mboxer

Create NotebookLM-ready Markdown source packs from Gmail MBOX exports, with local SQLite, JSONL, and CSV outputs for search, RAG, archive review, and LLM workflows.

mboxer is a local-first email archive processor designed around a common problem:

You can export Gmail as an MBOX file,
but a raw MBOX archive is not useful for NotebookLM, RAG, review, or analysis.

mboxer turns that raw archive into organized, structured, reusable knowledge assets.

Gmail / Google Takeout
  → MBOX file
  → local SQLite index
  → organized Markdown source packs
  → NotebookLM, RAG, search, review, JSONL, and future tools

Why this exists

Gmail archives often contain years of valuable personal, professional, legal, financial, operational, project, and organizational history.

Google Takeout makes it possible to export that history as an .mbox file, but the exported file is not immediately useful for modern AI workflows.

NotebookLM works best with readable, focused, well-organized source documents.

RAG systems work best with structured, chunkable records.

Spreadsheets work best with clean rows and metadata.

Local review works best when everything is inspectable before anything is uploaded.

mboxer bridges that gap.

Primary use case: Gmail MBOX to NotebookLM

The main selling point of mboxer is converting Gmail MBOX exports into clean, category-organized Markdown files that can be used as NotebookLM sources.

Instead of uploading one giant raw archive, mboxer creates structured source packs like:

exports/notebooklm/
  finance/
    invoices/
      2024/
        finance-invoices-2024-001.md
  legal/
    contracts/
      2023-2024/
        legal-contracts-2023-2024-001.md
  projects/
    product-launch/
      2026/
        projects-product-launch-2026-001.md
  operations/
    vendor-correspondence/
      2025/
        operations-vendor-correspondence-2025-001.md

The goal is to make exported Gmail content easier to:

• upload into NotebookLM
• organize by topic or category
• review before upload
• split into useful source packs
• preserve context from email threads
• exclude sensitive or irrelevant material
• reuse later for RAG, search, or analysis

What mboxer produces

NotebookLM Markdown source packs

Markdown is the primary output format.

Each exported file preserves useful email context:

• subject
• sender and recipients
• date
• thread hints
• category
• source account
• cleaned body text
• attachment references

Export output is split by category, year, and size band to respect NotebookLM source limits. A JSON manifest is written alongside each export run.

SQLite database

SQLite is the durable local project index.

The schema tracks:

• accounts and MBOX sources
• messages with normalized metadata and body text
• Gmail label associations
• thread groupings with participant and date ranges
• ingest runs with resumable checkpoint keys
• ingest errors per run
• attachments with SHA-256, content type, and extraction status
• classifications per message and per thread
• category taxonomy with locked/global flags
• category proposals for review and approval
• export items and export run records
• security findings per message

JSONL exports

JSONL is intended for RAG pipelines, embeddings, local LLM tools, and structured downstream processing.

Each line represents one message with clean body text, metadata, and classification context. Account key is injected into the output path automatically to keep multi-account exports separated.

CSV exports

CSV export is planned for spreadsheet review, filtering, auditing, and manual cleanup.

Current implementation status

The core pipeline is implemented and working.

Implemented:

• MBOX ingest into SQLite using Python's mailbox stdlib
• resumable ingest with per-run checkpoint keys and batch commits
• deduplication via INSERT OR IGNORE on message identity
• multi-account separation with per-account keyed storage
• message normalization: subject, sender, recipients, dates, body text, body hash, word count
• Gmail label parsing and storage
• thread grouping with participant aggregation and date ranges
• attachment extraction to disk with SHA-256 and content-type tracking
• rule-based classification at both message and thread level
• thread-level rule classification with message inheritance
• assign (confidence 1.0) and assign_hint (confidence 0.75) rule actions
• category taxonomy with locked categories and proposal workflow
• category review, approval, and rejection via CLI
• security scan and scrub hooks
• NotebookLM Markdown export with category directories, year bands, and size-limit profiles
• export dry-run mode
• JSONL export
• JSON export manifests
• five NotebookLM limit profiles: standard, plus, pro, ultra, ultra_safe
• CLI with subcommands for all pipeline stages
• YAML config loading with deep key access and environment variable support
• pyproject.toml packaging with optional pdf and dev extras

In progress / planned:

• CSV export
• LLM-based classification via Ollama (config shape is present, wiring is not complete)
• local web UI for category review
• incremental export tracking
• scrub profiles for PII redaction before cloud upload

Project identity

Project name:      mboxer
Python package:    mboxer
CLI command:       mboxer
Default database:  var/mboxer.sqlite
Entry point:       mboxer.cli:main
Python requires:   >=3.11

Source layout

src/mboxer/
  cli.py              # argparse CLI: all subcommands
  config.py           # YAML config loading, path helpers
  ingest.py           # MBOX ingest pipeline
  normalize.py        # message normalization and body extraction
  classify.py         # rule-based classification (message + thread)
  taxonomy.py         # category management and proposal workflow
  accounts.py         # account CRUD and resolution
  attachments.py      # attachment extraction and storage
  limits.py           # NotebookLM limit profiles and validation
  naming.py           # slugify and category path normalization
  db/
    schema.sql        # full SQLite schema
    schema.py         # init_db helper
    migrations/       # future migration scripts
  exporters/
    notebooklm.py     # Markdown source pack exporter
    jsonl.py          # JSONL exporter
    manifest.py       # JSON manifest writer
  security/
    scan.py           # security scan runner
    scrub.py          # scrub hooks
    policy.py         # export policy helpers

config/
  mboxer.example.yaml   # full annotated config example

tests/
  test_accounts.py
  test_classify.py
  test_config.py
  test_db.py
  test_export.py
  test_first_run.py
  test_ingest.py
  test_limits.py
  test_manifest.py
  test_migration.py
  test_naming.py
  test_normalize.py
  test_scrub_export.py
  test_taxonomy.py
  test_thread_classify.py

Quick start

python -m venv .venv
source .venv/bin/activate

pip install -e .

mboxer --help

Copy and customize the example config:

cp config/mboxer.example.yaml config/mboxer.yaml

First run

Complete walkthrough from a fresh checkout to a dry-run export.

1. Initialize the database

mboxer init-db --config config/mboxer.yaml

2. Register your account

mboxer account add primary-gmail \
  --display-name "Primary Gmail" \
  --email user@example.com \
  --config config/mboxer.yaml

3. Verify the account was registered

mboxer account list --config config/mboxer.yaml

4. Ingest a small test archive first (see warning below)

mboxer ingest data/mboxes/primary-gmail/sample.mbox \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --source-name "Sample" \
  --extract-attachments \
  --resume

5. Classify with rules

mboxer classify \
  --config config/mboxer.yaml \
  --account primary-gmail

6. Review categories

mboxer review-categories \
  --config config/mboxer.yaml \
  --account primary-gmail

7. Run a security scan

mboxer security-scan \
  --config config/mboxer.yaml \
  --account primary-gmail

8. Dry-run export to verify output shape

mboxer export notebooklm \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --profile ultra_safe \
  --dry-run

9. Real export when ready

mboxer export notebooklm \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --profile ultra_safe \
  --out exports/notebooklm

Warning: test with a small MBOX before ingesting large archives.

Gmail MBOX exports can exceed several gigabytes for long-lived accounts. Before ingesting a full archive:

1. Extract a small slice of messages into a separate .mbox file and ingest that first.
2. Run mboxer export notebooklm --dry-run to verify the output shape.
3. Review the generated exports locally before uploading anything to a cloud service.

--resume makes ingest restartable, but a full ingest of a large archive still takes significant time and disk space. Running --dry-run on exports is free and fast.

Getting a Gmail MBOX file

You can export Gmail data from Google Takeout / Google Data Request.

The typical flow is:

1. Request an export of your Gmail data.
2. Download the archive from Google.
3. Extract the downloaded archive locally.
4. Locate the .mbox file.
5. Ingest the .mbox file with mboxer.
6. Export organized Markdown files for NotebookLM.

Example:

mboxer ingest data/mboxes/archive.mbox \
  --config config/mboxer.yaml \
  --source-name "Primary Gmail Archive" \
  --account primary-gmail \
  --extract-attachments \
  --resume

Intended workflow

mboxer ingest data/mboxes/archive.mbox \
  --config config/mboxer.yaml \
  --source-name "Primary Gmail Archive" \
  --account primary-gmail \
  --extract-attachments \
  --resume

mboxer classify \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --level thread

mboxer review-categories \
  --config config/mboxer.yaml \
  --account primary-gmail

mboxer security-scan \
  --config config/mboxer.yaml \
  --account primary-gmail

mboxer export notebooklm \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --profile ultra_safe \
  --out exports/notebooklm

mboxer export jsonl \
  --config config/mboxer.yaml \
  --account primary-gmail \
  --out exports/rag/messages.jsonl

Multi-account support

mboxer supports multiple separate Gmail accounts and archives in the same local project.

Each ingested source is tracked by account, source name, import run, and original MBOX file.

Example account keys:

primary-account
work-account
business-archive
organization-archive
project-archive

To export multiple accounts into a single NotebookLM run:

mboxer export notebooklm \
  --config config/mboxer.yaml \
  --accounts primary-account,work-account \
  --profile ultra_safe \
  --out exports/notebooklm

NotebookLM source-pack strategy

NotebookLM exports are Markdown-first and organized by category directories.

Filenames remain meaningful even if the folder hierarchy is flattened during upload.

category-topic-year-sequence.md

Examples:

finance-invoices-2024-001.md
legal-contracts-2023-2024-001.md
projects-product-launch-2026-001.md
operations-vendor-correspondence-2025-001.md
research-literature-review-2024-001.md
support-customer-requests-2025-001.md

Export profiles

NotebookLM limit profiles are defined in config/mboxer.example.yaml.

Profile	Max sources	Reserved	Target sources	Target words/source
standard	50	10	40	300,000
plus	100	20	80	300,000
pro	300	50	250	300,000
ultra	600	75	525	300,000
ultra_safe	600	100	450	225,000

Use ultra_safe as the default for large NotebookLM-oriented workflows where you want to preserve headroom for manual sources, attachments, PDFs, and later additions.

Limit overrides can be passed directly on the CLI:

mboxer export notebooklm \
  --profile ultra_safe \
  --max-sources 400 \
  --target-words 200000

Classification strategy

Classification runs in two passes.

Rule-based (deterministic, no network required):

Rules match on sender domain, sender address fragment, and subject keywords. Each rule assigns a category_path, sensitivity, notebooklm_priority, and export_profile.

At thread level, a matching rule is applied to the whole thread and then inherited down to all messages in the thread.

Rules support two assignment modes:

• assign — confident match, confidence 1.0
• assign_hint — soft match, confidence 0.75

LLM-based (optional, local-first):

Config accepts an Ollama endpoint and model name. LLM classification is wired in the config shape and CLI but is not yet fully connected to the pipeline.

Classification can be scoped by account and run at message or thread level:

mboxer classify --level thread --account primary-gmail

Category taxonomy

Categories are slash-delimited paths that become directory hierarchies in exports.

medical
medical/hospital-billing
medical/pharmacy
legal
legal/law-firm-correspondence
finance
household/utilities
postal/usps-informed-delivery
noise/marketing
noise/spam

Locked categories are defined in config and cannot be deleted.

The classifier can propose new categories. Proposals appear in review-categories and require explicit approval before being used in exports:

mboxer approve-category <proposal_id>
mboxer reject-category <proposal_id>

Security stance

mboxer assumes mail archives contain sensitive material.

Raw exports are local-only by default.

The security pipeline:

ingest
  → normalize
  → classify
  → security-scan
  → scrub
  → review
  → export

The config controls which redaction passes are applied before export:

security:
  redact_phone_numbers: true
  redact_ssn_like_numbers: true
  redact_credit_card_like_numbers: true
  redact_email_addresses: false
  redact_physical_addresses: false
  scan_attachments: true
  quarantine_unsafe_attachments: true

Cloud-oriented exports should use reviewed, scrubbed, or metadata-only profiles.

Development

pip install -e ".[dev]"

pytest

Linting and type checking:

ruff check src/
mypy src/

Optional PDF support:

pip install -e ".[pdf]"

Design goals

mboxer should be:

• NotebookLM-friendly
• Gmail MBOX-focused
• local-first
• privacy-conscious
• resumable
• inspectable
• useful without a cloud service
• useful with local LLMs
• useful with future RAG systems
• safe for sensitive archives
• flexible enough for multiple Gmail accounts
• structured enough to support future application features

Non-goals

mboxer is not intended to be:

• a Gmail client
• a replacement for Gmail search
• a hosted SaaS product
• a tool that uploads raw email archives by default
• a black-box AI classifier
• a cloud-first archive processor

Releases

Releases are automated via .github/workflows/release.yml.

On every push to master (which is effectively every merged PR when branch protection requires PR-only changes), the workflow:

1. Finds the highest existing v* tag (by semantic version order).
2. Increments its patch component to produce the next version. If no prior v* tag exists, starts from v0.1.0.
3. Creates and pushes the new tag.
4. Publishes a GitHub Release from that tag with autogenerated release notes.

Assumptions and caveats:

• Only the patch component is bumped automatically. To release a minor or major version, push the tag manually before merging to master.
• The workflow runs on any direct push to master, not only merged PRs. If direct pushes are enabled, each push creates a new release.
• The automated tag is pushed using GITHUB_TOKEN. Due to a GitHub Actions limitation, tag pushes made by GITHUB_TOKEN do not re-trigger other workflows. This means the build-and-publish (PyPI) job in the same file will not run automatically for auto-created tags; PyPI publishing requires a manually pushed v*.*.* tag.

License

MIT