Agent Memory and State: A Practical Guide
Bottom line: Memory turns a stateless tool caller into a useful assistant. The trick is remembering the right things without overflowing the context window or leaking sensitive data.
Three layers of agent memory
Short-term memory
The current conversation history. It gives the agent immediate context but is limited by the model context window.
Working memory
Intermediate facts the agent creates while reasoning, such as plan steps or tool outputs from the current task.
Long-term memory
Facts that persist across sessions, such as user preferences, project settings, or learned knowledge.
Managing the context window
Context windows keep growing, but they are not infinite. Long conversations need compression. Common strategies include sliding windows, summarization, and retrieval of only the most relevant prior turns.
Retrieval-augmented memory
Store important facts as embeddings in a vector database. When the agent receives a query, retrieve the facts that are semantically closest to the current task. This scales memory far beyond the context window and keeps only useful information in the prompt.
Structured state machines
In frameworks like LangGraph, state is an explicit object. The agent reads from state, writes to state, and transitions between nodes. Explicit state makes debugging easier and lets you resume interrupted tasks.
Memory design checklist
- Decide what facts are worth remembering and what can be forgotten.
- Tag memories by user, project, and expiration date.
- Use summaries for long conversations instead of full transcripts.
- Retrieve memories before each turn, not all at once.
- Let users view, edit, and delete what the agent remembers.
Privacy and safety
Long-term memory can store passwords, personal data, or confidential details. Encrypt stored memories, enforce access controls, and allow users to opt out. Never store sensitive information in plain text logs.
Published 2026-06-12
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