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Short-Term Memory

Master how AI agents manage conversation context and working memory

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Managing Limited Memory

Since context windows are finite, agents need strategies to manage what stays in memory and what gets dropped. The right approach depends on your use case: chat apps, document Q&A, or long-running agents.

Let's explore the four main strategies and when to use each one.

Interactive: Strategy Comparison

🔄 Sliding Window

Drop the oldest messages when the window fills up. Simple FIFO (first-in, first-out) queue.

✅ Pros
  • • Simple to implement
  • • Low overhead
  • • Predictable behavior
❌ Cons
  • • Loses early context
  • • No prioritization
  • • Poor for long sessions
Best For:

Short chat sessions, customer support, simple Q&A where recent context is most important.

messages = messages[-max_messages:] # Keep last N messages

Interactive: Compression Simulator

See how summarization reduces token usage. Higher compression = more aggressive summarization.

4:1
2:1 (Mild)5:1 (Moderate)10:1 (Aggressive)

Before Compression

4,096 tokens

After Compression

1024 tokens

3072
Tokens Saved
75%
Reduction
$0.031
Cost Saved

Implementation Tips

💡 Always Keep System Prompt

The system prompt should never be dropped or summarized—it defines the agent's behavior and must stay intact.

💡 Preserve Recent Messages

Always keep the last 3-5 messages in full. Users expect agents to remember what was just said.

💡 Monitor Token Usage

Track token counts per request. Set alerts when approaching context limits to trigger compression proactively.

💡 Test Strategy Changes

A/B test different strategies with real users. What works theoretically may not align with user expectations.

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