A zero-dependency Python tool for AI agents to capture, store, and retrieve real-time learnings and error resolutions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install adaptive-learning-agents
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 adaptive-learning-agents 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 Adaptive Learning Agent?
Adaptive Learning Agent is an open-source framework designed to bridge the gap between AI mistakes and future performance. By systematically recording failures, user feedback, and successful patterns, it creates a local knowledge base that allows your AI to evolve. This Openclaw Skills component ensures that once a bug is fixed or a prompt is optimized, the knowledge is persisted and searchable for future sessions.
Operating entirely locally with zero external dependencies, it prioritizes privacy and speed. Developers can integrate it into any Python-based AI workflow to transform transient interactions into a permanent, structured learning repository. This is a foundational piece for building resilient Openclaw Skills that do not repeat the same mistakes.
Adaptive Learning Agent Use Cases
- Automated bug discovery and resolution tracking for persistent error handling.
- Systematic prompt optimization to record which variations yield the best LLM outputs.
- API behavior documentation to manage quirks and workarounds for different providers.
- Knowledge sharing across teams by exporting local learnings into portable JSON formats.
- Pre-task review of unresolved errors to ensure continuous improvement before starting new workflows.
How Adaptive Learning Agent Works
- The agent encounters a failure, a user correction, or identifies a successful pattern during execution.
- The specific insight or error is passed to the core methods like record_learning or record_error.
- Data is categorized by type (e.g., technique, bug-fix, constraint) and stored locally within the .adaptive_learning directory.
- Before starting a new task, the agent queries the local database using search_learnings to find relevant previous experiences.
- The retrieved context is used to inform the AI logic, effectively preventing repeated errors and applying proven best practices.
Adaptive Learning Agent Setup
The Adaptive Learning Agent is a pure Python skill with zero external dependencies, making it extremely easy to integrate into your Openclaw Skills projects. Simply include the source file in your directory.
# Navigate to your project directory
cd your-ai-project
# Import the agent into your Python script
# Ensure adaptive_learning_agent.py is in your path
from adaptive_learning_agent import AdaptiveLearningAgent
# Initialize the learning engine
agent = AdaptiveLearningAgent()
Adaptive Learning Agent Data Schema & Taxonomy
The skill organizes its data locally in the .adaptive_learning/ directory, utilizing a JSON-based structure to ensure portability and human-readability. The metadata taxonomy includes several key fields:
| Field | Description |
|---|---|
| content | The core insight or learning captured |
| category | Taxonomy classification: technique, bug-fix, api-endpoint, constraint, best-practice |
| source | Origin of data: user-correction, error-discovery, successful-pattern, user-feedback |
| context | Detailed environment or scenario where the learning applies |
| timestamp | ISO-8601 formatted date of creation |
All errors are tracked with specific fields for error_description, solution, and prevention_tip to facilitate automated recovery within Openclaw Skills.
Adaptive Learning Agent Advanced Features
- Semantic search and keyword filtering to locate specific knowledge bits across thousands of recorded entries.
- Batch export and import capabilities for synchronizing learnings across distributed Openclaw Skills.
- Detailed statistical reporting on error resolution rates and learning density over time.
- Privacy-first architecture with zero telemetry or cloud dependencies for secure enterprise use.
- Automatic categorization of raw feedback into actionable technical constraints or best practices.
SKILL.md
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