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Comprehensive, type-safe Python library for building AI-powered applications. Embed Noxus capabilities directly into your Python projects.

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Key Features

Complete Platform Access

Programmatically manage flows, agents, conversations, knowledge bases, and all platform features through a unified Python interface.

Type Safety

Full type hints and Pydantic models provide excellent IDE support, autocomplete, and compile-time error checking.

Async Support

Built-in async/await patterns for high-performance, concurrent operations and non-blocking I/O.

Event Streaming

Real-time streaming of flow executions, conversation responses, and agent actions.

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Common Use Cases

Integrate conversational AI, flows, or knowledge base search directly into your Python applications:
from noxus_sdk.client import Client

client = Client(api_key="your_api_key")

# Get a flow and run it
flow = client.workflows.get("wf_123")
run = flow.run(
    body={"user_query": "Analyze this data"}
)

# Query knowledge base
results = client.knowledge_bases.search(
    kb_id="kb_456",
    query="What's our refund policy?"
)
Build custom interfaces, dashboards, or tools on top of Noxus:
# Create a custom chatbot interface
conversation = client.conversations.create(
    agent_id="agent_789",
    name="Customer Support Session"
)

# Stream responses
for chunk in conversation.chat_stream("How do I reset my password?"):
    print(chunk.content, end="", flush=True)
Programmatically create, manage, and execute complex AI flows:
# Build flow programmatically
flow = client.flows.create_builder()
flow.add_node("generate_text", {
    "prompt": "Write a summary",
    "model": "gpt-4"
})
flow.add_node("save_to_file", {
    "filename": "summary.txt"
})
flow.connect("generate_text", "save_to_file")

# Deploy and run
deployed = flow.deploy("Document Summarizer")
result = deployed.run({"document": "..."})
Integrate AI processing into data pipelines and ETL flows:
import pandas as pd

# Process data through Noxus flows
df = pd.read_csv("customer_feedback.csv")

# Pre-fetch the workflow object
flow = client.workflows.get("sentiment_analysis")

for idx, row in df.iterrows():
    run = flow.run(
        body={"text": row['feedback']}
    )
    result = run.wait(output_only=True)
    df.at[idx, 'sentiment'] = result['sentiment']
    df.at[idx, 'category'] = result['category']

df.to_csv("analyzed_feedback.csv")
Create automation scripts for recurring AI tasks:
import schedule
import time
from datetime import datetime

# Pre-fetch the workflow object
flow = client.workflows.get("daily_analytics")

def daily_report():
    # Generate daily AI-powered report
    run = flow.run(
        body={"date": str(datetime.today())}
    )
    result = run.wait(output_only=True)
    
    # Email results
    send_email(result['report'])

schedule.every().day.at("09:00").do(daily_report)

while True:
    schedule.run_pending()
    time.sleep(60)

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Getting Started

1

Install the SDK

pip install noxus-sdk
2

Get API Key

Generate an API key from your Noxus workspace settings
3

Initialize Client

from noxus_sdk.client import Client

client = Client(api_key="your_api_key_here")
4

Start Building

Explore flows, conversations, and knowledge bases

SDK Documentation

Complete SDK documentation with examples and API reference