AgenticAI Framework — Memory Module Documentation¶
Overview¶
The memory module provides mechanisms for agents to store, retrieve, and manage information across their lifecycle. It supports both short-term and long-term memory, enabling context-aware and stateful agent behavior.
Key Class¶
Memory¶
Location: agenticaiframework/memory.py
The Memory class is a simple key-value store for agent data.
Core Methods:
- store(key, value) — Store a value under a given key.
- retrieve(key) — Retrieve a value by key.
- clear() — Clear all stored data.
Example Usage¶
from agenticaiframework.memory import Memory
memory = Memory()
memory.store("user_name", "Alice")
print(memory.retrieve("user_name")) # Output: Alice
memory.clear()
Use Cases¶
- Session Management — Store user session data for conversational agents.
- Context Retention — Maintain context between multiple agent actions.
- Caching — Store frequently accessed data to improve performance.
Extending Memory¶
You can implement custom memory backends by creating a new class in memory.py:
class PersistentMemory:
def __init__(self, storage_path):
self.storage_path = storage_path
self.data = {}
def store(self, key, value):
self.data[key] = value
self._save()
def retrieve(self, key):
return self.data.get(key)
def clear(self):
self.data.clear()
self._save()
def _save(self):
with open(self.storage_path, "w") as f:
f.write(str(self.data))
Best Practices¶
- Use in-memory storage for short-lived agents.
- Use persistent storage for long-running agents or when data must survive restarts.
- Avoid storing sensitive data unless encrypted.