Skip to main content
Glama
System-R-AI

System R Risk Intelligence

Official
by System-R-AI

systemr

Python SDK for agents.systemr.ai — AI-native risk intelligence for trading agents.

PyPI Python License: MIT

Install

pip install systemr

Quick Start

from systemr import SystemRClient

client = SystemRClient(api_key="sr_agent_...")

# Position sizing ($0.003)
result = client.calculate_position_size(
    equity="100000",
    entry_price="185.50",
    stop_price="180.00",
    direction="long",
)
print(result["shares"], result["risk_amount"])

# Risk validation ($0.004)
risk = client.check_risk(
    symbol="AAPL",
    direction="long",
    entry_price="185.50",
    stop_price="180.00",
    quantity="100",
    equity="100000",
)
print(risk["approved"], risk["score"])

# Strategy evaluation ($0.10 - $1.00)
eval_result = client.basic_eval(r_multiples=["1.5", "-1.0", "2.3", "-0.5", "1.8"])
print(eval_result["g_score"], eval_result["verdict"])

Get an API Key

import httpx

resp = httpx.post("https://agents.systemr.ai/v1/agents/register", json={
    "owner_id": "your-id",
    "agent_name": "my-trading-agent",
    "agent_type": "trading",
})
data = resp.json()
print(data["api_key"])  # sr_agent_... (save this, shown only once)

API Reference

Agent Management

Method

Description

client.get_info()

Get agent info

client.list_agents()

List owner's agents

client.update_mode(mode)

Change mode (sandbox/live/suspended/terminated)

Position Sizing

Method

Cost

client.calculate_position_size(equity, entry_price, stop_price, direction)

$0.003

Risk Validation

Method

Cost

client.check_risk(symbol, direction, entry_price, stop_price, quantity, equity)

$0.004

Evaluation

Method

Cost

Description

client.basic_eval(r_multiples)

$0.10

G metric + verdict

client.full_eval(r_multiples)

$0.50

G + rolling G + System R Score

client.comprehensive_eval(r_multiples)

$1.00

Full analysis + impact

Billing

Method

Description

client.get_pricing()

Operation prices (no auth)

client.get_balance()

Current USDC balance

client.deposit(amount)

Record deposit

client.get_transactions()

Transaction history

client.get_usage()

Usage summary

Error Handling

from systemr import SystemRClient, AuthenticationError, InsufficientBalanceError, SystemRError

client = SystemRClient(api_key="sr_agent_...")

try:
    result = client.calculate_position_size(...)
except AuthenticationError:
    print("Invalid API key or agent inactive")
except InsufficientBalanceError:
    print("Deposit USDC to continue")
except SystemRError as e:
    print(f"API error {e.status_code}: {e.detail}")

Context Manager

with SystemRClient(api_key="sr_agent_...") as client:
    result = client.check_risk(...)
# connection automatically closed

MCP (Model Context Protocol)

System R is also available as an MCP server for AI assistants like Claude and ChatGPT. See the MCP documentation for configuration.

License

MIT

-
security - not tested
A
license - permissive license
-
quality - not tested

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/System-R-AI/system-r-risk-intelligence'

If you have feedback or need assistance with the MCP directory API, please join our Discord server