Synapse Core
Seven open tools for building AI systems that learn, adapt, and stay sovereign. Research origins, production-ready implementations, Apache 2.0 — free for commercial use.
Can AI systems learn from their own experience without retraining?
Can systems reduce dependency on external LLM APIs?
Can we measure and manage cognitive load in AI agents?
Can structured debate improve decision quality?
Each started as a research experiment. Each is a real tool you can use today.
Can we measure cognitive load in AI agents?
Real-time monitoring of agent stress across five metrics: context pressure, reasoning complexity, temporal stress, uncertainty, and error recovery.
pip install cognitive-load-monitor
Can agents learn from experience across sessions?
Memory, Evolution, Optimization. Episodic and semantic memory with policy adaptation that makes LangChain, Autogen, or CrewAI agents learn from experience.
pip install synapse-meo
Can resilience be cognitive-aware?
Retry with six backoff strategies, a circuit breaker that learns, and rate limiting with cognitive awareness — stability that adapts to system state.
pip install smart-resilience
How do you orchestrate multi-agent systems with cognitive primitives?
Production multi-agent orchestration: ParallelSwarm with five consensus strategies, TreeOrchestrator routing, and a WisdomLayer for meta-learning.
pip install synapse-orchestra
Can adversarial deliberation improve decision quality?
A multi-agent debate framework: proposal, challenge, rebuttal, vote. Fluid resourcing, coalition formation, and Q-learning agents.
Clone from GitHub
Can systems grow from LLM-dependent to sovereign?
The Cognitive Loop: a self-developing system with tri-factor Hebbian learning, a typed ConceptGraph, and maturity tracking from Infancy to Sovereign.
pip install synapse-clm
Which learning mechanisms actually work?
A research platform for validating learning mechanisms: self-organizing networks, STDP, evolutionary algorithms, and metaplasticity.
Clone from GitHub
NEURON validates new learning mechanisms. CLM hardens them for production. Users close the loop in the real world.