Note: We make Atlas. This is a comparison written by the team that built it, not a neutral third-party review. Where Heptabase has the better answer for a given research job, the article says so plainly. See the table rows where Heptabase wins and the "When to choose Heptabase" section below. The goal is to give you the data you need to choose the right tool for the kind of work in front of you, not to convince you Atlas is the answer to every research job.
Atlas is a visual research workspace for people whose work depends on understanding a body of papers: a thesis, a treatment decision, a major-purchase teardown, a literature review. Heptabase is a visual note-taking tool: card-based notes arranged on whiteboards, where each card can hold rich content and the whiteboard is the unit of thinking, designed for visual learners and researchers who think spatially. Both tools touch a researcher's daily work; the wedge is what happens after the first answer. Atlas deconstructs each paper into a Knowledge Map (a visual map of the argument), projects a whole corpus into a Semantic Map, runs every answer through claim-source-justification (the citation-grounded surface that explains why a passage supports a claim), and compounds prior work into a persistent knowledge graph so projects get smarter the longer you use Atlas. Heptabase's brand, design, and integration with whiteboard-based thinking are genuinely strong, the visual card-on-whiteboard paradigm is well-executed for users who think spatially, and the export-to-markdown workflow is clean. If you need to trust the answers (for a thesis, a treatment plan, a brief, a hire), the visual maps, claim-source-justification, and compounding graph are where Atlas earns the comparison.
How is Atlas different?
Heptabase and Atlas overlap at the surface: both touch the work of reading and reasoning over sources. But they diverge on three capabilities that decide whether the output is shareable, defensible work. This section walks through the three differences, in order.
1. Visual maps of every paper and project
Atlas builds two kinds of visual map automatically as you read. A Knowledge Map deconstructs each paper into its argument structure: claims, evidence, definitions, and labeled relations between them (motivates, causes, enables, contradicts), laid out as a multi-level zoom. You see the paper's spine at the top level and drop into the supporting passages with a click. A Semantic Map projects your whole project (sources, notes, chats, citations) into a spatial canvas where related items cluster by topic, and you can re-project the same canvas under a new topic angle without re-reading anything. The Semantic Map is how 200 papers stop being a folder and start being a corpus.
"It's like an ultimate GPT. I can finally see what I've read." Kyle Lao, NUS researcher
Heptabase does not have a per-paper claim-evidence deconstruction or a topic-angle re-projection across an entire project. If you've ever spent an afternoon trying to recover the structure of a paper you read three weeks ago, the Knowledge Map is the surface that pays for itself first. Visual maps make a body of papers legible at a glance, and the multi-level zoom of the Knowledge Map is the surface Atlas is built around.
2. Every claim traces to a source, and Atlas explains why the source supports it
The hallucination problem in AI research tools isn't "the model made something up." It's "the model put a citation next to a claim that the cited passage doesn't justify." Atlas renders every answer as a claim-source-justification triple: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can click into the source paragraph and read the highlighted sentences in context.
The benchmark Atlas runs internally is the H/V ratio: the proportion of generated sentences whose citation does not survive a passage-level re-check, divided by the proportion that does. Atlas targets H/V < 0.1 on the citation-grounding benchmark, and we publish how the benchmark is constructed in Verifiable AI Research (2026): What It Actually Means. Heptabase's answers may include citations or links to sources, but they're grounded at the sentence-citation level (or not at all), not at the claim-justification level. For most casual question-answering the gap doesn't matter. For a thesis sentence, a legal brief paragraph, or a treatment-decision summary, it does. The wedge in one sentence: every claim traces to its source, and Atlas explains why the source justifies it.
3. Your projects compound: the second month is 10× the first
Heptabase treats each session (or project, or workspace) as a separable container: work goes in, an answer comes out, and the next session starts fresh. Atlas builds a persistent per-user knowledge graph across projects: every citation you jump to, every annotation you make, every Knowledge Map and Semantic Map you generate accumulates into a four-layer graph (citations + mentions + KMs + SMs) that the next chat can draw from. Open a new project on a related topic and Atlas can pull in the relevant sources, prior annotations, and chat history without re-ingesting.
This is the capability we hear about most from long-term users: the second month is 10× the first because the graph has something to work with. John Tan, a postdoc using Atlas for a multi-year literature review, describes it as "the only tool where the work I did last semester is still doing work for me this semester." Put plainly: projects get smarter the longer you use Atlas. Heptabase does not have an equivalent persistent compounding graph across projects, which is the wedge for sustained, multi-month research.
Try Atlas: Sign up for an evaluation sample (10 sources · 5 lifetime AI chats) and run a Knowledge Map on one of your own papers. Used by researchers at NUS, NTU, SMU, and eight other universities.
Comparing Atlas and Heptabase
Both Atlas and Heptabase touch a researcher's daily work, but they live in different categories. Atlas spans paper deconstruction, project navigation, source-cited AI answers, and compounding context across a research corpus; Heptabase spans card-based notes on whiteboards with PDF annotation and visual organisation. Heptabase's integration with whiteboard-based visual thinking is broader; Atlas's research depth at the citation surface is deeper. The rest of this article walks through the five capability surfaces where the two tools differ: per-paper deconstruction, project-level navigation, source-cited answering, literature-grounded annotations, and compounding context across projects. Each section is a two-column table where every row is a real capability, and at least one row in each table is one where Heptabase wins or ties.
Paper deconstruction (Knowledge Map)
The Knowledge Map is Atlas's per-paper surface. It deconstructs a single paper into a multi-level argument structure with labeled relations between claims, faithful-to-source nodes (the node text comes from the paper, not from a generated summary), and hierarchical breadcrumbs that let you read down from the high-level thesis to a specific paragraph.
| Atlas | Heptabase |
|---|---|
| Multi-level argument structure ✓ | Card-based notes per paper with PDF highlight extraction |
| Labeled relations (motivates, causes, enables) ✓ | ✗ |
| Faithful-to-source node text ✓ | ✗ |
| Hierarchical breadcrumbs ✓ | ✗ |
| ✗ | Whiteboard with cards arranged spatially ✓. canvas, not auto-deconstruction |
Good to know: The bottom row belongs to Heptabase. Atlas does not ship that surface. The Knowledge Map's payoff is recovering a paper's argument three weeks after you first read it, when topic chips alone are no longer enough.
Project / corpus view (Semantic Map)
The Semantic Map is Atlas's per-project surface. It projects all the sources, notes, chats, and citations in a project into a spatial embedding where related items cluster by topic. Re-project the same canvas under a different topic angle without re-ingesting anything.
| Atlas | Heptabase |
|---|---|
| Spatial embedding of sources + notes + chats ✓ | Whiteboard with linked cards |
| Auto-labeled topic clusters ✓ | ✗ |
| Topic-angle re-projection ✓ | ✗ |
| Cross-project view ✓ | ✗ |
| ✗ | Spatial arrangement of cards across boards ✓. arrangement, not reasoning |
Good to know: Heptabase's strength on that row is genuine. If your work depends on it, that's the boundary. The Semantic Map's payoff is when 200 papers stop being a folder and start being a corpus you can re-project under different topic angles without re-reading.
Citation-grounded answers
Atlas produces claim-source-justification triples: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can jump to the source paragraph, read the highlighted sentences, and check whether the reasoning holds.
| Atlas | Heptabase |
|---|---|
| Claim-source-justification triples ✓ | Card-to-highlight backlinks |
| Reasoning traces (why this passage supports this claim) ✓ | ✗ |
| Jump-to-source with passage highlight ✓ | ✗ |
| H/V ratio < 0.1 benchmark published ✓ | ✗ |
| ✗ | PDF highlight extraction onto cards ✓. extraction, not claim-source-justification |
Good to know: Both tools have a citation surface; the wedge is whether the surface explains why a passage justifies a claim, not just which passage was cited. For everyday Q&A the gap is invisible; for a thesis sentence or a brief paragraph it's the whole game.
Literature-grounded annotations
Atlas auto-annotates each paper on ingest. Citations inside the paper become first-class objects: Atlas resolves the cited source (when open-access), pulls the relevant passage, and lets you see how a citation in the paper builds up its argument across multiple sources without leaving the document.
| Atlas | Heptabase |
|---|---|
| Auto-annotate on ingest ✓ | PDF highlights surfaced as cards |
| Multi-citation synthesis (how citations build the argument) ✓ | ✗ |
| Resolve cited sources (open-access) ✓ | ✗ |
| Exact passage / page / paragraph anchors ✓ | ✗ |
| ✗ | Card mind map view per whiteboard ✓. view, not deconstruction depth |
Good to know: Literature-Grounded Annotations resolve citations inside the paper you're reading. When a paper cites a source that's open-access, Atlas pulls in the cited passage. It is not a web-grounding feature; it is a way to see how a single paper builds its argument across the sources it cites.
Compounding context across projects
Atlas builds a four-layer persistent graph (citations + mentions + KMs + SMs) across all your projects, so chats, annotations, and maps from one project become context for the next.
| Atlas | Heptabase |
|---|---|
| Persistent per-user knowledge graph ✓ | Per-whiteboard organisation |
| Citations + mentions + KMs + SMs accumulate ✓ | ✗ |
| Chat history reusable across projects ✓ | ✗ |
| Cross-project source reuse ✓ | ✗ |
| ✗ | Markdown export and offline mode ✓. export, not citation grounding |
Good to know: Compounding is the slowest capability to demonstrate in a demo and the biggest payoff in week eight. If your work is many small, unrelated projects, Heptabase's session-isolated design is the right choice; isolation is a feature, not a gap. Compounding pays off for sustained, multi-month research.
Price comparison
Atlas is a paid product. There is no perpetual no-cost plan; you get a short evaluation sample (10 sources · 5 lifetime AI chats), and after that you pay $20/mo or $204/yr for Atlas Pro. At the paid tier, Atlas is the only tool with Knowledge Map, Semantic Map, claim-source-justification, and compounding graph. You aren't paying for chat tokens; you're paying for capabilities that Heptabase doesn't have at any tier.
| Atlas | Heptabase |
|---|---|
| Free: ✗ (evaluation sample only: 10 sources · 5 lifetime AI chats) | Free: 7-day free trial; no perpetual no-cost plan ✓ |
| Pro: $20/mo or $204/yr (1,000 sources · 1,000 chats/month · all features) | Paid: Plus $11.99/mo or $107.88/yr |
| Pro unlocks Knowledge Map, Semantic Map, claim-source-justification, compounding graph ✓ | ✗ |
When to choose Atlas vs Heptabase
- Want paper structure deconstructed multi-level? Go with Atlas. (Knowledge Map)
- Want answers that explain how each citation justifies the claim? Go with Atlas. (claim-source-justification)
- Want your projects to compound over months? Go with Atlas. (4-layer graph)
- Want a whiteboard with cards arranged spatially for visual thinking? Go with Heptabase.
- Tied: extracting highlights from PDFs onto a visual canvas**: both work fine; Heptabase designed for the canvas-card pattern and Atlas designed for the Knowledge Map form. The wedge only opens up once you're building a corpus you'll return to.
Recommendations by user type
- PhD researchers: Atlas. Lit-review-heavy years 1–2 benefit most from the Knowledge Map (deconstruct each paper without re-reading). Thesis-writing years 3–4 benefit from claim-source-justification (every thesis sentence anchored to a passage). Heptabase works for one-off tasks; the multi-year compounding graph is what makes Atlas the right tool here.
- Students doing literature reviews and thesis research: Atlas, scoped to research workflows (dissertation, thesis, literature review). The Knowledge Map is the largest time-saver in the lit-review phase, and the compounding graph keeps prior work accessible across semesters.
- Knowledge workers (consultants, analysts, PMs, journalists): Atlas when claim-source-justification and a compounding graph matter; Heptabase when free-form whiteboard spatial arrangement of cards matters more.
- Personal researchers with stakes (medical, legal, major-purchase, deep autodidact): Atlas. Burst-usage research where the stakes are high (medical, legal, major-purchase, deep autodidact) is exactly where citation-grounded reasoning earns its keep. Heptabase is a fine starting tool; Atlas is the tool you graduate to once you realize you'll need to defend the answer.
The honest one-liner across all four segments: if the research compounds, Atlas is the bet; if each session is self-contained and the next one starts fresh, Heptabase's form is genuinely the better fit, and we'll say so plainly. The expensive mistake is using a session-isolated tool for compounding work (every project pays the re-ingestion tax) or using a corpus tool for one-off questions where simpler tools are faster. A useful diagnostic: ask whether you expect to come back to the same corpus in three months. If yes, the project-graph approach carries its weight; if no, lighter tools win on friction. Most research workflows we hear from at universities (Cambridge, Harvard, MIT, Stanford) sit firmly on the "yes" side: the corpus is the same corpus across semesters, advisors, and grant cycles, which is the cohort Atlas is built for. The corollary is that picking the right tool is mostly a question about your work pattern, not a question about which feature list is longer; both tools do their job well within the form they're built for.
Migrating from Heptabase to Atlas
Heptabase is built around two primitives: cards (rich-text blocks that hold notes, highlights, or PDF excerpts) and whiteboards (the canvases where you arrange those cards spatially, draw arrows between them, and group them into sections). Exports leave the Heptabase app as a folder of markdown files plus an attachments/ directory containing the original PDFs you imported. The export is clean, well-structured, and easy to script against, which makes the migration itself a fairly mechanical exercise rather than a research project in its own right.
What migrates well into Atlas: card text (each card becomes a source paragraph or a note you can paste into a project), journal entries (Heptabase's daily-note feature exports as one markdown file per day, which Atlas treats as ingested context once uploaded), and the underlying PDFs themselves (the attachments folder is the high-value payload, because every PDF you re-upload to Atlas gets a Knowledge Map on ingest, recovering the argument structure that Heptabase did not extract). PDF annotations and highlights survive the round-trip as plain-text excerpts, so the substance of what you marked up is preserved even if the visual highlight layer is not.
What does not migrate: the whiteboard spatial layout (where you placed each card on the canvas), the arrows you drew between cards on a whiteboard, the section groupings (coloured rectangles around clusters of cards), and the per-whiteboard mind-map view. Atlas's visual surfaces are the Knowledge Map (per paper, argument-structured) and the Semantic Map (per project, embedding-projected), not free-form canvases. There is no one-to-one mapping for "this card sits 200 pixels right of that card and is connected by a green arrow." If your Heptabase corpus encodes meaning in the spatial layout itself, that meaning is what you will need to manually re-encode in Atlas, usually as project notes describing the relationships, or by relying on the Semantic Map's auto-clustering to surface the same groupings via topic similarity rather than your manual placement. Most migrators describe this as a one-afternoon exercise: re-upload the PDFs, paste card markdown as notes, and let Atlas deconstruct the corpus from scratch.
A worked example: literature-review section from 8 papers
Imagine the same job in both tools: you have eight papers on transformer interpretability, and you need to write the 600-word "prior work" section of a paper that motivates a new sparse-autoencoder method. The output has to cite each source, position your contribution against them, and be defensible to a reviewer who will check the citations.
In Heptabase, the workflow is card-driven and spatial. You import each PDF as a source, read it, highlight passages, and pull each highlight onto a card on a new whiteboard. Once all eight papers are processed, you arrange the cards spatially: cluster by method (probing classifiers in one corner, dictionary learning in another, circuit analysis along the bottom edge), draw arrows where one paper builds on another, and write a synthesis card in the centre summarising the lineage. The visual layout is the thinking. When you sit down to write, you read the whiteboard left to right and translate the spatial structure into prose. The strength of this workflow is that the spatial arrangement itself encodes your interpretation; the weakness is that re-reading each highlight to recover the argument context takes time, and the citations in your prose are anchored only to the highlight you pulled out, not to a reasoning trace explaining why that highlight justifies your sentence.
In Atlas, the workflow is map-driven and citation-grounded. You upload the eight PDFs and Atlas builds a Knowledge Map for each one automatically, surfacing the argument structure (claim nodes, evidence nodes, labelled relations like motivates and contradicts) without you having to read every paper end-to-end first. You skim the eight maps to identify the lineage, then open a chat in the project and ask "summarise how each paper positions itself against probing-classifier baselines, with citations." Atlas returns the answer as claim-source-justification triples: each sentence has a passage attached and a one-sentence explanation of why that passage justifies the claim. You jump to source on the citations you intend to use, verify the highlighted sentences are doing the work, paste the verified prose into your draft, and adjust voice. The Semantic Map of the project shows the same eight papers clustered by topic, so you can spot the gap your contribution fills. The total time is dominated by reading the maps and verifying citations, not by manual highlight extraction, and the citations in your draft are anchored to a reasoning trace your reviewer can audit.
The two workflows produce comparable section quality; the difference is how the work is structured. Heptabase rewards spatial-visual reasoning over a small corpus you have personally processed; Atlas rewards argument-structured reasoning over a larger corpus where you need defensible citations.
When Heptabase is the right call
Heptabase is the right tool, and we will say so plainly, in four situations. First, when your thinking is genuinely spatial-visual: if you reason by arranging ideas on a 2D canvas, drawing arrows between them, and re-clustering them as your understanding shifts, Heptabase's whiteboard-with-cards paradigm is best-in-class for that mode. Atlas's Knowledge Map and Semantic Map are structured surfaces (argument-structured and embedding-projected respectively), not free-form canvases you can lay out yourself.
Second, when your workflow is PDF-annotation-first with cards-from-highlights: if the core motion is "highlight a passage, drag it onto a card, place the card next to a related card from another paper," Heptabase has the cleanest implementation of that loop. Atlas extracts citations and produces Knowledge Maps, but the manual highlight-to-card-on-whiteboard motion is not what Atlas is built around.
Third, when you want freeform whiteboard idea organisation outside of papers: brainstorming canvases, mood boards, multi-board projects where the boards themselves are the unit of thought. Atlas is opinionated about projects-as-corpora-of-sources, not projects-as-boards.
Fourth, when you are a single researcher doing visual-thinking workflows on a self-contained corpus you will not revisit in six months: the compounding-graph payoff of Atlas does not earn its keep below a certain corpus-size and recurrence threshold. If your honest answer to "will I come back to this corpus" is no, Heptabase's session-isolated form is the lower-friction tool. The article's bias is toward research that compounds; if your work does not, Heptabase is the right recommendation.
Common objections and edge cases
"I already have 200 cards in Heptabase. Is it worth migrating, or should I just keep both?" Many researchers run both: keep the existing whiteboards in Heptabase for the visual reasoning they already encode, and start new corpus-building work in Atlas where the Knowledge Map and compounding graph matter. There is no integration between the two, so sources have to be uploaded to each separately, but the workflows do not conflict and the cognitive overhead of switching is small once each tool has a clear job. The migration becomes worth doing wholesale only when the cost of dual-tool context-switching exceeds the cost of re-uploading your PDFs, which is usually after several months of accumulating new sources in Atlas.
"My corpus is mostly PDFs with hand-drawn diagrams the OCR layer mangles. Does that affect either tool?" Both tools struggle with PDFs whose text layer is poor or absent (image-only scans of older papers, diagram-heavy slide decks). Heptabase's card workflow at least lets you screenshot the diagram and paste it onto a card manually. Atlas's Knowledge Map relies on a usable text layer to extract claims and evidence, so for diagram-heavy or scanned sources, the map will be sparser. The pragmatic move is to OCR the PDFs (any standard tool works) before uploading to either system; the quality of the text layer dominates the quality of downstream extraction.
"I write in a language other than English. Does the Knowledge Map work for non-English papers?" Atlas's deconstruction pipeline is strongest on English-language sources because the underlying models are most accurate there; non-English sources are supported but the Knowledge Map quality varies by language. Heptabase, because it does not run automatic argument-extraction, is language-agnostic: your cards and whiteboards work the same in any language. If your corpus is predominantly non-English and you rely on automatic deconstruction, that gap is worth checking on your own papers before committing to either tool.