Hebbrix Python SDK

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

🚀 Features

• ✅ Core API Coverage - Typed resources for memories, search, and ProofLoop
• ✅ 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
• ✅ ProofLoop - Learn from outcomes with automatic, verifiable evidence receipts
• ✅ Sync + Async - Equivalent core memory and ProofLoop workflows
• ✅ 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"],
        user_id="customer-7",
        agent_id="support-agent",
        content="User prefers dark mode and loves Python",
        importance=0.9,
        wait_for_index=True,
    )

    # 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())

Blocking applications can use the same create/search/ProofLoop fields:

from hebbrix import SyncMemoryClient

with SyncMemoryClient(api_key="mem_sk_your_api_key") as client:
    collection = client.collections.create(name="My Agent")
    client.memories.create(
        collection_id=collection["id"],
        user_id="customer-7",
        content="User prefers concise answers",
        wait_for_index=True,
    )
    results = client.search(
        "How should answers be formatted?",
        collection_id=collection["id"],
        user_id="customer-7",
    )

ProofLoop: search → decision → outcome → proof

search = await client.search_with_proof(
    "What should the agent do next?",
    collection_id="collection-42",
    user_id="customer-7",
)
decision = await client.proofloop.decide(
    policy_key="agent.next_action",
    candidates=[{"action_key": "act"}, {"action_key": "ask"}],
    collection_id="collection-42",
    user_id="customer-7",
    proof_context=search["proof_context"],
)
await client.proofloop.record_outcome(
    decision["decision_id"], success=True, idempotency_key="run-123-result"
)
proof = await client.proofloop.proof(decision["decision_id"])

📚 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

Built with ❤️ by the Hebbrix team