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Memory Consolidation

Managing Context Windows

Master how AI agents manage limited context windows to maintain coherent, efficient conversations

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The Context Window Challenge

Every LLM has a context window—a hard limit on how many tokens it can process at once. GPT-4 handles 8K-128K tokens, Claude 200K, but agents need to work within these constraints while maintaining coherent, contextual conversations.

The problem: As conversations grow, agents must decide what to keep, what to summarize, and what to discard—all while preserving critical context for effective reasoning and tool use.

Interactive: Context Window Comparison

⚠️ Limited space: Only recent messages fit
user5 tokens

What is machine learning?

assistant8 tokens

ML is...

user2 tokens

Example?

Total: 15 / 4,000 tokens used

🎯 Why Context Windows Matter

💰

Cost Efficiency

Tokens cost money. Every API call charges per token—keeping context lean saves budget while maintaining quality.

⚡

Speed & Latency

Larger contexts take longer to process. Smaller, focused windows reduce latency and improve user experience.

🎯

Focus & Relevance

Too much context creates noise. Strategic pruning helps agents focus on what matters for the current task.

🧠

Quality Reasoning

Models perform best with relevant context. Effective window management improves reasoning and decision-making.

💡 The Core Challenge

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Token Limits Are Hard: Once you hit the limit, the model refuses or truncates input. There's no overflow—you must manage it proactively.
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Context = Coherence: Remove too much and the agent forgets critical details. Keep too much and you waste tokens on irrelevant history.
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No One-Size-Fits-All: Different tasks need different strategies. Customer support needs recent context; research needs deep history.