Token Reduction Engine (TRE)

Company Brain Core OS — Free, local, deterministic knowledge base for your agent. Start here if you need persistent facts before optimizing token spend.

Cut AI costs. Not quality. Not accuracy.

v1.0.0

Built and dogfooded by CertainLogicAI — Saves us 79%+ on every cached query. Measurement beats hope.

Part of the CertainLogic Stack

This skill works standalone. You don't need anything else to start cutting token costs today.

Works even better with Company Brain Core OS (clawhub install company-brain-os):

• Brain stores validated facts — TRE caches deterministic responses
• AgentPathfinder — audits which cache policies led to good outcomes
• Smart Router — routes non-cached queries to cheaper model tiers

All four are independent. Pick the one that solves your problem. Add others when you need them.

How It Works — Three Layers

TRE sits between your agent and the LLM. It intercepts queries and decides: cache hit, deterministic lookup, or expensive LLM call.

User Query → TRE Decision Layer → Result (cache | brain | LLM)

Layer 1: Query Cache (Answer Cache)

Stores LLM responses so repeated queries return instantly — zero tokens.

• SHA-256 hash of exact query as cache key
• LRU eviction — keeps hot answers, drops cold ones
• TTL-based expiry — stale data auto-purges (default: 3600s)
• Hallucination Guard — hedged answers ("I think", "maybe", "not sure") are shown to user but NOT cached

# After first LLM call:
tre.cache_answer("What is our refund policy?", "30 days, no questions.")

# Every subsequent call:
answer = tre.get_cached_answer("What is our refund policy?")
# → Instant. Zero tokens. No LLM hit.

Layer 2: Deterministic Lookup

For questions with factual answers, skip the LLM entirely.

• Intent classification — matches query to known domain (strategy, product, security, etc.)
• Facts DB overlay — structured key-value pairs with source attribution
• Zero LLM cost — pre-validated facts served direct from SQLite

"What products do we offer?" → Intent: product → Facts DB → Instant answer
"How does the brain work?" → Intent: strategy → Facts DB → Instant answer
"Who wrote the security policy?" → Intent: security → Facts DB → Instant answer

Layer 3: Intent Filtering (Sanity Gate)

Prevents expensive LLM calls that can't possibly have good answers.

• Forbidden intents — blocks "brain.delete_brain", "brain.purge", malicious commands
• Required fields gate — rejects malformed queries before they hit the LLM
• Domain alignment — refuses answers outside declared intent scope (prevents hallucinated guesses)

Major Benefits

1. 60-85% Token Cost Reduction

• Cache hit rate: up to 79% (measured on Brain workloads — your results depend on query repetition)
• Deterministic path: <10ms vs 500-2000ms LLM round-trip
• Cost comparison: $0 per cached answer vs $0.01-0.15 per LLM call

2. Zero Hallucination on Cached Facts

• Hallucination Guard detects hedged language: "I think", "maybe", "probably", "not sure"
• Flagged responses are shown but not cached — prevents poisoned cache
• SH A-256 verification on every write — tamper detection for audit trails
• Source attribution — every cached fact traces back to who loaded it and when

3. Instant Sub-10ms Responses

• Instant responses — SQLite-backed local cache serves answers from RAM (no network round-trip)
• Cold → hot acceleration — cache warmup brings first-query latency from ~700ms to sub-50ms

4. Agent Won't Run Amok

• Forbidden command list — "brain.delete_brain", "brain.purge" are blocked before execution
• Intent scope enforcement — agent refuses queries outside declared domain
• SHA-256 write verification — any tampered cache entry is rejected on read
• AgentPathfinder integration — HMAC-signed audit trail on every decision

5. Works With Any LLM Stack

• OpenAI (GPT-4o, GPT-4o-mini)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus)
• Local models (Ollama, llama.cpp, vLLM)
• Multi-provider setups — TRE sits in front of all of them

# Works with any backend
llm_provider: openai  # or anthropic, local, azure, etc.
tre:
  cache_ttl: 3600
  max_cache_size: 10000
  hallucination_guard: true

6. Persisted Across Restarts

• Cache auto-saves to disk on every write
• Load persisted cache on startup — no warm-up period
• Configurable persistence path — store anywhere (local disk, shared volume, etc.)

7. Standalone — No Lock-in

• Runs on localhost — no external API, no SaaS dependency
• MIT License — free to fork, modify, commercialize
• No telemetry — your queries never leave your machine
• 50-line integration — drop into existing agent in minutes

Standalone Usage

clawhub install certainlogic-tre

from tre import cache_answer, get_cached_answer, get_metrics

# After getting an LLM response
tre.cache_answer(query, answer)

# Next time — zero tokens
cached = tre.get_cached_answer(query)
if cached:
    answer, token_count = cached
    print(f"Instant! Saved {token_count} tokens.")

# Check your savings
print(tre.get_metrics())
# {'cache_hits': 853, 'cache_misses': 223, 'cache_hit_rate_percent': 79.3}

See references/CONFIGURATION.md for full config options (TTL, cache size, guard sensitivity, forbidden commands).

See references/API.md for Python API reference.

Integration with Company Brain Core OS

When Brain is installed, TRE gets smarter:

• Facts DB pre-populates cache — Brain's 52+ facts are available instantly
• Intent classification improves — Brain's domain model refines routing
• Query classification — Brain categorizes new queries, expanding cache coverage

# Install both
clawhub install company-brain-os certainlogic-tre

# Brain loads facts → TRE caches them → instant responses for common questions

Metrics (Measured Ourselves)

Free vs Pro

Pro pricing: TBD — join the waitlist at certainlogic.ai/tre

License

MIT — free to use, modify, distribute.

Built with brutal honesty by CertainLogic