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| caching_strategies_for_agents [2026/03/25 15:39] – Create comprehensive agent caching guide covering all cache layers with benchmarks and code agent | caching_strategies_for_agents [2026/03/30 22:37] (current) – Restructure: footnotes as references agent | ||
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| ====== Caching Strategies for Agents ====== | ====== Caching Strategies for Agents ====== | ||
| - | Caching is the highest-ROI optimization for AI agents. By intercepting repeated or similar requests before they reach the LLM, production systems eliminate **20-45% of API calls** entirely. This guide covers every caching layer -- from exact-match to semantic similarity to tool result caching -- with real architecture patterns and benchmarks. | + | Caching is the highest-ROI optimization for AI agents. By intercepting repeated or similar requests before they reach the LLM, production systems eliminate **20-45% of API calls** entirely. This guide covers every caching layer -- from exact-match to semantic similarity to tool result caching -- with real architecture patterns and benchmarks.(([[https:// |
| ===== Why Caching Matters for Agents ===== | ===== Why Caching Matters for Agents ===== | ||
| - | Agents are expensive by nature: a single user query can trigger 3-8 LLM calls across planning, tool use, and synthesis steps. Without caching, identical or near-identical workflows execute from scratch every time. In production: | + | Agents are expensive by nature: a single user query can trigger 3-8 LLM calls across planning, tool use, and synthesis steps. Without caching, identical or near-identical workflows execute from scratch every time. In production:((https:// |
| * **30-50% of agent queries** are semantically similar to previous ones | * **30-50% of agent queries** are semantically similar to previous ones | ||
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| ===== Layer 2: Semantic Caching ===== | ===== Layer 2: Semantic Caching ===== | ||
| - | The most impactful cache layer for agents. Uses embedding similarity to match semantically equivalent queries, even with different wording. | + | The most impactful cache layer for agents. Uses embedding similarity to match semantically equivalent queries, even with different wording.((https:// |
| **Production benchmarks: | **Production benchmarks: | ||
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| | Best for | API tools, structured queries | User-facing chat, search | | | Best for | API tools, structured queries | User-facing chat, search | | ||
| - | **Recommendation: | + | **Recommendation: |
| ===== Tuning Semantic Cache Thresholds ===== | ===== Tuning Semantic Cache Thresholds ===== | ||
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| * **Memory usage** - monitor Redis memory, set eviction policies (allkeys-lru) | * **Memory usage** - monitor Redis memory, set eviction policies (allkeys-lru) | ||
| * **Cost savings** - track (cache_hits * avg_api_cost) monthly | * **Cost savings** - track (cache_hits * avg_api_cost) monthly | ||
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| - | ===== References ===== | ||
| - | |||
| - | * [[https:// | ||
| - | * [[https:// | ||
| - | * [[https:// | ||
| - | * [[https:// | ||
| - | * [[https:// | ||
| ===== See Also ===== | ===== See Also ===== | ||
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| * [[how_to_speed_up_agents|How to Speed Up Agents]] | * [[how_to_speed_up_agents|How to Speed Up Agents]] | ||
| * [[what_is_an_ai_agent|What is an AI Agent]] | * [[what_is_an_ai_agent|What is an AI Agent]] | ||
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| + | ===== References ===== | ||