superbrain-fabric-sdk 5.1.0

Superbrain Fabric v5.1 — The Soul Expansion: LCC, Memory History, Knowledge Graph & MIRROR stability.

Superbrain Fabric Python SDK (V5.1) 🧠✨

Superbrain Fabric is a high-performance, distributed cognitive RAM fabric. This SDK allows your AI agents to treat memory as an active, self-reflecting participant in their reasoning loops.

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💎 The Soul Expansion (V5.1 Breakthroughs)

V5.1 transitions Superbrain Fabric from a fast storage layer into a Cognitive Organism.

📉 Layered Cognitive Compression (LCC)

Achieve 11-38x token reduction before hit the network.

• Level 1 (Deterministic): Prunes structural noise (HTML/JSON boilerplate).
• Level 2 (Semantic): Prevents redundant writes via Jaccard-deduplication.
• Level 3 (Extractive): Consolidates long contexts into extractive summaries.

🕰️ Memory History (The Hippocampus)

100% auditable lineage. Retrieve the full versioned history of any memory block to understand why an agent modified a belief.

🕸️ Knowledge Graph (The Cortical Mesh)

Distributed relational memory. Link memories via explicit edges (supports, contradicts, part-of) and traverse them via recursive discovery.

🪞 MIRROR Tier (Stability)

Use Lyapunov-inspired stability loops to protect critical reasoning blocks from the automatic decay cycles.

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🚀 Quickstart

1. Installation

pip install superbrain-fabric-sdk

2. Basic Cognitive Write

from superbrain import SuperbrainFabricClient

# Connect to the Fabric
client = SuperbrainFabricClient("coordinator:50050")

# Write a memory with automated LCC Level 3 and Mirror protection
ptr_id = client.write_memory(
    "Long research context...", 
    liveliness=0.9, 
    tag="strategy", 
    lcc_level=3,
    mirror_reinforcement=True
)

print(f"Memory anchored at: {ptr_id}")

3. Relational Discovery (Knowledge Graph)

# Link two cognitive blocks
client.add_edge(source_ptr, target_ptr, relation="supports", weight=1.0)

# Query the mesh
context = client.query_graph(source_ptr, depth=2)

4. Audit Trail (History)

# See how a memory evolved over time
history = client.get_memory_history(ptr_id)
for snap in history:
    print(f"Version {snap['version']} tag: {snap['tag']}")

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🛡️ Security & Performance

• E2EE: Enable via encryption_key=b'...' for AES-256-GCM SDK-level protection.
• Low Latency: Automated SHM Bypass (<15μs) for co-located agents.
• Durable Mode: WAL-backed recovery support.

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MIT License · Built by Anispy