AI Agent Memory: Short-Term, Long-Term, and Episodic

Memory is what separates a capable AI agent from a stateless chatbot. Without memory, every interaction starts from scratch, every lesson is forgotten, and every user must re-explain their preferences and context. Agent memory systems aim to give AI the ability to accumulate knowledge, maintain context across long tasks, and learn from past experiences, mirroring the memory capabilities that make human cognition so effective.

Understanding the different types of memory, how they work, and when to use each is essential for building agents that feel intelligent and responsive rather than forgetful and frustrating.

Short-Term Memory: The Working Context

Short-term memory in AI agents corresponds to the information actively available during a single task or conversation. For LLM-based agents, this is primarily the context window: the text provided to the model at each inference step, including the system prompt, conversation history, tool results, and any retrieved context.

The context window is the agent's working memory. Everything the agent can currently "think about" must fit within it. Current frontier models offer context windows of 128K to 1M tokens, which seems enormous but fills quickly when an agent is accumulating tool results, error messages, and intermediate reasoning across dozens of steps.

Managing Context Window Pressure

As an agent works through a complex task, its context accumulates rapidly. Without active management, the context fills with old tool outputs and intermediate thoughts that are no longer relevant, pushing out important information or hitting the context limit. Strategies for managing this include:

• Rolling summarization: Periodically summarize older portions of the conversation into a compressed representation
• Selective inclusion: Only include recent and relevant interactions, filtering out routine tool calls that have been processed
• Scratchpad patterns: Maintain a structured scratchpad that the agent updates with current status, replacing rather than appending

The context window is not memory in the traditional sense. It is more like a desk: the agent can only work with what is currently on the desk, and adding more items requires removing others.

Long-Term Memory: Persistent Knowledge

Long-term memory persists across sessions and conversations. It stores information that the agent should remember indefinitely: user preferences, learned facts, project context, and organizational knowledge. Unlike short-term memory, which is bounded by the context window, long-term memory is stored externally and retrieved as needed.

Implementation Approaches

Vector store memory embeds memories as vectors and retrieves relevant ones using semantic similarity search. When the agent needs to recall something, it searches the memory store with a query related to the current context. This approach is excellent for retrieving factually relevant memories but may miss important context that is not semantically similar to the current query.

Structured memory stores information in predefined schemas like key-value pairs, user profiles, or knowledge graphs. This provides more reliable retrieval for specific types of information, such as user preferences ("The user prefers dark mode") or project status ("The deployment is scheduled for Friday").

Hybrid memory combines both approaches, using structured storage for well-defined information and vector storage for unstructured memories. This provides the reliability of structured retrieval with the flexibility of semantic search.

Episodic Memory: Learning from Experience

Episodic memory records specific experiences and their outcomes, allowing the agent to learn from past successes and failures. Unlike long-term factual memory, which stores what is true, episodic memory stores what happened, providing a narrative record of past tasks that can inform future behavior.

For example, if an agent previously attempted to book a flight and discovered that the user's preferred airline does not fly to certain destinations, that episode is stored. When a similar task arises, the agent can retrieve this episode and avoid repeating the same mistake.

How Episodic Memory Works

Episodic memories typically include the task or goal, the actions taken, the outcomes (success or failure), any lessons learned, and metadata like timestamp and relevance tags. When the agent encounters a new task, it searches episodic memory for similar past experiences, retrieves relevant episodes, and uses them to inform its approach.

The concept draws from the Generative Agents research from Stanford, where simulated agents maintained detailed records of their experiences and used reflection to extract higher-level insights from accumulated episodes.

Memory in Practice: Design Patterns

The Conversation Buffer

The simplest memory pattern stores the full conversation history and includes it in every prompt. This works well for short interactions but becomes impractical as conversations grow. A conversation buffer window keeps only the last N messages, trading completeness for efficiency.

The Summary Buffer

A summary buffer maintains a running summary of the conversation that is updated at regular intervals. Older messages are compressed into the summary, freeing context space while preserving key information. The tradeoff is that summarization inevitably loses some detail.

The Entity Memory

Entity memory tracks information about specific entities (people, projects, companies) mentioned in conversations. As the agent learns new facts about entities, it updates their records. This provides persistent, structured knowledge about the things the agent interacts with.

The best memory system is the one that matches your agent's usage pattern. A personal assistant benefits from entity and preference memory. A coding agent benefits from project context memory. A research agent benefits from episodic memory of past searches.

Memory Challenges

Implementing effective memory systems requires addressing several challenges:

• Relevance filtering: Not everything should be memorized. Storing too much creates noise that drowns out important information during retrieval.
• Staleness: Information changes over time. Memories that were accurate last month may be wrong today. Mechanisms for updating and expiring memories are essential.
• Contradiction resolution: When new information contradicts stored memories, the system must decide which to trust. Generally, more recent information should override older memories, but this is not always correct.
• Privacy: Memory systems that store personal information must comply with privacy regulations and user expectations about data retention and deletion.

As AI agents take on longer, more complex tasks and build ongoing relationships with users, memory systems will become increasingly sophisticated. The agents that feel most intelligent will be those with the best memory, not because they store the most information, but because they remember the right things at the right times.