Three lines of code. Persistent AI memory.

from hebbrix import Hebbrix

hebbrix = Hebbrix()  # Uses HEBBRIX_API_KEY env var

# Store a memory
hebbrix.memories.create(
    content="User prefers dark mode and concise responses"
)

# Search memories
results = hebbrix.search("user preferences")

# That's it. Knowledge graph, 5-layer search,
# and auto-learning happen behind the scenes.

import openai

# Drop-in OpenAI replacement with memory
client = openai.OpenAI(
    base_url="https://api.hebbrix.com/v1",
    api_key="your_hebbrix_key"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What do I prefer?"}]
)
# Hebbrix auto-searches relevant memories
# and injects them before forwarding to the LLM