Alternative to Elicit
Elicit vs. Agent Bayes: Which One Fits Your Academic Workflow?
Elicit screens thousands of papers into extraction tables. Agent Bayes builds a citation-grounded mindmap from the library you curated. Which fits your work?
Choose Elicit if
- You are running a formal systematic review and need to screen thousands of papers.
- A table with consistent extracted columns is the deliverable your method requires.
- You need to search the open literature, not just papers you already hold.
- PRISMA-style workflow and screening audit trails matter to your protocol.
- You want paper alerts on a running search.
Choose Agent Bayes if
- You already have the papers and the hard part is working out what they add up to.
- Claims need author, year, and printed page range to survive a supervisor's check.
- Your sources contradict each other and flattening that into a table cell loses the point.
- You want one workspace that grows over months, with version history and snapshots.
- Your corpus includes non-English papers.
- You would rather pay for what you use than a flat 49 USD a month.
Elicit and Agent Bayes at a glance
| Axis | Elicit | Agent Bayes |
|---|---|---|
| What it reads | An index of over 138 million papers, plus PDFs you upload on paid tiers. | Only the knowledge bases you built. Retrieval never leaves your curated corpus. |
| Output shape | Extraction tables, research reports, and a systematic review workflow. | A mindmap of cited claims you and the agent develop together, plus prose synthesis on request. |
| Does the work accumulate | The unit of work is a query and its table. | The unit of work is a project. Multiple mindmaps, each with conversation history, version stack, and snapshots. |
| Citation quality | Cells and answers cite the source paper, with source viewing on paid tiers. | Author, year, printed page range, and the exact chunk, opened in a built-in PDF reader at that page. |
| When sources disagree | Recorded per row. Synthesis across rows is left to you. | Preserved as sibling nodes with separate evidence chains, and expandable into a cited prose discussion. |
| Screening scale | 5,000 papers on Pro, up to 40,000 on Enterprise. | Not a screening tool. Built for depth over a corpus you already selected. |
| Non-English sources | Index coverage is largely English-language. | Indexed bullet points are always written in English, so any source language is searchable together. |
| Checking your own wording | Not offered. | AI verification scores your claim against its attached citations and tightens phrasing that overstates. |
| Pricing | Free tier, Pro at 49 USD per month, Scale at 169 USD per month, Enterprise custom, as of 3 August 2026. | Metered credits charged in 0.25 increments, reserved up front and settled on actual usage, with a ledger you can inspect. |
What Elicit gets right
Elicit is a serious tool built by people who understand evidence synthesis, and the extraction table is the right idea. Give it a question, let it screen the literature, define your columns, and get back a grid where every row is a paper and every column is a field you specified. For a systematic review that is exactly the artifact the method demands, and no mindmap will replace it.
The scale is real. As of 3 August 2026 the Pro tier at 49 USD a month screens 5,000 papers and runs a dedicated systematic review workflow, and Enterprise reaches 40,000 papers with 40 columns and PRISMA-grade accuracy claims. The free tier gives unlimited search across more than 138 million papers, unlimited summaries, and Zotero import, which is more than most free tiers offer.
Elicit also searches. Agent Bayes does not. If you are starting a project with no library, Elicit finds the papers and Agent Bayes cannot.
Where researchers hit the limit
A table is a shape, and shapes lose things. The moment two papers disagree about a mechanism rather than a number, the disagreement has to fit in a cell or it does not survive. Extraction is excellent at "what sample size, what effect, what population" and poor at "these two schools of thought have been arguing since 1998 and here is where the argument actually turns".
Work does not accumulate. You run a query, you get a table, you export it. Come back in three weeks with a sharper question and you run another query and get another table. There is no single place where your understanding of the field is developing, no version history over an argument, and no record of what you concluded last month and why.
The corpus is not yours. Searching 138 million papers sounds like an advantage until you notice that the tool decided which twelve of them to put in your table. You did not vet the journals. You did not decide the inclusion criteria before the retrieval ran. For a screening workflow with explicit protocol that is fine, because the protocol does the vetting. For everyday synthesis it means your evidence base is chosen by a ranker.
Provenance stops at the paper. A citation to a paper is not a citation to the passage that supports the claim. When your supervisor asks which page, you go looking.
How Agent Bayes approaches it differently
Agent Bayes starts from the opposite end. You decide what goes into a knowledge base. Every retrieval the agent performs stays inside it, without exception, and you can narrow it further with document tags, author, year, or publication filters.
Then indexing does real work on each PDF. OCR preserves multicolumn layout, tables, figure captions, and footnotes. The document is segmented into semantic chunks and each chunk is distilled into structured bullet points written in English regardless of the source language, so a French or Japanese paper is searchable next to an English one.
The output is a mindmap you and the agent build together. Ask it to expand a branch, investigate an angle, or strengthen a claim with more evidence. Then edit any node yourself, move branches, attach or remove citations, and pin nodes as context for the next instruction. Every claim links to author, year, printed page range, and the exact chunk, and clicking it opens the PDF at that page.
When sources conflict, the agent is built not to average them. Competing positions become sibling nodes with separate evidence chains, and you can ask for a prose discussion that walks through each school of thought and shows where the evidence genuinely diverges.
The work persists. Multiple mindmaps per project, conversation history on each, a rolling version stack with undo and redo, named snapshots that restore as new versions, and provenance recorded for every change whether the agent made it or you did.
And you can check yourself. Select nodes and Agent Bayes reads the full text behind each attached citation, scores how well the evidence supports the claim as written, and tightens phrasing that overstates.
Alternative to Elicit, or a different job?
For a systematic review, yes, and the division is obvious. Screen in Elicit, because 5,000 papers is its job. Then index the included studies in Agent Bayes and build the synthesis there, because every claim in a published review needs a page you can point to.
The honest caveats: Agent Bayes does not search the open literature, does not run paper alerts, does not screen at Elicit's scale, and does not write your draft. It produces the structure and the cited claims you write from.
Questions researchers ask about Elicit
Is Agent Bayes an Elicit alternative?
Only for part of what Elicit does. If you use Elicit to screen a large candidate set into an extraction table, Agent Bayes does not replace that and is not trying to. If you use Elicit to understand a body of literature you already collected, Agent Bayes covers that job with better provenance and an artifact that persists.
Can Agent Bayes do systematic reviews?
Not the screening half. Elicit's Pro tier screens 5,000 papers and Enterprise reaches 40,000, and that is not our shape. Agent Bayes helps with the synthesis half, once the included studies are decided: mapping what they found, keeping conflicting results apart, and tracing every statement back to a page.
Why does page-level citation matter if Elicit already links the paper?
Because a link to a paper is not evidence that the paper says what the tool claims. Agent Bayes stores the exact chunk of source text behind each claim and the printed page range it sits on, so you can check it in one click and cite it without hunting. When a reviewer asks where a sentence came from, that difference is the whole conversation.
Which is cheaper?
It depends on volume. Elicit Pro is 49 USD a month whether you use it or not. Agent Bayes meters credits per action, so a light month costs less and recurring credits carry over for two months before expiring. Indexing an average 20 to 30 page PDF costs about 20 credits and a typical research query costs 10 to 20.
Can I use both?
Yes, and for a systematic review that is the sensible arrangement. Screen in Elicit, then index the included studies in Agent Bayes and build the synthesis where every claim carries its page.
Bring your own papers and see what they actually say
Index your library, ask the agent to map it, and open any claim at the page it came from. Zotero users can send papers across without re-uploading anything.