Feynman vs Lumi: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Feynman and Lumi — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Feynman
Companion
Open-source AI research agent that reads papers, ranks literature, drafts research and plans experiments from the terminal or a local workbench.
Key features
- Cited Research Briefs: Asking a research question returns a synthesized brief where each claim is tied to the paper or web source it came from, rather than an unsourced summary.
- PaperRank Scoring: Ranks papers on a topic with transparent evidence for citations, methodology, reproducibility and provenance so reading order is a decision you can inspect.
- Paper Access Resolver: Resolves a single DOI, arXiv ID, OpenAlex ID, PMID, PMCID or title against OpenAlex, arXiv/alphaXiv, DOI and Europe PMC, with optional full-text fetching.
- Local Science Workbench: `feynman serve` opens a standalone app with projects, sessions, chat, notebooks, compute, artifact previews and provenance in one place.
- Claim Auditing and Replication: Compares a paper's stated claims against what its code actually does, and generates replication plans with compute targets and gated experiment steps.
- Local and Hosted Models: Works with hosted providers via OAuth or API key and with local runtimes including LM Studio, Ollama, vLLM and a LiteLLM proxy.
- Skills-Only Install: The research skill library can be installed on its own into Claude, Codex or OpenCode projects without the terminal app or bundled runtime.
- Science Artifacts: Reports, data files, spreadsheets, notebooks, LaTeX, chemistry sketches and genomes are browsable together with versions, lineage and execution logs.
Best for
- Deciding What to Read: Ranking a fresh literature pile on a topic by reproducibility and methodology instead of citation count alone.
- Writing a Literature Review: Producing a review that separates where the field agrees from where questions remain open, with citations attached.
- Verifying a Paper's Claims: Auditing whether the results a paper reports are supported by the code and data it released.
- Planning a Replication: Turning a published finding into a concrete replication plan with a compute target and staged experiment steps.
- Running Deep Research Passes: Launching a multi-agent deep dive on a topic that synthesizes findings and verifies them before reporting.
- Keeping Research Local: Running the whole pipeline against a local model so unpublished work and private data never leave the machine.
- Adding Research Skills to a Coding Agent: Installing the skills bundle into an existing Claude or Codex project to get research workflows without a second app.
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