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

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

Conduit

Conduit AI

Paid

AI chat, voice, and internal agents purpose-built for hospitality — automate guest communication and back-of-house ops.

Chat Agents: Reply instantly across email, WhatsApp, OTAs, socials, and every guest messaging channel with a single agent.
Voice Agents: Answer calls 24/7 for restaurants, amenities, group sales, and front desk with human-sounding voices.
Internal Agents: AI teammate that works alongside staff inside the tools they already use, answering questions and taking action across systems.
Multilingual Responses: Greet and support every guest in their language across every market you serve.
Native Integrations: Connect every tool your team runs (PMS, channel manager, CRM) or have Conduit build a custom integration.
Observability: Inspect every step an agent takes — tools used, decisions made, and the reason behind each transfer.
Proactive Workflows: Workflows fire before guests need to ask, driving upgrades, feedback, and gap-night fills automatically.
Performance Reporting: Track automation rate, response time, resolution rate, and guest satisfaction across every property.

Hotel Groups: Run a unified omnichannel inbox across a multi-property portfolio with revenue management and channel manager automation.
Independent Hotels: Deploy an AI concierge and after-hours receptionist that handles reviews, guest memory, and room upgrades.
Short-Term Rentals: Automate Airbnb / vacation rental guest support end-to-end including maintenance and housekeeping coordination.
Voice Front Desk: Never miss a booking or amenity request — the voice agent handles calls around the clock.
Escalation Handling: Surface what AI couldn't resolve so operators can fill knowledge gaps and let the agent learn.

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