Litmaps vs. Agent Bayes: Which One Fits Your Research Needs?

What Litmaps gets right

Most discovery tools answer a question once. Litmaps keeps answering it. Seed a map with papers you already have, watch it expand into the surrounding citation neighbourhood, and then let it monitor for new work that fits. As of 3 August 2026 the Pro tier at 10 USD a month gives unlimited articles and maps with daily configurable alerts, with education pricing on an academic email. The free tier holds 100 articles and one map.

Two design choices deserve credit. Seeding from several papers rather than one handles interdisciplinary topics better than single-seed tools, because your topic may not have a canonical starting paper. And arranging the map by date and citation count communicates how a field actually developed, which a force-directed cluster does not.

Monitoring is the part Agent Bayes has no answer to. If you need to know when something relevant publishes, Litmaps is the tool.

Where it stops

Litmaps works on metadata and the citation graph. It never opens a paper.

So it can tell you a new study appeared, and that it sits close to work you care about, and that it has been cited eleven times. It cannot tell you what the study found, whether its sample supports the claim, or that it contradicts the paper you built your second chapter on.

That last one is the real gap. Alerts create work. Every new paper is a question you now have to answer, and the tool that told you about it cannot help you answer it.

Nor does the map hold anything you concluded. You cannot write a claim into a Litmap, attach evidence to it, or come back in a month to see what you decided and why.

What Agent Bayes does with the papers Litmaps finds

It reads them. Each PDF runs through OCR that preserves multicolumn text, tables, figure captions, footnotes, and headers, then semantic chunking, then distillation into structured bullet points that stand on their own. Those bullet points are always written in English regardless of the source language, so a French or Japanese paper is searchable next to your English ones.

The map holds claims. The agent writes cited claims into a mindmap and you shape it: rewrite any node, restructure branches, attach or remove citations, embed images, and pin nodes as context for the next instruction.

Every claim carries its page. Author, year, printed page range, and the exact chunk of source text, opened in a built-in PDF reader at that page.

New evidence does not quietly overwrite old conclusions. When a paper you just indexed contradicts something already on the map, the competing position appears as a sibling node with its own evidence chain rather than replacing what you had. Version history records what changed, and you can replay the agent's edits with additions, deletions, and word-level rewrites highlighted before you accept them.

Work accumulates. Projects hold several mindmaps, each with conversation history, a rolling version stack for undo and redo, and named snapshots that sit outside that stack and restore as new versions.

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 Litmaps, or the half it does not do?

Litmaps watches the field and tells you something changed. Agent Bayes holds what you concluded, what supports it, and what would have to move if the new paper is right.

Agent Bayes does not search the open literature, does not monitor for new work, and has no free tier. Litmaps covers the first two and costs 10 USD a month.