6 Agentic Frameworks Compared: 24 Implementations Across 4 Use Cases

<p>The provided information outlines how various conversational AI frameworks handle persistent context across multiple interactions. Below is a summary of each framework's approach to memory management:</p>

AutoGPT

• Short-term: Conversation buffer (in-memory list)
• Long-term: Manual JSON file persistence
• Control: Developer explicitly saves/loads memory
• Pattern: Simple append-to-buffer, no automatic summarization

Example:

python
 
1import json
2from openai import OpenAI
3
4class PersistentAutoGPT:
5    def __init__(self):
6        self.conversation_buffer = []
7    
8    def save_memory(self, file_path='memory.json'):
9        with open(file_path, 'w') as f:
10            json.dump(self.conversation_buffer, f)
11    
12    def load_memory(self, file_path='memory.json'):
13        try:
14            with open(file_path, 'r') as f:
15                self.conversation_buffer = json.load(f)
16        except FileNotFoundError:
17            pass
18    
19    def add_to_conversation(self, message):
20        self.conversation_buffer.append(message)
21
22# Usage
23p_auto_gpt = PersistentAutoGPT()
24p_auto_gpt.add_to_conversation("User: Hello")
25p_auto_gpt
26
27[Read the full article at Towards AI - Medium](https://pub.towardsai.net/6-agentic-frameworks-compared-24-implementations-across-4-use-cases-932496dba80c?source=rss----98111c9905da---4)
28
29---
30
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