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

A side-by-side comparison of Cadenya and GBrain — 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.

Paid

A shared team brain that syncs memory, connected apps and scheduled skills to ChatGPT, Claude, Cursor and any AI your team uses.

Shared Team Chat: The whole team prompts in one shared chat where anyone can follow up and see how answers were worked out.
Plain-File Memory: Team knowledge lives as markdown notes in folders you can open, correct and copy, and every AI answers from the fix.
Connect Once Credentials: Connect Gmail, Calendar, Drive, Stripe and more once; AIs receive call results, never passwords.
Per-AI Permission Levels: Set each AI to Off, Read, Draft, Manage or Full per account, with every action on one record.
Scheduled Skills: One-click skills like Team Standup, Customer Comms Tracker and Inbox Cleanup run daily or once.
Multi-AI Sync: Sync the same memory to Claude, ChatGPT, Cursor, Claude Code, Codex and your terminal.
Tools Plan: A password manager for AI that puts your applications into every AI you use for individuals.

Team Standups: Summarize the team's work across GitHub, Gmail and Calendar and email it out each morning.
Customer Follow-Up: Track customers and prospects owed a reply and flag overdue ones daily.
Issue Triage: Turn customer bugs and requests from email and support chats into deduplicated tracker issues.
Meeting Prep: Build sourced briefs on candidates, guests or investors before you meet.
Inbox Cleanup: Archive mail you never read on a schedule.
Pricing Decisions: Combine call notes and billing data in a shared chat to evaluate a price change.