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RDK Python SDK

RDK (Reinforcement Development Kit) is a lightweight Python library for tracing LLM applications. It provides automatic instrumentation for popular LLM providers and frameworks, giving you complete visibility into your AI application’s behavior.

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Why RDK?

Building LLM applications requires understanding what’s happening under the hood. RDK helps you:
  • Debug faster - See exactly what prompts are sent and responses received
  • Monitor costs - Track token usage across all your LLM calls
  • Optimize performance - Identify slow calls and bottlenecks
  • Ensure compliance - Automatically redact PII from traces

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Key Features

Auto-instrumentation

Automatic tracing for Anthropic, OpenAI, LangChain, and Gemini SDKs

PII Redaction

Built-in redaction for emails, phone numbers, SSNs, API keys, and custom patterns

Minimal Overhead

Async batching with configurable flush intervals

Framework Agnostic

Works with FastAPI, Flask, or any Python application

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Supported Integrations

ProviderAuto-instrumentationNotes
AnthropicYesAuto-instrumented at init()
OpenAIYesAuto-instrumented at init()
LangChainYesAuto-instrumented at init()
Google GeminiYesAuto-instrumented at init()
BAMLManualRequires b = instrument_baml(b) after init()

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Quick Example

from rdk import init, observe
from anthropic import Anthropic

# Initialize RDK
init(
    endpoint="https://collector.021labs.ai",
    api_key="your-api-key"
)

# All LLM calls are automatically traced
@observe(name="my-chat")
def chat(message: str) -> str:
    client = Anthropic()
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        messages=[{"role": "user", "content": message}]
    )
    return response.content[0].text

result = chat("Hello, world!")

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Next Steps

Quickstart

Get up and running in 5 minutes

Integrations

Set up your LLM provider