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SDK for interacting with AutoAgents.ai API

Project description

AutoAgents Core Python SDK

Enterprise-level AI Agent Building Platform Python SDK

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PyPI version License MIT

Professional Python SDK for AutoAgents AI platform, providing intuitive APIs for intelligent conversation, file processing, knowledge base management, and more.

Table of Contents

Why AutoAgents Core?

AutoAgents Core Python SDK is a comprehensive toolkit that transforms how developers interact with AI-powered automation systems. Built for modern Python applications, it provides seamless integration with the AutoAgents AI platform.

Core Features

Intelligent Conversation

  • Streaming Chat: Real-time conversation with multi-turn interactions
  • Reasoning Process: Display AI thinking and decision-making steps
  • Multi-modal Support: Handle text, images, and files in unified interface

File Processing

  • Multi-format Support: Automatic processing of PDF, Word, images, and more
  • Smart Analysis: Extract insights and content from documents
  • Batch Operations: Handle multiple files efficiently

Knowledge Base Management

  • Complete CRUD Operations: Create, read, update, delete knowledge bases
  • Advanced Search: Semantic search and content retrieval
  • Content Organization: Structured storage and management

Pre-built Agents

  • PowerPoint Generation: Create presentations from templates and data
  • React Agents: Interactive problem-solving agents
  • Workflow Automation: Complex multi-step task orchestration
  • Data Science Tools: Analytics and visualization capabilities

Modern Architecture

  • Async Support: High-performance asynchronous API calls
  • Type Safety: Full Pydantic type validation
  • Extensible Design: Modular components for custom solutions

Why Choose AutoAgents Core AI Python SDK?

  • Developer-First: Intuitive APIs designed for modern Python development
  • Production-Ready: Battle-tested in enterprise environments
  • Comprehensive: Everything needed for AI automation in one package
  • Well-Documented: Extensive examples and clear API documentation

Quick Start

Prerequisites

  • Python 3.11+
  • AutoAgents AI platform account

Installation

pip install autoagents-core

Get API Keys

  1. Log in to AutoAgents AI platform
  2. Navigate to Profile → Personal Keys
  3. Copy your personal_auth_key and personal_auth_secret

First Conversation

from autoagents_core.client import ChatClient

# Initialize client
client = ChatClient(
    agent_id="your_agent_id",
    personal_auth_key="your_auth_key", 
    personal_auth_secret="your_auth_secret"
)

# Start conversation
for event in client.invoke("Hello, please introduce artificial intelligence"):
    if event['type'] == 'token':
        print(event['content'], end='', flush=True)
    elif event['type'] == 'finish':
        break

File Processing

# Upload and analyze files
for event in client.invoke(
    prompt="Please analyze the main content of this document",
    files=["document.pdf"]
):
    if event['type'] == 'token':
        print(event['content'], end='', flush=True)

Knowledge Base Management

from autoagents_core.client import KbClient

# Initialize knowledge base client
kb_client = KbClient(
    personal_auth_key="your_auth_key",
    personal_auth_secret="your_auth_secret"
)

# Create knowledge base
result = kb_client.create_kb(
    name="Technical Documentation",
    description="Store technical documents"
)

# Query knowledge base list
kb_list = kb_client.query_kb_list()

Slide Generation

from autoagents_core.slide import SlideAgent

# Create slide agent
slide_agent = SlideAgent()

# Generate presentation
slide_agent.fill(
    prompt="Create a presentation about AI development",
    template_file_path="template.pptx",
    output_file_path="output.pptx"
)

Advanced Workflow Automation

from autoagents_core.graph import FlowGraph

# Create workflow graph
graph = FlowGraph(
    personal_auth_key="your_auth_key",
    personal_auth_secret="your_auth_secret"
)

# Add workflow nodes and compile
graph.add_node("chat_node", "chat", {"prompt": "Analyze this data"})
graph.add_node("ppt_node", "slide", {"template": "report.pptx"})
graph.add_edge("chat_node", "ppt_node")

# Deploy workflow
graph.compile(workflow_name="data_analysis_pipeline")

Getting Agent ID

  1. Open Agent details page
  2. Click "Share" → "API"
  3. Copy Agent ID

Examples

Explore the playground/ directory for comprehensive examples:

  • playground/client/ - Chat and API examples
  • playground/slide/ - PowerPoint generation examples
  • playground/kb/ - Knowledge base management
  • playground/react/ - React Agent examples
  • playground/datascience/ - Data analysis tools

Contributing

We welcome contributions! Please feel free to submit issues and pull requests.

Development Setup

git clone https://github.com/your-repo/autoagents-core-python-sdk.git
cd autoagents_core-python-sdk
pip install -e .[dev]

License

MIT License

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