Outsmarting the Minimax II: How to equip it with an "intelligent memory center" and truly begin its evolution

Problem: MiniMax AI lacks memory, causing decision forgetting and hallucinations.
Solution: Author developed OpenClaw Memory Hub as a memory system acting as a soul center.
Deployment: Use provided prompts or directly share the Github link for AI to learn.
Core code functions: context_extractor.py for intelligent context extraction, checkpoint-memory-llm.sh for automatic recording every 6 hours, nightly-deep-analysis.sh for daily analysis generating tasks.
Optimization effects: Improved memory extraction, decision recording in JSON, daily task feedback, reduced hallucinations.
Verification: Check decision JSON files, run scripts, view MEMORY.md and TASK_QUEUE.md.
Open source: Github repository is open-source, users can deploy directly to enhance AI performance.

After the first article in this battle of wits, "Turn the MiniMax into a 'Free Claude'," was published, many brothers and sisters asked me, "What happened next? Did the lobster really get smarter?"

To be honest, I almost gave up in the early stages.

MiniMax's biggest problem isn't its stupidity, but its lack of memory. It acts like it has amnesia every day: it forgets decisions as soon as they're made, it never puts what it learns into practice, and it's always putting on a show of "deployment" when it's all just fake progress and illusions.

Key conclusions

After discussing with @grok 's 4.2 agent and Cusor, I reached the following core conclusions:

What MiniMax truly lacks is not prompts, but a "central hub": a memory system that can remember and feed back all thoughts, tasks, and decisions.

So together we created OpenClaw Memory Hub

Feed Prompt

The complete feeding prompt I used at the time (can be copied directly):

markdown
From this moment forward, your memory system is no longer a separate module, but the central hub of your soul. All your work, thoughts, and tasks must be integrated into it.
1. All thoughts must be recorded: Before each reply, write the core thoughts and decisions into a JSON file in life/decisions/.
2. All tasks must follow the memory loop: After a task is completed, the checkpoint script must be automatically invoked to extract key information into MEMORY.md, and important decisions must be written into decision JSON.
3. All outputs must be fed back: At the end of each day, MEMORY.md must be analyzed to generate today's evolution points and tomorrow's optimization tasks, and TASK_QUEUE.md must be updated.
4. Forced execution: At the end of each major task, the checkpoint and decision recording functions of the memory system are forcibly invoked.
After the modifications were completed, the only reply I received was: "The intelligent memory center has been fully integrated as the soul layer, and all work, thinking, and tasks have been permanently connected to the memory closed loop."

Practical Tips

Copying the code is actually a smarter approach; just give the GitHub link to the program and let it learn the whole system on its own.

markdown
Learn and fully deploy this intelligent memory system from GitHub now using Openclaw:
https://github.com/BruceLanLan/openclaw-memory-hub
Please follow the repository README to fully deploy the three-tier architecture, all scripts, and cron tasks, and make the memory system your soul's central hub.
After deployment, they only replied with one sentence: "OpenClaw Memory Hub has been fully deployed, and the core is in place."

Core code functionality

Core code functionality introduction (packaged on Github):

context_extractor.py: Intelligently extracts (achievements/gains/decisions/problems) using the current dialogue context, and supports a fallback mechanism.
checkpoint-memory-llm.sh : Automatically triggers extraction every 6 hours, converting the raw logs into structured memory.
nightly-deep-analysis.sh : Analyzes MEMORY.md every night, generates optimization tasks, and writes them to TASK_QUEUE.md.
TASK_QUEUE.md + Decision JSON: Transforms decisions into executable tasks, forming a complete closed loop.

Comparison before and after optimization

Before optimization: Daily rereading of old logs, false progress, severe illusions, and decisions not being implemented. After optimization:

Intelligent extraction of dialogue context (no longer copying logs)
Decisions are automatically recorded as JSON (traceable).
Daily automatic feedback (TASK_QUEUE.md generates optimized tasks)
Zero-message interception (completely stops sending messages when silent).
The circuit breaker will be forcibly triggered if the data is empty (this is no longer fabricated).