What is short-term memory in an AI agent?

Short-term memory in an AI agent is information kept for a limited period so the agent can maintain continuity during a current conversation, task, or nearby sequence of interactions.

It helps the agent remember recent information without treating every new step as unrelated.

What short-term memory can contain

Short-term memory may include:

• recent user messages
• recent tool results
• temporary preferences
• recently discussed entities
• recent decisions
• intermediate task results
• summaries of the current conversation

The information is usually useful for the current interaction but may not need to remain available indefinitely.

Example

Suppose a user says:

> Compare Vendor A and Vendor B.

A few messages later, the user asks:

> Which one had the lower setup cost?

Short-term memory can help the agent remember that the discussion is about Vendor A and Vendor B and reuse the comparison from earlier in the same interaction.

Without that memory, the user might need to repeat the context.

Short-term memory is usually temporary

The defining feature is its limited lifetime.

It may last for:

• one conversation
• one workflow run
• one session
• a short series of related tasks

After that period, the information may be discarded unless it is important enough to be stored in long-term memory.

Short-term memory helps maintain conversational continuity

A conversation often contains references such as:

• “that option”
• “the second one”
• “use the same format”
• “continue from where we stopped”

To understand these references, the agent needs access to recent interaction history.

Short-term memory helps preserve that local continuity.

Short-term memory can reduce repeated questions

Suppose the agent already asked:

Preferred output format: CSV

If that information is still relevant in the current session, the agent should not ask for it again a few minutes later.

Short-term memory helps avoid unnecessary repetition.

Short-term memory vs. long-term memory

Short-term memory stores information that is useful for the current or recent interaction.

Long-term memory stores information intended to remain useful across future sessions or tasks.

For example:

Short-term: The user selected Option B during the current comparison.

Long-term: The user generally prefers concise reports.

A short-term fact may later be promoted into long-term memory if it has lasting value.

Short-term memory vs. working memory

The two concepts are related but not identical.

Short-term memory refers to recently retained information that remains available for a limited period.

Working memory is the smaller set of information the agent is actively using and manipulating for the current reasoning step.

For example:

Short-term memory might contain:

• the last several user messages
• recent search results
• earlier decisions in the current task

Working memory for the current step might contain only:

• the current question
• two relevant results
• one constraint

Working memory is therefore usually more focused on immediate processing.

Short-term memory vs. context

Short-term memory may be one source of context.

The system can retrieve relevant recent information and include it in the model's active context.

Not every item in short-term memory needs to be included in every model call.

For example, an old message from the same session may remain stored but be omitted if it is irrelevant to the current step.

Short-term memory can be implemented in different ways

Depending on the system, it may be represented by:

• recent conversation history
• session storage
• workflow state
• a temporary database record
• a rolling summary
• a bounded message buffer

The implementation can vary, but the purpose is the same: preserve useful recent information for a limited period.

It should remain selective

Keeping every recent detail can still create too much context.

A good short-term memory system may:

• keep only the latest messages
• summarize older parts of the session
• retain important decisions
• remove redundant information
• drop details that are no longer relevant

This helps the agent maintain continuity without overwhelming the context window.

Short-term memory can expire

Some systems remove short-term memories after:

• the session ends
• the workflow completes
• a time limit is reached
• the information is no longer relevant

This prevents temporary interaction data from being kept longer than necessary.

Example

Suppose an agent is helping plan a trip.

During the current session, short-term memory may contain:

• destination: Mumbai
• travel date: Friday
• preferred departure: morning
• budget: ₹8,000
• two shortlisted flights

When the user later asks:

> Which of the two is better?

The agent can use the recent memory to understand the question.

Once the trip-planning session ends, some of these details may no longer need to remain in short-term memory.

In short

Short-term memory in an AI agent is temporary memory used to retain recent information across a current conversation, task, or session.

It helps maintain continuity, reduce repeated questions, and preserve recent decisions, while information with lasting value may instead be stored in long-term memory.