AI Agent Memory: Complete Guide to Persistence Solutions

AI agents are stateless by default. Every request starts fresh with no memory of previous interactions. This is a fundamental limitation—but it's solvable.

This guide covers every approach to giving agents persistent memory, from simple conversation buffers to enterprise-grade control planes.

The Memory Hierarchy

Memory Types Explained

# LangChain example
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
memory.save_context({"input": "Hi"}, {"output": "Hello!"})
memory.save_context({"input": "What's my name?"}, {"output": "I don't know yet."})

# Retrieves all messages as context
history = memory.load_memory_variables({})
# {'history': 'Human: Hi\nAI: Hello!\nHuman: What\'s my name?\nAI: I don\'t know yet.'}

from langchain.memory import ConversationSummaryMemory

memory = ConversationSummaryMemory(llm=llm)

# As conversation grows, it summarizes older parts
# "The human introduced themselves as John and asked about..."

from langchain.memory import VectorStoreRetrieverMemory
from langchain.vectorstores import Pinecone

# Store conversations as vectors
vectorstore = Pinecone.from_existing_index("conversations", embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
memory = VectorStoreRetrieverMemory(retriever=retriever)

# Retrieves semantically relevant messages
# Query: "What did we discuss about billing?"
# Returns: The 5 most relevant messages about billing

import redis
import json

r = redis.Redis()

def save_state(user_id, state):
    r.set(f"agent_state:{user_id}", json.dumps(state))
    r.expire(f"agent_state:{user_id}", 86400 * 30)  # 30 day TTL

def get_state(user_id):
    data = r.get(f"agent_state:{user_id}")
    return json.loads(data) if data else {}

# Store structured state, not just messages
save_state("user_123", {
    "name": "John",
    "preferences": {"language": "en"},
    "open_tickets": ["ticket_456"],
    "conversation_summary": "Discussed billing issue..."
})

import agentmemo

# Register agent
agent = agentmemo.agents.register({
    "name": "support-agent",
    "framework": "langchain"
})

# Store structured state (persists forever)
agentmemo.state.write({
    "agent_id": agent.id,
    "component": "customer_context",
    "key": "user_123",
    "value": {
        "name": "John",
        "history_summary": "Long-standing customer, previous billing issues resolved",
        "preferences": {"channel": "email", "language": "en"},
        "current_issue": {"ticket_id": "456", "status": "investigating"}
    }
})

# Store workflow (any agent can execute)
agentmemo.workflows.create({
    "name": "billing-support",
    "definition": workflow_markdown,
    "designed_by": "opus"
})

# Handoff to another agent
agentmemo.handoffs.create({
    "to_agent": "specialist-agent",
    "context": state,
    "workflow": "billing-support"
})

Choosing the Right Memory Type

Hybrid Approaches

In practice, most production agents use multiple memory types:

class HybridMemoryAgent:
    def __init__(self):
        # Short-term: conversation buffer
        self.conversation = ConversationBufferMemory()
        
        # Medium-term: vector store for semantic retrieval
        self.knowledge = VectorStoreRetrieverMemory(...)
        
        # Long-term: control plane for state and workflows
        self.state = agentmemo.state
        self.workflows = agentmemo.workflows
    
    async def process(self, user_id, message):
        # Load persistent state
        user_state = await self.state.read(user_id)
        
        # Get relevant knowledge
        relevant = await self.knowledge.retrieve(message)
        
        # Add to conversation
        self.conversation.add_message(message)
        
        # Process with full context
        response = await self.llm.complete(
            context={
                "user_state": user_state,
                "relevant_knowledge": relevant,
                "conversation": self.conversation.messages
            }
        )
        
        # Update persistent state
        await self.state.write(user_id, updated_state)
        
        return response

Memory Best Practices

1. Separate What from Why

Store structured state (what the agent knows) separately from conversation logs (what was said).

2. Use Appropriate TTLs

Not everything needs to live forever. Set expiration for temporary state.

3. Summarize Before Storing

Store "Customer John has billing concerns about invoice #456" not 50 raw messages.

4. Plan for Handoffs

Memory should be complete enough that a different agent can pick up the work.

5. Audit State Changes

Log when state is read/written for debugging and compliance.