🧠 Cognitive Memory System (CMS)

Absolute Enterprise Agentic Architecture

Cognitive Memory System (CMS) is the "long-term brain" for autonomous agents. Unlike standard RAG systems, CMS implements a multi-tiered memory architecture (Episodic, Semantic, and Goal-driven) with a self-correcting reinforcement learning loop. It doesn't just store data; it reasons, prioritizes, and forgets intentionally to maintain peak performance.

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🚀 Key Features

• Hybrid Retrieval Engine: Combines FAISS (dense vector search) and BM25 (sparse keyword search) with dynamic weight adjustment based on query intent.
• Multi-Role LLM Routing: Native support for specialized agents—Planner, Executor, Evaluator, Rewriter, and Guardrail—optimizing cost and latency.
• Autonomous Goal Tree: Implements a hierarchical goal system with dynamic priority decay, urgency multipliers, and sub-goal dependency management.
• Memory Consolidation: Automatically abstracts raw episodic logs into "Semantic Rules" and "Patterns" using asynchronous workers.
• Cognitive Self-Correction: Detects factual contradictions and penalizes "hallucinated" memories through a hard-relevance gate.
• Enterprise Concurrency: Thread-safe operations using custom RWLock for high-frequency agentic loops.

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🏗️ Architecture Overview

The system operates on four distinct layers:

1. Working Memory: A short-term buffer for the immediate interaction context.
2. Episodic Memory: Chronological logs of experiences (State-Action-Result-Reward).
3. Semantic Memory: Consolidated "world knowledge" and extracted rules.
4. Goal Memory: A strategic tree defining the agent's long-term objectives and current focus.

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📁 Project Structure

absolute-agentic-arch/
├── src/
│   ├── memory/
│   │   ├── vector_store.py      # FAISS & BM25 Hybrid implementation
│   │   ├── long_term_goal.py    # Goal Tree & Priority logic
│   │   └── episodic_memory.py   # Experience logging & Consolidation
│   ├── core/
│   │   ├── engine.py            # Main Agent Loop & Decision System
│   │   └── concurrency.py       # Threading & RWLock utilities
│   ├── utils/
│   │   └── validators.py        # JSON Schema & Hallucination filters
├
├── requirements.txt             # Dependencies (LangChain, FAISS, rank_bm25)
└── main.py                      # System Entry Point

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🛠️ Installation

1. Clone the repository:

   git clone https://github.com/yourusername/absolute-agentic-arch.git
   cd absolute-agentic-arch

2. Install dependencies:

   pip install -r requirements.txt

3. Required Libraries:

   • faiss-cpu or faiss-gpu
   • langchain_community
   • sentence-transformers
   • rank_bm25
   • numpy

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💻 Quick Start

from src.core.engine import CognitiveMemory
from langchain_openai import ChatOpenAI

# Initialize LLM
llm = ChatOpenAI(model="gpt-4-turbo")

# Initialize Brain
memory = CognitiveMemory(db_path="./memory_db", llms=llm)

# 1. Add a goal
memory.add_memory("Build a luxury AI frontend", category="goal", priority=0.9)

# 2. Agent Step: Observe state and determine action
state = "User wants a high-end website template with GSAP animations."
action, score = memory.agent_step(state)

print(f"Decision: {action} | Confidence Score: {score}")

# 3. Shutdown gracefully
memory.shutdown()

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🧠 Cognitive Mechanics

The Reinforcement Loop

Every interaction is evaluated. If a retrieved memory leads to a successful outcome (High Reward), its win_rate and importance are boosted. Conversely, memories that lead to "Failure Patterns" are penalized or flagged as "Contradictions."

Dynamic Forgetting (Pruning)

To prevent "Context Pollution," the system performs Eviction based on a score of importance + confidence + (visits * 0.01). Low-value, noisy memories are automatically archived to maintain a slim, efficient vector space.

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🤝 Contribution

Building a digital consciousness is a team effort. Feel free to open issues or submit PRs regarding RAG optimization, memory decay algorithms, or new LLM router roles.