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

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

Digger

Freemium

Background agent cloud that runs TypeScript agents on hardware-isolated Linux microVMs, with secrets the agent never sees.

Agent as a TypeScript Function: Declare a model, tools and MCP servers inside one exported function and deploy it live with `opencomputer deploy` — the platform owns the agent loop, sessions, streaming and versions.
Full Linux MicroVM Per Session: Each run gets a real machine with bash, a read-write filesystem, apt/npm/pip package installs and full network egress, isolated at the hardware level via KVM.
Durable Steerable Sessions: Runs stream to the client, accept mid-run steering, hibernate automatically when idle and resume exactly where they left off instead of restarting.
Origin-Bound Secret Injection: Secrets are defined per connection and injected only after a request leaves the sandbox, so the agent can act with a token it can never read or exfiltrate.
Checkpoint and Fork: Named snapshots work like git branches for VMs — fork a prepared machine to try five approaches in parallel, or restore one at any time.
Scheduled Autonomous Runs: A `defineSchedule` cron entry keeps a deployed agent running on its own, with sessions, streaming, MCP and Slack delivery handled by the platform.
Model-Agnostic Passthrough: The model is just a string that can change per request; token usage is passed through at raw API rates with no markup, or drops to zero with your own key or subscription.
Bare Sandboxes API: `Sandbox.create()` exposes the same microVMs directly — checkpoint, fork and live resize — for teams that want to bring their own harness and own the loop.

Codebase Hygiene Bots: Deploy an agent that finds stale feature flags still referenced in code and opens a cleanup pull request for each one on a weekday schedule.
Background Coding Agents: Hand off long refactors or migrations to an agent that clones the repo, runs the test suite in a real shell and works while you sleep.
Media and Data Pipelines: Run agents that need heavyweight binaries such as ffmpeg or headless Chromium, which serverless function runtimes cannot host.
Parallel Approach Exploration: Checkpoint a machine once the environment is prepared, then fork it to evaluate several agent strategies from an identical starting state.
Safe Third-Party API Automation: Let an agent operate on GitHub, Slack or an internal API through origin-bound credentials it is structurally unable to read or redirect.
Custom Agent Harness Hosting: Use the bare sandbox API as compute for an in-house agent framework, keeping your own loop while outsourcing VM lifecycle and scaling.