videointermediate

Build a Persistent Memory AI Agent in Python (AutoGen + Groq Guide)

By Nidhi ChouhanMay 10, 2026youtube

This guide demonstrates how to build persistent memory AI agents using AutoGen and Groq in Python. It covers implementing memory systems that allow agents to retain user interactions across sessions, leveraging AutoGen's multi-agent framework with Groq's fast LLM inference. The tutorial includes practical code examples and a GitHub repository for hands-on learning of stateful agent development.

Key Points

•Use AutoGen framework to create multi-agent systems with collaborative capabilities
•Implement persistent memory storage to enable agents to recall previous user interactions
•Integrate Groq API for fast, efficient LLM inference in agent workflows
•Design agent architectures that maintain conversation history across sessions
•Leverage agent roles and responsibilities for specialized task handling
•Store and retrieve memory data efficiently to improve agent context awareness
•Test agent memory retention with multi-turn conversations
•Deploy stateful agents that provide personalized user experiences

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Workflow Diagram

Start Process

Step A

Step B

Step C

Complete

Quality★★★★★

Concepts

Coding Assistance Memory Systems Multi-Agent Systems Agent Teams Tool Use

Artifacts (2)

AutoGen Persistent Memory Agentpythonscript

# Example structure for persistent memory agent with AutoGen + Groq
import autogen
from groq import Groq
import json
import os

# Initialize Groq client
client = Groq(api_key=os.getenv('GROQ_API_KEY'))

# Memory storage
memory_store = {}

def load_user_memory(user_id):
    """Load persistent memory for user"""
    if user_id in memory_store:
        return memory_store[user_id]
    return {"conversations": [], "context": {}}

def save_user_memory(user_id, memory):
    """Save user memory to persistent storage"""
    memory_store[user_id] = memory

def create_agent_with_memory(user_id):
    """Create AutoGen agent with persistent memory"""
    user_memory = load_user_memory(user_id)
    
    config_list = [
        {
            "model": "mixtral-8x7b-32768",
            "api_key": os.getenv('GROQ_API_KEY'),
            "api_type": "groq"
        }
    ]
    
    agent = autogen.AssistantAgent(
        name="MemoryAgent",
        system_message=f"You are a helpful assistant with memory of past interactions. Previous context: {user_memory['context']}",
        llm_config={"config_list": config_list}
    )
    
    return agent, user_memory

def process_user_input(user_id, user_input):
    """Process input and maintain memory"""
    agent, memory = create_agent_with_memory(user_id)
    
    # Add to conversation history
    memory['conversations'].append({"role": "user", "content": user_input})
    
    # Get response from agent
    response = agent.generate_reply(messages=[{"role": "user", "content": user_input}])
    
    # Store response
    memory['conversations'].append({"role": "assistant", "content": response})
    memory['context'].update({"last_interaction": user_input})
    
    # Save memory
    save_user_memory(user_id, memory)
    
    return response

# Example usage
if __name__ == "__main__":
    user_id = "user_123"
    response = process_user_input(user_id, "Hello, remember me!")
    print(f"Agent: {response}")

Memory Configurationjsonconfig

{
  "memory_config": {
    "storage_type": "json",
    "persistence_path": "./user_memories",
    "max_history_length": 100,
    "auto_save_interval": 5
  },
  "groq_config": {
    "model": "mixtral-8x7b-32768",
    "temperature": 0.7,
    "max_tokens": 1024,
    "timeout": 30
  },
  "autogen_config": {
    "seed": 42,
    "timeout": 60,
    "max_consecutive_auto_reply": 10
  }
}