KnowOps Frameworks
_index Link to heading
We develop open-source frameworks for building reliable AI systems through cognitive architecture.
Each framework addresses a different aspect of KnowOps—executive function, memory management, and behavioral regulation—and they’re designed to work together as an integrated cognitive stack.
PrefrontalOS Link to heading
The executive function layer for AI systems
What it provides: Link to heading
- Goal tracking and hierarchical decomposition
- Attention allocation and priority management
- Metacognitive monitoring of reasoning quality
- Working memory management
- Decision-making transparency and audit trails
Key features: Link to heading
- Event-sourced cognitive state tracking
- Explicit goal representation with success criteria
- Attention regulation based on task priority
- Metacognitive confidence scoring
- Integration with LLM reasoning chains
Status: Link to heading
Theoretical Framework - Core architecture defined, reference implementation in design
Ideal for: Link to heading
Teams building autonomous agents that need transparent decision-making and goal management.
CortexGraph Link to heading
The memory layer for AI knowledge
What it provides: Link to heading
- Temporal knowledge graphs with decay
- Context-aware retrieval strategies
- Fact verification and contradiction detection
- Multi-modal memory integration
- Knowledge synthesis and consolidation
Key features: Link to heading
- SQLite-backed persistent memory
- Time-weighted decay for forgetting
- Semantic similarity search
- Knowledge graph visualization
- Markdown export for human review
Status: Link to heading
Reference Implementation - Functional prototype for research and validation
Ideal for: Link to heading
AI systems that need long-term memory, knowledge synthesis, and context management across sessions.
STOPPER Protocol Link to heading
The behavioral regulation layer for AI safety
What it provides: Link to heading
- Loop detection and breaking mechanisms
- Failure mode identification
- Recovery protocols for common errors
- Behavioral audit logging
- Convergence validation for iterative processes
Key features: Link to heading
- Pattern matching for repetitive reasoning
- DBT-inspired emotional regulation metaphors
- Cascade failure prevention
- Configurable stopping conditions
- Integration with cognitive monitoring
Status: Link to heading
Published Methodology - Peer-reviewed research, reference patterns available
Ideal for: Link to heading
Production AI deployments that need reliability guarantees and failure recovery mechanisms.
How They Work Together Link to heading
Our frameworks are designed as composable layers:
┌─────────────────────────────────────┐
│ PrefrontalOS │
│ (Executive Function Layer) │
│ Goal tracking, attention, │
│ metacognitive monitoring │
└─────────────────────────────────────┘
↕
┌─────────────────────────────────────┐
│ CortexGraph │
│ (Memory Layer) │
│ Temporal storage, retrieval, │
│ context management │
└─────────────────────────────────────┘
↕
┌─────────────────────────────────────┐
│ STOPPER Protocol │
│ (Behavioral Regulation Layer) │
│ Loop detection, failure recovery, │
│ resilience mechanisms │
└─────────────────────────────────────┘
Use them individually or together depending on your needs.
Get Implementation Help Link to heading
Need help integrating these frameworks into your AI system?