> ## Documentation Index
> Fetch the complete documentation index at: https://docs.021labs.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Get started with RDK in 5 minutes
## Installation
Install RDK using pip or uv:
```bash pip theme={null}
pip install rdk --extra-index-url https://pypi.fury.io/021labs/
```
```bash uv theme={null}
uv add rdk --extra-index-url https://pypi.fury.io/021labs/
```
## Basic Setup
### 1. Set your API key
```bash theme={null}
export RDK_API_KEY="your-api-key"
```
### 2. Initialize and trace
Call `init()` to start tracing — all LLM calls are captured automatically:
```python Anthropic theme={null}
from rdk import init, shutdown
from anthropic import Anthropic
init()
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello!"}]
)
shutdown()
```
```python OpenAI theme={null}
from rdk import init, shutdown
from openai import OpenAI
init()
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
shutdown()
```
```python Gemini theme={null}
from rdk import init, shutdown
import google.generativeai as genai
init()
model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content("Hello!")
shutdown()
```
Use `@observe` to group multiple calls into a single trace:
```python Anthropic theme={null}
from rdk import observe
from anthropic import Anthropic
@observe(name="chat-completion")
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
```
```python OpenAI theme={null}
from rdk import observe
from openai import OpenAI
@observe(name="chat-completion")
def chat(message: str) -> str:
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": message}]
)
return response.choices[0].message.content
```
```python Gemini theme={null}
from rdk import observe
import google.generativeai as genai
@observe(name="chat-completion")
def chat(message: str) -> str:
model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content(message)
return response.text
```
### 3. Shutdown gracefully
Flush remaining traces before your app exits:
```python theme={null}
from rdk import shutdown
shutdown()
```
## Complete Example
```python theme={null}
from rdk import observe, shutdown
from anthropic import Anthropic
@observe(name="summarize")
def summarize(text: str) -> str:
client = Anthropic()
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=500,
messages=[{"role": "user", "content": f"Summarize this text:\n\n{text}"}]
)
return response.content[0].text
result = summarize("Your long text here...")
print(result)
shutdown()
```
## Custom Configuration
Need custom settings? Call `init()` before your first `@observe` call:
```python theme={null}
from rdk import init
init(
sample_rate=0.1, # Capture 10% of traces
redact_pii=True, # Remove emails, phones, SSNs, etc.
)
```
## Configuration Options
| Parameter | Type | Default | Description |
| ---------------------- | -------- | ------------------------------ | -------------------------------------------------- |
| `api_key` | str | `RDK_API_KEY` env var | Your RDK API key |
| `endpoint` | str | `https://collector.021labs.ai` | Collector URL |
| `batch_size` | int | 10 | Number of spans per batch |
| `flush_interval` | float | 5.0 | Seconds between auto-flushes |
| `timeout` | float | 30.0 | HTTP request timeout in seconds |
| `sample_rate` | float | 1.0 | Fraction of traces to capture (0–1) |
| `redact_pii` | bool | False | Enable built-in PII redaction |
| `redactor` | Callable | None | Custom redaction function |
| `debug` | bool | False | Enable verbose debug logging |
| `enabled` | bool | True | Set to `False` to disable all tracing |
| `mode` | str | `"default"` | Operating mode: `"default"`, `"test"`, or `"eval"` |
| `on_error` | Callable | None | Callback for transport errors |
| `instrument_langchain` | bool | True | Auto-instrument LangChain |
| `instrument_anthropic` | bool | True | Auto-instrument Anthropic SDK |
| `instrument_openai` | bool | True | Auto-instrument OpenAI SDK |
| `instrument_gemini` | bool | True | Auto-instrument Gemini SDK |
See [init()](/api-reference/init) for full documentation.
## Environment Variables
| Variable | Effect |
| -------------- | ------------------------------------------ |
| `RDK_API_KEY` | API key for authentication |
| `RDK_ENDPOINT` | Override the default collector URL |
| `RDK_MODE` | Operating mode (`default`, `test`, `eval`) |
## What Gets Traced?
RDK automatically captures all LLM calls:
* **Token Usage** — Prompt, completion, and total tokens
* **Cost** — Calculated automatically based on model pricing
* **Timing** — Start time, end time, and duration
* **Model Info** — Model name and provider
* **Errors** — Exception messages
Input messages and output content are **not** captured by default. Enable them with `capture_input=True` and `capture_output=True` on `@observe`.
## Next Steps
Set up Claude tracing
Protect sensitive data
Write tests without real API calls
Simulate tool calls for fast iteration
Full parameter reference