Install
$ agentstack add mcp-block-model-ledger ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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 · Quickstart · Concepts · 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
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.
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:
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 — and it can't drift, because the API reference is generated from source and every example runs in CI:
- Quickstart — install to your first dependency trace in 60 seconds
- Concepts — DataNode, Snapshot, and Composite, in three ideas
- Agents (MCP) — the eight-tool agent surface, with a worked transcript
- Connectors — discover from SQL, REST, GitHub, or your own platform
- Backends — in-memory, SQLite, JSON, Snowflake, or remote HTTP
- Governance — how the primitives map to SR 11‑7/SR 26‑2, the EU AI Act, and NIST AI RMF
- API reference — generated from the source
Architecture
flowchart LR
subgraph Sources
C1[SQL / REST / GitHub / Prefectconnectors]
end
subgraph Core
L[Ledgerappend-only event log,point-in-time reconstruction]
G[Dependency graph]
V[Compliance profilesSR 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 ·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. All commits require DCO sign-off.
Security
See [SECURITY.md](SECURITY.md) for how to report vulnerabilities privately.
License
Apache-2.0. See LICENSE.
Created and maintained by Vignesh Narayanaswamy at Block.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: block
- Source: block/model-ledger
- License: Apache-2.0
- Homepage: https://block.github.io/model-ledger/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.