vizra 0.1.0

A simple AI agent framework for Python

Vizra - Simple AI Agent Framework for Python

A lightweight, class-based AI agent framework for Python that uses litellm for LLM integration.

Installation

pip install vizra

Quick Start

Define an Agent

from vizra import BaseAgent

class CustomerSupportAgent(BaseAgent):
    name = 'customer_support'
    description = 'Helps customers with their inquiries'
    instructions = 'You are a friendly customer support assistant.'
    model = 'gpt-4o'
    tools = [OrderLookupTool, RefundProcessorTool]

# Or load instructions from a file
class AdvancedSupportAgent(BaseAgent):
    name = 'advanced_support'
    description = 'Advanced support agent with complex instructions'
    instructions_file = 'prompts/advanced_support.md'  # Path relative to your project
    model = 'gpt-4o'
    tools = [OrderLookupTool, RefundProcessorTool]

Run the Agent

from my_agents import CustomerSupportAgent  # Import your agent

# Simple usage
response = CustomerSupportAgent.run('How do I reset my password?')

# With context for conversation continuity
from vizra import AgentContext

context = AgentContext()
agent_runner = CustomerSupportAgent.with_context(context)

response1 = agent_runner.run("Hi, I need help")
response2 = agent_runner.run("Can you check my order?")

Define Tools

from vizra import ToolInterface, AgentContext
import json

class OrderLookupTool(ToolInterface):
    def definition(self) -> dict:
        return {
            'name': 'order_lookup',
            'description': 'Look up order information by order ID',
            'parameters': {
                'type': 'object',
                'properties': {
                    'order_id': {
                        'type': 'string',
                        'description': 'The order ID to look up',
                    },
                },
                'required': ['order_id'],
            },
        }

    def execute(self, arguments: dict, context: AgentContext) -> str:
        order_id = arguments['order_id']
        # Your implementation here
        return json.dumps({"order_id": order_id, "status": "shipped"})

Features

• Class-based agent definition
• Tool integration with automatic execution loop (max 3 iterations)
• Context management for conversation history
• Support for multiple LLM providers via litellm
• Hook methods for monitoring and customization
• File-based instruction loading
• Simple and intuitive API

Hooks

Agents can override hook methods to add custom behavior:

class MonitoredAgent(BaseAgent):
    def before_llm_call(self, messages, tools):
        print(f"Making LLM call with {len(messages)} messages")
    
    def after_llm_response(self, response, messages):
        print(f"Response received with {response.usage.total_tokens} tokens")
    
    def before_tool_call(self, tool_name, arguments, context):
        print(f"Calling tool: {tool_name}")
    
    def after_tool_result(self, tool_name, result, context):
        print(f"Tool {tool_name} returned: {result}")

License

MIT