fx vs Lumi: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of fx and Lumi — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
fx
Vercel Labs
Vercel Labs' tiny open-source coding agent — a ~6 MB native CLI written in Zig, built for speed, embeddability and Unix-style ergonomics.
Key features
- Tiny Native Binary: The whole agent ships as a roughly 6 MB executable designed for instant installation and for embedding in resource-constrained environments and agent sandboxes.
- Instant Time to Prompt: fx cold starts in about 10 microseconds and performs no unnecessary work or I/O before accepting user input, which matters for programmatic invocation.
- Minimal Memory Footprint: A single-digit-megabyte memory baseline lets you pack many concurrent instances onto one machine.
- Shell-Like Ergonomics: Scroll history is preserved by default and output is deliberately sparse, so the CLI composes like a Unix tool instead of taking over the terminal.
- Context Efficiency: A minimal system prompt and tool surface reduce token spend and improve time-to-first-token performance.
- WebAssembly Builds: Optimal fx.wasm builds from the Zig toolchain shrink the binary further and make the network stack pluggable, enabling the in-browser demo.
- Model and Provider Agnostic: Works with local models, LLM gateways, direct provider API access or existing subscriptions rather than locking you to one vendor.
- Extensible Small Core: Capabilities are added through skills, plugins and MCPs, following a Unix-like philosophy of a small core with composable extensions.
Best for
- Sandboxed Agent Execution: Ship a full coding agent inside a container or sandbox where a large runtime would not fit.
- Embedding in Larger Systems: Use fx as the agent harness inside your own product or internal platform rather than building a loop from scratch.
- CI and Scripted Automation: Invoke a coding agent from pipelines and scripts where fast cold starts and quiet output matter more than an interactive UI.
- Agent Harness Research: Experiment with system prompt and tool design on a deliberately minimal, readable Apache-2.0 codebase.
- Local-Model Coding: Run agentic coding against a locally hosted model without any dependency on a specific cloud provider.
- Browser-Based Demos and Playgrounds: Compile to WebAssembly and run the agent client-side with networking delegated to browser fetch.
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