Memory Systems for Agents

Agent Memory Systems

# 1. Short-term memory (conversation history)
class ShortTermMemory:
    def __init__(self, max_messages: int = 20):
        self.messages  = []
        self.max_msgs  = max_messages

    def add(self, role: str, content: str):
        self.messages.append({"role":role,"content":content})
        if len(self.messages) > self.max_msgs:
            self.messages = self.messages[-self.max_msgs:]

# 2. Long-term memory (vector store)
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.schema import Document

class LongTermMemory:
    def __init__(self):
        self.store = Chroma(embedding_function=OpenAIEmbeddings())

    def remember(self, text: str, metadata: dict = {}):
        self.store.add_documents([Document(page_content=text, metadata=metadata)])

    def recall(self, query: str, k: int = 5) -> list[str]:
        docs = self.store.similarity_search(query, k=k)
        return [d.page_content for d in docs]

# 3. Entity memory (track key entities)
# 4. Episodic memory (past task results)
# 5. Procedural memory (tool usage patterns)