Docs K  Search
Docs/Getting started/Overview
Documentation

Welcome to Hebbrix

Hebbrix gives your AI persistent memory. Build applications with durable memory, adaptive retrieval, and learning from reported outcomes.

01. Why Hebbrix?

Traditional LLMs forget everything after each conversation. Hebbrix fixes this by providing a memory layer that stores, retrieves, and can learn from reported outcomes. Your AI can now remember user preferences, past conversations, and build knowledge temporal graphs that preserve how facts and relationships change over time.

Versioned Memory

Operational tiers, current truth, history, provenance, and explicit deletion controls.

Smart Retrieval

Hybrid search finds exactly what you need, even from old conversations.

Drop-in Ready

Works with OpenAI, Anthropic, and any LLM. Just change the base URL.

02. Explore the API

03. Quick Example

python
import asyncio
from hebbrix import MemoryClient

async def main():
    async with MemoryClient(api_key="mem_sk_...") as client:
        coll = await client.collections.create(name="my-agent")

        # Store a memory
        await client.memories.create(
            collection_id=coll["id"],
            content="User prefers dark mode",
        )

        # Search memories
        results = await client.search(
            query="user preferences",
            collection_id=coll["id"],
        )

asyncio.run(main())

04. Ready to Build?

Get started in minutes with our quickstart guide or explore the full API reference.

Ask the docs
reading · this page

Hi! I'm the Hebbrix docs assistant. Ask me anything about this page: setup, code examples, endpoints, pricing, or integrations.