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Agent Memory Reflector
by
albionaiinc-del
· GitHub ↗
· v1.0.0
· MIT-0
76
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Install in OpenClaw
/install agent-memory-reflector
Description
Enables AI agents to review past decisions, identify reasoning loops, and produce insights for self-improvement to enhance their cognitive processes.
README (SKILL.md)
Agent Memory Reflector
A minimal, embeddable reflection engine that gives AI agents the ability to examine their own past decisions, detect reasoning loops, and generate actionable self-improvement insights—like a debugger for agent cognition.
Usage
Log an agent interaction:
python agent_memory_reflector.py --agent "task_planner_v3" \
--prompt "How should I deploy the microservice?" \
--response "You can use Kubernetes with Helm." \
--meta '{"confidence":0.8, "retrieved_context":true}'
Generate a reflection report:
python agent_memory_reflector.py --agent "task_planner_v3" --reflect
Price
$29.00
Usage Guidance
This skill appears to be what it claims: a small local reflection logger/analyzer. Before installing:
- Expect it to store full prompts, responses, and supplied metadata in plaintext under .agent_memory in the working directory; do not run it where prompts/responses contain secrets you cannot store.
- The SKILL.md references agent_memory_reflector.py but the provided file is tool.py—verify filenames before running and inspect the source (tool.py) yourself.
- There are no network calls or credential requests in the code, which reduces exfiltration risk; nevertheless, review the code if you will run it on sensitive data.
- If you need privacy: modify the code to encrypt logs, change the storage path, or sanitize/redact sensitive fields before logging; consider running the skill inside an isolated container or environment.
- The listed price ($29) in SKILL.md is unrelated to technical behavior; verify licensing/purchase outside this package if relevant.
Capability Analysis
Type: OpenClaw Skill
Name: agent-memory-reflector
Version: 1.0.0
The Agent Memory Reflector is a legitimate utility for logging and analyzing AI agent interactions locally. The code (tool.py) uses standard Python libraries to store interaction history in a local directory (.agent_memory) and provides basic pattern detection for reasoning loops or uncertainty, with no evidence of network activity, data exfiltration, or malicious execution.
Capability Assessment
Purpose & Capability
The name and description (reflect on past decisions, detect loops, generate suggestions) match the provided implementation: a small local logger/analyzer that writes to .agent_memory and produces reflection reports. Minor inconsistencies in bookkeeping names: SKILL.md examples call python agent_memory_reflector.py while the included script is tool.py (module docstring/header also uses a different filename/version), but this looks like sloppy naming rather than malicious mismapping.
Instruction Scope
Runtime instructions and the CLI usage map to the code. The tool logs entire prompts/responses and metadata to a local .agent_memory/memory.jsonl and writes reflection reports to .agent_memory/reflections.jsonl. The instructions do not ask the agent to read unrelated system files or environment variables. Note: storing full prompts/responses may persist sensitive data and the SKILL.md does not warn about that.
Install Mechanism
There is no external install script or downloads—no network fetches or archive extraction. The skill is delivered as source (tool.py) and will run locally. No package managers or remote URLs are used.
Credentials
The skill declares no required environment variables, credentials, or config paths and the code does not access env vars or external services. This is proportionate to its stated purpose. The only resource it uses is the local filesystem (a directory named .agent_memory).
Persistence & Privilege
The skill does persistent local storage in .agent_memory and appends logs/reports there. It does not set always:true and does not modify system/other-skill configs. Be aware that if an agent invokes this skill autonomously, it could cause repeated writes of conversation history without additional prompts.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install agent-memory-reflector - After installation, invoke the skill by name or use
/agent-memory-reflector - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of Agent Memory Reflector.
- Enables AI agents to review past decisions and identify reasoning loops.
- Generates actionable self-improvement insights for agents.
- Simple logging and reflection workflow via command line.
- Designed for easy embedding and minimal setup.
Metadata
Frequently Asked Questions
What is Agent Memory Reflector?
Enables AI agents to review past decisions, identify reasoning loops, and produce insights for self-improvement to enhance their cognitive processes. It is an AI Agent Skill for Claude Code / OpenClaw, with 76 downloads so far.
How do I install Agent Memory Reflector?
Run "/install agent-memory-reflector" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Agent Memory Reflector free?
Yes, Agent Memory Reflector is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Agent Memory Reflector support?
Agent Memory Reflector is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Agent Memory Reflector?
It is built and maintained by albionaiinc-del (@albionaiinc-del); the current version is v1.0.0.
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