Experimental infrastructure for long-term memory systems in AI agents.
The architecture is being actively designed and validated. APIs and internal modules may change.
Cognitive Memory Engine is an experimental infrastructure layer designed to provide advanced memory capabilities for AI agents, assistants, and multi-agent systems.
Unlike traditional vector memory wrappers, this project aims to model cognitive-style memory, including:
- Episodic memory (events and interactions)
- Semantic memory (facts and knowledge)
- Procedural memory (skills and instructions)
- Memory relations and knowledge graphs
- Reinforcement and decay mechanisms
- Event-driven memory lifecycle
- Multi-agent memory isolation and sharing
Modern AI agents lack structured, persistent, and evolvable memory systems.
This project explores how to build:
- Long-term AI memory infrastructure
- Cognitive-style memory ranking
- Knowledge graph-based reasoning
- Self-evolving memory importance
- Multi-agent memory ecosystems
| Type | Description |
|---|---|
| Episodic | Conversations, events, experiences |
| Semantic | Facts and knowledge |
| Procedural | Instructions and workflows |
| Working | Temporary reasoning context |
Memories can be connected via:
- relates_to
- derived_from
- contradicts
- reinforces
- contextual relationships
This enables graph-based reasoning.
The system tracks memory lifecycle events:
- Memory created
- Memory accessed
- Memory updated
- Memory consolidated
- Memory linked
Memory can be:
- Private
- Shared
- Public
And partitioned by namespace.
API Layer
↓
Orchestration Layer
↓
Service Layer
↓
Storage Layer
↓
Plugin System
- Memory Orchestrator
- Ranking Engine
- Knowledge Graph Relations
- Event Bus
- Write-Ahead Operation Log
- Plugin Registry
concept/
Experimental architecture and evolving implementation
docs/
Architecture and research notes
examples/
Integration examples (planned)
Current AI memory solutions are mostly:
- Simple vector search
- Short-term context buffers
- Static retrieval systems
Cognitive Memory Engine explores:
- Memory evolution
- Memory reinforcement
- Long-term knowledge structuring
- Agent memory collaboration
This project investigates:
- Cognitive memory modeling
- Memory ranking algorithms
- Memory consolidation pipelines
- Graph-based AI reasoning
- Multi-agent knowledge sharing
✔ Memory storage abstraction ✔ Vector search integration ✔ Cognitive ranking engine ✔ Event-driven architecture ✔ Knowledge graph relations ✔ Plugin-based extensibility ✔ Multi-agent memory model
- Memory storage and retrieval
- Ranking engine
- Event architecture
- Basic relations
- Decay and reinforcement engine
- Multi-agent improvements
- Plugin stabilization
- Memory consolidation pipelines
- Knowledge graph traversal
- Background processing
- AI assistants with long-term memory
- Multi-agent collaboration systems
- Offline AI cognitive infrastructure
- Research into AI memory architectures
- Local AI memory servers
Currently under development.
Instructions will be added when core modules stabilize.
This project is in early experimental stage.
Contributions, architecture discussions, and research ideas are welcome.
Apache 2.0
Inspired by:
- Cognitive science memory models
- Agentic AI architectures
- Long-term AI reasoning research
Developed by:
- Oskar Gerlicz-Kowalczuk
The long-term goal is to create fully human like memory reasoning.