oximy 0.0.2

Zero-overhead observability for LLM applications

Oximy Python SDK

Zero-overhead observability for LLM applications.

This library provides a lightweight wrapper for LLM client libraries that captures telemetry and enforces policies without blocking your application.

Full documentation is available at docs.oximy.com.

Installation

pip install oximy

Quick Start

from oximy import Oximy
from openai import OpenAI

oximy = Oximy(
    api_key=os.getenv("OXIMY_API_KEY"),
    project_id=os.getenv("OXIMY_PROJECT_ID"),
)

openai = oximy.wrap(OpenAI())

# Use exactly as before - telemetry is automatic
response = await openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}],
)

Configuration

oximy = Oximy(
    # Required
    api_key="ox_xxxxxxxx",
    project_id="proj_xxxxxxxx",
    
    # Optional
    environment="production",
    service="chat-api",
    version="1.2.3",
    metadata={"team": "platform"},
    timeout=100,
    debug=False,
    
    # Policy violation callback
    on_policy_violation=lambda violation: print(violation.rule_name, violation.action),
)

Per-Request Context

response = await openai.chat.completions.create(
    {
        "model": "gpt-4o",
        "messages": [{"role": "user", "content": "Hello"}],
    },
    oximy={
        "user_id": "user_123",
        "session_id": "sess_456",
        "trace_id": "trace_789",
        "tags": ["support", "billing"],
        "metadata": {"ticket_id": "TICKET-123"},
    },
)

Provider Support

Works with any OpenAI-compatible client.

OpenAI

openai = oximy.wrap(OpenAI())

Anthropic

from anthropic import Anthropic

anthropic = oximy.wrap(Anthropic())

OpenRouter

openrouter = oximy.wrap(
    OpenAI(
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
    )
)

Azure OpenAI

azure = oximy.wrap(
    OpenAI(
        api_key=os.getenv("AZURE_OPENAI_KEY"),
        base_url="https://your-resource.openai.azure.com/v1",
    )
)

Groq

groq = oximy.wrap(
    OpenAI(
        api_key=os.getenv("GROQ_API_KEY"),
        base_url="https://api.groq.com/openai/v1",
    )
)

Streaming

Streaming is fully supported. Telemetry is sent after the stream completes.

stream = await openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True,
)

async for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Captured Data

Category	Data
Request	Model, messages, tools, parameters
Response	Content, finish reason, tool calls
Usage	Input tokens, output tokens, cached tokens
Cost	Estimated cost (USD)
Timing	Duration, time-to-first-token
Errors	Type, message, status code
Context	User ID, session ID, trace ID, tags

Policy Enforcement

Policies are configured in the Oximy dashboard and enforced automatically. No code changes required.

Modes:

• Shadow - Log violations without blocking
• Quarantine - Log and alert, allow through
• Enforce - Block or redact violations

Local Rules (evaluated in SDK, < 5ms):

• Regex patterns (API keys, secrets)
• Deny/allow lists (models, keywords, MCP servers, tool calls)
• Contains matching (SQL injection patterns)
• Token limits
• Rate limits (per user/session/global)
• Cost limits (per user/session/global)

SLM Rules (evaluated via API, 50-200ms):

• AI-powered PII detection with pseudoanonymization
• Prompt injection detection
• Custom content classification

Fail-Open Design

The SDK never blocks your application:

• 100ms telemetry timeout
• Silent failure on network errors
• Zero runtime dependencies (except httpx)
• Falls back to enabled if config fetch fails

Requirements

• Python 3.12+
• httpx (for async HTTP requests)

Support

• Documentation: docs.oximy.com
• Email: [e-mail]