OCTOBER 2026 CALIBRATION POLICY · 30/30 CI TESTS PASSING · SELF-ASSESSED SECURITY

The Epistemic AI Backbone &
Token Optimization Engine

Autonomous agent frameworks are hemorrhaging token budgets by dumping 30 massive tool schemas on every single loop while hallucinating on frozen neural weights. ChronoFact is an open-source native Rust engine that slashes prompt tokens by 81.8% to 90.9%, intercepts knowledge cutoff drift in under 5ms, and gates memory with a strict 0.72 relevance threshold.

Clone Open-Source EngineLaunch Interactive SimulatorInspect Live Machine Receipts
81.8% - 90.9%
Prompt Token Reduction
Prunes 9-10 of 11 tool schemas via unigram cosine similarity
< 0.15 ms
Deterministic Invariant Verifier
Sub-millisecond token-overlap & invariant rules with zero LLM inference cost
0.00 ms
Tool Cache Hit Latency
Instant in-memory SHA-256 hash lookup with zero LLM roundtrip
0 Tokens
Cross-Domain Memory Leak
Strict 0.72 relevance gate halts context pollution on task switch
// HANDS-ON INTERACTIVE LAB

Pillar 4: TF-IDF Tool Pruning Simulator

Experience how ChronoFact extracts unigrams from user queries, calculates cosine similarity against tool schema vectors, and prunes unneeded tools before the prompt hits the LLM.

SELECT TEST QUERY SCENARIOFrontend UI
TF-IDF RELEVANCE THRESHOLD0.45
0.10 (Aggressive Retain)0.45 (Optimal Production)0.90 (Hyper-Strict)
REAL-TIME PRUNING TELEMETRY
83%Token Reduction
1635 / 1980 tok pruned
Est. ~$4.91 saved / 1k turns
TOOL SCHEMA MATRIX (2 RETAINED · 9 PRUNED)Prompt Payload: 345 tokens
read_file
Read local filesystem source files
180 tok
write_file
Write or edit code in filesystem
PRUNED
terminal_exec
Execute shell and powershell commands
PRUNED
sql_query
Execute PostgreSQL or SQLite queries
PRUNED
git_commit
Create commits and branch operations
PRUNED
web_search
Perform live web research queries
PRUNED
memory_store
Persist architectural rules in semantic memory
PRUNED
deploy_k8s
Deploy container workloads to Kubernetes
PRUNED
figma_inspect
Inspect design tokens and CSS frames
165 tok
auth_rotate
Rotate API keys and session credentials
PRUNED
docker_build
Build OCI container images with buildx
PRUNED
// VERIFIED PRODUCTION TELEMETRY

Authentic Machine Receipts & Local State

Captured directly from Chrome headless on http://localhost:5173 connected to our native Rust daemon. You can test live turn simulation below.

CUMULATIVE TOKENS SAVED
3,420
+1,710 tok / avg turn
SCHEMAS PRUNED
19
81.8% elimination ratio
ESTIMATED USD SAVED
$0.0103
Claude 3.5 / Opus pricing
INTERCEPTED DRIFT EVENTS
37
Sub-5ms interception
ChronoFact Live Telemetry Cockpit
chronofact.exe stdio & HTTP proxy event ledger (SQLite WAL)● LIVE
[20:20:39][PILLAR 1]Intercepted Astra 6 query · Cutoff Delta: +187d · Training Freeze: +218d
[20:20:41][PILLAR 4]Pruned 9 of 11 tool schemas · Slashed 1,620 prompt tokens (81.8%)
[20:20:42][PILLAR 2]Grounding fetch SRC-1 · SSRF shield: PASS · Indirect injection: SANITIZED
[20:20:44][PILLAR 3]Gated memory evaluation · Cosine: 0.18 < 0.72 · Zero context pollution
[20:20:45][PILLAR 4]SHA-256 tool cache hit · 0.00ms response · Zero LLM token cost
// ARCHITECTURAL BLUEPRINT

The 4 Epistemic Pillars

Engineered from scratch in native Rust to eradicate hallucinations, drift, and token bloat across the full agent lifecycle.

PILLAR 4 · 81.8% TO 90.9% TOKEN REDUCTION

Pure Rust TF-IDF Tool Router

Stop dumping dozens of JSON schemas into every prompt. ChronoFact extracts unigrams, computes cosine similarity against tool schema definitions, and strips unneeded tools. Responses are cached via SHA-256 for instant 0.00ms hit latency.

src/cost/router.rs
pub fn route_tools(query: &str, tools: &[ToolSchema], threshold: f32) -> Vec<ToolSchema> {
    let q_unigrams = extract_unigrams(query);
    tools.iter()
        .filter(|t| compute_tool_similarity(&q_unigrams, t) >= threshold)
        .cloned()
        .collect()
}
// DETERMINISTIC INVARIANT AUDITOR

Sub-Millisecond Invariant & Lexical Verifier

Test how ChronoFact decomposes model responses into atomic propositions and checks policy invariants and token-overlap in <0.15ms with zero LLM inference cost.

// HORIZON AUDIT

October 2026 Model Freeze Matrix

ChronoFact automatically tracks cutoff deltas and weight freeze dates across all leading model providers.

Model IdentifierProviderOfficial CutoffTraining FreezeStatus & Interceptor Rule
GPT-6 AstraOpenAISep 3, 2026Aug 2026Active Frontier Flagship
GPT-6.1 SolOpenAISep 29, 2026Sep 2026Active Frontier Flagship
Claude 3.5 SonnetAnthropicApr 30, 2024Apr 2024RETIRED EOL (Oct 28, 2025) · Flags Active Successor
Claude Opus 5.5 / Sonnet 5.5AnthropicApr 1, 2026Mar 1, 2026Active Frontier Flagship
Grok 4.7xAISep 2026Aug 2026Active Frontier Flagship (Grok 3 Superseded)
// RAPID DEPLOYMENT

Zero-Dependency Quickstart

Run ChronoFact as a standalone binary or connect it to Google Antigravity, Cursor AI, and Claude Desktop via Model Context Protocol.

Antigravity MCP Configuration (antigravity.json)
{
  "mcpServers": {
    "chronofact": {
      "command": "C:\\chronofact\\target\\release\\chronofact.exe",
      "args": ["mcp"]
    }
  }
}
// FREQUENTLY ASKED QUESTIONS

Architectural Details & Specifications

Everything you need to know about ChronoFact's mathematical models, memory gating, and token economics.

What is ChronoFact and how does it solve agent hallucinations?−
ChronoFact is an open-source epistemic AI backbone written in native Rust. Unlike naive wrapper frameworks that blindly feed user prompts into LLMs, ChronoFact sits between your agents and model providers via Model Context Protocol (MCP) or an HTTP proxy. It executes four deterministic pillars: (1) Temporal Horizon Calibration to detect knowledge cutoff and weight freeze divergence, (2) Dynamic Source Grounding with SSRF and prompt injection protection, (3) Relevance-Gated Memory to eliminate context pollution, and (4) TF-IDF Tool Pruning with SHA-256 caching to cut token overhead by 80% to 90%.
How does Pillar 4 TF-IDF tool pruning slash prompt tokens by 81.8% to 90.9%?+
What is the difference between Knowledge Cutoff and Training Freeze Date?+
How does ChronoFact handle October 2026 frontier models like OpenAI Astra 6, Sol 6.1, and Claude 3.5 Sonnet EOL?+
How does Pillar 3 Relevance-Gated Memory prevent context pollution?+
How does the 20ms NLI Contradiction Engine verify factual claims?+
How does ChronoFact connect to Google Antigravity, Cursor AI, and Claude Desktop?+
Is ChronoFact fully local and does it leak API keys or private data?+