MinimalLLMAgent 0.1.2

A minimal LLM agent with memory management.

Overview

pip install MinimalLLMAgent

• Homepage: https://yuelin301.github.io/posts/Minimal-LLM-Agent
• PyPI Page: https://pypi.org/project/MinimalLLMAgent/
• GitHub Page: https://github.com/YueLin301/min_llm_agent

Features:

• simple and unified
• memory management
• a terminal simulation that allows for web-style interaction

Models & Pricing:

supported_platform_name_list = ["OpenAI", "Grok", "DeepSeek", "Gemini", "Ali"]

• OpenAI
• Grok
• DeepSeek
• Gemini
• Ali

Examples

See the demo folder.

1: String Input

from min_llm_agent import min_llm_agent_class

if __name__ == "__main__":

    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")

    question = "What is the capital of France?"
    response = llm_agent(question)

    print(f"Question: {question}")
    print(f"Answer by model {llm_agent.model_name}: {response}")
    
    # llm_agent.print_memory(memory_item_separator="/")
    llm_agent.print_memory()

Question: What is the capital of France?
Answer by model gpt-4o-mini: The capital of France is Paris.
======================================
Memory:
--------------------------------------
[0] (user): What is the capital of France?
[1] (assistant): The capital of France is Paris.
======================================

2: Dict Input

from min_llm_agent import min_llm_agent_class

if __name__ == "__main__":

    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")

    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant.",
        },
        {
            "role": "user",
            "content": "1+1=?",
        },
        {
            "role": "user",
            "content": "1+2=?",
        }
    ]
    response = llm_agent(messages)

    print(f"Messages: {messages}")
    print(f"Answer by model {llm_agent.model_name}: {response}")
    
    llm_agent.print_memory()

Messages: [{'role': 'system', 'content': 'You are a helpful assistant.'}, {'role': 'user', 'content': '1+1=?'}, {'role': 'user', 'content': '1+2=?'}]
Answer by model gpt-4o-mini: 1 + 1 = 2 and 1 + 2 = 3.
======================================
Memory:
--------------------------------------
[0] (system): You are a helpful assistant.
[1] (user): 1+1=?
[2] (user): 1+2=?
[3] (assistant): 1 + 1 = 2 and 1 + 2 = 3.
======================================

3: Interact

from min_llm_agent import min_llm_agent_class

if __name__ == "__main__":

    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")
    llm_agent.interact()

==========================

This is Yue's minimal LLM agent, powered by the model "gpt-4o-mini".

- To submit a query: start a new line, type '/', and press Enter.
    - Line breaks are allowed and recognized as a part of the query.
- Query 'q' or 'Q' to exit.
- Query 'm' or 'M' to print the memory.

See more details on: https://github.com/YueLin301/min_llm_agent

>>>>>>>>>>>>>>>>>>>>>>>>>>
[0] Question:
> 1+1=
/

<<<<<<<<<<<<<<<<<<<<<<<<<<
[0] Answer by the model gpt-4o-mini:
1 + 1 = 2.


>>>>>>>>>>>>>>>>>>>>>>>>>>
[1] Question:
> how are you
/

<<<<<<<<<<<<<<<<<<<<<<<<<<
[1] Answer by the model gpt-4o-mini:
I'm just a computer program, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?


>>>>>>>>>>>>>>>>>>>>>>>>>>
[2] Question:
> m
/
==========================
Memory:
--------------------------
[0] (user): 1+1=
[1] (assistant): 1 + 1 = 2.
[2] (user): how are you
[3] (assistant): I'm just a computer program, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?
==========================
>>>>>>>>>>>>>>>>>>>>>>>>>>
[2] Question:
> q
/

1a: Memoryless Query

from min_llm_agent import min_llm_agent_class

if __name__ == "__main__":

    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")

    question = "What is the capital of France?"
    response = llm_agent(question, with_memory=False)

    print(f"Question: {question}")
    print(f"Answer by model {llm_agent.model_name}: {response}")

    llm_agent.print_memory()

Question: What is the capital of France?
Answer by model gpt-4o-mini: The capital of France is Paris.
======================================
Memory:
--------------------------------------
======================================

2a: More Keywords

• JSON mode
• temperature

See more detailed keywords on OpenAI API Reference.

from min_llm_agent import min_llm_agent_class

if __name__ == "__main__":

    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")

    messages = [
        {"role": "system", "content": "Extract the event information. Output in JSON format, including the event name, date, and participants."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ]

    response = llm_agent(messages, response_format={"type": "json_object"}, temperature=0.5)

    print(f"Messages: {messages}")
    print(f"Answer by model {llm_agent.model_name}: {response}")

    llm_agent.print_memory()

Messages: [{'role': 'system', 'content': 'Extract the event information. Output in JSON format, including the event name, date, and participants.'}, {'role': 'user', 'content': 'Alice and Bob are going to a science fair on Friday.'}]
Answer by model gpt-4o-mini: {
  "event_name": "Science Fair",
  "date": "Friday",
  "participants": ["Alice", "Bob"]
}
================================================
Memory:
------------------------------------------------
[0] (system): Extract the event information. Output in JSON format, including the event name, date, and participants.
[1] (user): Alice and Bob are going to a science fair on Friday.
[2] (assistant): {
  "event_name": "Science Fair",
  "date": "Friday",
  "participants": ["Alice", "Bob"]
}
================================================

4: Memory Management

from min_llm_agent import min_llm_agent_class
from LyPythonToolbox import lyprint_separator
from pprint import pprint

if __name__ == "__main__":
    llm_agent = min_llm_agent_class(platform_name="OpenAI", model_name="gpt-4o-mini")

    response = llm_agent("1+1=?")
    response = llm_agent("1+2=?", with_memory=False)
    llm_agent.print_memory()
    
    lyprint_separator("|")
    llm_agent.reset_memory()
    print("Reset memory...")
    response = llm_agent("1+3=?")
    llm_agent.print_memory()
    
    lyprint_separator("|")
    print("Set memory...")
    llm_agent.set_memory([{"role": "system", "content": "You are a self-interested and rational player."}])
    llm_agent.print_memory()

    lyprint_separator("|")
    response = llm_agent("You are playing a coordination game. State your strategy in only a sentence.", role="user")
    print("Get memory and pprint...")
    memory = llm_agent.get_memory()
    pprint(memory)

    lyprint_separator("|")
    print("Append memory...")
    llm_agent.append_memory({"role": "user", "content": "1+4=?"})
    llm_agent.print_memory()

================================================
Memory:
------------------------------------------------
[0] (user): 1+1=?
[1] (assistant): 1 + 1 = 2.
================================================
||||||||||||||||||||||||||||||||||||||||||||||||
Reset memory...
================================================
Memory:
------------------------------------------------
[0] (user): 1+3=?
[1] (assistant): 1 + 3 = 4.
================================================
||||||||||||||||||||||||||||||||||||||||||||||||
Set memory...
================================================
Memory:
------------------------------------------------
[0] (system): You are a self-interested and rational player.
================================================
||||||||||||||||||||||||||||||||||||||||||||||||
Get memory and pprint...
[{'content': 'You are a self-interested and rational player.',
  'role': 'system'},
 {'content': 'You are playing a coordination game. State your strategy in only '
             'a sentence.',
  'role': 'user'},
 {'content': 'I will choose the strategy that aligns with the most commonly '
             'played option by other players to ensure mutual coordination and '
             'benefit.',
  'role': 'assistant'}]
||||||||||||||||||||||||||||||||||||||||||||||||
Append memory...
================================================
Memory:
------------------------------------------------
[0] (system): You are a self-interested and rational player.
[1] (user): You are playing a coordination game. State your strategy in only a sentence.
[2] (assistant): I will choose the strategy that aligns with the most commonly played option by other players to ensure mutual coordination and benefit.
[3] (user): 1+4=?
================================================

How to Use

API Key

For security reasons, this project does not maintain any API key files. You need to configure the API key yourself in the environment variables. Check the following guidelines to see how it is done:

• OpenAI guideline
• Alibaba guideline

Resources:

• LLM Comparisons
• OpenAI
  • Models and pricing
  • How to create API keys
  • Billing
• Grok
  • Models and pricing
  • API Guides
  • A query example
  • A query example - OpenAI lib
• DeepSeek
  • Models and pricing
  • API
  • Billing
• Gemini
  • Models and pricing
  • API
  • A query example
  • A query example - OpenAI lib
  • Billing
• Alibaba
  • Models and pricing
  • API Guides
  • A query example
  • A query example
  • Dashboard

An Example Set Sp for MacOS Users:

1. Append the following API configurations to the end of the ~/.zshrc file.

export OPENAI_API_KEY="sk-xxx"
export OPENAI_BASE_URL="https://api.openai.com/v1"

export XAI_API_KEY="xai-xxx"
export XAI_BASE_URL="https://api.x.ai/v1"

export DEEPSEEK_API_KEY="sk-xxx"
export DEEPSEEK_BASE_URL="https://api.deepseek.com"

export GEMINI_API_KEY=""
export GEMINI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"

export DASHSCOPE_API_KEY="sk-xxx"
export DASHSCOPE_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"

2. Run source ~/.zshrc to update.