Cadenya vs Context.dev: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Cadenya and Context.dev — 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.

Freemium

A single REST API that turns any URL into LLM-ready markdown, crawls whole sites, and returns brand, logo, and structured company data.

Web Scraping API: Converts any URL into markdown, HTML, raw bytes, sitemaps, screenshots, or images, with JS rendering, anti-bot bypass, and premium proxies included at one credit per page.
Site Crawling: Crawls an entire domain page by page so teams can build knowledge bases or ground RAG pipelines in fresh content instead of a model's training cutoff.
Schema-Based Extraction: The Extract endpoint crawls a site and returns structured data shaped to a JSON Schema you supply, removing hand-written parsers.
Answers Endpoint: Takes a research task plus the JSON shape you want back, researches the web, and returns a structured answer in one API call, with a cheaper fast mode.
Brand Intelligence: Retrieves logos, colors, fonts, styleguides, descriptions, socials, and addresses for a domain, powering programmatic theming and automated brand kits.
Logo Link CDN: Serves any company's logo through a direct image URL on a separate quota that does not consume API credits.
Entity Enrichment and Classification: Extracts products, enriches people from an email or profile URL, searches company news, and returns NAICS/SIC codes or transaction identification.
SDKs, MCP Server, and CLI: Official TypeScript, Python, Ruby, Go, and PHP SDKs plus an MCP server and CLI let agents and applications integrate without custom HTTP plumbing.

Grounding AI Agents: Give an LLM agent live web access so answers reflect the current web rather than the model's training cutoff.
RAG Knowledge Bases: Crawl documentation sites, academic journals, or PDFs at scale to build and refresh a retrieval corpus.
Support Chatbot Ingestion: Turn a customer's whole website into the knowledge base behind an AI support bot, as SiteGPT does after migrating from a competing scraper.
Automated Brand Kits and Theming: Pull a company's logo, colors, and fonts from its domain to theme an app or generate on-brand assets programmatically.
Onboarding Autofill: Enrich a new signup's company profile from their email domain so onboarding forms prefill instead of asking users to type.
Website Change Monitoring: Run concurrent monitors against competitor or supplier pages and react when content changes.
Structured Research Pipelines: Use the Answers endpoint to run repeatable web research tasks that return machine-readable JSON for downstream automation.