| I built Supe because I wanted Claude to analyze game binaries but not modify them. The constraints grew into something more general: a cognitive architecture for AI agents. The problem: Most agent frameworks treat memory as flat storage. Store a key, get a value. That's not how useful memory works. What Supe provides: 1. Neural Memory - Hebbian learning ("fire together, wire together"). Cards connected by synaptic links that strengthen with co-activation and decay with disuse. Spreading activation for recall. Hubs emerge naturally. 2. Validation Gates - Python functions that run before/after tool executions. Block `rm -rf`, enforce read-only mode, whitelist commands. Code, not configuration. 3. Proof-of-Work - SHA256 hashes chain every execution. Tamper with logs and proofs won't verify. 4. Cognitive Hierarchy - Moments (sessions) → Cards (knowledge units) → Buffers (raw data). Not flat. 5. Semantic Relations - 7 typed connections: CAUSES, IMPLIES, CONTRADICTS, SUPPORTS, DEPENDS_ON, EQUALS, TRANSFORMS. Example gate: @agent.register_gate("safe")
def safe(record, phase) -> GateResult:
if "rm -rf" in record.tool_input.get("command", ""):
return GateResult("safe", False, "BLOCKED")
return GateResult("safe", True, "OK")
Example neural recall: neural.add_card(1, {"title": "OAuth"})
neural.add_card(2, {"title": "Login"})
neural.connect(1, 2) # Hebbian learning
results = neural.recall("authentication") # Spreading activation
343 tests. MIT license. Works with Claude SDK.pip install supe |
How do you handle cases where the "brain" makes decisions that need to be auditable or reversible?