How to give your AI agent a memory — a practical deep-dive
This post breaks down the main ways to add memory to an AI agent so it can remember past conversations and tasks. The choice of memory design directly affects how many tokens you spend and how much the LLM costs to run. It's a practical guide for anyone building their own agent.
By default, an AI agent forgets everything once a conversation ends. To fix this, developers need to design a memory system. The post covers four main approaches: passing the full conversation history every time, storing a compressed summary of past interactions, keeping a structured list of facts and rules, and using vector search to pull only the most relevant pieces of memory when needed.
Each approach involves a different trade-off between cost, complexity, and accuracy. Sending the full history is simple but token costs grow fast. Summaries save tokens but may lose important details. Vector search (also called RAG) is the most token-efficient but requires more setup. Understanding these trade-offs helps developers pick the right design for their agent — whether it handles short chats or long-running tasks.
Key points
- There are four main memory designs: full history, summary, fact store, and vector search
- Your memory design choice directly controls token usage and LLM cost
- Summaries cut costs but can drop useful context
- Vector search (RAG) retrieves only relevant memory, saving tokens but adding complexity
- The best choice depends on your agent's use case — short conversations vs. long tasks
Quick term guide
- memory
- A ChatGPT feature that lets it use details from past chats in future chats.
- AI agent
- An AI program that can inspect information and suggest what to do next.
- tokens
- Tokens are small pieces of text that AI systems count when reading or writing.
- developers
- Developers are people who build software, apps, or websites.
- vector search
- A search method that finds text with similar meaning, not only the same words.
- token costs
- Token costs are the fees paid for the text an AI model reads and writes.
- token cost
- The money or usage spent when sending text to an AI model and getting text back.
- context
- The information an AI uses to understand your request, such as files, notes, and past messages.