharma companies. The vendor cites over 10,000 users. Tamarind Bio pricing Tamarind Bio links to a separate pricing page and offers demos; prices are not on the homepage. Tamarind Bio alternatives SciSpace helps find and read research papers, rather than run design models. Scite analyzes how papers cite each other, a literature use case. Citrine Informatics applies machine learning to materials and chemical R&D data.
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What is Connected Papers? Connected Papers is a tool for discovering and exploring academic papers as a visual graph of related work. You start from a paper and see a map of connected papers to find prior and derivative work. Key capabilities of Connected Papers Paper graphs: visual map of related papers Free tier: 5 graphs per month with all features Unlimited graphs: on Academic and Business Academic plan: for institutions, non-profits and personal use Business plan: for commercial use All features on free: no feature gating How Connected Papers works You enter a paper, and Connected Papers generates a graph that places related papers by similarity so clusters of connected work are visible. Every plan includes all features, and paid plans only remove the monthly graph limit. Who uses Connected Papers? Researchers, students and analysts exploring a field use it. Academic users, non-profits and personal users take the Academic plan, while commercial users take Business. Connected Papers pricing Free includes 5 graphs a month. Academic is $6 a month ($72 billed annually) and Business is $20 a month ($240 billed annually), both with unlimited graphs. Connected Papers alternatives Alternatives include Litmaps for citation maps, Semantic Scholar for paper search, Iris.ai for research reading, Humata for chatting with documents, and ChatPDF for PDF questions.
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What is Avidnote? Avidnote is an AI research workspace for writing, reading and analyzing academic work. It supports paper summarization and Q&A, quantitative data analysis, interview transcription and coding, and reference management. Key capabilities of Avidnote Academic writing: AI-assisted writing and rewriting of research papers. Paper reading: Summaries and Q&A on papers. Data analysis: Quantitative research analysis. Interview transcription: Automatic transcription and coding. Reference management: Organize projects and sources. Private by design: Data not used to train the AI. How Avidnote works You upload papers, data or recordings, then ask Avidnote to summarize, analyze, transcribe or draft text inside project folders. Plans meter usage in AI words per month plus storage and transcription hours. Who uses Avidnote? Students and academics. The site lists endorsements from Princeton, Cornell, Kent, Harvard, MIT and Oxford. Avidnote pricing Free is $0 with 20,000 AI words and 300 MB. Professional is $19 per month with 200,000 words, 5 GB and 10 hours of transcription. Premium is $59 per month with 2 million words and 100 hours. Ultimate is $99 per month with unlimited words and 300 hours. A 30-day money-back guarantee applies. Avidnote alternatives Paperguide and Humata are AI tools for reading and chatting with documents, and Litmaps maps citations. Avidnote adds writing, data analysis and transcription.
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What is Txyz.ai? Txyz.ai (TXYZ) is an AI research platform that claims access to millions of papers and over 500,000 users. It helps users discover papers, read them with AI assistance and write with a research-aware agent. Uploaded papers are private to the user. Key capabilities of Txyz.ai Paper search: knowledge discovery across papers. AI-assisted reading: chat with a paper, with answers pointing to document locations. Writing agent and Pages: writing workflows with saved pages. Library: a Starter Library on Free and full Library access on paid plans. File analysis: upload up to 100 pages, or 300 on Ultra. API: AI access for businesses and labs. How Txyz.ai works You upload or find a paper, ask questions in chat and get answers that reference where in the document the evidence appears, so you can verify. The writing agent helps draft in Pages, and deleting a paper removes it from the database. Who uses Txyz.ai? Students, researchers and academics who read many papers. Companies, labs and educational institutions can enquire about API access. Txyz.ai pricing Free costs $0 and includes file upload up to 100 pages and up to 5 writing Pages. Pro is $96 per year and Ultra is $288 per year, with yearly billing marked as saving 20 percent and a monthly option available. Txyz.ai alternatives SciSpace is a research platform with paper discovery, PDF chat and writing tools. Consensus answers research questions with evidence drawn from published studies. Scite shows how later papers cite a study, supporting or contrasting its claims.
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AI research agents gather, analyze, and synthesize information across the web and documents, producing cited reports and answers to complex questions far faster than manual research. This guide explains what research agents are, how they work, what matters, and how to choose one.
AI research agents gather, analyze, and synthesize information across the web and documents, producing cited reports and answers to complex questions far faster than manual research. This guide explains what research agents are, how they work, what matters, and how to choose one.
AI research agents are autonomous or semi-autonomous tools that plan a research task, search across web sources and documents, read and evaluate findings, and synthesize a structured, cited answer or report.
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See where research agents fits in a complete stack, with the other software, AI agents and services each business needs.
They are used for market and competitive research, due diligence, literature review, analyst work, and answering complex questions that require gathering and reasoning over many sources.
Distinct from a single search query, research agents perform multi-step investigation, decomposing a question, iterating across sources, and citing evidence. Buyers weigh source coverage and quality, citation and accuracy, depth of reasoning, and data privacy.
Given a research question, the agent plans sub-questions, searches across the web and connected documents, reads and extracts relevant evidence, and synthesizes findings into a cited report, often iterating to fill gaps.
Platforms combine an LLM, web search and document retrieval, multi-step planning/agent loops, and citation tracking, with guardrails against hallucination and controls over sources.
Users pose a question, optionally connect their own documents and set scope, then review the agent's cited output, verifying sources and refining the query as needed.
Decompose a complex question into sub-questions and investigate each systematically.
Search across the web and your connected documents to gather relevant evidence.
Produce structured, readable reports that summarize and connect findings.
Cite sources for claims so users can verify accuracy and follow up.
Connect your documents and constrain sources for focused, trusted research.
Export reports and integrate findings into docs and workflows.
Compress hours or days of gathering and synthesis into minutes.
Investigate more sources than manual research practically allows.
Get organized, cited reports ready to review and use.
Spend time evaluating findings instead of collecting them.
Run consistent research processes across topics and time.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| General research agents | Web research and reports | Any | Fast, broad, cited | Verify sources and accuracy |
| Document/knowledge research | Research over your documents | Mid-market to enterprise | Grounded in trusted content | Setup and connectors |
| Domain research tools | Market, legal, scientific research | Any | Domain depth and sources | Narrower scope |
| Embedded research assistants | Research inside other tools | Any | In-context, no switching | Depth varies |
Technology: Technology teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Healthcare: Healthcare teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Financial Services: Financial Services teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Retail & E-commerce: Retail & E-commerce teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Education: Education teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Professional Services: Professional Services teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Manufacturing: Manufacturing teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Media: Media teams use AI research agents to run market and competitive research, due diligence, and literature review, gathering and synthesizing many sources into cited reports while keeping humans to verify and decide.
Assess which sources the agent searches and whether they're relevant and trustworthy for your domain.
Verify claims are cited and accurate; test on questions where you know the answer.
Evaluate how well the agent decomposes and investigates complex questions, not just summarizes.
Confirm you can connect your own documents and constrain scope for trusted research.
Check whether your queries and documents are used to train shared models.
Understand usage or seat pricing and how it scales with research volume.
Research agents are getting better at deep, multi-step investigation with transparent reasoning and citations.
Grounding in trusted, current sources and your own documents is improving reliability.
Agents are integrating into analyst and knowledge workflows to produce ready-to-use deliverables.
Buyers should prioritize source quality, citation accuracy, reasoning depth, and data privacy.
An AI research agent is a tool that autonomously plans and carries out a research task, decomposing a complex question, searching across the web and documents, reading and evaluating sources, and synthesizing a structured, cited report or answer. Unlike a single search query, it performs multi-step investigation and citing, used for market research, due diligence, literature review, and analyst work.
They accelerate research dramatically but can hallucinate facts or misattribute citations, so treat output as a fast, reviewable draft rather than ground truth. Choose agents that cite sources you can verify, ground answers in trustworthy or your own documents, and let you check claims. Test on questions where you know the answer before relying on it.
A search engine returns links and a chatbot answers from a single pass. A research agent performs multi-step work: it plans sub-questions, searches and reads many sources iteratively, and synthesizes a cited report. This makes it suited to complex questions that require gathering and reasoning over lots of information rather than a quick lookup.
Many can connect to your documents and knowledge sources so research is grounded in trusted, internal content, and you can constrain scope for focused investigation. This improves reliability and relevance. Confirm the connectors you need and how your documents are secured before connecting sensitive material.
It depends on the vendor. Check whether your queries and connected documents are used to train shared models, where data is processed, and what retention and security policies apply. For sensitive research, look for no-training guarantees and enterprise data controls.
They excel at gathering and synthesizing information at scale, market and competitive analysis, due diligence, literature and background review, and answering complex multi-source questions. They're weakest where sources are sparse or unreliable, or where nuanced judgment is required, so pair them with human verification and expertise.
Common models are usage-based (per research run or tokens) or per-seat subscriptions, sometimes within a broader AI assistant or platform. Estimate your research volume and depth needs, and weigh source coverage and data privacy alongside cost.
Prioritize source coverage and quality for your domain, citation accuracy and anti-hallucination safeguards, depth of multi-step reasoning, the ability to connect your own documents, data privacy, and pricing. Test on real research questions and verify the cited output before relying on it.