# Frenchie

> Your agent's best friend. The toolkit for product teams building with AI agents: Frenchie Method for requirement-to-release workflows, plus Frenchie Kit for OCR, transcription, extraction, and image generation through MCP tools and slash commands.

Frenchie is a closed-source managed service built by LAB94. The primary integration path is stdio MCP (`npx @lab94/frenchie install`); HTTP is available at `mcp.getfrenchie.dev` for hosted agents. Method text workflows do not require an API key. Hosted Kit processing uses pay-as-you-go credits: $1 buys 100 credits, 1 credit per OCR page, 2 credits per transcription minute, DOCX 0.5 credit/page, XLSX 0.5 credit/sheet, CSV/TSV 0.5 credit/file, PPTX 1 credit/slide, 20 credits per generated image, 100 free credits on signup, no card required, credits don't expire.

## Core pages

- [Homepage](https://www.getfrenchie.dev/): Product overview for Frenchie Method and Frenchie Kit
- [Frenchie Method Principles](https://www.getfrenchie.dev/method/principles): Artifact-driven, test-driven, worktree-driven AI-agent workflow from requirement to release
- [Frenchie Kit](https://www.getfrenchie.dev/kit): MCP file tools for OCR, transcription, document extraction, and image generation
- [Pricing](https://www.getfrenchie.dev/pricing): Pay-as-you-go pricing with usage examples and billing FAQ
- [Extract to Markdown](https://www.getfrenchie.dev/extract-to-markdown): DOCX, XLSX, CSV, TSV, and PPTX extraction for AI agents
- [Docs](https://www.getfrenchie.dev/docs): How Frenchie Method and Frenchie Kit work together
- [Method Docs](https://www.getfrenchie.dev/docs/method): Install and use the requirement-first, test-case-first Method workflow
- [Kit Docs](https://www.getfrenchie.dev/docs/kit): Install, invoke, troubleshoot, migrate Kit file tools
- [Invocation by agent](https://www.getfrenchie.dev/docs/tools): Per-agent install and usage guides
- [Troubleshooting](https://www.getfrenchie.dev/docs/troubleshooting): Symptom-first fixes for common errors
- [Migrate](https://www.getfrenchie.dev/docs/migrate): Version-to-version upgrade guide
- [Changelog](https://www.getfrenchie.dev/changelog): Release history
- [About LAB94](https://www.getfrenchie.dev/about): The studio behind Frenchie
- [Terms & Privacy](https://www.getfrenchie.dev/terms): Legal

## Agent integrations

- [Frenchie in Claude Code](https://www.getfrenchie.dev/docs/tools/claude-code): Slash commands work out of the box. Results auto-save.
- [Frenchie in Cursor](https://www.getfrenchie.dev/docs/tools/cursor): Natural language works. One setting toggle on first install.
- [Frenchie in Codex (Desktop, CLI, IDE)](https://www.getfrenchie.dev/docs/tools/codex): @frenchie, /frenchie, or natural language — all three work.
- [Frenchie in Google Antigravity](https://www.getfrenchie.dev/docs/tools/antigravity): Invoke MCP servers by name, not by skill name.
- [Frenchie in VS Code (GitHub Copilot)](https://www.getfrenchie.dev/docs/tools/vscode): Slash commands work once MCP is registered.
- [Frenchie in Claude Desktop](https://www.getfrenchie.dev/docs/tools/claude-desktop): Natural language. Requires --global install.
- [Frenchie in Windsurf](https://www.getfrenchie.dev/docs/tools/windsurf): Natural language. User-level install.
- [Frenchie in Gemini CLI](https://www.getfrenchie.dev/docs/tools/gemini-cli): Natural language. Terminal-launched, no PATH gap.
- [Frenchie in Zed](https://www.getfrenchie.dev/docs/tools/zed): Assistant panel, user-level install.

## Comparisons

- [Frenchie vs Marker](https://www.getfrenchie.dev/compare/marker): Marker is a library you run. Frenchie is a server your agent calls.
- [Frenchie vs LlamaParse](https://www.getfrenchie.dev/compare/llamaparse): LlamaParse fits the LlamaIndex stack. Frenchie fits the MCP ecosystem.
- [Frenchie vs Whisper](https://www.getfrenchie.dev/compare/whisper): Whisper is a transcription model you run. Frenchie is a managed MCP server.
- [Frenchie vs AssemblyAI](https://www.getfrenchie.dev/compare/assemblyai): AssemblyAI goes deep on audio intelligence. Frenchie keeps it narrow.
- [Frenchie vs Deepgram](https://www.getfrenchie.dev/compare/deepgram): Deepgram is built for realtime. Frenchie is built for batch async.
- [Frenchie vs Replicate](https://www.getfrenchie.dev/compare/replicate): Replicate is a model-hosting platform. Frenchie is an MCP tool your agent calls.
- [Frenchie vs Fal.ai](https://www.getfrenchie.dev/compare/fal): Fal.ai optimizes for low-latency image generation. Frenchie optimizes for agent ergonomics.
- [Frenchie vs Pandoc](https://www.getfrenchie.dev/compare/pandoc): Pandoc is a document converter you run. Frenchie is an MCP tool your agent calls.
- [Frenchie vs Unstructured](https://www.getfrenchie.dev/compare/unstructured): Unstructured is a document-processing platform. Frenchie is a narrow MCP file utility.

## Extraction hub

- [Extract to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown): Give your agent Word docs, Excel workbooks, CSV files, TSV exports, or PowerPoint decks. Frenchie turns them into Markdown your agent can read, cite, and work with.
- [Word to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown/word-to-markdown): Contracts, briefs, policies, and reports land as DOCX all the time. Frenchie gives your agent Markdown instead of asking it to guess through a binary file.
- [Excel to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown/excel-to-markdown): Frenchie turns XLSX workbooks into sheet-by-sheet Markdown so your agent can inspect assumptions, compare tabs, and write the follow-up without a custom parser.
- [CSV to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown/csv-to-markdown): CSV is simple until your agent has to reason over it. Frenchie handles quoting, headers, truncation, and output shape so the agent gets a clean table.
- [TSV to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown/tsv-to-markdown): TSV files show up in data exports, logs, and research workflows. Frenchie turns them into Markdown tables without asking your agent to parse tabs by hand.
- [PowerPoint to Markdown for AI Agents](https://www.getfrenchie.dev/extract-to-markdown/powerpoint-to-markdown): Frenchie extracts PowerPoint decks slide by slide, so your agent can write a summary, briefing note, or follow-up without opening presentation software.

## Use cases

- [Meeting transcription for AI agents](https://www.getfrenchie.dev/use-cases/meeting-transcription): Drop a recording. Get a clean transcript back. Let your agent summarize, extract decisions, or answer follow-ups directly.
- [Sales call transcription inside your CRM workflow](https://www.getfrenchie.dev/use-cases/sales-call-transcription): Frenchie transcribes sales calls into clean Markdown so your agent can extract next steps, objections, and commitments without relying on your memory or your rep's notes.
- [Podcast transcription for show notes, SEO, and accessibility](https://www.getfrenchie.dev/use-cases/podcast-transcription): Drop an episode. Get Markdown your agent can turn into chapter markers, pull-quote cards, SEO-ready show notes, and full accessibility transcripts.
- [Research paper parsing — text, tables, and figures](https://www.getfrenchie.dev/use-cases/research-papers): Frenchie parses PDF research papers into clean Markdown, pulling figures out as separate PNG files so your agent can cite specific content instead of summarizing the whole thing.
- [Contract and legal document OCR for agent review](https://www.getfrenchie.dev/use-cases/legal-documents): Frenchie parses legal PDFs — scanned or native — into clean Markdown so your agent can flag clauses, extract dates, and diff redlines without you scrubbing formatting by hand.
- [Invoice OCR and receipt extraction for bookkeeping agents](https://www.getfrenchie.dev/use-cases/invoices): Frenchie parses invoices and receipts into structured Markdown so your agent can extract line items, totals, tax, and dates straight into your accounting tool.
- [Handwritten notes to searchable Markdown](https://www.getfrenchie.dev/use-cases/handwritten-notes): Frenchie reads handwritten pages — notebook scans, whiteboard photos, meeting jottings — and returns Markdown your agent can search, summarize, and reference.
- [Excel workbook extraction for AI agents](https://www.getfrenchie.dev/use-cases/excel-workbook-analysis): Frenchie extracts XLSX files sheet by sheet so your agent can compare tabs, explain changes, and write reports without a custom spreadsheet parser.
- [CSV cleanup and reporting for AI agents](https://www.getfrenchie.dev/use-cases/csv-data-cleanup): Frenchie converts CSV files into Markdown tables so your agent can spot bad rows, explain fields, and draft lightweight reports.
- [Word document review for AI agents](https://www.getfrenchie.dev/use-cases/word-document-review): Frenchie extracts DOCX files into Markdown so your agent can review policies, briefs, contracts, and reports from a clean text version.
- [PowerPoint briefing extraction for AI agents](https://www.getfrenchie.dev/use-cases/powerpoint-briefing-extraction): Frenchie extracts PPTX decks slide by slide so your agent can draft summaries, meeting prep, and follow-up questions.
- [Office file intake workflows for AI agents](https://www.getfrenchie.dev/use-cases/office-file-intake): Frenchie gives your agent one extraction tool for the Office and spreadsheet files that show up in real project work.
- [Product mockups for AI agents](https://www.getfrenchie.dev/use-cases/product-mockups): Describe a product image. Your agent generates it, saves it next to your work, and moves on.
- [Blog covers and social images for AI agents](https://www.getfrenchie.dev/use-cases/blog-covers): Your agent drafts a blog post, then generates a matching cover image in the same flow. No separate design pass.

## Blog

- [How Frenchie Method keeps agent work easy to review](https://www.getfrenchie.dev/blog/how-frenchie-method-keeps-agent-work-reviewable): Fast agent work is only useful when a human can understand what changed, why it changed, and how it was checked.
- [Why test cases come before implementation plans](https://www.getfrenchie.dev/blog/test-cases-before-implementation-plans): Frenchie Method asks for proof before planning the code change, because the plan should be shaped by how the team will verify it.
- [Why Frenchie Method uses worktrees](https://www.getfrenchie.dev/blog/why-frenchie-method-uses-worktrees): Worktrees give fast agent work a clean place to happen, so parallel tasks do not contaminate the main checkout.
- [Document extraction, without the heavy platform](https://www.getfrenchie.dev/blog/document-extraction-is-not-document-intelligence): Frenchie keeps extraction intentionally small: read the file, return Markdown, let the agent do the reasoning.
- [Excel to Markdown for AI agents](https://www.getfrenchie.dev/blog/excel-to-markdown-for-ai-agents): Why agents need spreadsheet extraction before they can reason over workbooks, and how Frenchie turns XLSX files into sheet-aware Markdown.
- [Pandoc, Unstructured, or Frenchie?](https://www.getfrenchie.dev/blog/pandoc-unstructured-or-frenchie): A practical guide to three different extraction shapes: local conversion, document processing infrastructure, and MCP-native file extraction for agents.
- [Read, listen, create: why your AI agent needs all three](https://www.getfrenchie.dev/blog/read-listen-create): Your agent already reasons. Give it three hands: read files, listen to recordings, create images. How image generation completes the Frenchie toolkit.
- [Why we built Frenchie: the MCP tools gap](https://www.getfrenchie.dev/blog/why-we-built-frenchie): Agents write code and reason about systems. Drop a scanned PDF or a 30-minute recording in front of one and most hit a wall. That wall is why Frenchie exists.
- [How Frenchie handles 30-minute audio without blocking your agent](https://www.getfrenchie.dev/blog/async-transcription-without-blocking): Sync transcription freezes your agent for five minutes. Async job handling is the detail that makes transcription usable inside a live agent workflow.
- [Inside Frenchie: from scanned PDF to clean Markdown in 3 seconds](https://www.getfrenchie.dev/blog/inside-frenchie-pdf-to-markdown): What happens between dropping a scanned contract into your agent and getting back searchable Markdown with tables and figures intact. The OCR pipeline.
- [MCP for beginners: what it is and why Frenchie picks it](https://www.getfrenchie.dev/blog/mcp-for-beginners): Model Context Protocol is the quiet standard reshaping how AI agents get tools. What it actually is, why it matters, why Frenchie bet the whole product on it.
- [Pay-per-use vs subscription: why Frenchie ships flat $1 = 100 credits](https://www.getfrenchie.dev/blog/pay-per-use-vs-subscription): Every SaaS eventually debates usage-based vs subscription pricing. For a narrow tool like Frenchie, the answer was clear before the first line of billing code.

## Key facts

- Built on: Model Context Protocol (MCP) plus installable agent skills
- Frenchie MCP server: the protocol surface agents call for Kit capabilities; local stdio is primary, hosted HTTP is the fallback for web agents
- Frenchie Method: workflow layer for requirement discovery, design, test-case-first planning, implementation plans, walkthroughs, PR review, security review, release-check, and cleanup
- Frenchie Kit: file and multimodal tools for OCR, transcription, extraction, and generation
- artifact-driven workflow: important decisions are written to `.frenchie/docs/feature/[feature-name]/`
- worktree-driven workflow: agent implementation happens in isolated worktrees by default
- Release-check: readiness output only; release actions are explicit and user-approved
- Test-case-first planning: test cases come before implementation plans
- Agent workflow: Method guides the work; Kit adds file context when needed
- Transports: stdio (local, primary) + HTTP (hosted, fallback)
- Tier-A auto-install: Claude Code, Cursor, Codex, Antigravity, Claude Desktop. Other MCP clients can use the same skill pack with manual config.
- Capabilities: OCR (PDF/image → Markdown), transcription (audio/video → Markdown), extraction (DOCX/XLSX/CSV/TSV/PPTX → Markdown), image generation (text prompt → PNG/JPEG/WebP)
- OCR formats: PDF, PNG, JPG, WebP
- Transcription formats: MP3, M4A, WAV, MP4, MOV, WebM
- Image generation output formats: PNG (default), JPEG, WebP. Sizes: 1024x1024, 1536x1024, 1024x1536, or auto. Quality: low/medium/high/auto. Background: transparent/opaque/auto (transparent rejected with JPEG).
- Extraction formats: DOCX, XLSX, CSV, TSV, PPTX. DOCX/XLSX/PPTX max 50 MB; CSV/TSV max 20 MB.
- Extraction pricing: DOCX 0.5 credit/page, XLSX 0.5 credit/sheet, CSV/TSV 0.5 credit/file, PPTX 1 credit/slide.
- Image generation rate limits: 50 images/hour and 250 images/day per user.
- Max file size: 2 GB per file
- Result retention: 30 minutes after first delivery, then deleted
- Languages: 50+ including Thai, Japanese, Chinese, Arabic, French, Spanish
- Pricing: $1 = 100 credits; 1 credit/OCR page; 2 credits/transcription minute; 20 credits/generated image; 100 free credits on signup
- Shared result contract: every capability returns `{ status, jobId, creditsUsed, resultExpiresAt, result: { kind: "markdown" | "image", ... } }`. Agents branch on `result.kind`.
- Contact: support@getfrenchie.dev
- npm package: https://www.npmjs.com/package/@lab94/frenchie

## Optional

- [Full site content](https://www.getfrenchie.dev/llms-full.txt): Every page and blog post aggregated into a single Markdown document for full-context AI ingestion
- [RSS feed](https://www.getfrenchie.dev/blog/feed.xml): Blog updates in RSS 2.0
- [Sitemap](https://www.getfrenchie.dev/sitemap.xml): Canonical URL index