ApeAgent

ApeAgent is a framework created by Macaque Consulting to develop intelligent agents capable of executing specific functions (tools) sequentially or in parallel, while maintaining conversation context. This multi-agent approach is ideal for complex systems requiring efficient token usage and detailed process control..

Installation

pip install apeagent

Multi-Agent System Advantages

The multi-agent orchestration in ApeAgent optimizes token usage and enhances model accuracy in complex environments. Key benefits include:

• Token consumption reduction: Each agent uses only the necessary context for each query, optimizing costs and speed.
• Detailed process control: Specialized agents enable granular control and easy debugging, perfect for systems that require precise task management.
• Reduced hallucinations: By dividing tasks into specialized agents, the system minimizes incoherent or inaccurate responses.
• Capability for self-supervision or "judge" systems: You can include an agent to verify responses from other agents before presenting them to the user.
• Efficiency and parallelism: Agents can execute tasks in parallel and maintain context in long conversations.

Agent Configuration Parameters

When configuring an agent in ApeAgent, you can adjust its behavior through several parameters:

• name: Agent name, without spaces.
• instructions: System role message that guides the agent (defines its purpose).
• functions: Tools or agents the agent can access, listed as a list.
• model: Model to use (currently integrates with OpenAI models).
• temperature: Controls the agent's response creativity.
• parallel_tool_calls: Allows the agent to run tools in parallel.
• memory_enabled: Allows the agent to keep conversation context.
• memory_max_conversations: Max number of interactions the agent stores in memory.
• debug: Enables console output for debugging.

Defining Tools

Tools are functions the agent can use to perform specific tasks. They must be defined with the @Agent.tool decorator and include typing and descriptive comments to help the model understand their purpose and use.

Simple Tool Example

from apeagent import Agent
from typing import Dict

@Agent.tool
def add(x: float, y: float) -> Dict[str, float]:
    """Adds two numbers and returns the result."""
    return {"result": x + y}

Basic Agent Configuration

calculator_agent = Agent(
    name="Calculator",
    instructions="You are an agent that can perform simple mathematical operations.",
    functions=[add],
    model="gpt-4o",
    temperature=0.0
)

Advanced Example: Multi-Agent System with Hierarchy

Tool for Weather Consultation

@Agent.tool
def get_weather(city: str) -> Dict[str, str]:
    """Provides current weather information for a specific city using the OpenWeather API."""
    # Weather query implementation...
    ...

Configuring Specialized Agents and Main Agent

weather_agent = Agent(
    name="Weather_Agent",
    instructions="You are an agent specialized in providing weather information.",
    functions=[get_weather],
    model="gpt-4o",
    temperature=0.0
)

main_agent = Agent(
    name="Main_Agent",
    instructions="You are a versatile assistant that can: 1. Provide weather information. 2. Perform mathematical operations.",
    functions=[weather_agent, calculator_agent],
    model="gpt-4o",
    temperature=0.5,
    parallel_tool_calls=True,
    memory_enabled=True,
    memory_max_conversations=40,
    debug=True
)

Using the Main Agent

weather_query = "What's the weather like in Barcelona?"
weather_response = main_agent.call(weather_query)
print("Agent (weather):", weather_response)

add_query = "Add 7 and 5"
add_response = main_agent.call(add_query)
print("Agent (addition):", add_response)

Output Example

Agent (weather): The temperature in Barcelona is 20°C with clear skies.
Agent (addition): The result is 12.