Lumi vs ShogunAI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Lumi and ShogunAI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Lumi
A Google PAIR prototype that adds AI-powered annotations, granular summaries, and custom Q&A to arXiv research papers.
Key features
- Granular Summaries: Generates summaries at multiple granularities (section- or paragraph-level) to surface key ideas and make long papers easier to skim and comprehend.
- Inline Annotations: Attaches contextual, sentence- or paragraph-specific annotations directly onto the paper text to explain terminology, methods, or results in place.
- Custom Q&A: Lets users ask targeted questions about a paper and receive context-aware answers derived from the document content to clarify methods, results, or motivations.
- arXiv Integration: Built specifically to work with arXiv papers, enabling quick access to preprints and their metadata while preserving original paper structure.
- Open-Source Prototype: Source code available under an Apache-2.0 license on GitHub, allowing inspection, reuse, and community-driven improvements.
- Research Navigation Aids: Provides tools to jump between sections, references, and highlighted insights to streamline literature review workflows.
- Contextual Highlighting: Highlights important sentences and phrases based on AI analysis to draw attention to key contributions and claims.
- Collaboration-Friendly Outputs: Produces shareable annotations and summaries that can be used to coordinate reading lists and group discussions.
- Inline annotations layered on top of arXiv papers
- Granular and multi-level summaries for sections and full papers
- Custom Q&A over the paper content (user-driven queries)
- Lightweight AI layer integrated into the reading interface
- Browser/web-based reading experience (lumi.withgoogle.com)
- Open-source codebase on GitHub (Apache-2.0) allowing local integration and extension
- Designed for improved paper navigation and comprehension
Best for
- Rapid literature review: Quickly generate section-level summaries across many arXiv papers to triage and prioritize reading lists.
- Clarifying complex passages: Ask focused questions about specific paragraphs or figures to get concise, context-aware explanations.
- Teaching and learning: Instructors and students use inline annotations and summaries to make advanced papers accessible in coursework.
- Collaborative annotation: Teams annotate papers with AI-generated notes to share insights and discussion points during journal clubs or research meetings.
- Relevance triage: Determine whether a paper contains needed methods or results without reading it end-to-end by scanning AI-highlighted passages and summaries.
- Research discovery: Identify related work and key contributions faster by surfacing dominant themes and claims within a paper.
- Accelerating literature reviews and paper digestion for researchers
- Explaining complex methods or equations within academic papers
- Creating Q&A study aids from research articles
- Annotating and sharing insights on arXiv papers within teams
- Prototyping integrations that enhance document-based workflows
ShogunAI
ShogunAI
A local-first macOS memory and execution assistant that remembers your workday on-device and finishes work inside the tools you already use.
Key features
- On-Device Memory Layer: Captures mail, meetings, documents and screen context locally and indexes them into an encrypted store on your Mac, with no cloud copy by default.
- Contextual Recall with Sources: Answers plain-language questions across Mail, chat, docs and calendar from a single search, attaching the source and timestamp to every hit so answers can be checked.
- Execution Layer with Three Autonomy Levels: Reversible work runs automatically, drafts wait for review, and anything leaving your Mac stops for explicit approval — with every action logged as what ran, on what evidence, and what left the device.
- Inline Draft at the Caret: Press Option and ShogunAI reads the field around your cursor plus the memory behind it, then writes the continuation directly in the app you are already typing in as a local write you send yourself.
- Meeting Minutes, Not Recordings: Transcribes a meeting as it starts and on completion writes a summary, the decisions made and the commitments it heard, filing next actions into your work state with one tap; audio is never written to disk.
- Two-Way Live Translation: Set the language you speak and the language they speak — their speech reaches you in yours and yours reaches them in theirs, with only text retained afterwards.
- Daily Brief: Assembles what moved overnight, what is still open and what you promised someone before the day starts, rather than on request.
- Shared Memory Across Models and Agents: The same structured state of people, projects, commitments and open loops reaches Claude, Cursor, ChatGPT and anything driven over MCP, CLI or REST, so no session starts cold.
Best for
- Eliminating Cold Starts: Stop re-pasting last week's decisions and open threads at the beginning of every model session — every assistant starts from the same live memory of your work.
- Closing Open Loops: Surface the follow-up that is due today, draft the reply with the correct file attached, and hold it for approval before it reaches the recipient.
- Meeting Follow-Through: Turn a call into decisions, commitments and filed next actions automatically instead of re-listening to a recording.
- Answering 'What Did We Decide?': Recall a specific decision from a Notion brief or Gmail thread weeks later, with the source and time attached so it can be verified.
- Privacy-Constrained Work: Run an assistant over sensitive client or company context on machines where a cloud-indexed copy of the workday is not acceptable.
- Cross-Language Collaboration: Hold live meetings with counterparts in another language and keep only the translated text afterwards.
- Consultant and Founder Context Switching: Keep separate projects, people and commitments straight across many concurrent engagements without manual note discipline.