Axora 🌌

A local-first LLM orchestration library with native support for Hugging Face, PEFT/LoRA, QLoRA — without hiding the model and giving advanced users the full control.

Axora is a thin orchestration layer — not an abstraction barrier.

Why Axora?

Use Axora if you:

• Want full control over your model (no hidden layers)
• Prefer explicit agent logic over auto-magic chains
• Are working locally with Hugging Face / quantized models

Avoid Axora if you:

• Want plug-and-play integrations (use LangChain)
• Need production-ready pipelines out of the box

Note:

⚠️ Status

• Early development — APIs may change
• Currently focusing on agentic systems (v0.1.5)

⚠️ LM Studio Support

• Inference only
• Agent integration coming soon

🤝 Contributing

• Contributions welcome
• Please avoid low-quality / spam PRs

Features

• Local-First: Built for running LLMs on your own hardware efficiently.
• Native PEFT/QLoRA: Seamless integration with Hugging Face's PEFT for efficient model loading.
• Persistent Memory: Vector-based memory using FAISS with automatic text embedding (SLM).
• Strict ReAct Agents: Deterministic agent loops using JSON-based tool calls.
• Model Transparency: Easy access to the underlying Hugging Face model and tokenizer.

Installation

Using python

pip install Axora

Using Anaconda/Miniforge

conda activate < ENV NAME >
pip install Axora

(Note: Requires Python 3.12+)

Quick Start

#setup imports and optional vector DB
from Axora import Agent, AxoraHFModel, VectorDB, Tool

#Optional
db = VectorDB()
db.add("Information to be added into the vector db to be checked, it will automatically searched up by the model when generating responses")

model = AxoraHFModel(
    model_name ="unsloth/Llama-3.2-3B-Instruct",
    quantization="4bit",
    vector_db=db #optional
)

model.generate("What is an apple?")

# OR

model = AxoraLMSModel()
model.generate("What is an apple")

#define functions for the agent
def add(a,b):
    "Adds 2 numbers together"
    print(f"Adding {a} and {b} via tool call")
    return a + b

def subtract(a,b):
    "Subtracts b from a"
    print(f"Subtracting {b} from {a} via tool call")
    return a - b

tools = [
    Tool(name="add", func=add, description="Adds two numbers together. Input should be in the format: 'a,b' where a and b are numbers."),
    Tool(name="subtract", func=subtract, description="Subtracts b from a. Input should be in the format: 'a,b' where a and b are numbers.")
] 

#Setup and run the agent
agent = Agent(
    "AddSubtract_Agent",
    model, 
    tools=tools,
    description = "An agent for only addition and subtraction tasks."
)

agent.run("what is 1+1?")

# MULTI AGENT setup
# The coordinator model routes tasks between agents based on their descriptions.
def add(a,b):
    "Adds 2 numbers together"
    print(f"Adding {a} and {b} via tool call")
    return a + b

def subtract(a,b):
    "Subtracts b from a"
    print(f"Subtracting {b} from {a} via tool call")
    return a - b

def multiply(a,b):
    "Multiplies 2 numbers together"
    print(f"Multiplying {a} and {b} via tool call")
    return a * b

def divide(a,b):
    "Divides a by b"
    print(f"Dividing {a} by {b} via tool call")
    if b == 0:
        return "Error: Division by zero"
    return a / b

tools1 = [
    Tool(name="add", func=add, description="Adds two numbers together. Input should be in the format: 'a,b' where a and b are numbers."),
    Tool(name="subtract", func=subtract, description="Subtracts b from a. Input should be in the format: 'a,b' where a and b are numbers.")
] 

tools2 = [
    Tool(name="multiply", func=multiply, description="Multiplies two numbers together. Input should be in the format: 'a,b' where a and b are numbers."),
    Tool(name="divide", func=divide, description="Divides a by b. Input should be in the format: 'a,b' where a and b are numbers."),
]

agent1 = Agent(
    "AddSubtract_Agent",
    model, 
    tools=tools1,
    description = "An agent for only addition and subtraction tasks."
)

agent2 = Agent(
    "MultiplyDivide_Agent",
    model, 
    tools=tools2,
    description = "An agent only for multiplication and division tasks."
)

#create a multi agent system

MOA = MultiAgent(agents=[agent1,agent2],coordinator_model=model)
response = MOA.run("What is the result of (15 + 5) * (10 - 2)?")
print("Final Response from MultiAgent System: ",response)

Project Structure

• Axora/chains/: Agentic workflows and ReAct implementations.
• Axora/model/: Hugging Face model loading and generation wrappers.
• Axora/memory/: FAISS-backed persistent vector storage.
• Axora/tools/: Tool registry and definition system.
• Axora/experimental/: Experimental features do not use these in production , these will be changed / deleted later on.

Non-goals

This library does not aim to be a full ML framework or replace existing tools like LangChain. It focuses on providing a clean, minimal interface for local-first LLM orchestration.

Notes for devlopers

in order to compile Axora into a python package , please run:

python -m build