Memory Fundamentals
Understanding the core concepts of how AI agents store, retrieve, and use information over time.
Think of AI agent memory like a human brain during a conversation. You remember what was said earlier (short-term memory), you can recall facts you learned years ago (long-term memory), and you can look things up in books when needed (external memory). AI agents work similarly - they keep track of recent interactions, store important information for later use, and can search databases or documents when they need specific facts.
When to Add Memory
- ✓Multi-turn conversations: Users need context from previous messages (chatbots, assistants)
- ✓Personalization: Adapting behavior based on user preferences or history
- ✓Large knowledge bases: Need to reference docs/data beyond context window limits
- ✓Session continuity: Users expect the agent to "remember" across sessions
When Memory May Not Be Needed
- ✗Stateless operations: Single-turn queries with no context needed (e.g., "translate this")
- ✗Privacy-sensitive: When storing user data creates compliance risks
- ✗Short context: All needed info fits in the context window comfortably
Start Here
Begin with contextual memory (conversation history in the prompt). Only add external memory (vector DBs, databases) when you hit context limits or need persistence across sessions. Over-engineering memory too early adds complexity without clear benefits.
- • Context window (tokens in current conversation)
- • Recent user messages and AI responses
- • Current task state and progress
- • Temporary variables and calculations
Example: ChatGPT remembering your previous questions in the same conversation
- • User preferences and patterns
- • Historical conversation summaries
- • Learned facts and relationships
- • Domain-specific knowledge
Example: GitHub Copilot learning your coding style over time
Parametric Memory
Knowledge encoded in the model's weights during training. This is like your brain's built-in knowledge.
Contextual Memory
Information held in the current context window. Limited by token limits but immediately accessible.
External Memory
Information stored outside the model that can be retrieved when needed. Like having access to a library.
Episodic Memory
Memories of specific events and experiences, often with temporal and contextual information.
Attention Is All You Need - Explained
Deep dive into the transformer architecture that powers modern AI memory
RAG vs Long Context: When to Use What
Practical comparison of retrieval vs context window approaches
Building Memory-Enabled AI Agents
Hands-on tutorial for implementing agent memory systems
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
The foundational RAG paper that started the retrieval revolution
MemGPT: Towards LLMs as Operating Systems
Novel approach to managing memory hierarchies in LLMs
Lost in the Middle: How Language Models Use Long Contexts
Critical analysis of how models actually use long context windows
The Reversal Curse: LLMs trained on A is B fail to learn B is A
Important findings about bidirectional memory in language models
Context Window Limits
Even large models have finite context windows. GPT-4 has 128k tokens, but that's still limited for long conversations or large documents.
Memory Consistency
Ensuring that stored memories remain accurate and don't contradict each other over time.
Retrieval Accuracy
Finding the right information at the right time. Vector similarity doesn't always match semantic relevance.
Privacy & Security
Protecting sensitive information while maintaining useful memory capabilities across sessions.