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getmem.ai Memory
by
NimbleV2023
· GitHub ↗
· v1.0.2
· MIT-0
85
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Install in OpenClaw
/install getmem
Description
Persistent memory for AI agents via getmem.ai. Call mem.get() before each LLM call to inject context, and mem.ingest() after each turn to save the conversation.
README (SKILL.md)
getmem.ai Memory Skill
Persistent memory for your AI agent via getmem.ai.
Setup
Set your API key in the environment:
export GETMEM_API_KEY=gm_live_YOUR_KEY_HERE
Get your key at https://platform.getmem.ai — $20 free credit on signup.
Usage
import getmem_ai as getmem, os
mem = getmem.init(os.environ["GETMEM_API_KEY"])
# Before each LLM call — get relevant memory context
result = mem.get(user_id, query=user_message)
context = result["context"] # inject into system prompt
# After each turn — save both user + assistant messages
mem.ingest(user_id, messages=[
{"role": "user", "content": user_message},
{"role": "assistant", "content": reply},
])
How it works
mem.get()fetches only the relevant memories for the current query (semantic search)- Context is injected into your system prompt — typically 200-800 tokens
mem.ingest()saves the full conversation exchange asynchronously- Memory persists indefinitely — no TTL, no purge
Token savings
Standard approach: full conversation history every turn = 10,000-40,000 tokens With getmem: only relevant context = 200-800 tokens. Save up to 95% on context tokens.
Links
- Website: https://getmem.ai
- Platform: https://platform.getmem.ai
- PyPI: https://pypi.org/project/getmem-ai/
- npm: https://npmjs.com/package/getmem
- OpenClaw plugin: clawhub:getmem-openclaw
Usage Guidance
This skill appears to be a legitimate getmem.ai integration, but there are important mismatches and privacy implications you should consider before installing:
- The SKILL.md requires a GETMEM_API_KEY and includes a pip install for getmem-ai, but the registry metadata does not declare that secret or the install step. Ask the publisher to correct the metadata so the required secret is explicit.
- Using the skill will send user and assistant messages to getmem.ai and the documentation says memory persists indefinitely. Do not enable this skill for agents that will handle sensitive, regulated, or confidential data unless you confirm retention, encryption, access controls, and deletion policies with getmem.ai.
- Verify the PyPI package (getmem-ai) is the official SDK (check maintainer, code repository, and recent releases) before installing. Prefer installing in an isolated/sandboxed environment first.
- If you need memory but cannot risk sending data externally, consider a local or self-hosted memory solution instead.
If you want to proceed, request that the publisher update the registry metadata to declare GETMEM_API_KEY as a required secret and to provide a homepage/source repository and clear privacy/retention documentation.
Capability Analysis
Type: OpenClaw Skill
Name: getmem
Version: 1.0.2
The getmem skill is a legitimate integration for the getmem.ai persistent memory service. It facilitates storing and retrieving conversation context via the 'getmem-ai' PyPI package to optimize token usage. The skill's behavior is transparent, requires a user-provided API key, and contains no evidence of malicious intent, obfuscation, or unauthorized data access beyond its stated purpose.
Capability Tags
Capability Assessment
Purpose & Capability
The SKILL.md and install metadata describe installing the getmem-ai package and calling mem.get()/mem.ingest(), which is coherent with a persistent-memory skill. However, the registry metadata claims no required env vars or install spec while the SKILL.md explicitly requires GETMEM_API_KEY and includes a pip install entry — a clear metadata/instruction mismatch.
Instruction Scope
Runtime instructions tell the agent to send user and assistant messages to getmem.ai (mem.ingest) and to fetch contextual memory (mem.get). This is expected for a memory service, but it means full conversation content is transmitted to an external service and stored indefinitely (no TTL/purge). The instructions also reference os.environ['GETMEM_API_KEY'] even though the registry metadata doesn't declare it. Transmitting potentially sensitive data off-host with long retention is a material privacy/security risk.
Install Mechanism
The SKILL.md includes a pip install (getmem-ai on PyPI), which is a common and expected distribution method (moderate risk). The registry metadata earlier reported no install spec — another inconsistency between registry and SKILL.md. No downloads from arbitrary URLs are used, which reduces installation risk.
Credentials
The skill requires an API key (GETMEM_API_KEY) according to SKILL.md, but the registry metadata lists no required env vars or primary credential. The missing declaration of a secret-level environment variable is an incoherence and increases the chance a user won't realize they're providing a long-lived secret that allows an external service to store conversation data.
Persistence & Privilege
The skill does not request always:true and does not claim elevated system privileges. Autonomous invocation is allowed by default (normal). Note: combining autonomous invocation with remote indefinite storage increases blast radius for data exfiltration, but that is a behavioral/privacy concern rather than an OS-privilege escalation.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install getmem - After installation, invoke the skill by name or use
/getmem - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
Fix: remove JSON metadata block that triggered SUSPICIOUS flag; use clean YAML frontmatter; update OpenClaw plugin reference to clawhub:getmem-openclaw
v1.0.1
No user-facing changes in this version.
- No file changes detected.
- Functionality and documentation remain the same.
v1.0.0
- Initial release of the getmem skill for persistent AI memory using getmem.ai.
- Remembers users across sessions; fetch relevant memory context before LLM calls with mem.get().
- Ingest and persist conversations after each turn with mem.ingest().
- Significantly reduces token usage by retrieving only relevant context (200–800 tokens).
- Easy setup via API key and simple usage with clear Python code examples.
- Supports both Python and npm packages and integration with OpenClaw.
Metadata
Frequently Asked Questions
What is getmem.ai Memory?
Persistent memory for AI agents via getmem.ai. Call mem.get() before each LLM call to inject context, and mem.ingest() after each turn to save the conversation. It is an AI Agent Skill for Claude Code / OpenClaw, with 85 downloads so far.
How do I install getmem.ai Memory?
Run "/install getmem" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is getmem.ai Memory free?
Yes, getmem.ai Memory is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does getmem.ai Memory support?
getmem.ai Memory is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created getmem.ai Memory?
It is built and maintained by NimbleV2023 (@nimblev2023); the current version is v1.0.2.
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