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

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

Aside

Aside Computer Inc.

Freemium

A Chromium desktop browser with a built-in agent that signs in and completes real work across your logged-in sites.

Agentic Browsing: The agent operates your logged-in websites directly - clicking, typing and navigating - so tasks that need no public API still get done.
Local Memory: Browsing history is distilled into on-device memory files so the agent already knows which tools and accounts a recurring task involves.
Agent Password Manager: Credentials are autofilled into pages through hardware-backed encryption and Secure Enclave storage, never handed to the model.
Human Approval Gates: Sensitive steps such as payments, posts and outbound messages pause for your confirmation before the agent proceeds.
Access Audit Log: Every credential use and scoped permission grant is recorded so you can see exactly what the agent touched and when.
Routines: Scheduled recurring tasks, such as a 9am daily briefing, run on their own and drop results back into the browser.
Bring Your Own Model: Connect an existing ChatGPT or Claude subscription or your own API key rather than paying twice for inference.
Sandboxed Execution: Filesystem and network access are isolated with guardrails so agent runs cannot reach beyond what the task needs.

Operations Backfill: Push the same record update through several internal dashboards that have no shared API.
Recruiting Prep: Reopen a candidate profile viewed yesterday and assemble interview notes from the sites already visited.
Inbox and Comment Triage: Draft replies, follow-ups and comment responses across email and social accounts under review.
Daily Briefing: Schedule a routine that gathers overnight metrics and trending topics into one morning summary.
Sales Research: Work through prospect sites and CRM screens to collect context before an outreach sequence.
Spreadsheet and Document Work: Have the agent edit local files and web spreadsheets as part of a longer task.

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