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Documentation Index

Fetch the complete documentation index at: https://docs.runcascade.com/llms.txt

Use this file to discover all available pages before exploring further.

Get up and running with Cascade in minutes. This guide covers installation, API key setup, and sending your first trace.

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Install the SDK

Install the Cascade SDK from PyPI: 
pip install cascade-sdk
Requires Python 3.8 or higher.

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Set your credentials

Get your API key and endpoint from the Cascade Dashboard. Both are provided per customer. Create a .env file in the root of your project:
.env
CASCADE_API_KEY="csk_live_..."
CASCADE_ENDPOINT="https://your-endpoint.runcascade.com/v1/traces"
Or pass them directly in code: 
from cascade import init_tracing

init_tracing(api_key="csk_live_...", endpoint="https://your-endpoint.runcascade.com/v1/traces")

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Initialize tracing

Call init_tracing() once at the top of your application. Everything else is automatic. 
from cascade import init_tracing

# With a project name (recommended - organizes traces in the dashboard)
init_tracing(project="customer_support_chatbot")

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Send your first trace

Wrap your agent’s entry point with trace_run() and use wrap_llm_client() to automatically capture all LLM interactions:
my_agent.py
from cascade import init_tracing, trace_run, wrap_llm_client
from anthropic import Anthropic

# Initialize tracing
init_tracing(project="my_first_agent")

# Wrap your LLM client - all calls are now automatically traced
client = wrap_llm_client(Anthropic())

# Create a traced agent run
with trace_run("SimpleAgent"):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=100,
        messages=[
            {"role": "user", "content": "Say hello and introduce yourself in one sentence."}
        ]
    )
    print(response.content[0].text)
Run the script: 
python my_agent.py
Open the Cascade Dashboard to see your trace with full details including prompts, completions, token counts, latency, and cost.

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Next steps

Sessions & Tracing

Learn about tracing runs, sub-agents, tools, and LLM clients.

Evaluation SDK

Score your agent’s performance with built-in and custom evaluators.

Integrations

Auto-instrument LangGraph, OpenAI Agents, and Claude Agents.

Model providers

Trace any LLM provider through a unified wrapper.