AI Brain AGI Documentation
Complete technical guide for integrating Cognitive Hypergraph Engine memory, Autonomous Swarm Matrix, and cross-session context recall.
📚 Quick Navigation Index (6 Sections) ▼
AGI Cognitive Memory Model
Unlike legacy flat vector databases that rely on static cosine similarity, AI Brain deploys a Neuromorphic Cognitive Hypergraph Engine. It connects multi-dimensional concepts, execution histories, and agent intentions into a unified, non-lossy persistent graph.
Installation & Environment Setup
Install the official AI Brain AGI Memory SDK across Python or Node.js environments.
💻 Terminal Setup & Auth Python / CLI
# Install AI Brain Neuromorphic AGI Memory SDK
pip install aibrain-sdk --upgrade
# Environment Setup with Zero-Trust TLS Authentication
export AIBRAIN_API_KEY="sk_live_hypergraph_9942a"
export AIBRAIN_CLUSTER_ENDPOINT="us-central1-agihub.aibrain.io"Cognitive Hypergraph Memory API
Store and recall long-term agent experiences with sub-15ms associative graph retrieval.
🐍 Python SDK Code Python 3.9+
import aibrain
# 1. Initialize AGI Neuromorphic Hypergraph Client
brain = aibrain.HypergraphClient(
api_key="sk_live_hypergraph_9942a",
tier="ENTERPRISE_AGI_CLUSTER"
)
# 2. Ingest Cross-Session Memory
memory_node = brain.remember(
agent_id="executive_agent_01",
context={
"user_intent": "Deploy AGI agent swarm",
"tech_stack": ["Google Cloud", "Gemini 1.5 Pro"],
"rule": "Zero context decay across sessions"
},
reasoning_weight=0.98,
decay_protection=True
)
# 3. Sub-15ms Multi-Hop Associative Recall Query
graph_result = brain.recall(
agent_id="executive_agent_01",
query="What architecture rules were specified?",
depth=4, # 4-hop cognitive graph traversal
top_k=5
)
print("Recall Latency:", graph_result.latency_ms, "ms")
print("Retrieved Graph Nodes:", graph_result.nodes)Swarm Matrix Consensus Protocol
Synchronize state across multiple concurrent agents (Coder, Planner, Reviewer) without data collisions or context drift.
🐝 Swarm Matrix Code Consensus Grid
import aibrain
# 1. Connect Autonomous Agent Swarm Grid
swarm = aibrain.SwarmGrid(
swarm_id="swarm_alpha_dev",
agents=["planner_agent", "coder_agent", "evaluator_agent"],
consensus_protocol="BYZANTINE_HYPERGRAPH_AGREEMENT"
)
# 2. Broadcast Verified Task Artifact across Swarm Nodes
swarm.broadcast_state(
source_agent="coder_agent",
state_key="verified_agi_memory_spec",
payload={"status": "PASSED_TESTS", "code_coverage": "99.8%"},
consensus_mode="IMMEDIATE_SYNC"
)
# 3. Read Synchronized Swarm State with Zero Collision
current_state = swarm.read_state(
agent_id="evaluator_agent",
state_key="verified_agi_memory_spec"
)
print("Swarm Sync Status:", current_state.status)Enterprise HTTP REST API Reference
Direct REST API endpoints for non-Python environments (Go, Rust, C++, Java).
Enterprise Performance & Investor ROI
Empirical benchmark metrics demonstrating AI Brain's superiority over standard LLM context windows.