Persistent memory engine for AI agents. Local-first, sub-millisecond, zero cloud.
Native Python binding for Cortex — a Rust memory engine with 4-tier memory, Bayesian beliefs, people graph, and HNSW vector search.
pip install cortex-ai-memoryfrom cortex_python import PyCortex
# Open or create a memory database
cx = PyCortex("memory.db")
# Ingest memories
cx.ingest("Met Alice at the Q3 planning meeting", "slack", user_id="alice_123")
cx.ingest("User prefers dark mode", "cli")
# Retrieve relevant memories
results = cx.retrieve("What do I know about Alice?", limit=5)
for memory_id, score, text in results:
print(f"[{score:.2f}] {text}")
# Generate LLM-ready context (token-budgeted)
context = cx.get_context(2000, channel="slack")
# Structured knowledge
cx.add_fact("Alice", "works_at", "Acme Corp", 0.95, "slack")
cx.add_preference("timezone", "Asia/Shanghai", 0.9)
# Bayesian beliefs
cx.observe_belief("user_likes_python", True, 0.8)
beliefs = cx.get_beliefs(0.5)
# People graph
cx.add_person("Alice", "slack", "alice_123")
# Consolidation (run periodically)
scanned, promoted, swept, patterns = cx.run_consolidation()For semantic search, pass embeddings from any provider:
import numpy as np
# Use any embedding model (OpenAI, ollama, sentence-transformers, etc.)
def embed(text):
# your embedding function here
...
cx.ingest("I live in Shanghai", "cli", embedding=embed("I live in Shanghai"))
results = cx.retrieve("where do I live?", 5, embedding=embed("where do I live?"))- 4-tier memory: Working, Episodic, Semantic, Procedural
- HNSW vector search: Sub-millisecond at 100K+ memories
- Bayesian beliefs: Self-correcting with evidence
- People graph: Cross-channel identity resolution
- Conversation compression: Automatic session summarization
- Contradiction detection: Catches conflicting facts
- Chinese + English: Native bilingual NLP
- Zero cloud: 100% local, your data stays on your device
- 3.8MB binary: Pure Rust, zero runtime dependencies
MIT