speedyfoxai

speedyfoxai / openclaw-jarvis-memory

Public

Qdrant - long term memory and Redis - short term memory.

32
13
89% credibility
Found Feb 21, 2026 at 27 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A user-friendly blueprint to give OpenClaw AI persistent multi-layer memory for saving and semantically searching conversations across sessions.

How It Works

1
📰 Discover smart AI memory

You find a simple blueprint to make your AI assistant remember conversations forever, just like a personal Jarvis.

2
📥 Place in your AI home

Copy the ready folder into your AI workspace where it fits perfectly without mess.

3
🚀 Launch safely

Click run on the installer that backs up everything first and sets up your remembering system in seconds.

4
🔌 Wake the helpers

Start the quiet background friends that hold short and long memories securely.

5
🧪 Save your first thought

Type a quick save command in chat and watch your words stored safely.

6
💭 Search old chats

Ask 'q topic' and get back past conversations by meaning, not exact words.

🎉 AI remembers you always

Now your assistant recalls every talk across days, feeling truly personal and helpful.

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AI-Generated Review

What is openclaw-jarvis-memory?

This Python project adds a Jarvis-like memory layer to OpenClaw AI assistants, solving the problem of session-only recall with Redis for short-term buffering and Qdrant for long-term semantic storage. Users get real-time capture via heartbeat or cron, daily markdown logs for audits, and commands like "save mem", "save q", or "q topic" for instant persistence and cross-session search. Check the Qdrant GitHub repo for Docker setup or Python client examples to pair with its vector capabilities.

Why is it gaining traction?

It stands out by being fully portable—memories export to standard Redis, Qdrant, and files, avoiding vendor lock-in unlike built-in OpenClaw tools. One-command installer handles crons, backups, and skills setup, while token-free cron capture saves costs on LLM calls. Developers dig the hybrid search blending exact Redis matches with Qdrant semantic recall, plus uninstaller for safe testing.

Who should use this?

OpenClaw users building personal AI agents who need conversation history across reboots, like researchers tracking project notes or hobbyists simulating Jarvis. Ideal for Python devs already running Qdrant Docker or Redis, especially those experimenting with long-term memory from the Qdrant GitHub examples.

Verdict

Grab it if you're in the OpenClaw ecosystem—solid docs and automation make setup painless despite 24 stars signaling early maturity. Credibility score of 0.9% reflects low adoption, but robust installer and RESTORE.md mitigate risks; test on a fresh instance first.

(198 words)

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