semvec/pypi-packages huggingface dataset:
moltrust 0.1.0
MolTrust SDK - Trust Layer for the Agent Economy
License: MIT
Requires Python: >=3.9
Keywords: ai, agents, trust, identity, did, verifiable-credentials, reputation

# MolTrust SDK

**Trust Layer for the Agent Economy.**

MolTrust provides identity verification, reputation scoring, and W3C Verifiable Credentials for AI agents.

## Install
```bash
pip install moltrust
```

## Quickstart
```python
from moltrust import MolTrust

mt = MolTrust(api_key="mt_your_key")

# Register an agent
agent = mt.register("MyAgent")
print(agent.did)  # did:moltrust:a1b2c3d4...

# Issue a Verifiable Credential
vc = mt.issue_credential(agent.did)
print(vc.is_signed)  # True

# Verify the credential
result = mt.verify_credential(vc)
print(result.valid)  # True
```

## Features

- **Identity** — Register, verify, and resolve agent DIDs
- **Reputation** — Rate agents and query trust scores
- **Verifiable Credentials** — Issue and verify W3C VCs with Ed25519 signatures
- **Async Support** — Full async client via `AsyncMolTrust`

## Integration Examples

### LangChain Tool
```python
from langchain.tools import tool
from moltrust import MolTrust

mt = MolTrust(api_key="mt_...")

@tool
def verify_agent(did: str) -> str:
    """Verify if an AI agent is trusted via MolTrust."""
    if mt.verify(did):
        rep = mt.get_reputation(did)
        return f"Verified. Trust score: {rep.score}/5 ({rep.total_ratings} ratings)"
    return "Agent not found."
```

### Pre-Transaction Check
```python
mt = MolTrust(api_key="mt_...")

def safe_transact(counterparty_did: str):
    rep = mt.get_reputation(counterparty_did)
    if not rep.is_trusted:
        raise Exception(f"Not trusted (score: {rep.score})")
    vc = mt.issue_credential(counterparty_did, "TransactionCredential")
    return vc
```

## Standards

- **W3C DID:web** — Decentralized Identifiers
- **W3C Verifiable Credentials** — Tamper-proof credentials
- **Ed25519** — Elliptic curve signatures
- **Lightning Network** — Bitcoin L2 payments

## Links

- **API Docs:** https://api.moltrust.ch/docs
- **DID Document:** https://api.moltrust.ch/.well-known/did.json
- **Website:** https://moltrust.ch
- **X:** [@moltrust](https://x.com/moltrust)

## License

MIT — CryptoKRI GmbH, Zurich, Switzerland
dg-kit 0.1.2
Data Governance Kit provides programmatic access to data governance metadata with integrations for different Logical and Physical Modeling tools.
License: MIT
Requires Python: >=3.10

## Data Governance Kit (dg_kit)

Data Governance Kit helps you access Data Governance information programmatically.
It provides core objects that model Physical Model, Logical Model, Business Information,
and related governance metadata. Integrations let you pull this data from tools like
dbt, Oracle Data Modeler, and Notion, with more connectors planned in upcoming releases.

This toolkit is handy for building Data Governance CI gates, strengthening Data Ops
practices, and keeping governance checks close to your delivery workflows.

## Requirements
- Python >= 3.10

## Install
```bash
pip install -e .
```

Optional extras:
```bash
pip install -e ".[dbt]"
pip install -e ".[notion]"
```

## Quick Start

### Parse an Oracle Data Modeler project
```python
from dg_kit.integrations.odm.parser import ODMParser

parser = ODMParser("path/to/model.dmd")
bi = parser.parse_bi()
lm = parser.parse_lm()

print(lm.version, len(lm.entities))
```

### Parse a dbt project into a physical model
```python
from dg_kit.integrations.dbt.parser import DBTParser

pm = DBTParser("path/to/dbt_project").parse_pm()
print(pm.version, len(pm.tables))
```

### Validate with conventions
```python
from dg_kit.base.convention import Convention, ConventionValidator
from dg_kit.base.enums import ConventionRuleSeverity

convention = Convention("example")

@convention.rule(
    name="has-entities",
    severity=ConventionRuleSeverity.ERROR,
    description="Logical model must contain at least one entity",
)
def has_entities(lm, pm):
    return set() if lm.entities else {("no entities")}

issues = ConventionValidator(lm, pm, convention).validate()
```

### Sync to Notion data catalog
```python
from dg_kit.integrations.notion.api import NotionDataCatalog

catalog = NotionDataCatalog(
    notion_token="secret",
    dc_table_id="data_source_id",
)
rows = catalog.pull()
print(len(rows))
```

## Development
Run tests:
```bash
pytest
```

Export requirements with uv:
```bash
uv export --extra dbt --extra notion --group test -o requirements.txt
```