Write down which research job you need covered before you look at any tool: finding papers, mapping how they connect, running a formal review or reusing open data. Most alternatives do one of those well rather than all four. Run one real question through two tools from the matching group and compare what each finds.
A good research tool earns its place on the day your reading list stops growing and starts making sense. If you're leaving Elicit, you'll know whether it did that for you or not. The ten alternatives below are for students, academics and researchers who want another way to find, map and read papers, and the trial method comes first, because watching two tools handle a question you already know well is the quickest way to tell them apart.
An afternoon trial with a question you know
- Pick a research question you've already worked on, where you know the key papers by heart.
- Write down the five papers any decent search should find, and one obscure paper only a thorough search would turn up.
- Run the question through two tools from the group that matches your job, using the same wording in each.
- Check each result list against your five papers and your obscure one, and note anything new that looks relevant.
- Open three of the cited sources in each tool and make sure the summary matches what the paper says.
- Export a few references into your reference manager and see whether they arrive clean.
That last check catches more problems than you'd think. A tool that finds the right papers but sends messy references into your library will eat hours at the writing stage.
What changed about the tool you're leaving
Part of the reason people look around is that the tool they started with has changed shape. Its own homepage now pitches it as AI for research and decision-making, covering evidence synthesis, designing experiments, drafting proposals and weighing tradeoffs. That's a much wider promise than finding and summarising papers, and it may or may not be what you need.
Its plans page lists a free Basic tier with limited usage of its Research Agent and Research Reports features. If you were relying on those every day, limited usage is the line to read closely, because it's often where a free account stops covering real research work.
The other half of the story is that many alternatives don't try to do everything. Several do one narrow job, such as drawing a citation map from a few seed papers or offering open metadata you can download and reuse. A narrow tool can be the better choice if that one job is the part you care about, and it can sit next to whatever search tool you already use.
So the honest question isn't which tool replaces it. It's which of its jobs you were using, and whether one tool or two would cover them better.
Thesis and review advice: use two tools
Use two tools, not one: a search tool to find papers and a map tool to see how they connect. That pairing covers most of what students and early-career researchers need, and it means no single tool decides what you read. Always open the paper itself before you cite it; an AI summary is a reason to read, never a replacement for reading.
If you want a wider look at this kind of software, my guide to AI research assistants covers more general tools, and these deep research prompts help you get better answers from whichever one you settle on.
Search, maps, reviews and open data: four jobs
AI search. Consensus, Semantic Scholar and Undermind all help you find papers from a question. They differ in depth: a quick search engine for a first pass, an AI search engine focused on peer-reviewed work, and a slower deep search that reads full texts.
Citation maps. ResearchRabbit, Litmaps and Connected Papers start from papers you already have and show you what's connected to them. They're for the stage when you know a few key works and want to see the field around them.
Systematic reviews and agents. Paperguide is built for literature and systematic reviews with reference management. Asta and Iris.ai lean on AI agents and controlled models, which suits teams working at scale more than a student with one essay.
Open data. OpenAlex is a catalogue rather than a reading tool. It's the one for anyone who needs to count, filter or analyse scholarly works through an API or a bulk download.
Ten research tools at a glance
| Tool | Type of tool | Who it helps |
|---|---|---|
| Asta | Research agents | Trying AI agents on scientific questions |
| Connected Papers | Visual paper graphs | Seeing related papers as a graph |
| Consensus | AI academic search | Asking questions of peer-reviewed research |
| Iris.ai | Enterprise research AI | Teams that need control over what AI reads |
| Litmaps | Citation mapping | Watching a field grow out from seed papers |
| OpenAlex | Open scholarly data | Reusing catalogue data through an API or download |
| Paperguide | Review platform | Systematic reviews with reference management |
| ResearchRabbit | Citation discovery | Finding related papers from ones you rate |
| Semantic Scholar | Academic search engine | Free searching of scientific literature |
| Undermind | Deep literature search | Thorough searches where a missed paper matters |
All ten, in alphabetical order
In no particular order beyond the alphabet. Each line reflects what the tool's own makers say it does.
Asta
Best for: Trying AI agents on scientific research inside Ai2's agentic ecosystem, which the vendor says advances scientific discovery.
Connected Papers
Best for: Visual browsing for papers related to your field of work, aimed, the vendor says, at researchers and applied scientists.
Consensus
Best for: Asking plain questions of peer-reviewed literature through an AI academic search engine built for finding, organising and analysing science.
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Iris.ai
Best for: Expert teams who want control over what a model reads and a measure of how it answers; the vendor says AI needs an expert harness.
Litmaps
Best for: Mapping papers out from a few seed articles with visualisations; the vendor says it speeds up the whole literature review.
OpenAlex
Best for: Researchers who want an open catalogue of works, authors, institutions and topics, with a web app, an API and bulk download.
Paperguide
Best for: Literature and systematic reviews with research agents and reference management built in; the vendor stresses that its systematic reviews are auditable.
ResearchRabbit
Best for: Building citation maps and tracking research trends from papers you already rate, which the vendor says finds research you've been missing.
Semantic Scholar
Best for: A free, AI-powered search across scientific literature, in the project's own words, for a quick first pass on any topic.
Undermind
Best for: Deep searches that read full texts and cite every source, per the vendor, for questions where a missed paper hurts.
Keeping your library when you switch
Whatever you move to, your reference library is the thing to protect. Export it from your reference manager before you change anything, and keep your notes and highlights somewhere that doesn't depend on any one research tool. Saved searches and collections inside a tool rarely travel with you, so write down the search wording that worked and the seed papers you started from. That way, rebuilding a map or a search in a new tool takes minutes rather than an afternoon.
Do you make academic research or literature search tools? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What's a good free alternative for finding papers?
Several tools in this list offer a free plan or are free to use. Start with a free academic search engine for a first pass, then add a citation map tool once you have a few key papers.
What's the difference between AI search and a citation map?
AI search starts from a question and finds papers. A citation map starts from papers you already have and shows what's connected to them. Most literature reviews need both.
Can AI tools run a systematic review for me?
Some platforms are built to support systematic reviews and keep the steps auditable, but you still set the question, the criteria and the final decisions. Treat the tool as help, not the reviewer.
How do I check that an AI summary is accurate?
Open the cited paper and read the part the summary refers to. If the claim isn't there, don't use it. Do this for every source you plan to cite.
What's open scholarly data used for?
It's used to count, filter and analyse research at scale, for example to see who publishes on a topic or how a field has grown. You reach it through a web app, an API or a bulk download.
