Cadenya vs Graphis: Features, Pricing & Which Is Better (2026)

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

Cadenya

Paid

A managed agent runtime that layers your tools, agents and objectives so teams can test agent behavior safely and iterate fast.

Unified Tool Layer: Connect MCP servers, OpenAPI specs and existing endpoints once, and expose them to every agent through a single managed interface.
Model-Agnostic Variations: Set a default model and run canary variations on other providers side by side to compare behaviors before promoting a change.
Progressive Tool Discovery: Tool schemas stay out of the context window until an agent asks for them, with configurable max tools per search, search hints and a rerank threshold, so every request gets smaller.
Live Token Metering: Track cost as it accrues across loops, active variations, memory entries and widgets, so usage is visible rather than discovered on the invoice.
Webhooks and SSE Streaming: Push agent events — assistant messages, tool results, approval requests, sub-agent spawns, compaction, timeouts — into your own apps in real time.
Memory Layers: Attach stored documents such as playbooks and policy sets to an agent so its guidance persists across objectives.
Outcome Feedback Scoring: Collect scored comments on each objective, attributed to the variation and model that produced it, to see which behaviors actually work.
Embeddable Widgets: Drop an agent experience into any frontend as a widget rather than building the conversational surface yourself.

Operational Exception Handling: Run an agent that detects stalled shipments or orders and reroutes them through your dispatch API within policy.
Safe Model Migration: Evaluate a new frontier model as a canary variation against live objectives before switching the default.
Wrapping Existing APIs: Turn internal OpenAPI endpoints into agent-callable tools without rewriting the services behind them.
Embedding Agent Chat in a Product: Ship a conversational agent surface into an existing frontend using widgets instead of building it in-house.
Cost Control at Scale: Use progressive discovery and live metering to keep context size and per-loop cost down as agent traffic grows.
Agent Quality Review: Compare scored feedback across variations to understand which prompt or model changes improved real outcomes.

Graphis / Graphis AI

Freemium

All-in-one AI workspace that helps designers, marketers, and creators manage AI-driven content projects and collaboration.

AI Content Project Management: Centralized workspace to create, organize, and track AI-driven content projects for campaigns and client work.
Team Collaboration: Shared project spaces and access controls that enable creatives and agency teams to work together on prompts, assets, and iterations.
Creative Workflow Consolidation: Brings prompts, outputs, assets, and project history into a single environment to streamline iteration and review.
Multi-role Support for Agencies: Designed to support agency workflows with organization-level project coordination and client-focused content pipelines.
Built-for-Creatives UX: Interface and tooling crafted by creatives to match the needs of designers, marketers, and content creators integrating AI into their processes.
Simple Authentication and Access: Supports modern sign-in flows (e.g., Google login) for quick team onboarding and access management.
Web-based collaborative workspace for creative teams
AI content project management and organization
Tools to integrate generative workflows into design and marketing tasks
Team accounts and authentication with Google single sign-on
Centralized management of creative projects and assets

Agency Campaign Management: Plan and manage AI-generated assets across client campaigns, keeping briefs, prompts, iterations, and final outputs organized.
Social Content Production: Rapidly generate and iterate social posts, captions, and visuals with a shared workspace for marketers and designers to review.
Creative Iteration and Review: Store AI outputs and version history to enable designers to compare generations, refine prompts, and finalize assets.
Cross-functional Team Collaboration: Allow designers, copywriters, and marketers to co-manage projects, comment on outputs, and align creative direction.
Standardizing AI Workflows: Create repeatable processes for prompt reuse, output curation, and asset management to maintain brand consistency.
Agency Client Delivery: Package and present AI-driven deliverables in organized project spaces for client review and approval.
Agency-level management of AI-generated campaigns and creative projects
Design teams incorporating generative content into production workflows
Marketing teams producing and iterating on AI-assisted assets and copy
Cross-functional collaboration on content projects with centralized access
Organizing and tracking AI-driven creative deliverables for clients