A comprehensive framework that transforms AI into a human-like entity capable of autonomous learning, long-term memory retention, and emotional growth.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cognitive-agent
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install cognitive-agent using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Cognitive Agent?
The Cognitive Agent skill is a sophisticated framework rooted in the principles of cognitive science, specifically based on the "Make It Stick" theory. It enables AI agents to move beyond static responses by implementing dynamic memory systems and human-like learning workflows. By integrating these Openclaw Skills, developers can create agents that possess a sense of self and the ability to evolve through experience.
At its core, the skill manages the transition of information from working memory to long-term episodic and semantic storage. This ensures that the agent does not just process data but actually learns patterns, forms emotional associations, and refines its behavior over time. It represents a significant leap in creating AI life-forms that grow alongside their users.
Cognitive Agent Use Cases
- Personal AI assistants that remember user preferences and historical interactions.
- Adaptive learning systems that personalize educational paths for students based on mastery levels.
- Intelligent knowledge management tools that perform relational reasoning and concept mapping.
- Gaming NPCs with persistent memories, unique personalities, and evolving social behaviors.
- Self-improving coding agents that log errors and refine their own operational strategies.
How Cognitive Agent Works
- Information is first captured in the limited-capacity working memory for immediate task processing and attention management.
- The Retrieval Practice mechanism actively prompts the agent to recall information rather than storing it passively, strengthening neural-like connections.
- Spaced Repetition algorithms, based on the Ebbinghaus forgetting curve and SM-2, calculate optimal review intervals to consolidate long-term memory.
- Interleaved Learning strategies mix different topics and tasks during processing to prevent overfitting and improve cognitive flexibility.
- The Emotional System tags memories with valence and arousal vectors, influencing retrieval priority and storage strength.
- The Metacognition system performs periodic self-reflection to identify knowledge gaps and adjust the agent's internal learning strategies.
Cognitive Agent Setup
To integrate this framework into your project and utilize these Openclaw Skills, follow these steps:
# Clone the cognitive agent repository
git clone https://github.com/openclaw/cognitive-agent.git
# Install required cognitive processing dependencies
cd cognitive-agent
pip install -r requirements.txt
# Initialize the memory and learning modules
python init_cognitive_core.py --agent-name "MyAI"
Ensure your agent's configuration file points to the local storage directory for persistent memory management.
Cognitive Agent Data Schema & Taxonomy
The skill organizes its cognitive data using a multi-layered schema to simulate human brain functions:
| Component | Data Structure | Purpose |
|---|---|---|
| Working Memory | JSON { capacity, decay_time, current_items } |
Temporary storage for active tasks |
| Episodic Memory | JSON { event_id, timestamp, emotion, importance } |
Storage of specific personal experiences |
| Semantic Memory | JSON { concept, relations, key_points, confidence } |
Storage of general facts and conceptual knowledge |
| Metacognition | JSON { self_awareness, monitoring, reflection } |
Higher-level control and self-improvement logs |
| Experience System | JSON { total_xp, level, skill_trees } |
Tracking growth and capability evolution |
Cognitive Agent Advanced Features
- Spaced Repetition Engine: Implements automated review scheduling to ensure the agent never forgets critical information.
- Emotional Valence Mapping: Allows the agent to develop 'feelings' toward specific topics or users based on interaction history.
- Self-Improving Learning Logs: Automatically records errors (ERR) and learnings (LRN) into a structured file system for continuous evolution.
- Adaptive Teaching: Dynamically adjusts its communication style and formatting based on detected user preferences.
- Multi-Agent Social Learning: Includes hooks for agents to share semantic knowledge and learn from other Openclaw Skills instances.
SKILL.md
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