Your AI keeps forgetting. Give it lasting memory.
Your repo docs are the easy part — your agent already reads those. LLMtoMD is for the rest: the PDFs, scanned specs, Office files, and Notion exports it can't read locally — turned into a knowledge base your agents search over MCP, across hundreds of files and across Cursor, VS Code, and Claude.
Paste a URL, get clean Markdown
See exactly what your AI gets — no account needed.
Public web pages, free to try.
Plugs into the tools your team already uses
Your AI writes great code — then forgets the plan
AI coding tools are brilliant at generating code, but they lose context as a project grows: requirements, architectural decisions, business rules, and past conversations slip out of the window. LLMtoMD turns your PRDs, FRDs, API docs, meeting notes, designs, and websites into structured, AI-ready Markdown your agents can search and reuse across the whole development lifecycle.
- Stop re-explaining the same project context every session
- Keep generated features consistent with the spec
- One searchable source of truth for the entire build
Without a memory layer
“As we discussed, the auth flow should… wait, what did we decide about refresh tokens?” — context lost, the agent guesses, and you paste the spec again.
With LLMtoMD
The agent queries your FRD and decision log directly, pulls the exact requirement, and implements it — no re-explaining, no drift from the spec.
You asked it to fix one function. It rewrote half the file.
AI coding agents drift. git diff shows what changed — not what the agent was allowed to change. Scope guard records the approved boundary before it edits, then flags anything it touched outside that boundary, checked against your stored spec. You approve the extra changes or revert — nothing is auto-deleted.
- Record the files an agent may touch — by file, folder, or glob — right from the agent.
- Catch out-of-scope edits before they land, with an AI verdict on whether your spec justifies them.
- Block the commit locally with a pre-commit hook — only file paths leave your machine, never your code.
$ git commit -m "fix credits rounding" LLMtoMD scope check — ✗ needs review 2 in scope · 1 outside the approved boundary ✓ app/billing/credits.py ✓ app/billing/utils/round.py ✗ app/auth/session.py unexplained — nothing in the spec touches auth → commit blocked. approve the wider scope or revert.
One pipeline for every file type
From clean digital PDFs to scanned paper and audio recordings — all converted to consistent, LLM-friendly Markdown.
Documents
- Word (.docx)
- RTF
- Plain text
- EPUB
Office
- PowerPoint
- Excel
- CSV
- ODT / ODS / ODP
Images & scans
- PNG / JPG
- WebP / GIF
- Handwriting (AI Vision)
Web & data
- URLs & web pages
- HTML
- JSON
- XML
Audio & video
- MP3 / WAV / M4A
- MP4 / MOV
- Transcribed with timestamps
Archives
- ZIP
- Batch ingestion
- Watched storage folders
More than a converter — a data layer for AI
Token-efficient output
Clean headings, tables, and lists instead of raw text dumps — up to ~70% fewer tokens than pasting a PDF, so prompts stay lean.
AI Vision for scans
Scanned PDFs, photos, and handwritten notes are read by a vision model, not just OCR — the documents other tools give up on.
Semantic search
Every document is chunked and embedded automatically, so you can search your library by meaning, not just keywords.
Document Q&A
Ask a question and get a cited answer drawn from your own documents — retrieval-augmented out of the box.
Structured extraction
Define reusable field schemas (invoice number, total, parties…) and pull structured data from any document or batch.
Knowledge graph
Entities are linked across your whole corpus into a queryable graph — see how documents and concepts connect.
More than a converter
Naive converters flatten your documents into a wall of text. LLMtoMD keeps the structure your AI actually needs.
From messy files to clean Markdown in seconds
Upload or connect
Drop a file, paste a URL, POST to the API, or point a watched folder at your storage.
We convert & enrich
Layout-aware conversion, AI Vision for scans, plus summaries, topics, entities, and embeddings.
Use it anywhere
Read it in the app, export RAG-ready JSONL, query it, or pull it through the API or MCP server.
Built for everyone shipping with AI
Whether you're vibe-coding a side project, founding a startup without an engineering team, or running a production codebase — LLMtoMD gives your AI the context it keeps forgetting.
Vibe coders
Upload your requirements, specs, and user stories once — your AI tools retrieve them instead of you re-explaining.
- Less context loss in long sessions
- Consistent features across the build
- Fewer prompts to rewrite
- Better code from real requirements
Non-technical founders
Turn your idea, feature requests, and customer feedback into an AI knowledge base that keeps agents aligned with your vision.
- Ship software without deep technical skills
- Keep AI aligned to business rules
- One source of truth for the product
- Faster delivery with AI workflows
Software engineers
A development intelligence layer between your docs and your coding agents — architecture, APIs, standards, and decisions on tap.
- Architecture & API references
- Engineering standards & guidelines
- Historical decisions & specs
- More accurate, on-standard code
Designed to keep your models grounded
Every conversion is shaped for retrieval — not just readable, but structured so your chunkers, vector stores, and agents get clean, citable context.
- Predictable chunk boundaries and explicit headings keep retrieval grounded
- Export documents as RAG-ready JSONL — drop straight into a vector store
- Per-document metadata (summary, topics, entities) for better provenance
- Classify-and-route auto-extraction stores structured fields on convert
{"id":"c1","document_id":"doc_8f2",
"ordinal":0,
"text":"# Master Services Agreement\n...",
"embedding":[0.013,-0.041, ...],
"metadata":{"doc_type":"contract",
"topics":["liability","termination"]}}API-first, with an MCP server for your AI tools
REST API
Push files, track jobs, fetch Markdown, run extraction, and export RAG packages — all with typed, documented endpoints and API keys.
MCP server
Connect LLMtoMD to Claude, ChatGPT, or Cursor so your assistant can convert and retrieve documents inside the chat.
Watched sources & webhooks
Point a source at a storage prefix and new files convert automatically — perfect for ETL jobs and ingestion pipelines.
# Convert a file in one call curl -F file=@invoice.pdf \ -H "X-API-Key: $LLMTOMD_KEY" \ https://api.llmtomd.com/v1/ingest # → queued · converted · ready as Markdown
Learn everything your agent can do
Step-by-step guides and a full command reference — connect your agent, convert and upload documents, search your library, and automate with the API.
Stop paying to repeat yourself
Context repetition is a hidden tax on AI-assisted development. Every time you re-paste the same requirements, architecture, and API specs into Claude, ChatGPT, Gemini, Cursor, or your VS Code agent, you pay for it in tokens.
- Store project knowledge once — not in every prompt
- Agents retrieve only the relevant sections, on demand
- No more re-uploading 50-page specs
- Less context-window waste, fewer repeated tokens
- Lower debugging and troubleshooting costs
Cheaper debugging: agents pull error logs, API references, and prior implementation details from your knowledge base instead of you resubmitting them every cycle.
# Without a memory layer
- paste FRD.pdf (52 pages) into every prompt
- the same context, re-sent again and again ✗
# With LLMtoMD
- search_documents("refund policy")
- → just the 2 relevant chunks ✓Trusted across teams and industries
Legal teams
Turn contracts, briefs, and case files into AI-searchable Markdown for faster review.
Researchers
Process papers, textbooks, and literature into clean text for LLM-powered analysis.
Healthcare
Convert clinical notes and records into structured text for AI-assisted workflows.
AI & ML engineers
Build RAG pipelines and training datasets from any document source, at scale.
Operations
Digitize specs, manuals, and SOPs into operational knowledge bases.
Knowledge teams
Power docs, wikis, and AI assistants with clean, structured Markdown.
Trust built in by default
Encrypted in transit
All traffic is served over TLS; uploads go to private, access-controlled storage.
Auto-deleted
Source files are purged automatically within 24 hours of conversion.
Your data stays yours
We never sell your data or use your documents to train third-party models.
Simple, transparent pricing
Start free and upgrade when you need more. Standard conversions are unlimited by plan credits; AI Vision credits apply only to scanned and image documents.
Free
To try it out
- 100 credits / month
- Connect your AI tools (MCP)
- Semantic search & Q&A
Starter
For individuals
- 2,000 credits / month
- 100 MB file limit
- AI Vision & transcription
Business
For teams
- 25,000 credits / month
- 500 MB file limit
- Full REST API & webhooks
Frequently asked questions
What does LLMtoMD do?+
It converts documents of almost any type — PDFs, Office files, images, audio, and web pages — into clean, structured Markdown that's optimized for large language models, RAG pipelines, and AI agents.
How do I give my AI coding agent my project's context?+
Convert your specs, docs, and notes into AI-ready Markdown with LLMtoMD, then connect it to your agent over MCP. Your agent — Claude Code, Cursor, or VS Code — retrieves the exact relevant context on demand instead of you re-pasting it or hoping the model still remembers it mid-build.
How do I convert a PDF or website to Markdown for an LLM?+
Paste a URL or upload a PDF, DOCX, or PPTX and LLMtoMD returns clean, structured Markdown optimized for LLMs — preserving headings, tables, and lists, and reading scanned pages with AI Vision. Use it in the dashboard, via the REST API, or directly from your AI agent over MCP.
What is an MCP server, and how does LLMtoMD use it?+
MCP (Model Context Protocol) is an open standard that lets AI coding tools connect to external data and tools. LLMtoMD runs an MCP server, so your agent can search your documents, ask questions, and pull clean Markdown directly. It's one line to connect in Claude Code, or one click for Cursor and VS Code.
Isn't it easier to just put all my context in one file in the project root?+
That works until it doesn't: a single context file is loaded in full every time — burning tokens on mostly-irrelevant text — goes stale, and doesn't scale past a few notes. LLMtoMD keeps a searchable knowledge base and serves your agent only the passages it needs over MCP, so your context stays fresh, relevant, and cheap.
How is this different from a plain PDF-to-text tool?+
Beyond conversion, LLMtoMD preserves structure (headings, tables, lists), reads scans with AI Vision, and adds an AI layer: semantic search, document Q&A, structured field extraction, a cross-document knowledge graph, and RAG-ready exports.
Do you store my files?+
Source files are processed to produce your Markdown and then auto-deleted within 24 hours. We never sell your data or use your documents to train third-party models. See our Privacy Policy for details.
How does LLMtoMD help my AI coding tools?+
It acts as a persistent memory layer. Upload your PRDs, FRDs, API docs, and decisions once, and your coding agents — Claude, ChatGPT, Cursor, VS Code, and others — retrieve the exact context they need via the MCP server, so they stop forgetting requirements and drifting from the spec as a project grows.
Will this reduce my AI token costs?+
Yes. Instead of re-pasting the same requirements, architecture, and specs into every prompt, agents retrieve only the relevant sections on demand. Storing context once — rather than re-sending a 50-page document repeatedly — cuts wasted tokens during both development and debugging.
Is there an API?+
Yes. Every capability is available through a documented REST API with API keys, plus an MCP server so assistants like Claude, ChatGPT, and Cursor can convert and retrieve documents directly.
What are credits?+
Standard conversions run against your monthly plan allowance. Credits meter premium AI operations — AI Vision, audio transcription, and semantic analysis — so you only pay for the heavy lifting when you use it.
Can I cancel anytime?+
Yes. Paid plans are month-to-month (or annual) and you can cancel at any time from your billing settings. See our Refund Policy for details.
LLMtoMD is the persistent memory and knowledge layer for AI-powered software development — helping vibe coders, founders, and engineering teams build faster, hold context, and cut AI token costs across the entire development lifecycle.
Convert your first document free
Turn your documents into AI-ready Markdown in seconds. No credit card required to start.