Build a Local Multi-Agent System with TinyLlama

Overview

This tutorial demonstrates how to orchestrate a team of specialized AI agents locally using an efficient manager-agent architecture powered by TinyLlama. We focus on building structured task decomposition, inter-agent collaboration, and autonomous reasoning loops without relying on external APIs.

Setting Up the Environment

To set up your environment, ensure you have the necessary libraries:

!pip install transformers torch accelerate bitsandbytes -q

Core Components

Task and Agent Structures

We define Task and Agent data structures to manage tasks effectively. Each task is assigned distinct attributes making orchestration cleaner:

@dataclass
class Task:
   id: str
   description: str
   assigned_to: str = None
   status: str = "pending"
   result: Any = None
   dependencies: List[str] = None
   
   def __post_init__(self):
       if self.dependencies is None:
           self.dependencies = []

Agent Registry

We register various agents with their roles and expertise:

AGENT_REGISTRY = {
   "researcher": Agent(...),
   "coder": Agent(...),
   "writer": Agent(...),
   "analyst": Agent(...)
}

LocalLLM Class

This class facilitates local model management.

class LocalLLM:
   def __init__(self, model_name: str = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"):
       self.tokenizer = AutoTokenizer.from_pretrained(model_name)
       # additional code

ManagerAgent Class

The ManagerAgent handles goal decomposition and task execution.

class ManagerAgent:
   def decompose_goal(self, goal: str) -> List[Task]:
       # code implementation

Execution Workflow

We explain how tasks are executed while respecting dependencies. The ManagerAgent orchestrates the entire process smoothly:

def execute_goal(self, goal: str) -> Dict[str, Any]:
   tasks = self.decompose_goal(goal)
   # method implementation

Conclusion

We’ve designed and operated a complete multi-agent orchestration system locally with minimal dependencies. The implementation showcases the modular and powerful nature of local agentic patterns.