.causal
The Knowledge Graph Format for AI
Binary format with embedded deterministic inference. Zero hallucination. Every fact is traceable.
30-40x
faster than SQLite
50-200%
fact amplification
0%
hallucination
The Problem
🗄️
SQLite
Stores facts but can't reason. Query "COVID → fatigue" returns nothing even when the connection exists.
🔍
Vector RAG
Finds similar text but no logic. Returns relevance scores, not causal chains.
🤖
LLMs
Reasons creatively but hallucinates. Invents plausible but false connections.
.causal = Storage + Reasoning
Pre-computed inference at write time. Zero-latency logic at query time.
Try It Live
Real Long COVID Knowledge Graph: Loading...
Quick Start
from dotcausal import CausalWriter, CausalReader
# Create a knowledge graph
writer = CausalWriter()
writer.add_triplet("SARS-CoV-2", "damages", "mitochondria", confidence=0.9)
writer.add_triplet("mitochondrial dysfunction", "causes", "fatigue", confidence=0.85)
writer.save("knowledge.causal") # Inference runs automatically
# Query - finds the inferred chain!
reader = CausalReader("knowledge.causal")
results = reader.search("fatigue")
for r in results:
tag = "[INFERRED]" if r['is_inferred'] else "[EXPLICIT]"
print(f"{tag} {r['trigger']} → {r['outcome']}")
# Output:
# [EXPLICIT] mitochondrial dysfunction → fatigue
# [INFERRED] SARS-CoV-2 → fatigue ← This was computed, not stored!