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`.markit/config.json` (created by `markit init`):

```json
{
  "llm": {
    "provider": "openai",
    "apiBase": "https://api.openai.com/v1",
    "model": "gpt-4.1-nano",
    "transcriptionModel": "gpt-4o-mini-transcribe"
  }
}

import { Markit } from "markit-ai";

const markit = new Markit();

// Convert a file by path
const { markdown } = await markit.convertFile("report.pdf");
console.log(markdown);

// Convert a URL
const { markdown: webMd } = await markit.convertUrl("https://example.com/article");

// Convert a Buffer with explicit type hint
import { readFileSync } from "fs";
const buffer = readFileSync("document.docx");
const { markdown: docMd } = await markit.convert(buffer, { extension: ".docx" });

import OpenAI from "openai";
import { Markit } from "markit-ai";

const openai = new OpenAI(); // reads OPENAI_API_KEY from env

const markit = new Markit({
  describe: async (image: Buffer, mime: string) => {
    const res = await openai.chat.completions.create({
      model: "gpt-4.1-nano",
      messages: [
        {
          role: "user",
          content: [
            { type: "text", text: "Describe this image in detail." },
            {
              type: "image_url",
              image_url: {
                url: `data:${mime};base64,${image.toString("base64")}`,
              },
            },
          ],
        },
      ],
    });
    return res.choices[0].message.content ?? "";
  },

  transcribe: async (audio: Buffer, mime: string) => {
    const res = await openai.audio.transcriptions.create({
      model: "gpt-4o-mini-transcribe",
      file: new File([audio], "audio.mp3", { type: mime }),
    });
    return res.text;
  },
});

const { markdown } = await markit.convertFile("photo.jpg");

import Anthropic from "@anthropic-ai/sdk";
import OpenAI from "openai";
import { Markit } from "markit-ai";

const anthropic = new Anthropic(); // reads ANTHROPIC_API_KEY from env
const openai = new OpenAI();       // reads OPENAI_API_KEY from env

// Mix providers: Claude for images, OpenAI Whisper for audio
const markit = new Markit({
  describe: async (image: Buffer, mime: string) => {
    const res = await anthropic.messages.create({
      model: "claude-haiku-4-5",
      max_tokens: 1024,
      messages: [
        {
          role: "user",
          content: [
            {
              type: "image",
              source: {
                type: "base64",
                media_type: mime as "image/jpeg" | "image/png" | "image/gif" | "image/webp",
                data: image.toString("base64"),
              },
            },
            { type: "text", text: "Describe this image." },
          ],
        },
      ],
    });
    return (res.content[0] as { text: string }).text;
  },

  transcribe: async (audio: Buffer, mime: string) => {
    const res = await openai.audio.transcriptions.create({
      model: "gpt-4o-mini-transcribe",
      file: new File([audio], "audio.mp3", { type: mime }),
    });
    return res.text;
  },
});

import { Markit, createLlmFunctions, loadConfig, loadAllPlugins } from "markit-ai";

// Reads .markit/config.json and env vars automatically
const config = loadConfig();

// Load any installed plugins
const plugins = await loadAllPlugins();

// Create instance with built-in providers + plugins
const markit = new Markit(createLlmFunctions(config), plugins);

const { markdown } = await markit.convertFile("report.pdf");

// my-plugin.ts
import type { MarkitPluginAPI } from "markit-ai";

export default function (api: MarkitPluginAPI) {
  api.setName("my-format");
  api.setVersion("1.0.0");

  api.registerConverter(
    {
      name: "myformat",
      accepts: (info) => [".myf", ".myfmt"].includes(info.extension ?? ""),
      convert: async (input: Buffer, info) => {
        // info.extension, info.mimeType, info.fileName available
        const text = input.toString("utf-8");
        const markdown = `# Converted\n\n\`\`\`\n${text}\n\`\`\``;
        return { markdown };
      },
    },
    // Optional: declare so it appears in `markit formats`
    { name: "My Format", extensions: [".myf", ".myfmt"] },
  );
}

// better-pdf-plugin.ts
import type { MarkitPluginAPI } from "markit-ai";

export default function (api: MarkitPluginAPI) {
  api.setName("better-pdf");
  api.setVersion("1.0.0");

  // Runs before built-in PDF converter, effectively replacing it
  api.registerConverter({
    name: "pdf",
    accepts: (info) => info.extension === ".pdf",
    convert: async (input: Buffer, info) => {
      // Custom PDF extraction logic
      const markdown = `# PDF Content\n\n(extracted with custom logic)`;
      return { markdown };
    },
  });
}

import type { MarkitPluginAPI } from "markit-ai";

export default function (api: MarkitPluginAPI) {
  api.setName("gemini-provider");

  api.registerProvider({
    name: "gemini",
    envKeys: ["GOOGLE_API_KEY"],
    defaultBase: "https://generativelanguage.googleapis.com/v1beta",
    defaultModel: "gemini-2.0-flash",
    create: (config, prompt) => ({
      describe: async (image: Buffer, mime: string) => {
        // Call Gemini API here
        return "Image description from Gemini";
      },
    }),
  });
}

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import { Markit } from "markit-ai";
import { readdirSync, readFileSync, writeFileSync } from "fs";
import { extname, basename, join } from "path";

const markit = new Markit();
const inputDir = "./docs";
const outputDir = "./docs-md";

const files = readdirSync(inputDir);
for (const file of files) {
  const ext = extname(file);
  if (![".pdf", ".docx", ".html"].includes(ext)) continue;

  const buffer = readFileSync(join(inputDir, file));
  const { markdown } = await markit.convert(buffer, { extension: ext });
  const outName = basename(file, ext) + ".md";
  writeFileSync(join(outputDir, outName), markdown);
  console.log(`Converted: ${file} → ${outName}`);
}

import { Markit } from "markit-ai";

const markit = new Markit();
const urls = [
  "https://example.com/article-1",
  "https://example.com/article-2",
  "https://en.wikipedia.org/wiki/TypeScript",
];

const results = await Promise.all(
  urls.map(async (url) => {
    const { markdown } = await markit.convertUrl(url);
    return { url, markdown };
  })
);

for (const { url, markdown } of results) {
  console.log(`\n## ${url}\n\n${markdown.slice(0, 200)}...`);
}

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