Agent Memory: 7 Types, and 2 don't actually remember

Your agent does not have a memory problem. It has seven different types of memory. Most teams only build two.

The first thing you must understand: the model remembers nothing. An LLM is a pure function. It takes an input and gives an output. It carries no state between calls. What feels like memory is just a layer re-sending history with every request. You pay for those tokens every single time.

Most engineering efforts collapse into two patterns: conversation history and RAG. These are two of the seven types. The problem? They do not make your agent smarter over time.

Here are the seven types of memory:

• Working: Everything in the current context window. • Semantic: Facts, preferences, and domain knowledge. • Episodic: A log of past events and what worked or failed. • Procedural: Skills, workflows, and tool patterns. • Retrieval: Pulling knowledge via similarity search. • Parametric: Knowledge baked into the model weights. • Prospective: Future intentions and scheduled tasks.

Two of these are not real memory. RAG is just a delivery mechanism. It is the plumbing, not the water. It moves data from a store into the working memory. If you only use a vector database, you built a pipe and forgot the liquid.

To build an agent that actually learns, you need the consolidation loop. This means turning episodic memory into semantic memory.

The process works like this:

  1. The agent experiences an event (Episodic).
  2. The agent sees the same pattern repeat many times.
  3. The agent abstracts that pattern into a permanent rule (Semantic).

Now, the agent does not need to reason through twelve examples. It simply applies one fact.

How to prioritize your build:

  • Manage working memory as a budget. It is your highest cost. Use summarization and eviction early.
  • Separate your stores. Keep facts, events, and rules in different places.
  • Use a scheduler for prospective memory. Do not use a vector store for things that need to happen on a specific date.
  • Draw a hard line for parametric memory. Use the model for reasoning, but use your own stores for volatile data like interest rates or product rules.

Most agents today are just a context window and a vector DB. The agents that win are the ones that can turn yesterday's mistakes into tomorrow's rules.

Source: https://dev.to/shudiptotrafder/agent-memory-7-types-and-2-of-them-arent-memory-6oi

Optional learning community: https://t.me/GyaanSetuAi