engram-causal 0.1.5

Causal reasoning layer for AI systems. Answers why instead of what.

engram-causal

Causal reasoning layer for AI systems. Answers "why" instead of just "what".

Extract cause-effect relationships from your AI agent sessions, store them in a causal graph, and inject root causes into debugging workflows without burning tokens on re-investigation.

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The Problem in 30 Seconds

Your AI agent debugs a payment failure (6+ files read, 5+ token-expensive turns). Next week: same failure, same re-investigation, same tokens wasted.

Why? Because memory systems don't store causality — they store facts.

Engram solves this:

# Structured event ingestion (zero LLM cost)
engram ingest ./logs/session.json

# Query: "Why did payment fail?"
engram why "payment_failed"
# Returns: "Token validation bug in auth_middleware.py:47 (conf: 0.94)"
# Tokens used: 0 (pure graph traversal)

Next time your agent sees that setup, it knows the root cause instantly.

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Key Features

✅ Three-Track Extraction — Rules (free) → SHA256 cache (free) → Claude once, then cached (~$0.0003)

✅ Persistent Causal Graph — Store relationships, survive sessions

✅ Why-Query Engine — Backward DFS traversal for root causes (zero LLM)

✅ EU AI Act Ready — Full audit trail + confidence scores

✅ Structured Events — Parse OpenTelemetry, typed JSON logs with deterministic rules

✅ Low Cost — Most queries run on cached triples or graph math, not LLM

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Install

pip install engram-causal

# Set your Anthropic API key (optional, only for free-text extraction)
export ANTHROPIC_API_KEY=sk-ant-...

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Quick Start

Extract causal triples from text

engram extract "The database timed out because the connection pool was exhausted"

Output:

1 triple(s) extracted:
  'connection pool exhausted' → 'database timed out' (conf=0.89, mechanism=external-event)

Ingest structured events (zero LLM)

engram ingest ./logs/events.json

Query root causes

engram why "database_timeout"

Start the API server

engram serve
# API: http://localhost:8000
# Graph viewer: http://localhost:8000/graph

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Use Cases

• AI Agent Debugging — Capture why decisions failed, prevent repeated mistakes
• Explainable Systems — EU AI Act Article 13 compliance, auditable reasoning chains
• Root Cause Analysis — Trace failures back to origin across sessions
• Incident Response — Structured post-mortems with causal chains

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What's Different

	Vector DB	Chat Memory	Engram
Stores	Embeddings	Facts & history	Cause-effect chains
Query style	"Find similar"	"What happened?"	"Why did this happen?"
Explainability	❌ Black box	❌ Fact-based only	✅ Causal + confidence
LLM cost	High (reprocess)	Medium (caching)	Ultra-low (graph math)

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Documentation

Full docs: https://engram.viberank.co.in

GitHub: https://github.com/karankartikeya/engram

Report issues: https://github.com/karankartikeya/engram/issues

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License

MIT