Context.dev vs Yolk: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Context.dev and Yolk — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Context.dev
Context.dev
A single REST API that turns any URL into LLM-ready markdown, crawls whole sites, and returns brand, logo, and structured company data.
Key features
- 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.
Best for
- 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.
Yolk
elkowar (GitHub)
Cross-platform dotfile manager that embeds templates inside config files using Rhai scripting for dynamic, in-place templating.
Key features
- Embedded Templating: Allows template expressions to be included inside comments within the actual configuration file so template and generated output stay in the same file.
- Rhai Scripting: Uses the Rhai scripting language for template expressions and configuration logic, enabling conditionals, functions, and system-aware templates.
- Dynamic Data Files: Supports a yolk.rhai file to provide custom or system-specific data sources for templates, allowing dynamic generation per host.
- In-place Modifications: Applies template-driven modifications directly to existing files without requiring synchronized separate template files.
- Version Control Friendly: Designed to keep templates and generated config together to simplify tracking changes and reduce divergence in Git repositories.
- Cross-Platform Support: Built to run across different operating systems and environments for consistent dotfile management on multiple machines.
- Safe Template Evaluation: Limits template logic to Rhai to reduce complexity and surface area compared to arbitrary shell-based templating.
- In-file templating: templates embedded inside comments of actual configuration files to avoid separate template artifacts
- Rhai-based templates: template expressions and user configuration are written in the Rhai scripting language
- Custom data sources: support for a yolk.rhai file to fetch dynamic or system-specific data for template rendering
- Cross-platform CLI: designed to run on multiple operating systems (cross-platform)
- Version-control friendly: templates live alongside generated configs, simplifying repo management
- Documentation and examples hosted in the project repository
Best for
- Managing personal dotfiles across multiple machines: keep one source configuration with embedded templates that adapt per-host via yolk.rhai.
- Maintaining synchronized configs in Git: store templated comments in tracked files so generated values and templates evolve together under version control.
- Generating system-specific configuration: use Rhai scripts to read system properties and populate config options for different OSes or environments automatically.
- Sharing standardized configs within a team: distribute a single file format containing both template and result so teammates can reproduce environments easily.
- Migrating or refactoring configuration files: apply in-place template transformations to modernize or standardize legacy config formats without separate template artifacts.
- Automating repetitive config edits: script common modifications (e.g., toggling options, inserting keys) using embedded templates and Rhai logic.
- Maintain and deploy dotfiles across multiple machines while keeping templates and generated configs in a single file
- Generate system-specific configuration files (e.g., different values per host) using Rhai scripts
- Embed templated values into configs for applications (editors, shells, tools) without separate template files
- Store templated configs in version control without divergent template and generated-file states
- Automate config updates via a CLI tool that processes in-file templates