Compare
How Agent Bayes compares
Ten tools researchers actually use, each measured on the same six things: what corpus it reads, how good its evidence is, what you are left holding, how it handles disagreement, how deeply it reads, and how much control you keep.
Most of these tools are not competitors. Six of the ten do a job Agent Bayes deliberately does not do, and the honest answer on those pages is to use both. The comparisons below say which is which, name what each tool does better than us, and link to the vendor so you can check the details yourself.
Own-corpus research workspaces
NotebookLM vs. Agent Bayes
Google's free notebook is excellent at answering questions about your sources. It is not built to hold a thesis argument together over six months.
Read the comparison →Atlas vs. Agent Bayes
The closest structural competitor. Both read only your own PDFs. The difference is whether the map holds papers or holds claims.
Read the comparison →Answer engines over the open literature
Elicit vs. Agent Bayes
Built for screening thousands of papers into tables. If your problem is depth on 60 papers rather than breadth across 5,000, the shapes do not match.
Read the comparison →Consensus vs. Agent Bayes
The Consensus Meter answers 'does the field agree' across 220 million papers. It does not leave you with anything you can build on.
Read the comparison →SciSpace vs. Agent Bayes
The broadest feature set in the category at the lowest price. Breadth is the product, and depth on any one job is the trade.
Read the comparison →scite.ai vs. Agent Bayes
Classifies 1.2 billion citation statements as supporting, contrasting, or mentioning. Answers a question Agent Bayes cannot, and does not answer the one it can.
Read the comparison →Discovery and citation mapping
Connected Papers vs. Agent Bayes
The fastest way to see the shape of an unfamiliar subfield. It never opens a PDF, which is where Agent Bayes starts.
Read the comparison →ResearchRabbit vs. Agent Bayes
Free, excellent at finding papers and watching a topic. It stops at the abstract, which is where the reading problem starts.
Read the comparison →Litmaps vs. Agent Bayes
Seed a map, then get told when relevant new work appears. Excellent at staying current, and it never reads a paper.
Read the comparison →General research agents
Or skip the reading and try it on your own papers
Agent Bayes only answers from the library you build. Upload a handful of PDFs, or send them across from Zotero, and see what the agent can defend.