FigureLabs vs Medley: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of FigureLabs and Medley — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

FigureLabs

Freemium

AI agent that creates publication-ready scientific figures via text-to-figure, image-to-figure, and vectorization.

Text-to-Figure Generation: Creates complete, composed scientific figures from plain-text descriptions, allowing users to specify panels, annotations, and figure layout that the agent renders automatically.
Image-to-Figure Conversion: Transforms input images (e.g., plots, microscopy snapshots, schematics) into polished figure components suited for publication, preserving scientific detail while improving presentation.
Vectorization and Editable Output: Converts raster graphics into vector representations so figures are editable and scalable for high-resolution publication needs.
Publication-Ready Styling: Applies formatting and styling conventions appropriate for academic journals, producing high-resolution outputs that reduce manual rework before submission.
Rapid Iteration: Generates and refines figures in seconds, enabling fast prototyping and repeated adjustments during manuscript or presentation development.
Precision Preservation: Focuses on preserving underlying data clarity and scientific details while enhancing visual clarity and label legibility for reproducible visuals.
Text-to-figure generation from natural-language prompts
Image-to-figure conversion (convert raster inputs into cleaned, publication-ready figures)
Vectorization of raster graphics to vector formats (SVG)
Fast generation workflow (seconds-scale) for rapid iteration
Outputs optimized for publication (high-resolution and editable vector formats)

Preparing manuscript figures for journal submission: generate composed, publication-ready multi-panel figures from descriptions and source images to accelerate paper submission.
Converting lab outputs into editable graphics: turn raster plots or microscope images into vectorized, editable figures for revision and scaling without quality loss.
Rapid prototyping of visual results: create multiple figure variants quickly to test layouts, annotations, and styles during manuscript drafting or poster design.
Recreating figures from text or notes: produce visual representations of experimental setups, workflows, or conceptual diagrams from written descriptions for methods or review articles.
Improving figure consistency across a manuscript: standardize styling, labels, and panel layouts across multiple figures to meet journal formatting requirements and improve readability.
Create publication figures for manuscripts, posters, and presentations
Convert hand-drawn or raster diagrams into editable vector figures
Rapidly prototype visualizations from experimental descriptions
Produce consistent, publication-ready figure sets with minimal manual redrawing

Free

Claude Code plugin that decomposes prompts into coordinated multi-agent plans and visualizes the plan at a shareable URL.

Slash-Command Integration: Activates directly inside Claude Code via the /mission command to produce a plan without leaving the chat interface.
Prompt Decomposition: Breaks a single user prompt into discrete subtasks with clear dependencies to turn vague requests into actionable steps.
Multi-Agent Coordination: Generates a coordinated plan that assigns roles or agent responsibilities and sequences work across multiple agents to tackle complex tasks.
Plan Visualization URL: Renders the produced plan structure at a shareable URL so users can inspect, review, and share the full task graph and execution plan.
Task Assignment & Sequencing: Determines ordering and handoffs between subtasks so parallel and dependent work is organized for execution by different agents.
Shareable Workflow Export: Enables distribution of the decomposed plan via link for collaboration, review, or external execution tracking.
Decomposes a single prompt into a coordinated multi-agent plan
Invoked within Claude Code via the /mission command
Generates a structured plan view accessible at a shareable URL
Orchestrates multiple agents/subtasks rather than relying on a single model
Focus on readable plan structure for inspection and collaboration

Complex Project Breakdown: Converting a high-level product or research brief into a multi-step plan with assigned agent roles and dependencies for coordinated execution.
Multi-step Code Development: Decomposing a feature request into design, implementation, testing, and deployment tasks that can be executed or reviewed by specialized agents.
Data Analysis Pipelines: Breaking down an analysis prompt into data-cleaning, transformation, modeling, and visualization subtasks that are assigned and sequenced.
Content Creation Workflows: Orchestrating ideation, drafting, editing, fact-checking, and formatting steps across different agents to produce polished content.
Collaborative Review & Handoff: Sharing the generated plan URL with teammates or stakeholders to review responsibilities, timelines, and handoffs before execution.
Experiment Orchestration: Designing and coordinating multi-step experiments or research tasks where different agents perform measurements, aggregation, and interpretation.
Breaking complex prompts into executable subtasks for multi-agent workflows
Orchestrating LLM agents to collaborate on a single objective
Sharing and reviewing decomposition and task assignments via a URL
Improving reliability and coverage by distributing work across multiple agents
Prompt engineering for complex, multi-step automation tasks