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Why Say the Same Thing 12,000 Times?

#agents #ai #llm #performance

Type: Technical Article
Date: 2026-07-09
Authors: Yuta Tu & ALICE (Pi Agent)

TL;DR

LLM Agents repeat the same system prompt every turn, wasting tokens and diluting attention. We built a sub-300-line Pi extension that deduplicates system prompts by hash comparison, achieving a 93% hit rate across 12,104 turns and saving ~290M tokens. We contrast two design philosophies: OS-level garbage collection (evict what you stuffed) vs. compiler-level dead code elimination (don't stuff it in the first place).

The Problem: Your Agent Keeps Repeating Itself

Every LLM Agent API call re-injects the full system prompt—identity, tool schemas, skill descriptions, operational guidelines, project context—verbatim into each request.

This content rarely changes during a session. Yet it's sent dozens, hundreds of times.

We measured ALICE on Pi Agent. Average system prompt: 104,478 characters (~26,120 tokens). In an 8-turn conversation, that's 208,960 tokens of system prompt traffic—only 12.5% of which is new information. Across 12,104 production turns, 93% of system prompts were identical to the previous turn. Without deduplication, that's ~290 million wasted tokens.

This isn't just a cost problem. Transformer self-attention allocates weight equally across all tokens. When 87.5% of input is static background, effective attention gets diluted. Liu et al. (2024) showed that longer contexts degrade recall for content in the middle.

Two Design Philosophies

OS-Level: Stuff First, Clean Later

Pichay (2026) proposed a transparent proxy between LLM and application, using demand paging to evict seldom-used tokens and page-fault them back when needed—analogous to virtual memory.

Across 857 production sessions and 4.45B tokens, they found 21.8% structural waste (unused tool schemas, duplicates, stale tool outputs). Their method recovered session usable space from 7% to 43%.

This is OS-level garbage collection. Waste is generated first, then cleaned.

Compiler-Level: Don't Stuff It at All

We took a different path.

Compilers perform dead code elimination—removing code that never executes, before generating machine code. Don't produce, don't process, don't occupy space.

Applied to system prompts: if the content hasn't changed, why send it again?

Our extension does one simple thing: before each API call, hash the system prompt. If it matches the previous turn, strip the system field from the payload. Only re-inject when content actually changes.

The "Never Replenish" principle: force_interval is set to 0. No time- or turn-based replenishment. The reasoning: there is zero evidence that periodic system prompt re-injection improves attention, but extra tokens definitely dilute it. Anthropic's official guidance agrees: "Treat context as a precious, finite resource."

Side-by-Side

Dimension OS-Level (Pichay) Compiler-Level (Ours)
Intervention point After token generation Before token generation
Core mechanism Eviction / page fault Hash comparison + strip
Dependency layer Proxy (extra service) Extension (built into agent)
Latency impact Page fault may delay Zero latency, pure intercept
Scope Multi-agent, multi-provider Single agent runtime

These approaches are complementary. Compiler layer catches static repetition; OS layer handles dynamic redundancy.

Implementation: A Sub-300-Line Extension

Built on Pi Agent's extension mechanism. Core logic in TypeScript.

Architecture 

Pi Extension Lifecycle:
  buildSystemPrompt() → assemble full system prompt (~26K tokens)
    ↓
  before_agent_start → extension chain injects persona/skills
    ↓
  agent-loop → execute tools, generate conversation
    ↓
  before_provider_request → ★ dedup extension intercepts here:
                              hash comparison → strip system field if unchanged
    ↓
  LLM API call (system field empty in payload)
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Key decisions:

  • Intercept at before_provider_request (not before_agent_start): preserves extension chain integrity, strips only at the final API boundary
  • Read-only capture: reads and strips system prompt; never modifies other extensions' output
  • Hash-based, not content-based: SHA-256 is fast and stable

Configuration 

{
  "enabled": true,
  "force_interval": 0,
  "force_on_change": true
}
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Every turn's decision is logged to JSONL. A CLI stats panel provides real-time savings data.

Results

As of 2026-07-09:

Metric Value
Total turns 12,104
Dedup hits 11,197 (93%)
Avg system prompt size 104,478 chars (~26,120 tokens)
Cumulative tokens saved ~290M
DeepSeek Cache Hit Rate 94.3%
Cache discount 99.2% (¥0.025/MTok vs ¥3.00/MTok)

DeepSeek's 94.3% cache hit rate shows that dedup improves provider-side caching too—saving both agent and provider compute.

At Claude Opus pricing ($15/MTok input), a 100-turn session saves ~$11. Across 100 monthly sessions: ~$80–100. Modest for individual devs, but for long-running agent systems, it means more meaningful content fits in the same context window.

Discussion

Complementary, Not Competing

Pichay handles dynamic redundancy (stale tool outputs, cross-request similarities). We handle static redundancy (unchanging system prompts). An ideal architecture uses both: compiler layer first, OS layer for what remains.

Limitations & Next Steps

Persona consistency: Does stripping the system prompt affect ALICE's personality? Across 12,000+ turns, we observed no discernible drift—conversation history appears to carry sufficient behavioral signal. But rigorous controlled experiments remain undone. This is our primary open question.

Cross-model validation: Tested primarily on DeepSeek and Anthropic Claude. GPT-series and open-source models need verification.

Multi-agent portability: Our implementation is tied to Pi Agent's extension mechanism. Porting to other runtimes (Claude Code, LangChain) requires equivalent interception hooks.

Practical Guidance

For LLM Agent developers:

  1. Measure first — hash your system prompt per turn to quantify waste
  2. Don't replenish on a timer — force_interval: 0 unless you have evidence your model needs it
  3. Re-inject on change — force_on_change: true ensures config updates take immediate effect
  4. Monitor output quality — watch for persona drift; check whether other extensions depend on persistent system prompts

Conclusion

Across 12,104 turns, a sub-300-line extension saved ~290 million tokens. Not because we invented a new algorithm, but because we asked what seemed like an overly simple question:

Why say the same thing 12,000 times?

The answer is: you don't have to.

References

  1. Mason, T. (2026). Demand Paging for LLM Context Windows. arXiv:2603.09023.
  2. Liu, N. F., et al. (2024). Lost in the Middle: How Language Models Use Long Contexts. TACL.
  3. Leviathan, Y., et al. (2025). Prompt Repetition Improves Non-Reasoning LLMs. arXiv:2512.14982.
  4. Anthropic. (2025). Effective Context Engineering for AI Agents. Official Guide.
  5. OpenAI. (2025). Prompt Caching in the API. Documentation.

Source code available as a Pi Agent extension. Statistics from 12,104 production conversation turns.

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