openstat-sdk 0.2.2

Native telemetry SDK for AI agents, trading bots, and on-chain workflows

OpenStat Python SDK

Native telemetry for AI agents, trading bots, and on-chain workflows.

OpenStat gives your app a structured timeline for agent runs: heartbeats, model completions, decisions, risk checks, paper orders, fills, positions, PnL snapshots, tool calls, errors, and chain transaction metadata.

Features

• Agent-native events - send OpenStat lifecycle, decision, risk, trading, model, tool, error, and chain transaction events with one Python client.
• Trading timeline helpers - record orders, fills, positions, and PnL snapshots using OpenStat-compatible event fields.
• Run correlation - preserve run_id, strategy, symbol, venue, agent identity, trace IDs, span IDs, tags, and metadata across the whole run.
• Vendor-neutral model telemetry - track provider, model, status, latency, token counts, and stage metadata without coupling your app to one LLM vendor.
• OpenTelemetry-ready config - generate OTLP/HTTP URLs and auth headers for traces, logs, and metrics exporters.
• Project scaffolding - run openstat init to add starter files to an existing Python app.
• Zero runtime dependencies - the core SDK uses only the Python standard library.

Installation

Minimal SDK:

pip install openstat-sdk

For local development and tests:

pip install -e ".[test]"

Quick Start

from openstat import OpenStatClient

client = OpenStatClient(
    api_key="ostat_...",
    service_name="paper-trader",
    environment="production",
)

run = client.start_agent_run(strategy="breakout")
run_id = run["run_id"]
agent = {"id": "agent-1", "name": "Paper Trader"}

client.record_run_lifecycle(
    run_id=run_id,
    agent=agent,
    status="running",
    strategy="breakout",
    symbols=["BTC-USD"],
    summary="Run started.",
)

client.record_decision(
    run_id=run_id,
    agent=agent,
    strategy="breakout",
    symbol="BTC-USD",
    venue="paper",
    action="enter_long",
    confidence=82,
    rationale_summary="Momentum and risk budget aligned.",
)

client.record_run_lifecycle(
    run_id=run_id,
    agent=agent,
    status="completed",
    strategy="breakout",
    symbols=["BTC-USD"],
    summary="Run completed.",
)

endpoint defaults to https://api.openstat.online. Set OPENSTAT_ENDPOINT=http://localhost:4000 only when sending telemetry to a local OpenStat API during development.

Trading Agent Example

Use the trading helpers when you want OpenStat to render the full paper-trading timeline for one agent run.

decision_id = "decision-lp-1"

client.record_decision(
    run_id=run_id,
    agent=agent,
    decision_id=decision_id,
    strategy="paper-lp-v1",
    symbol="ETH-USDC",
    venue="paper",
    action="open",
    confidence=76,
)

client.record_risk_check(
    run_id=run_id,
    agent=agent,
    decision_id=decision_id,
    result="approved",
    reason="Within exposure and volatility limits.",
    metadata={
        "strategy": "paper-lp-v1",
        "symbol": "ETH-USDC",
        "risk_score": 32,
    },
)

client.record_order(
    run_id=run_id,
    agent=agent,
    strategy="paper-lp-v1",
    symbol="ETH-USDC",
    venue="paper",
    side="buy",
    order_type="market",
    quantity="1000",
    price="1",
    decision_id=decision_id,
    metadata={
        "paper_notional_usd": 1000,
        "estimation_method": "shadow_yield_only_v1",
    },
)

client.record_fill(
    run_id=run_id,
    agent=agent,
    strategy="paper-lp-v1",
    symbol="ETH-USDC",
    venue="paper",
    side="buy",
    quantity="1000",
    price="1",
    fee="1.00",
)

client.record_position(
    run_id=run_id,
    agent=agent,
    strategy="paper-lp-v1",
    symbol="ETH-USDC",
    venue="paper",
    quantity="1000",
    average_price="1",
    metadata={"market_value": "1000"},
)

client.record_pnl_snapshot(
    run_id=run_id,
    agent=agent,
    strategy="paper-lp-v1",
    symbol="ETH-USDC",
    realized_pnl="0",
    unrealized_pnl="3.42",
    equity="10003.42",
    metadata={"estimated": True, "venue": "paper"},
)

For domain-specific systems such as LP research, keep OpenStat's accepted trading fields (side, quantity, price, fee, order_type) in the event data and put pool addresses, range state, paper notional, or estimation methods inside metadata.

CLI

Add editable OpenStat starter files to an existing Python project:

openstat init

Choose a target path:

openstat init --path ./my-agent

Overwrite existing starter files:

openstat init --force

The command creates:

File	Purpose
.env.openstat.example	Environment variable template
openstat_integration.py	Small client factory for your app
OPENSTAT.md	Local integration notes

Configuration

The SDK constructor accepts explicit values, but most apps keep secrets and deployment-specific settings in environment variables.

Variable	Description
OPENSTAT_API_KEY	API key from the OpenStat dashboard
OPENSTAT_ENDPOINT	API endpoint, defaults to hosted OpenStat
OPENSTAT_SERVICE_NAME	App or service family name
OPENSTAT_ENVIRONMENT	Deployment environment, such as dev or prod

OpenTelemetry

Generate OTLP/HTTP targets and headers for OpenTelemetry exporters:

from openstat import create_opentelemetry_http_config

otlp = create_opentelemetry_http_config(
    api_key="ostat_...",
    service_name="paper-trader",
)

print(otlp["traces"]["url"])
print(otlp["traces"]["headers"])

Examples

The repository includes a runnable trading-agent example:

python examples/trading_agent.py

Development

python -m pip install -e ".[test]"
python -m pytest

Build a local distribution:

python -m pip install build
python -m build

Links

• Homepage: https://openstat.online
• Documentation: https://docs.openstat.online
• Source: https://github.com/kezuflow/openstat

License

MIT