axi-easyagent 0.1.0

A lightweight Python AI agent framework

Axi-EasyAgent

中文文档 | English Documentation

A lightweight Python AI agent framework with conversation management, tool calling, and memory persistence capabilities.

How lightweight is it?

Why Axi-EasyAgent?

Do you know how much space a typical AI library takes nowadays? Up to 200MB! That's larger than a web browser!
If you only want AI to call your functions, how much of that 200MB do you actually need? The answer is right here.

Features

• 🤖 Smart Conversations: Streaming conversation support based on OpenAI-compatible APIs
• 🔧 Tool Calling: Automatically convert Python functions into AI-callable tools
• 💾 Memory Management: Built-in conversation memory system with persistent storage and auto-compression
• ⚡ Async Processing: Full async programming support for improved response efficiency
• 🔄 Streaming Output: Real-time streaming responses for enhanced user experience

Installation

pip install axi-easyagent

Quick Start

1. Environment Configuration

Configure environment variables, or hardcode them directly in your code (not recommended)

OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
OPENAI_API_KEY=your-api-key-here

Note: easyagent does not automatically read .env files. Please load them yourself.

2. Quick Start Example

import asyncio
from typing import Annotated
from easyagent import Agent

async def get_weather(city: str) -> str:
    """Get weather information"""
    return f"The weather in {city} is sunny"

def get_weather_detail(city: Annotated[str, "Can be precise to district, e.g.: Shanghai/Qingpu District"]) -> str:
    """Get detailed weather information"""
    return f"Detailed weather info for {city}: Temperature 25°C, Humidity 60%"

async def main():
    agent = Agent(
        "deepseek-v4-flash",
        tools=[get_weather, get_weather_detail],
        prompt="Keep responses brief"
    )
    
    while (msg := input("You: ")) != "q":
        async for output in agent.chat(msg):
            print(output, end="")
        print()

if __name__ == "__main__":
    asyncio.run(main())

3. Memory Persistence

import os
from easyagent import Agent, Memory

# Load existing memory or create new memory
if os.path.exists("./memory.json"):
    memory = Memory.load("./memory.json")
else:
    memory = Memory()
    memory.add_message("Hello!")  # role: user
    memory.add_message("You're bad!")  # role: assistant

agent = Agent("deepseek-v4-flash", memory=memory)

# Save memory after conversation
memory.save("./memory.json")

Core Components

Agent Class

The core agent class responsible for managing conversations, tool calls, and memory.

Parameters:

• model (str): Model name
• base_url (str, optional): API base URL
• api_key (str, optional): API key
• memory (Memory, optional): Memory instance
• prompt (str, optional): System prompt
• tools (list[Callable], optional): Available tools list
• complete_memory (bool): Whether to save tool call and thinking processes to memory
• max_tool_call (int): Maximum tool call limit

Memory Class

Conversation memory management class that inherits from list, supporting message CRUD operations and persistence. If you want to customize memory management, you can inherit from the Memory class and implement relevant methods.

Main Methods:

• add_user_message(message): Add user message
• add_assistant_message(message): Add assistant message
• load(json_file): Load memory from JSON file
• save(json_file): Save memory to JSON file
• compress(): Compress memory by removing reasoning content and tool call records