A minimal, embeddable reflection engine that enables AI agents to analyze past decisions and detect reasoning loops for continuous self-improvement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-memory-reflector
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 agent-memory-reflector 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 Agent Memory Reflector?
Agent Memory Reflector acts as a sophisticated debugger for agent cognition. It provides a structured framework for AI agents to examine their own historical interactions, allowing them to identify patterns of success or failure in their reasoning processes. By integrating this tool into your Openclaw Skills collection, you empower your autonomous agents with the self-awareness necessary to refine their logic and generate actionable insights for future tasks.
This engine is particularly valuable for complex multi-step workflows where agents might otherwise fall into repetitive loops or inconsistent decision-making. By formalizing the reflection process, developers can ensure their AI deployments become more reliable and efficient over time.
Agent Memory Reflector Use Cases
- Identifying and breaking recursive reasoning loops in autonomous task planners.
- Analyzing agent confidence scores against actual output quality to calibrate performance.
- Generating automated post-mortem reports for complex multi-agent orchestrations.
- Auditing decision-making history to ensure compliance with predefined safety or logic constraints.
How Agent Memory Reflector Works
- The agent logs every interaction, including the specific prompt, generated response, and associated metadata (such as confidence levels and context sources).
- The reflection engine stores these interaction logs in a structured format for longitudinal analysis.
- Upon triggering the reflection command, the engine parses the historical data to detect inconsistencies, circular logic, or decreasing confidence trends.
- The system generates a comprehensive reflection report containing specific recommendations for the agent to adjust its future reasoning strategies.
Agent Memory Reflector Setup
To integrate this component into your existing Openclaw Skills ecosystem, follow these CLI steps:
# Log a specific interaction for later analysis
python agent_memory_reflector.py --agent "your_agent_name" \
--prompt "Input prompt text" \
--response "Agent output text" \
--meta '{"confidence":0.9, "retrieved_context":true}'
# Generate the reflection and self-improvement report
python agent_memory_reflector.py --agent "your_agent_name" --reflect
Agent Memory Reflector Data Schema & Taxonomy
The skill organizes interaction logs and cognitive metadata using a clear taxonomy to ensure high-quality reflection output:
| Field | Description | Type |
|---|---|---|
agent |
Unique identifier for the specific AI agent version | String |
prompt |
The original input or instruction provided to the agent | String |
response |
The final output generated by the agent | String |
meta |
JSON object containing confidence, context flags, and latency | JSON |
reflection_report |
Generated analysis highlighting reasoning patterns | Markdown |
Agent Memory Reflector Advanced Features
- Multi-agent support for comparing performance across different model versions (e.g., comparing task_planner_v2 vs v3).
- Metadata-driven insights that weigh reflection based on custom confidence thresholds.
- Actionable insight generation that can be piped directly back into the agent's system prompt for immediate self-correction.
- Lightweight, embeddable architecture designed to work seamlessly with other Openclaw Skills.
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