just-agents-core

A lightweight, straightforward core library for LLM agents - no over-engineering, just simplicity!

🎯 Core Features

🪶 Lightweight and simple implementation
📝 Easy-to-understand agent interactions
🔧 Customizable prompts using YAML files
🤖 Support for various LLM models through litellm, including DeepSeek R1 and OpenAI o3-mini
🔄 Chain of Thought reasoning with function calls

🚀 Installation

pip install just-agents-core

🏗️ Core Components

BaseAgent

A thin wrapper around litellm for basic LLM interactions. Provides:

Simple prompt management
Direct LLM communication
Memory handling

ChatAgent

The fundamental building block for agent interactions. Here's an example of using multiple chat agents:

from just_agents.base_agent import ChatAgent
from just_agents.llm_options import LLAMA4_SCOUT

# Initialize agents with different roles
harris = ChatAgent(
    llm_options=LLAMA4_SCOUT, 
    role="You are Kamala Harris in a presidential debate",
    goal="Win the debate with clear, concise responses",
    task="Respond briefly and effectively to debate questions"
)

trump = ChatAgent(
    llm_options=LLAMA4_SCOUT,
    role="You are Donald Trump in a presidential debate",
    goal="Win the debate with your signature style",
    task="Respond briefly and effectively to debate questions"
)

moderator = ChatAgent(
    llm_options={
        "model": "groq/meta-llama/llama-4-maverick-17b-128e-instruct",
        "temperature": 0.0
    },
    role="You are a neutral debate moderator",
    goal="Ensure a fair and focused debate",
    task="Generate clear, specific questions about key political issues"
)

ChainOfThoughtAgent

Extended agent with reasoning capabilities and function calling:

from just_agents.patterns.chain_of_throught import ChainOfThoughtAgent
from just_agents import llm_options

def count_letters(character: str, word: str) -> str:
    """ Returns the number of character occurrences in the word. """
    count = word.count(character)
    return str(count)

# Initialize agent with tools and LLM options
agent = ChainOfThoughtAgent(
    tools=[count_letters],
    llm_options=llm_options.LLAMA4_SCOUT
)

# Get result and reasoning chain
result, chain = agent.think("Count the number of occurrences of the letter 'L' in 'HELLO'.")

📚 Motivation

Most existing agentic libraries are over-engineered, either directly or by using complex libraries under the hood. In reality, interactions with LLMs are mostly about strings, and you can create templates using f-strings and Python's native string templates.

It's easier to reason about code when you separate prompting from Python code into easily readable files (like YAML files). This library was created to provide a controlled, simple approach to LLM agent development without unnecessary complexity.

📦 Structure

The just-agents-core package provides the fundamental building blocks for LLM agents. For additional functionality:

just_agents_coding: For code execution in sandboxed environments
just_agents_tools: Additional tools like web search capabilities
just_agents_web: For serving agents as OpenAI-compatible REST API endpoints

For full usage examples and documentation, please refer to the main repository.

Metadata

Release files for just-agents-core 0.8.9

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Source distribution for just-agents-core 0.8.9
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