Metadata-Version: 2.4
Name: market2agent
Version: 2.0.0
Summary: Universal trust scoring for any entity on Earth.
Author-email: James Rausch <api@market2agent.ai>
Project-URL: Homepage, https://market2agent.ai
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: httpx>=0.24.0

# market2agent

Universal trust scoring for any entity on Earth. One API call, five pillars, one number.

## Install

```bash
pip install market2agent
```

## Quick start

```python
from market2agent import M2AClient

client = M2AClient("m2a_live_your_key")

# Score any entity
score = client.score("stripe.com")

print(score.grade)    # "A"
print(score.trust)    # 847
print(score.pillars)  # {"identity": 190, "competence": 185, ...}

if score.is_safe:
    proceed()
```

## What you can score

Pass anything — domains, companies, URLs, emails, social handles, agent IDs:

```python
client.score("stripe.com")
client.score("openai")
client.score("https://api.x.ai")
client.score("@elonmusk")
client.score("sketchy-payments.xyz")
```

## Five pillars

Every score is a weighted composite across five independent verification pillars:

- **Identity** — DNS, WHOIS, SSL, domain age
- **Competence** — Security headers, GitHub activity, infrastructure
- **Solvency** — Business registration, operational longevity
- **Reputation** — Blocklists, news sentiment, reviews
- **Network** — Social graph, partnerships, ecosystem position

## Compare

```python
result = client.compare("stripe.com", "sketchy-payments.xyz")
print(result.safer_entity)  # "stripe.com"
```

## Batch

```python
results = client.batch(["stripe.com", "shopify.com", "unknown.xyz"])
for r in results:
    print(f"{r.target}: {r.grade} ({r.score})")
```

## Async

```python
from market2agent import AsyncM2AClient

async with AsyncM2AClient("m2a_live_...") as client:
    score = await client.score("stripe.com")
```

## Get an API key

Email [api@market2agent.ai](mailto:api@market2agent.ai) or sign up at [market2agent.ai](https://market2agent.ai).

## License

MIT
