WTM API VS ContextForge.dev

WTM API's answer

WTM API focuses on structure preservation, not just text extraction. Unlike most scrapers that return raw text, it converts HTML to proper Markdown with headings, tables, code blocks, and links intact. No headless browser needed — pure server-side parsing means sub-second responses at a fraction of the cost.

ContextForge.dev's answer:

ContextForge is memory that lives at the MCP layer, so it works across every AI coding agent at once — Claude Code, Cursor, GitHub Copilot, ChatGPT, and Windsurf — not just one. Save a decision once and any client recalls it later with semantic search. It goes beyond a note store: automatic git sync turns your commits and PRs into searchable knowledge, plus task tracking, snapshots, and team sharing — all through a single MCP server you add with one command.

WTM API's answer

Speed and simplicity. One POST call, any URL, clean Markdown back in under 1 second. No SDKs to install, no headless browser to maintain, no complex configuration. Generous free tier (50 calls/month) and straightforward pricing. The output is immediately usable for LLMs, RAG pipelines, or content migration.

ContextForge.dev's answer:

Most memory tools are tied to a single agent or are just a key-value store. ContextForge is MCP-native, so it's portable across all your AI tools; it adds git sync so your codebase history becomes searchable context automatically; and it includes team features (shared spaces, collaborators) that solo-memory tools lack. Setup is one command, there's a genuine free-forever tier with no credit card, and paid plans start at just $9/month.

WTM API's answer

Developers building AI/LLM applications, RAG pipelines, content migration tools, or any system that needs web content in a structured, portable format. Also useful for teams moving content to Markdown-based platforms like Notion, Obsidian, or static site generators.

ContextForge.dev's answer:

Software developers and engineering teams who use AI coding assistants — Claude Code, Cursor, GitHub Copilot, ChatGPT, Windsurf — and are tired of re-explaining their project, architecture, and conventions every session. It fits solo developers working across multiple projects as well as small teams that need shared, persistent context.

WTM API's answer

I was building a RAG pipeline and needed web content in Markdown format. Existing tools gave me either raw text (losing all structure) or required a headless browser (slow and expensive). I built WTM API to solve my own problem - a fast, simple API that preserves the structure of any webpage as clean Markdown. Shipped the MVP in a weekend using Next.js, Supabase, Stripe, and Vercel, all on free tiers.

ContextForge.dev's answer:

ContextForge was born from a simple frustration: AI coding agents forget everything the moment a session ends. Every new conversation meant re-explaining the same architecture, naming conventions, and past decisions. ContextForge was built to give AI agents a permanent, searchable memory through the Model Context Protocol — so knowledge is captured once and reused forever, across sessions and projects. It even dogfoods its own memory to help build itself.

WTM API's answer

Next.js 16 (App Router), Supabase (auth and PostgreSQL), Stripe (subscription billing), Vercel (hosting), and Cheerio (server-side HTML parsing). The entire stack runs on free tiers with zero monthly infrastructure cost.

ContextForge.dev's answer:

Next.js 16 (App Router), React and Tailwind CSS for the dashboard, hosted on Vercel. Supabase (PostgreSQL) with pgvector powers the semantic vector search, and Deno edge functions serve the API. Embeddings use OpenAI text-embedding-3-small. The MCP client is a Node.js package (contextforge-mcp) on npm, implementing the Model Context Protocol.

WTM API's answer

• Early-stage AI startups building RAG pipelines
• Independent developers working on content tools
  
• Currently in public beta and growing the user base