Model Ledger
Model inventory and governance: trace deployed models and their dependencies as an append-only log
Open source Open in the app JSON README (API)
About
Model inventory and governance: trace deployed models and their dependencies as an append-only log
Details
- Kind
- MCP servers
- Topic
- Developer tools
- Publisher
- block
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.7.13
- Stars
- 14
- Forks
- 6
- Last push
- 2026-07-25T19:29:51Z
- Repository state
- ativo
- Language
- Python
- License
- Apache-2.0
- Added
- 2026-08-29 03:02:31
- Updated
- 2026-08-29 03:02:31
- Origin id
io.github.block/model-ledger
README
# model-ledger
**git for models** — know what models you have deployed, where they run, what they depend on, and what changed.
[](https://github.com/block/model-ledger/actions/workflows/ci.yml)
[](LICENSE)
[](https://python.org)
[](https://pypi.org/project/model-ledger/)
[](https://pypistats.org/packages/model-ledger)
[](https://block.github.io/model-ledger/)
📖 **[Documentation](https://block.github.io/model-ledger/)** ·
[Quickstart](https://block.github.io/model-ledger/quickstart/) ·
[Concepts](https://block.github.io/model-ledger/concepts/) ·
[Governance](https://block.github.io/model-ledger/governance/)
---
model-ledger is a model inventory for any organization with deployed models. It
**discovers** models, heuristic rules, and ETL across your platforms, maps the
**dependency graph** automatically, and records **every change as an immutable event**.
Unlike registries tied to a single platform (MLflow, SageMaker, W&B), it spans all of
them — as one connected graph — and it's built to be driven by AI agents through a
native MCP server.
Benchmarked at production scale: full inventory reconstruction over a ledger of 28.8k
models and 212k events runs in under a second ([CHANGELOG, v0.7.4](CHANGELOG.md)).
## Install
```bash
pip install model-ledger
```
## The graph builds itself
Every model is a `DataNode` with typed input and output ports. When an output port name
matches an input port name, `connect()` creates the dependency edge — no hand-wiring.
```python
from model_ledger import Ledger, DataNode
ledger = Ledger()
ledger.add([
DataNode("segmentation", platform="etl", outputs=["customer_segments"]),
DataNode("fraud_scorer", platform="ml", inputs=["customer_segments"], outputs=["risk_scores"]),
DataNode("fraud_alerts", platform="alerting", inputs=["risk_scores"]),
])
ledger.connect()
ledger.trace("fraud_alerts")
# ['segmentation', 'fraud_scorer', 'fraud_alerts']
```
Every mutation is recorded as an immutable **Snapshot** — an append-only event log that
gives you full history and point-in-time reconstruction, because nothing is overwritten.
## Talk to your inventory
The MCP server is a first-class surface — point Claude (or any MCP agent) at it:
```bash
pip install "model-ledger[mcp]"
claude mcp add model-ledger -- model-ledger mcp --demo
```
> **You:** if we deprecate `customer_features`, what breaks?
>
> **Claude:** 3 models consume it directly, 2 more transitively.
## Documentation
Everything lives at **[block.github.io/model-ledger](https://block.github.io/model-ledger/)** —
and it can't drift, because the API reference is generated from source and every example
runs in CI:
- **[Quickstart](https://block.github.io/model-ledger/quickstart/)** — install to your first dependency trace in 60 seconds
- **[Concepts](https://block.github.io/model-ledger/concepts/)** — DataNode, Snapshot, and Composite, in three ideas
- **[Agents (MCP)](https://block.github.io/model-ledger/guides/agents/)** — the eight-tool agent surface, with a worked transcript
- **[Connectors](https://block.github.io/model-ledger/guides/connectors/)** — discover from SQL, REST, GitHub, or your own platform
- **[Backends](https://block.github.io/model-ledger/guides/backends/)** — in-memory, SQLite, JSON, Snowflake, or remote HTTP
- **[Governance](https://block.github.io/model-ledger/governance/)** — how the primitives map to SR 11‑7/SR 26‑2, the EU AI Act, and NIST AI RMF
- **[API reference](https://block.github.io/model-ledger/reference/)** — generated from the source
## Architecture
```mermaid
flowchart LR
subgraph Sources
C1[SQL / REST / GitHub / Prefect<br/>connectors]
end
subgraph Core
L[Ledger<br/>append-only event log,<br/>point-in-time reconstruction]
G[Dependency graph]
V[Compliance profiles<br/>SR 11-7/SR 26-2 · EU AI Act · NIST AI RMF]
end
subgraph Surfaces
S1[Python SDK]
S2[CLI]
S3[REST API]
S4[MCP server · 8 tools]
end
B1[(in-memory · SQLite · JSON ·<br/>Snowflake · remote HTTP)]
C1 --> L
L --> G
L --> V
L --- B1
S1 --> L
S2 --> L
S3 --> L
S4 --> L
```
## For organizations
The OSS core handles discovery, graph building, change tracking, storage, the agent
protocol, and compliance validation — the SR 11‑7/SR 26‑2, EU AI Act Annex IV, and
NIST AI RMF profiles ship in `model_ledger.validate`. Your internal package provides
only the thin layer on top: connector configs, custom connectors for internal
platforms, and credentials. Thin config, not reimplemented logic.
## Contributing
See [CONTRIBUTING.md](https://github.com/block/model-ledger/blob/main/CONTRIBUTING.md).
All commits require DCO sign-off.
## Security
See [SECURITY.md](SECURITY.md) for how to report vulnerabilities privately.
## License
Apache-2.0. See [LICENSE](https://github.com/block/model-ledger/blob/main/LICENSE).
Created and maintained by [Vignesh Narayanaswamy](https://github.com/vigneshnarayanaswamy) at Block.
<!-- mcp-name: io.github.block/model-ledger -->