letta-filesystem-to-memfs

Documents

Migrates deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking, local lexical search, and QMD semantic search via the memfs-search skill. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating open_file, grep_file, and search_file behavior.

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Letta Filesystem to MemFS

Use this skill when a user wants the old Letta Filesystem behavior: upload documents, chunk them, attach them to an agent, and let the agent search/open them.

MemFS is not the same product. It is git-backed markdown memory. To mimic the old workflow, use the bundled CLI:

Extract PDFs/docs to markdown text.
Chunk the text into stable markdown files under documents/<corpus>/<doc>/chunks/.
Write a small pinned index under system/filesystem/<corpus>.md.
Index only the corpus chunk files in QMD for semantic search.
Review the MemFS git diff. Commit only if asked.

Quick workflow

# Set this to the skill directory shown in the skill load header.
SKILL_DIR="/path/to/letta-filesystem-to-memfs"

# From any directory. MEMORY_DIR should point at the target agent's memfs repo.
uv run --with pymupdf \
  "$SKILL_DIR/scripts/letta_fs_to_memfs.py" ingest \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  --source ./docs/ \
  --source ./guide.pdf \
  --source https://arxiv.org/pdf/2310.08560

cd "$MEMORY_DIR"
git status --short
git diff --stat

Search the chunk corpus lexically:

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" search \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  "memory hierarchy" \
  -n 5

Semantic search with QMD. The CLI creates a corpus-scoped QMD collection over chunk files only:

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd setup \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd query \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  "memory hierarchy" \
  -n 5

Use qmd reindex after changing corpus files, and qmd search / qmd vsearch when you specifically want keyword-only or vector-only search.

Layout

The ingest script writes:

system/filesystem/<corpus>.md
  Pinned index and operating instructions for the corpus.

documents/<corpus>/manifest.md
  Corpus manifest.

documents/<corpus>/<doc-slug>/manifest.md
  Per-document metadata and chunk table.

documents/<corpus>/<doc-slug>/chunks/chunk-0001.md
  Chunk content with frontmatter metadata.

documents/<corpus>/chunks.jsonl
  Machine-readable chunk export for custom indexing or debugging.

Old API mapping

Old Filesystem	MemFS mimic
`folders.create`	`--corpus <name>` creates `documents/<corpus>/`
`folders.files.upload`	`letta_fs_to_memfs.py ingest --source <file-or-directory-or-url>`
OCR/chunk/embed job	Extract + chunk locally; `qmd setup` / `qmd reindex` for semantic search
`agents.folders.attach`	Enable MemFS, then review and sync repo changes when appropriate
`open_file`	Read chunk markdown files by path
`grep_file`	`rg` or `letta_fs_to_memfs.py search`
`search_file`	`letta_fs_to_memfs.py qmd query` over the corpus chunk collection

Heuristics

Use system/filesystem/<corpus>.md for the small always-visible index only.
Do not pin full documents into system/; it will bloat the prompt.
Keep chunks outside system/, usually under documents/<corpus>/....
Passing a directory to --source recursively ingests supported files (.pdf, .md, .txt, .json, .csv, .html, .xml).
Use --glob / --exclude for messy directories. Defaults exclude .git, node_modules, .venv, and __pycache__.
URL downloads default to --max-download-mb 100; set 0 for unlimited.
Re-ingesting the same document slug replaces that document's old chunk directory, so stale chunks do not survive chunk-size changes.
Use chunk sizes around 2,000-4,000 characters with 200-500 character overlap.
Use the CLI's qmd subcommands when the user needs semantic search over many chunks.
Preserve source URLs, checksums, page markers, chunk numbers, and corpus names in the generated files.

CLI reference

uv run --with pymupdf "$SKILL_DIR/scripts/letta_fs_to_memfs.py" ingest --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" search --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd setup --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd query --help

Compatibility wrappers remain for older examples:

uv run --with pymupdf "$SKILL_DIR/scripts/ingest_documents.py" --memory-dir "$MEMORY_DIR" --corpus docs --source ./docs
uv run "$SKILL_DIR/scripts/search_corpus.py" --memory-dir "$MEMORY_DIR" --corpus docs --query "refund policy"

PDF notes

The ingest script uses PyMuPDF when it sees a PDF. Run it with uv run --with pymupdf ....

For scanned PDFs or complex tables, use the tools/extracting-pdf-text skill first, then ingest the extracted markdown/text file with this skill.

See references/design.md for design notes and edge cases.