Agent Memory

Give agents the ability to remember and learn across conversations.

When to Use This Skill

Invoke this skill when:

• Adding conversation history
• Implementing long-term memory
• Building personalized agents
• Managing context windows

Parameter Schema

Parameter	Type	Required	Description	Default
task	string	Yes	Memory goal	-
memory_type	enum	No	buffer, summary, vector, hybrid	hybrid
persistence	enum	No	session, user, global	session

Quick Start

from langchain.memory import ConversationBufferWindowMemory

# Simple buffer (last k messages)
memory = ConversationBufferWindowMemory(k=10)

# With summarization
from langchain.memory import ConversationSummaryBufferMemory
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=2000)

# Vector store memory
from langchain.memory import VectorStoreRetrieverMemory
memory = VectorStoreRetrieverMemory(retriever=vectorstore.as_retriever())

Memory Types

Type	Use Case	Pros	Cons
Buffer	Short chats	Simple	No compression
Summary	Long chats	Compact	Loses detail
Vector	Semantic recall	Relevant	Slower
Hybrid	Production	Best of all	Complex

Multi-Layer Architecture

class ProductionMemory:
    def __init__(self):
        self.short_term = BufferMemory(k=10)    # Recent
        self.summary = SummaryMemory()           # Compressed
        self.long_term = VectorMemory()          # Semantic

Troubleshooting

Issue	Solution
Context overflow	Add summarization
Slow retrieval	Cache, reduce k
Irrelevant recall	Improve embeddings
Memory not persisting	Check storage backend

Best Practices

• Use multi-layer memory for production
• Set token limits to prevent overflow
• Add metadata (timestamps, importance)
• Implement TTL for old memories

Related Skills

• rag-systems - Vector retrieval
• llm-integration - Context management
• ai-agent-basics - Agent architecture

References

• LangChain Memory
• MemGPT Paper