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52 Listings in Research Agents Available
What is Causaly? Causaly is a biomedical research AI agent offering an AI research platform that maps cause-and-effect relationships across biomedical literature. Founded in 2018 and based in London, United Kingdom, Causaly helps pharma and biotech R&D teams automate biomedical research work and get results faster. Key capabilities of Causaly Biomedical knowledge graph Target discovery Evidence summaries Hypothesis generation Source-linked answers Report generation How Causaly works Causaly takes text and scientific literature as input and produces text and insights. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Slack, Microsoft 365, Google Drive and Salesforce, so the agent works inside existing workflows. Who uses Causaly? Causaly is built for pharma and biotech R&D teams. It suits teams that want biomedical knowledge graph and target discovery without adding headcount, while keeping people in control of review and final decisions. Causaly vs BenchSci Causaly is often compared with BenchSci. Causaly stands out for biomedical knowledge graph and evidence summaries. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Keenious? Keenious is an AI research tool used by universities that helps students, researchers and faculty discover, analyze and manage academic sources. It offers Microsoft Word and Google Docs extensions. Key capabilities of Keenious Source discovery: Finds research relevant to your text Word extension: Works inside Microsoft Word Google Docs extension: Works inside Google Docs AI analysis: AI responses about sources File upload: Upload files up to 3 MB free and 20 MB on Plus Export: Plus exports results as RIS, BibTeX and more How Keenious works You write or paste text and Keenious returns relevant academic sources. You can chat with AI about results, upload files and, on Plus, export references in formats such as RIS and BibTeX. Who uses Keenious? Students, researchers and faculty, with institutional plans that add SSO, SAML, IP-based authentication and library integration. Keenious pricing The Free plan costs $0 and includes 5 AI responses per conversation and 3 conversations per day. Plus is billed annually with unlimited responses and conversations and a 30 percent student discount. Institutional plans are quoted. Keenious alternatives Alternatives include Scite and Litmaps for citation research, Scholarcy for summaries and Causaly for biomedical research. Keenious works from your writing.
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What is Sourcely? Sourcely is a source finder AI agent offering an AI tool that finds academic sources to support essays and research from your text. Sourcely helps students automate source finder work and get results faster. Key capabilities of Sourcely Source discovery from text Summaries Citation export Credibility filters Source-cited answers Reference manager export How Sourcely works Sourcely takes text as input and produces citations and text. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Zotero, Mendeley, PubMed and arXiv, so the agent works inside existing workflows. Who uses Sourcely? Sourcely is built for students. It suits teams that want source discovery from text and summaries without adding headcount, while keeping people in control of review and final decisions. Sourcely vs Consensus Sourcely is often compared with Consensus. Sourcely stands out for source discovery from text and citation export. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, run by Ai2 (the Allen Institute for AI), a non-profit research institute founded in 2014 by Microsoft co-founder Paul Allen. It indexes over 200 million academic papers. Key capabilities of Semantic Scholar Paper search: searches over 200 million papers across scientific fields AI relevance ranking: orders results using AI signals Semantic Reader: augmented reading experience for papers Citation and influence analysis: shows how papers cite and influence each other Scholar's Hub: personalized research feed Developer API: access to the Semantic Scholar Academic Graph dataset How Semantic Scholar works Papers come from publisher partnerships, data providers and web crawls. You search, read results ranked with AI, open papers in the Semantic Reader, and explore citations. Developers use the API to retrieve the Academic Graph, and researchers use the S2ORC corpus. The service is free. Who uses Semantic Scholar? Researchers, students and scientists use it to find and read literature, and developers and academics use the API and datasets for research projects. Semantic Scholar pricing Semantic Scholar is completely free to use. There are no paid plans listed, and API access and datasets are provided for research purposes under Ai2 terms. Semantic Scholar alternatives Alternatives include Elicit for AI-assisted literature review, Consensus for evidence-based answers from papers, SciSpace for paper explanation, Scite for citation context, and Scholarcy for summaries.
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What is Latent Labs? Latent Labs is a protein design AI agent offering a frontier AI lab whose Latent-X web platform lets scientists design novel protein binders computationally. Founded in 2023 and based in London, United Kingdom, Latent Labs helps biologists and drug discovery teams automate protein design work and get results faster. Key capabilities of Latent Labs De novo binder design Structure prediction Web-based design Lab-validated models Model-driven design Cloud compute workflows How Latent Labs works Latent Labs takes sequences and structures as input and produces protein designs. It is powered by Latent Labs models, with the vendor managing prompts, models and updates. It connects to tools such as Python, Jupyter, PyMOL and Benchling, so the agent works inside existing workflows. Who uses Latent Labs? Latent Labs is built for biologists and drug discovery teams. It suits teams that want de novo binder design and structure prediction without adding headcount, while keeping people in control of review and final decisions. Latent Labs vs EvolutionaryScale Latent Labs is often compared with EvolutionaryScale. Latent Labs stands out for de novo binder design and web-based design. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Sharly AI? Sharly AI is a document summarization AI agent offering an AI document assistant that summarizes and answers questions across long documents and links. Sharly AI helps professionals and researchers automate document summarization work and get results faster. Key capabilities of Sharly AI Document summaries Multi-file chat Cross-document comparison Citations Cited document answers Multi-document chat How Sharly AI works Sharly AI takes documents and links as input and produces text and summaries. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Google Drive, Dropbox, Chrome extension and Web browsers, so the agent works inside existing workflows. Who uses Sharly AI? Sharly AI is built for professionals and researchers. It suits teams that want document summaries and multi-file chat without adding headcount, while keeping people in control of review and final decisions. Sharly AI vs Humata Sharly AI is often compared with Humata. Sharly AI stands out for document summaries and cross-document comparison. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is You.com? You.com is a search AI agent offering an AI search and research platform with research agents and enterprise APIs. Founded in 2020 and based in Palo Alto, California, USA, You.com helps professionals and enterprises automate search work and get results faster. Key capabilities of You.com AI search with citations Research agents Model choice Web search APIs Cited answers Export to reference managers How You.com works You.com takes text and files as input and produces text. It is powered by Multiple LLMs (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Zotero, Mendeley, Google Scholar and PubMed, so the agent works inside existing workflows. Who uses You.com? You.com is built for professionals and enterprises. It suits teams that want AI search with citations and research agents without adding headcount, while keeping people in control of review and final decisions. You.com vs Perplexity You.com is often compared with Perplexity. You.com stands out for AI search with citations and model choice. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Polymerize? Polymerize is an AI-native platform for materials and chemicals R&D that unifies data and predicts and designs formulations. Key capabilities of Polymerize Polymerize Connect: Centralizes experimental data across global teams. Polymerize Labs: 35+ domain-specific models for property prediction and formulation optimization. Polymerize One: Platform plus prototyping networks and expert consultation. Explainable AI: Recommendations with transparency and confidence scores. How Polymerize works The system unifies fragmented experimental data, applies domain-trained models built for polymers and chemicals, and returns explainable recommendations with confidence scores. Security includes end-to-end encryption and role-based access controls. Who uses Polymerize? R&D teams in materials and chemicals. The vendor cites 500+ teams across 12 industries including REHAU, Maxell, Samyang and Meraxis, and reports 50% fewer failed experiments within 3 months. Polymerize pricing Pricing is not disclosed. Companies request a demo. Polymerize alternatives Rowan and Tamarind Bio offer computational chemistry and biology tooling, and Semantic Scholar and Connected Papers help with literature. Polymerize focuses on formulations.
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What is Citrine Informatics? Citrine Informatics is an enterprise SaaS platform that applies generative AI and materials science to product development in chemicals and materials. It has three components: Citrine DataManager, Citrine VirtualLab and Citrine Catalyst. Key capabilities of Citrine Informatics Citrine DataManager: Captures organizational knowledge and data. Citrine VirtualLab: Runs virtual experiments and property specification. Citrine Catalyst: Digital assistance with research citations. Candidate search: Runs thousands of simulations to find promising candidates. Onboarding services: Professional data organization help. Cross-functional use: Extends to sales, supply chain, finance and compliance. How Citrine Informatics works Teams consolidate data in DataManager, define target properties, and use VirtualLab to run thousands of simulations and rank candidate materials or formulations. Catalyst answers questions with citations. The vendor claims companies can develop products 5 times faster. Who uses Citrine Informatics? R&D teams in chemicals, coatings, plastics, automotive, batteries and aerospace across 13+ verticals. Named customers include GM, LyondellBasell, Evonik, Rolls Royce, Lanxess and TotalEnergies. Citrine Informatics pricing Citrine does not publish prices. Deployment is customized and requires a demo request. Citrine Informatics alternatives Polymerize and MaterialsZone are materials informatics platforms, while Edison Scientific, Genspark and You.com are general research tools. Citrine is built for materials R&D.
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What is AnswerThis? AnswerThis is a literature review AI agent offering an AI research tool that answers questions with citations from academic papers and drafts literature reviews. AnswerThis helps researchers and graduate students automate literature review work and get results faster. Key capabilities of AnswerThis Cited answers Literature reviews Citation maps Paper library Cited answers from papers Literature synthesis How AnswerThis works AnswerThis takes text as input and produces text and citations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as arXiv, PubMed, Zotero and Google Scholar, so the agent works inside existing workflows. Who uses AnswerThis? AnswerThis is built for researchers and graduate students. It suits teams that want cited answers and literature reviews without adding headcount, while keeping people in control of review and final decisions. AnswerThis vs SciSpace AnswerThis is often compared with SciSpace. AnswerThis stands out for cited answers and citation maps. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Undermind? Undermind is an AI co-researcher that helps scientists find academic papers they might otherwise miss. It was founded by MIT quantum physics PhDs and is backed by Y Combinator, and it analyzes full texts rather than only abstracts. Key capabilities of Undermind Clarifying questions: asks about your research before searching Deep iterative search: evaluates hundreds of papers and follows citation trails Full-text analysis: reads papers beyond abstracts on Pro Research reports: iterates on reports and explores full texts New publication alerts: notifies you about relevant new papers MCP connection: agents from Claude and ChatGPT can use it How Undermind works The workflow has four steps: describe your research and answer clarifying questions, let Undermind explore and evaluate hundreds of papers by following citations, build and refine reports while reading full texts, and keep up with notifications. A typical search takes a few minutes, and the vendor reports its v2 engine found 85% of the 20 most relevant papers in 10 minutes in its own benchmark. Who uses Undermind? Scientists and academic researchers doing literature discovery and reviews use it. Teams can share a Team plan with management features and priority support. Undermind pricing Free costs $0 with basic chats and standard rate limits. Pro is $16 a month billed annually with advanced models, full-text analysis and 10x higher limits. Team is $15 per person a month billed annually. Enterprise is custom with SSO and dedicated support. Undermind alternatives Alternatives include Consensus for evidence summaries from papers, SciSpace for paper explanation, Scite for citation context, ResearchRabbit for visual literature mapping, and Scholarcy for article summaries.
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What is Edison Scientific? Edison Scientific offers Kosmos, an AI platform that acts as an embedded scientist to speed drug development and scientific R&D across the research pipeline. Key capabilities of Edison Scientific Scientific investigation: Reads literature, analyzes data, generates hypotheses and executes workflows autonomously. Full lifecycle support: Spans target identification through clinical development and regulatory filing. Team collaboration: Works through Slack, Teams and email. Organizational learning: Builds on proprietary data, experiment history and decision patterns. Model agnostic: Adapts to new frontier AI models. How Edison Scientific works Kosmos runs as an embedded agent that collaborates with scientists, steering investigations in real time and surfacing findings as researchers work. A customer quote on the site describes about 292 independent analysis trajectories executed within 24 hours. Forward-deployed scientists work inside client teams, and an on-premises deployment option is offered. Who uses Edison Scientific? Pharma, biotech and academic research groups. Named customers include Incyte Corporation, Population Health Partners, MIT, the Broad Institute, the UK Dementia Research Institute and Springer Nature. Edison Scientific pricing No pricing or tiers are published. Access is arranged with the vendor, and no free plan is listed. Edison Scientific alternatives Elicit and Consensus are AI research assistants focused on literature search, while Rowan and Tamarind Bio provide computational chemistry and biology tooling. Edison targets autonomous end-to-end investigations.
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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.
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.
