BigCactusLabs/dead-letter

**Turn .eml email exports into clean, local, LLM-ready Markdown.**
dead-letter converts .eml email exports into clean Markdown with YAML front matter — threads split, signatures stripped, attachments extracted, calendars parsed. One file or ten thousand.
Use it to build a readable email archive, move messages into Markdown-based knowledge systems, or prepare email for RAG and LLM pipelines without feeding raw MIME and base64 into your context window. No account, upload, or API key required.
⚡ Try it
If you already have uv, run dead-letter without installing it globally:
Or install with Homebrew or pip below. Agents and MCP clients can use the client-specific setup in llms-install.md.
🎯 Common use cases
- **Email → Markdown archives** — turn exported .eml collections into readable, portable Markdown with structured metadata
- **RAG and LLM ingestion** — normalize message text, thread structure, links, and attachment metadata before chunking or indexing
- **Agent workflows** — expose conversion and diagnostics directly to Claude, Codex, and other MCP clients
- **Knowledge bases** — move email into Markdown-first systems such as Obsidian, static archives, or local search pipelines
- **Digital preservation** — retain human-readable content and useful message structure without depending on one mail client
✨ Features
- **Full-fidelity conversion** — HTML sanitization, Gmail/Outlook thread segmentation, inline image handling, and calendar event summaries
- **CLI** — point it at a file or a directory and go
- **Local web UI** — dark command-center interface with drag-and-drop import, watch mode, conversion grade badges, processing history, and per-job diagnostics
- **Inbox/Cabinet workflow** — drop .eml files into an Inbox, let dead-letter organize the Markdown bundles into a Cabinet
- **Install validation** — dead-letter doctor checks your runtime environment
- **Conversion report** — opt-in JSON report with per-file diagnostics, including attachment referenced/retained counts for automation and audit
- **MCP server** — integrate with Claude Desktop, Claude Code, Codex, and other MCP clients
- **Claude plugin** — marketplace install in Claude Code or Cowork with five slash commands (/dead-letter:convert, /dead-letter:summarize, /dead-letter:triage, /dead-letter:cabinet, /dead-letter:mbox)
- **Portable Agent Skill** — teaches skill-aware agents when and how to convert .eml files
- **Python API** — from dead_letter import convert and you're off
🧠 Built for LLM Pipelines
Raw .eml files are noisy input for downstream LLM and retrieval pipelines — MIME headers, multipart boundaries, duplicated HTML/plain bodies, and encoded attachments all get mixed into the text path.
dead-letter normalizes that into Markdown with YAML front matter, so message text and metadata are ready for chunking or indexing without MIME parsing or base64 cleanup. Default convert() and convert_dir() runs write a single .md per message and keep attachment names in front matter.
To separate the filesystem artifacts too, bundle and Cabinet workflows write message.md plus retained decoded files under attachments/. The Markdown is ready for text ingestion, while PDFs, spreadsheets, calendar files, and other retained binary attachments stay cleanly split out for whatever downstream parser you already use.
For direct LLM integration, the MCP server lets clients call dead-letter's conversion tools without shelling out. Conversion is local; your chosen MCP host may still send returned email text to a remote model.
📊 Token-cost benchmarks
dead-letter's value isn't fewer tokens than every alternative — it's **fidelity per token**: keeping useful email structure without carrying raw MIME into context. Measured across an 11-message synthetic corpus of HTML threads, attachments, and newsletters (tokenizer o200k_base, structured thread mode):
- **~88% fewer tokens than the raw .eml** in the reported aggregate comparison — the attachment category's median is ~126k tokens raw vs ~180 converted.