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The Netra.init() function configures the SDK and starts the tracing system. Call it once at the start of your application, before making any LLM or database calls.

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Quick Start

from netra import Netra

Netra.init(
    app_name="my-ai-app",
    environment="production",
)

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Configuration Parameters

ParameterTypeDefaultDescription
app_name / appNamestringRequiredApplication name for identifying traces in the dashboard
environmentstring"default" (Py) / "local" (TS)Deployment environment (e.g., production, staging, development)
headersstring""Authentication headers, typically x-api-key=YOUR_KEY
trace_content / traceContentbooleantrueCapture prompt/completion content from LLM calls. Disable for privacy.
debug_mode / debugModebooleanfalseEnable verbose logging for troubleshooting
disable_batch / disableBatchbooleanfalseSend spans immediately instead of batching
enable_root_span / enableRootSpanbooleanfalseCreate a root span for the entire process (useful for workers)
resource_attributes / resourceAttributesdict/object{}Custom attributes added to every span
blocked_spans / blockedSpanslist/array[]Span name patterns to exclude (supports * wildcards)
enable_scrubbingbooleanfalseAuto-redact detected PII (Python only)
instrumentsSet/listAllSpecific instrumentations to enable
block_instruments / blockInstrumentsSet/list[]Instrumentations to disable
For instrumentation control details, see Instrumentation Selection.

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Complete Example

import os
from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

Netra.init(
    # Core settings
    app_name="production-ai-service",
    environment="production",
    headers=f"x-api-key={os.getenv('NETRA_API_KEY')}",

    # Content and privacy
    trace_content=True,
    enable_scrubbing=False,

    # Performance
    disable_batch=False,

    # Debugging
    debug_mode=False,

    # Long-running processes
    enable_root_span=False,

    # Custom metadata added to all spans
    resource_attributes={
        "service.version": os.getenv("APP_VERSION"),
        "deployment.region": os.getenv("AWS_REGION"),
        "team": "ml-platform",
    },

    # Span filtering (supports wildcards)
    blocked_spans=[
        "health-check",   # Exact match
        "internal.*",     # Prefix match
        "*.debug",        # Suffix match
    ],

    # Instrumentation control
    block_instruments={
        InstrumentSet.HTTPX,
        InstrumentSet.REQUESTS,
    },
)
PII scrubbing (enable_scrubbing) adds processing overhead. Only enable when handling sensitive data.

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Configuration Precedence

Configuration values are resolved in order of priority:
  1. Code parameters - Values passed to Netra.init()
  2. Netra environment variables - NETRA_* variables
  3. OpenTelemetry environment variables - OTEL_* variables
  4. Default values - SDK defaults

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Async Initialization (TypeScript)

The Netra.init() method in TypeScript is async and waits for all instrumentations to be ready before returning. Always await the call to ensure proper instrumentation:
TypeScript
async function main() {
  // init() is async and waits for instrumentations to be ready
  await Netra.init({
    appName: "my-ai-app",
    environment: "production",
  });
  // SDK is fully initialized, all instrumentations are patched
}
Always await Netra.init() to ensure libraries like OpenAI, Anthropic, and LangGraph are properly instrumented before use. This is especially important in frameworks like NestJS where modules are loaded after initialization.

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Shutdown

Ensure all pending spans are exported before application exit: 
Netra.shutdown()
Always call shutdown() before exit, especially for short-lived processes like serverless functions.

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Next Steps

Last modified on February 3, 2026