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Managing Context Windows

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

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Context Compression Techniques

When context exceeds token limits, compression reduces verbosity while preserving essential information. Instead of dropping entire messages, agents condense them into compact summaries.

Interactive: Compression Simulator

50%
No compressionHigh compression
Result91 tokens (was 182)

User: Python data structures help? Assistant: Lists (mutable, ordered), tuples (immutable), dicts (key-value), sets (unique). Lists use [], support append/insert/remove/slice [start:end], dynamic sizing.

Saved
50%
Tokens
91
Quality
High

🔧 Compression Methods

📝

LLM Summarization

Use the LLM itself to summarize old context. Prompt: "Summarize this conversation preserving key facts and decisions."

✅ Best quality, ❌ Costs API calls
✂️

Extractive Summarization

Select key sentences using TF-IDF, TextRank, or embedding similarity. Extract most important lines without rewriting.

✅ Fast, deterministic, ❌ Less fluent
🗜️

Prompt Compression

Remove filler words, redundant phrases, formatting. "Could you please help me understand" → "Explain"

✅ Very fast, ❌ Loses nuance
🔗

Entity-Focused Compression

Extract entities (names, dates, facts) and relationships. Store as structured data instead of full text.

✅ Very compact, ❌ Requires NER

💡 Compression Best Practices

•
Compress Old First: Recent messages are more relevant. Compress or remove messages older than N turns (e.g., > 10 turns ago).
•
Preserve Critical Facts: Never compress: user preferences, system instructions, current task goals, tool results.
•
Balance Cost vs Quality: Summarization costs tokens. Only compress when approaching limits (e.g., > 80% full).
•
Test Compression Ratios: 50% compression usually preserves quality. Above 70% risks losing important context.
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