Installation

model-ledger requires Python 3.10+. The core is deliberately tiny (httpx + pydantic only); everything else is an opt-in extra, so you install just the surfaces and backends you use.

pip install model-ledger          # core: SDK + dependency graph + connectors
# or
uv add model-ledger

Extras

Install	Adds	For
model-ledger	SDK, graph, SQL/REST/GitHub connectors	the core library
model-ledger[mcp]	MCP server (model-ledger mcp)	AI agents — Claude, Goose, Cursor
model-ledger[rest-api]	FastAPI app (model-ledger serve)	frontends, dashboards
model-ledger[cli]	Typer + Rich CLI	terminal use
model-ledger[snowflake]	Snowflake backend	production storage
model-ledger[introspect-sklearn]	scikit-learn introspector	extract algorithm/features from fitted models
model-ledger[introspect-xgboost]	XGBoost introspector	"
model-ledger[introspect-lightgbm]	LightGBM introspector	"
model-ledger[excel]	openpyxl	spreadsheet import/export
model-ledger[all]	Snowflake + pandas + httpx	the common production set

Combine them: pip install "model-ledger[mcp,rest-api,snowflake]".

Which extra for which surface

• Python SDK — core install is enough.
• Talk to it from an agent — [mcp], then claude mcp add model-ledger -- model-ledger mcp (see the Agent guide).
• Serve it over HTTP — [rest-api], then model-ledger serve (see Backends).
• From the terminal — [cli] (see the CLI guide).

Next: the 60-second quickstart.