gagiteck 0.2.1

Python SDK for Gagiteck AI SaaS Platform

Gagiteck Python SDK

Official Python client library for the Gagiteck AI SaaS Platform.

Installation

pip install gagiteck

Quick Start

Using the API Client

from gagiteck import Client

# Initialize the client
client = Client(api_key="ggt_your_api_key")

# List agents
agents = client.agents.list()
print(f"Found {len(agents['data'])} agents")

# Create an agent
agent = client.agents.create(
    name="My Assistant",
    system_prompt="You are a helpful assistant.",
    config={"model": "claude-3-sonnet"}
)
print(f"Created agent: {agent['id']}")

# Run an agent
response = client.agents.run(
    agent_id=agent["id"],
    message="Hello, how can you help me?"
)
print(response["content"])

Creating Agents Locally

from gagiteck import Agent, tool

# Define a custom tool
@tool
def search_database(query: str) -> str:
    """Search the database for information."""
    # Your search logic here
    return f"Results for: {query}"

# Create an agent with tools
agent = Agent(
    name="Research Assistant",
    model="claude-3-opus",
    tools=[search_database],
    system_prompt="You are a helpful research assistant.",
    memory_enabled=True,
)

# Run the agent
response = agent.run("Find information about AI agents")
print(response.text)

# Continue the conversation (memory enabled)
response = agent.run("Tell me more about the first result")
print(response.text)

Working with Workflows

from gagiteck import Client

client = Client(api_key="ggt_your_api_key")

# Create a workflow
workflow = client.workflows.create(
    name="Customer Onboarding",
    steps=[
        {"id": "verify", "name": "Verify Email", "agent_id": "agent_verifier"},
        {"id": "setup", "name": "Setup Account", "agent_id": "agent_provisioner", "depends_on": ["verify"]},
        {"id": "welcome", "name": "Send Welcome", "agent_id": "agent_messenger", "depends_on": ["setup"]},
    ]
)

# Trigger the workflow
run = client.workflows.trigger(
    workflow_id=workflow["id"],
    inputs={"user_email": "[e-mail]"}
)
print(f"Workflow run started: {run['id']}")

Features

• Simple API Client - Easy-to-use client for the Gagiteck REST API
• Local Agents - Create and run agents locally with memory support
• Custom Tools - Define tools using the @tool decorator
• Type Safe - Full type annotations for IDE support
• Async Support - Async client available (coming soon)

API Reference

Client

Client(
    api_key: str,           # Your API key (starts with 'ggt_')
    base_url: str = None,   # Custom API URL
    timeout: int = 30,      # Request timeout in seconds
    debug: bool = False,    # Enable debug logging
)

Agent

Agent(
    name: str,                      # Agent name
    model: str = "claude-3-sonnet", # LLM model
    system_prompt: str = None,      # System instructions
    tools: list = [],               # List of tools
    memory_enabled: bool = False,   # Enable conversation memory
    max_tokens: int = 4096,         # Max response tokens
    temperature: float = 0.7,       # Sampling temperature
)

Tool Decorator

@tool
def my_function(param: str) -> str:
    """Description of what the tool does."""
    return result

Error Handling

from gagiteck import Client, GagiteckError, AuthenticationError, APIError

try:
    client = Client(api_key="ggt_your_key")
    response = client.agents.run(agent_id="agent_123", message="Hello")
except AuthenticationError:
    print("Invalid API key")
except APIError as e:
    print(f"API error {e.code}: {e.message}")
except GagiteckError as e:
    print(f"Error: {e.message}")

Development

# Clone the repository
git clone https://github.com/ajaniethos-1/gagiteck-python.git
cd gagiteck-python

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests -v

# Run linting
ruff check gagiteck tests

# Run type checking
mypy gagiteck

Documentation

• Full Documentation
• API Reference
• Examples

License

MIT License - see LICENSE for details.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]