mm-ctx 0.10.0

Fast, multimodal context for agents.

mm-ctx

Fast, multimodal context for agents

---

Familiar UNIX CLI tools like find, grep, cat — with multimodal powers.

mm offers both a CLI and a Python API that enable agents to work with file types that LLMs can't natively read, including images, video, audio, PDFs, and other binary formats. Rust core for speed, Python for dev-ex, UNIX philosophy for composability.

Installation

# with pip
pip install mm-ctx

# with uv
uv pip install mm-ctx

# or run directly without installing
uvx --from mm-ctx mm --help

Alternative methods

# macOS / Linux (shell installer)
curl -LsSf https://vlm-run.github.io/mm/install/install.sh | sh

# Windows (PowerShell)
irm https://vlm-run.github.io/mm/install/install.ps1 | iex

CLI

Commands that mirror familiar Unix tools but operate on multimodal semantics. Indexing is implicit — every command auto-builds a metadata index on first use.

Metadata commands (find, wc with --format json) run in ~60ms on 700 files via the Rust fast path.

Sample files

Download sample files from vlm.run to try the examples below:

mkdir mm-samples && cd mm-samples
curl -LO https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.caption/bench.jpg
curl -LO https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.invoice/wordpress-pdf-invoice-plugin-sample.pdf
curl -LO https://storage.googleapis.com/vlm-data-public-prod/hub/examples/video/Timelapse.mp4
curl -LO https://storage.googleapis.com/vlm-data-public-prod/hub/examples/mixed-files/mp3_44100Hz_320kbps_stereo.mp3

Multimodal directory

With all 4 files downloaded, mm treats the folder as a multimodal workspace:

$ mm find mm-samples/ --tree

mm-samples  (4 files, 3.5 MB)
├── Timelapse.mp4  [3.0 MB]
├── bench.jpg  [253.8 KB]
├── mp3_44100Hz_320kbps_stereo.mp3  [286.0 KB]
└── wordpress-pdf-invoice-plugin-sample.pdf  [42.6 KB]

$ mm wc mm-samples/ --by-kind

kind      files  size      lines (est.)  tokens (est.)  tok_per_mb
audio     1      286.0 KB  0             85             304
document  1      42.6 KB   29            176            4.2K
image     1      253.8 KB  0             425            1.7K
video     1      3.0 MB    0             85             29
—————
total     4      3.5 MB    29            771            218

$ mm find mm-samples/ --columns name,kind,size,ext

name                                     kind      size     ext
bench.jpg                                image     259865   .jpg
Timelapse.mp4                            video     3113073  .mp4
mp3_44100Hz_320kbps_stereo.mp3           audio     292853   .mp3
wordpress-pdf-invoice-plugin-sample.pdf  document  43627    .pdf

$ mm cat mm-samples/wordpress-pdf-invoice-plugin-sample.pdf -n 10

wordpress-pdf-invoice-plugin-sample.pdf — pages 1-1 of 1:

--- Page 1 ---
INVOICE
Sliced Invoices
Suite 5a-1204 123 Somewhere Street
Your City AZ 12345
[e-mail]
Invoice Number: INV-3337
Invoice Date: January 25, 2016
Due Date: January 31, 2016
Total Due: $93.50
0.8s • 42.6 KB • 53.2 KB/s

$ mm cat mm-samples/bench.jpg -m accurate

<description>
This outdoor daytime photograph captures a peaceful park scene on a sunny day. The primary focus is a modern dark gray metal slat bench  positioned in the foreground on a patch of green grass. The bench is set upon a small concrete pad, and its curved backrest and armrests  create a sleek, contemporary silhouette. Behind the bench, a paved walkway cuts through a well-maintained lawn.
</description>

Tags: park, bench, outdoors, summer, grass, trees, street, urban, leisure, sunlight

Objects: metal bench, tree, car, white SUV, red car, concrete pad, walkway, grass, street, building

$ mm grep "invoice" mm-samples/

wordpress-pdf-invoice-plugin-sample.pdf
    2 Payment is due within 30 days from date of invoice. Late payment is subject to fees of 5% per month.
    3 Thanks for choosing DEMO - Sliced Invoices | [e-mail]
   10 [e-mail]

Quick start

mm --version                                                    # print version
mm find mm-samples/ --tree --depth 1                            # directory overview with sizes
mm wc mm-samples/ --by-kind                                     # file/byte/token counts by kind

# Default: --mode metadata (no LLM, local extraction only)
mm cat wordpress-pdf-invoice-plugin-sample.pdf                  # PDF text via pypdfium2
mm cat bench.jpg                                                # image dimensions, EXIF, hash
mm cat Timelapse.mp4                                            # video resolution, duration, codecs
mm cat wordpress-pdf-invoice-plugin-sample.pdf -n 20            # first 20 lines (metadata)

# --mode fast: kind's fast pipeline (short LLM caption for image/video; passthrough for code)
mm cat bench.jpg -m fast                                        # short VLM caption
mm cat Timelapse.mp4 -m fast                                    # mosaic → short VLM description

# --mode accurate: full LLM pipeline (requires a configured profile)
mm cat bench.jpg -m accurate                                    # LLM caption + tags + objects
mm cat Timelapse.mp4 -m accurate                                # keyframe mosaic → LLM description
mm cat mp3_44100Hz_320kbps_stereo.mp3 -m accurate               # Whisper transcript → LLM summary
mm cat wordpress-pdf-invoice-plugin-sample.pdf -m accurate      # LLM-structured invoice

Python API

mm is also a library. mm.Context is the one class you need to build a multimodal prompt incrementally, then hand the whole thing to a VLM. Backed by a Rust core: O(1) insert/lookup, sub-millisecond render at 10K items.

The public namespace is intentionally tiny:

import mm
mm.Context              # the one class you use
mm.Ref                  # Annotated[str, "mm.Ref"] typed alias for ref ids
mm.RefNotFoundError     # KeyError subclass raised by ctx.get on miss
mm.uuid7()              # UUIDv7 helper (time-ordered default session_id)

Build a prompt

import mm
from pathlib import Path
from PIL import Image

ctx = mm.Context(session_id=mm.uuid7())      # or omit; auto-mints a UUIDv7

img:  mm.Ref = ctx.put(Path("photo.jpg"))
img2: mm.Ref = ctx.put(Image.open("x.png"),
                       metadata={"note": "product hero shot"})
doc:  mm.Ref = ctx.put(Path("paper.pdf"),
                       metadata={"summary": "Attention is all you need",
                                 "tags": ["nlp", "transformer"]})
vid:  mm.Ref = ctx.put(Path("clip.mp4"),
                       metadata={"scene": 3, "actor": "A"})

ctx.put(obj, *, metadata=...) accepts a pathlib.Path, a str (file path or http(s):// URL), bytes, or a PIL.Image.Image. metadata is a single free-form JSON-serialisable dict — note / summary / tags are conventional keys used by rendering surfaces; anything else flows through to the VLM as a leading text block per item. Every put returns a short kind-prefixed ref id like img_a1b2c3, typed as mm.Ref.

Emit VLM-ready messages (OpenAI / Gemini)

from openai.types.chat import ChatCompletionMessageParam
from google.genai import types as genai_types

messages_openai: list[ChatCompletionMessageParam] = ctx.to_messages(format="openai")
messages_gemini: list[genai_types.ContentDict]    = ctx.to_messages(format="gemini")

Drop messages_openai directly into client.chat.completions.create(messages=...), or messages_gemini into model.generate_content(contents=...). Per-kind encoder overrides:

messages: list[ChatCompletionMessageParam] = ctx.to_messages(
    format="openai",
    encoders={"image": "tile", "video": "mosaic"},
)

Unspecified kinds fall back to sensible defaults (image-resize, video-frame-sample, document-rasterize).

Round-trip and resolve

obj: Path | Image.Image | bytes | str = ctx.get(img)   # instance: returns the stored object
row: dict | None = mm.Context.get(f"{ctx.session_id}/{img}")  # classmethod: cross-session DB lookup

Instance ctx.get(ref) returns the exact Python object you put — identity is preserved for in-memory items (no copy, no rehydrate). Classmethod mm.Context.get("<session>/<ref>") resolves against the global ~/.local/share/mm/mm.db when you only have a ref string and no live Context.

Missed a ref? ctx.get("img_a1b2cZ") raises mm.RefNotFoundError (a KeyError subclass) with a Levenshtein-based "did you mean" and the full context table inline — agent-friendly by default.

Render

ctx.print_tree()                  # insertion-order tree with metadata
print(ctx.to_md(mode="metadata")) # markdown: ref | kind | source | content
print(repr(ctx))                  # markdown summary: ref | kind | source

Context(session=019da4…, items=4)
├── [1] img_a1b2c3  image     /abs/path/photo.jpg
├── [2] img_9f0e12  image     PIL.Image(RGB, 1024×768)
│        └─ note: "product hero shot"
├── [3] doc_d4e5f6  document  /abs/path/paper.pdf
│        ├─ summary: "Attention is all you need"
│        └─ tags: [nlp, transformer]
└── [4] vid_7890ab  video     /abs/path/clip.mp4
         ├─ scene: 3
         └─ actor: "A"

Context("~/data") continues to support the directory-scan surface (to_polars, to_pandas, to_arrow, sql, show, info). See docs/api.md for the full spec — print_tree layouts, cross-session resolution, and the deferred save() API.

Integrations

Claude Code

Install the mm-cli-skill via the skill marketplace:

claude
> /plugin marketplace add vlm-run/skills
> /plugin install mm-cli-skill@vlm-run/skills
> Organize my ~/Downloads folder using mm

npx skills

Install mm-cli-skill globally so any CLI assistant or agentic tool can discover it:

npx skills add vlm-run/skills@mm-cli-skill

Universal assistants (OpenClaw, NemoClaw, OpenCode, Codex, Gemini CLI)

Install the mm-cli-skill globally first, then start your preferred tool:

# One-time setup
npx skills add vlm-run/skills@mm-cli-skill

# Then use any CLI assistant — it will discover mm automatically
openclaw "Organize my ~/Downloads folder using mm"
codex "Find all PDFs in ~/docs and summarize them with mm"

The skill exposes mm's capabilities to any tool that supports the skills protocol.

Command reference

Command	Purpose	Key flags
find	Find/list files, tree view, schema	--name, -i (ignore case), --kind, --ext, --min-size, --max-size, --sort, --reverse, --columns, --tree, --depth, --schema, --limit, --no-ignore, --format
cat	Content extraction (auto-detected by file type × mode)	--mode metadata/fast/accurate (default metadata), -p (pipeline), -n, --no-cache, -v, --encode.* (incl. --encode.strategy_opts KEY=VALUE), --generate.*, --list-pipelines, --list-encoders, --print-pipeline <kind>/<mode>, --format
grep	Content search across files	--kind, --ext, -C, --count, -i, --semantic, --pre-index, --no-ignore, --format
sql	SQL queries on file index, results, chunks, and embeddings	--dir, --pre-index, --format, --list-tables
wc	Count files, size, lines (est.), tokens (est.)	--kind, --by-kind, --format
bench	Benchmark suite	--rounds, --warmup, --mode, --format
config	Extraction mode settings	show, init, set, reset-db, reset-profiles, reset
profile	Manage LLM provider profiles	list, add, update, use, remove, --format

find — locate/list, tree, and schema

mm find ~/data --kind image                               # all images
mm find ~/data --kind video --sort size --reverse         # videos by size
mm find ~/data --ext .pdf --min-size 10mb                 # large PDFs
mm find ~/data --kind image --limit 5 --format json       # JSON output
mm find ~/data --name "test_.*\.py"                       # regex name match
mm find ~/data -n config                                  # substring name match
mm find ~/data -n CONFIG -i                               # case-insensitive (-i)

mm find ~/data --sort size --reverse --limit 20        # tabular listing
mm find ~/data --kind document --columns name,size,ext
mm find ~/data --tree --depth 2                        # hierarchical tree view
mm find ~/data --tree --kind video                     # tree filtered to videos
mm find ~/data --schema                                # column names, types, descriptions
mm find ~/data --format json                           # full metadata JSON
mm find ~/data --no-ignore                             # include gitignored files

cat — content extraction

--mode is one of metadata (default; local extraction, never an LLM call), fast (kind's fast pipeline; short LLM call for images/video), or accurate (LLM-heavy pipeline).

mm cat wordpress-pdf-invoice-plugin-sample.pdf                  # PDF text via pypdfium2 (metadata)
mm cat wordpress-pdf-invoice-plugin-sample.pdf -n 20            # first 20 lines (head)
mm cat wordpress-pdf-invoice-plugin-sample.pdf -n -20           # last 20 lines (tail)
mm cat bench.jpg                                                 # image dimensions, EXIF, hash (metadata)
mm cat bench.jpg -m fast                                         # short VLM caption
mm cat bench.jpg -m accurate                                     # full LLM caption + tags + objects
mm cat Timelapse.mp4 -m accurate                                 # mosaic → LLM description
mm cat bench.jpg -m fast -p resize                               # use named encoder (requires -m fast/accurate)
mm cat bench.jpg -m accurate -p my-pipeline.yaml                 # custom pipeline YAML
mm cat Timelapse.mp4 -m accurate --no-cache                      # force fresh LLM call
mm cat bench.jpg -m accurate -v                                  # verbose (shows pipeline tree)
mm cat --list-pipelines                                          # list registered pipelines
mm cat --list-encoders                                           # list registered encoders
mm cat --print-pipeline image/accurate                           # print a built-in pipeline's YAML source
mm cat bench.jpg -m accurate --encode.strategy_opts max_width=768  # override a single strategy_opts entry

wc — count files, size, tokens

mm wc ~/data --by-kind
mm wc ~/data --by-kind --format json

grep — content search

mm grep "attention" ~/data --kind document
mm grep "TODO" ~/data --kind code
mm grep "invoice" ~/data --count               # match counts per file
mm grep "Quantum Phase" ~/data -i              # case-insensitive search
mm grep "secret" ~/data --no-ignore            # search gitignored files
mm grep "revenue forecast" ~/data -s             # semantic (vector) search
mm grep "architecture" ~/data -s --pre-index      # auto-index before search

sql — query the index

Queries file metadata via scan + SQLite, or results and chunks from the persistent SQLite store.

mm find ~/data --schema                          # see available columns
mm sql "SELECT kind, COUNT(*) as n, ROUND(SUM(size)/1e6,1) as mb \
  FROM files GROUP BY kind ORDER BY mb DESC" --dir ~/data

# Query stored tables directly (auto-detected from table name)
mm sql "SELECT file_uri, summary FROM extractions LIMIT 10"
mm sql "SELECT file_uri, chunk_idx, LENGTH(chunk_text) FROM chunks"
mm sql "SELECT * FROM files WHERE kind='image'" --dir ~/data --pre-index  # index before query
mm sql --list-tables                              # show available tables

Output modes

• TTY: Rich formatted tables/panels
• Piped: plain TSV/text (machine-readable, no ANSI)
• **--format json**: JSON output on any command that supports it
• **--format csv**: Comma-separated values
• **--format dataset-jsonl**: JSONL for dataset export
• **--format dataset-hf**: HuggingFace Datasets format (requires --output-dir)

Verbose mode (--verbose / -v)

mm cat <file> [OPTIONS] --verbose shows the pipeline execution tree after content:

pipeline
  ├─ encode: resize · 0.0s → 1 parts (1 image)
  └─ generate: ollama · 2.3s · 354→195 tokens

Processing tiers

mm separates content tiers (what's stored) from pipeline modes (what runs). mm cat accepts --mode metadata|fast|accurate (default metadata).

Tier	What	LLM?
metadata (default)	Locally-extracted file content — PDF text, image dims/EXIF, video codec, transcript	never
fast	Output of the kind's fast pipeline	maybe¹
accurate	Output of the kind's accurate pipeline	yes

¹ Per-kind fast pipelines: image/video include a short LLM caption stage; audio/document/code do not. See pipelines/{kind}/fast.yaml.

Metadata-tier extraction (used by find, wc, the cat default, and as the input for fast/accurate pipelines) is Rust-native (~60ms / 700 files).

Performance

Benchmarked on Apple Silicon (M-series), 702 files (7.2GB):

Operation	Latency
Metadata scan (702 files)	8ms
CLI cold start (find --format json)	60ms
CLI cold start (find --schema --format json)	109ms
CLI cold start (sql)	300ms
Fast code extraction	~52ms
Fast image extraction	~61ms
Fast PDF text extraction	~220ms
Fast video metadata	<100ms
PDF page mosaic (per page)	~10ms
Video keyframe mosaic (48 frames)	~1s

Storage

mm uses a global SQLite database at ~/.local/share/mm/mm.db with sqlite-vec for vector search:

Table	Contents	Relationship
files	File metadata + content (one row per file, uri = absolute path)	—
extractions	LLM-generated summaries (many per file)	FK → files.uri
chunks	~2048-char content chunks (mode = 'fast' or 'accurate')	FK → extractions.id
chunks_vec	Embedding vectors (sqlite-vec virtual table)	FK → chunks.id
cache	Key-value result cache	—

The files table includes metadata columns (path, size, kind, etc.) and content columns (content_hash, text_preview, line_count, duration_s, exif_*, video_codec, etc.).

Use mm config reset-db to clear all databases and caches.

Pipelines — encode + generate

Pipelines are YAML configs under pipelines/{kind}/{mode}.yaml that pair an encoder with optional LLM generation parameters. When generate is null, the pipeline is encode-only (no LLM call). Encoders are Python classes under encoders/ that convert media files into VLM-ready Messages. See [docs/PIPELINES.md](docs/PIPELINES.md) and [docs/ENCODERS.md](docs/ENCODERS.md) for the full pipeline and encoder reference.

Pipeline fields can be overridden from the CLI:

mm cat photo.jpg -m accurate --encode.strategy tile --generate.max-tokens 1024
mm cat photo.jpg -m accurate --generate.temperature 0.5

# Override individual strategy_opts entries (repeatable KEY=VALUE form;
# values are coerced to int/float/bool when possible).
mm cat photo.jpg -m accurate --encode.strategy_opts max_width=768
mm cat video.mp4 -m accurate --encode.strategy_opts max_width=768 --encode.strategy_opts fps=5

# Print a built-in pipeline's YAML as a starting point for your own.
mm cat --print-pipeline image/accurate

Load explicit pipeline YAML(s) with -p (repeatable, dispatched by kind field):

mm cat photo.jpg -p my-image-pipeline.yaml
mm cat *.jpg *.mp4 -p image-pipeline.yaml -p video-pipeline.yaml
mm cat photo.jpg -p ~/custom.yaml --generate.max-tokens 512

Custom pipeline paths can also be set in ~/.config/mm/mm.toml:

[pipelines]
image.fast = "/path/to/my-image-fast.yaml"
video.accurate = "/path/to/my-video-accurate.yaml"

LLM Configuration using Profiles

For accurate mode, mm uses the openai Python SDK to call any OpenAI-compatible API. Provider settings are managed through profiles — named configurations stored in ~/.config/mm/mm.toml.

Quick setup

mm config init                # create config with default profile (local Ollama)
mm config show                # show resolved config with sources

Managing profiles

Each profile stores base_url, api_key, and model. You can have as many as you need — one per provider, one per use-case, etc.

# Add custom profiles
mm profile add openai --base-url https://api.openai.com/v1 --api-key sk-... --model gpt-4o
mm profile add openrouter --base-url https://openrouter.ai/api/v1 --model qwen/qwen3.5-27b

# Update reserved profiles (ollama, gemini, vlmrun)
mm profile update ollama --base-url http://localhost:11434 --model qwen3.5:9B

# List all profiles (● = active)
mm profile list

# Switch the active profile
mm profile use openai

# Update a field on an existing profile
mm profile update openai --model gpt-4o-mini --api-key sk-new-key

# Remove a profile (cannot remove the active one)
mm profile remove openai

Selecting a profile per-command

# --profile flag (one-off override, does not change active profile)
mm --profile openai cat photo.png -m accurate

# Environment variable
MM_PROFILE=openai mm cat photo.png -m accurate

Resolution order

Provider settings (base_url, api_key, model) come from the active profile, falling back to built-in defaults.

The active profile is resolved as:

--profile flag  >  MM_PROFILE env  >  active_profile in config file  >  "ollama"

Config file format

# ~/.config/mm/mm.toml
active_profile = "ollama"

[profile.ollama]
base_url = "http://localhost:11434"
api_key = ""
model = "qwen3.5:0.8"

[profile.gemini]
base_url = "https://openrouter.ai/api/v1"
api_key = "<OPENROUTER_API_KEY>"
model = "google/gemini-2.5-flash-lite"

[profile.vlmrun]
base_url = "https://api.vlm.run/v1/openai"
api_key = "<VLMRUN_API_KEY>"
model = "Qwen/Qwen3.5-0.8B"

License

MIT