Documentation

curl -X POST https://fetchmd.dev/api/convert \
  -H "Content-Type: application/json" \
  -H "X-API-Key: fmd_your_key_here" \
  -d '{"url":"https://example.com/article"}'

{
  "markdown": "# Getting Started with Stripe\n\nStripe is a suite of...",
  "url": "https://docs.stripe.com/get-started",
  "title": "Getting Started with Stripe",
  "tokenEstimate": 1843,
  "fetchTimeMs": 312,
  "htmlSize": 94200,
  "markdownSize": 7372,
  "compressionRatio": 92.2
}

curl -X POST https://fetchmd.dev/api/convert \
  -H "Content-Type: application/json" \
  -H "X-API-Key: fmd_your_key_here" \
  -d '{"url":"https://example.com"}'

-H "Authorization: Bearer fmd_your_key_here"

curl -X POST https://fetchmd.dev/api/keys \
  -H "Content-Type: application/json" \
  -d '{"email":"you@example.com"}'

{
  "key": "fmd_a3f9b2c1d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7",
  "existing": false
}

curl -X POST https://fetchmd.dev/api/youtube \
  -H "Content-Type: application/json" \
  -d '{"url":"https://www.youtube.com/watch?v=dQw4w9WgXcQ","timestamps":false}'

{
  "markdown": "# Never Gonna Give You Up\n\n**Source:** https://www.youtube.com/watch?v=dQw4w9WgXcQ\n**Channel:** RickAstleyVEVO\n\n## Transcript\n\nWe're no strangers to love...",
  "videoId": "dQw4w9WgXcQ",
  "title": "Rick Astley - Never Gonna Give You Up (Official Video)",
  "channelName": "RickAstleyVEVO",
  "language": "en",
  "segmentCount": 342,
  "durationSeconds": 212,
  "tokenEstimate": 1840,
  "markdownSize": 7362
}

curl -X POST https://fetchmd.dev/api/pdf \
  -H "Content-Type: application/json" \
  -d '{"url":"https://arxiv.org/pdf/2303.08774"}'

{
  "markdown": "# GPT-4 Technical Report\n\n**Source:** https://arxiv.org/pdf/2303.08774\n**Author:** OpenAI\n**Pages:** 98\n\n---\n\nWe report the development of GPT-4...",
  "title": "GPT-4 Technical Report",
  "author": "OpenAI",
  "pages": 98,
  "pdfSize": 1842034,
  "markdownSize": 184203,
  "tokenEstimate": 46050,
  "compressionRatio": 90.0
}

curl -X POST https://fetchmd.dev/api/github \
  -H "Content-Type: application/json" \
  -d '{"url":"https://github.com/vercel/next.js","includeCode":false}'

{
  "markdown": "# vercel/next.js\n\n> The React Framework for the Web\n\n**Stars:** 127,000...",
  "owner": "vercel",
  "repo": "next.js",
  "description": "The React Framework for the Web",
  "stars": 127000,
  "language": "JavaScript",
  "defaultBranch": "canary",
  "fileCount": 4821,
  "tokenEstimate": 12300
}

curl -X POST https://fetchmd.dev/api/bulk \
  -H "Content-Type: application/json" \
  -H "X-API-Key: fmd_your_key_here" \
  -d '{"urls":["https://docs.stripe.com/api","https://docs.github.com"]}'

curl -X POST https://fetchmd.dev/api/jobs \
  -H "Content-Type: application/json" \
  -H "X-API-Key: fmd_your_key_here" \
  -d '{"urls":["https://docs.stripe.com/api","https://docs.github.com","https://docs.anthropic.com"]}'

{
  "jobId": "job_01jt5k2n8r4p",
  "status": "processing",
  "totalUrls": 3,
  "pollUrl": "/api/jobs/job_01jt5k2n8r4p",
  "submittedAt": "2026-03-02T12:00:00.000Z"
}

curl https://fetchmd.dev/api/jobs/job_01jt5k2n8r4p \
  -H "X-API-Key: fmd_your_key_here"

{
  "jobId": "job_01jt5k2n8r4p",
  "status": "complete",
  "totalUrls": 3,
  "completedUrls": 3,
  "submittedAt": "2026-03-02T12:00:00.000Z",
  "completedAt": "2026-03-02T12:00:03.421Z",
  "results": [
    { "url": "https://docs.stripe.com/api", "title": "Stripe API Reference", "tokenEstimate": 8420, "ok": true },
    { "url": "https://docs.github.com", "title": "GitHub Docs", "tokenEstimate": 3210, "ok": true },
    { "url": "https://docs.anthropic.com", "title": "Anthropic", "tokenEstimate": 1840, "ok": true }
  ]
}

curl https://fetchmd.dev/api/quota \
  -H "X-API-Key: fmd_your_key_here"

{
  "plan": "pro",
  "used": 1842,
  "limit": 10000,
  "remaining": 8158,
  "resetsAt": "2026-04-01T00:00:00.000Z",
  "batchLimit": 500
}

{
  "error": "Monthly limit of 100 conversions reached. Upgrade at https://fetchmd.dev/pricing",
  "plan": "free",
  "limit": 100,
  "used": 100
}

from langchain.docstore.document import Document
import requests

def fetch_as_documents(urls: list[str], api_key: str) -> list[Document]:
    """Fetch URLs via FetchMD and return LangChain Documents."""
    docs = []
    for url in urls:
        res = requests.post(
            "https://fetchmd.dev/api/convert",
            headers={"Content-Type": "application/json", "X-API-Key": api_key},
            json={"url": url},
        )
        data = res.json()
        docs.append(Document(
            page_content=data["markdown"],
            metadata={"source": url, "title": data["title"], "tokens": data["tokenEstimate"]},
        ))
    return docs

# Use with any LangChain chain
docs = fetch_as_documents(
    ["https://docs.stripe.com/api", "https://platform.openai.com/docs"],
    api_key="fmd_your_key_here",
)

{
  "mcpServers": {
    "fetchmd": {
      "command": "npx",
      "args": ["-y", "@fetchmd/mcp-server"],
      "env": {
        "FETCHMD_API_KEY": "fmd_your_key_here"
      }
    }
  }
}

// RAG pipeline: ingest web content → chunk → embed → store
import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

async function ingestUrls(urls: string[], apiKey: string) {
  // 1. Fetch & convert to Markdown
  const markdowns = await Promise.all(
    urls.map(async (url) => {
      const res = await fetch("https://fetchmd.dev/api/convert", {
        method: "POST",
        headers: { "Content-Type": "application/json", "X-API-Key": apiKey },
        body: JSON.stringify({ url }),
      });
      return res.json();
    })
  );

  // 2. Split into chunks
  const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
  const allDocs = await Promise.all(
    markdowns.map((m) =>
      splitter.createDocuments([m.markdown], [{ source: m.url, title: m.title }])
    )
  );

  // 3. Embed and store
  const vectorStore = await MemoryVectorStore.fromDocuments(
    allDocs.flat(),
    new OpenAIEmbeddings()
  );

  return vectorStore;
}

async function safeConvert(url: string, apiKey: string) {
  const res = await fetch("https://fetchmd.dev/api/convert", {
    method: "POST",
    headers: { "Content-Type": "application/json", "X-API-Key": apiKey },
    body: JSON.stringify({ url }),
  });

  if (res.status === 429) {
    const { plan, limit } = await res.json();
    throw new Error(`Rate limited: ${plan} plan allows ${limit}/month. Upgrade at fetchmd.dev/pricing`);
  }

  if (res.status === 504) {
    throw new Error(`Timeout: ${url} took longer than 10s to respond`);
  }

  if (!res.ok) {
    const { error } = await res.json();
    throw new Error(error);
  }

  return res.json();
}

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

async function summarizeUrl(url: string, fetchmdKey: string) {
  // 1. Fetch content
  const res = await fetch("https://fetchmd.dev/api/convert", {
    method: "POST",
    headers: { "Content-Type": "application/json", "X-API-Key": fetchmdKey },
    body: JSON.stringify({ url }),
  });
  const { markdown, title, tokenEstimate } = await res.json();

  console.log(`Fetched "${title}" — ~${tokenEstimate} tokens`);

  // 2. Pass to Claude
  const message = await client.messages.create({
    model: "claude-sonnet-4-6",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: `Summarize the following article in 3 bullet points:\n\n${markdown}`,
      },
    ],
  });

  return message.content[0].type === "text" ? message.content[0].text : "";
}