hebbrix 2.0.1

Advanced Memory API for AI Agents with Reinforcement Learning - 3-line integration

Hebbrix Python SDK

Official Python SDK for the Hebbrix api - the only memory API with Reinforcement Learning.

🚀 Features

• ✅ Complete API Coverage - All 50+ endpoints supported
• ✅ Reinforcement Learning - Train AI agents to optimize memory operations
• ✅ Temporal Knowledge Graphs - Track facts over time with bi-temporal model
• ✅ Procedural Memory - Store and execute learned skills
• ✅ Working Memory - Short-term context buffer for conversations
• ✅ Memory Consolidation - Automatic compression of episodic memories
• ✅ Async/Await - Full async support with httpx
• ✅ Type Hints - Complete type annotations
• ✅ Clean API - Pythonic, intuitive interface

📦 Installation

pip install hebbrix

🔥 Quick Start

import asyncio
from hebbrix import MemoryClient

async def main():
    # Initialize client
    client = MemoryClient(api_key="mem_sk_your_api_key")

    # Create a collection
    collection = await client.collections.create(
        name="My AI Agent",
        description="Personal memory for my chatbot"
    )

    # Store a memory
    memory = await client.memories.create(
        collection_id=collection["id"],
        content="User prefers dark mode and loves Python",
        importance=0.9
    )

    # Search memories
    results = await client.search(
        query="What programming language does user like?",
        collection_id=collection["id"],
        limit=5
    )

    print(results)

    # Close client
    await client.close()

asyncio.run(main())

📚 Complete Documentation

Visit https://docs.hebbrix.com for full documentation.

🔗 Links

• Documentation: https://docs.hebbrix.com
• API Reference: https://api.hebbrix.com/docs
• GitHub: https://github.com/hebbrix/hebbrix
• Examples: https://github.com/hebbrix/examples

📄 License

MIT License - see LICENSE for details

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Built with ❤️ by the Hebbrix team