jzOcb

jzOcb / ai-agent-memory

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File-based memory system for AI Agents with automatic TTL, LLM compression, and multi-agent sharing

19
2
100% credibility
Found Feb 27, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A straightforward file-based system for organizing AI agent memories with priority tagging, automatic archiving of expired entries, and tools to generate summaries and insights from daily logs.

How It Works

1
🧠 Discover Memory Helper

You hear about a simple way to give your personal AI assistant a long-term memory using just everyday note files.

2
📋 Set Up Your Notes

Copy ready-made templates into a folder to create your main memory note, daily journals, and work buffer.

3
📝 Log Daily Thoughts

Start adding facts, projects, and notes with easy tags like permanent, medium-term, or short-term so your AI remembers what matters.

4
🧹 Add Auto-Cleanup

Set a daily helper to scan and archive old notes automatically, keeping your memory fresh and under control.

5
💡 Extract Key Insights

Run a smart summarizer on your journals to pull out lessons, patterns, and decisions into concise overviews.

6
🤝 Share with Others

Optionally put memories in a shared folder so multiple AI helpers can access and build on the same knowledge.

🚀 Smarter AI Achieved

Your AI now has an organized, growing memory that stays tidy and insightful, making it more helpful every day.

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AI-Generated Review

What is ai-agent-memory?

This Python-based ai agent memory system stores agent knowledge in plain markdown files, handling automatic TTL expiration, LLM-powered compression of logs into insights, and multi-agent sharing via shared directories. It solves the hassle of managing ai agent memory without databases—think daily logs feeding long-term MEMORY.md, with cleanup and reflection scripts keeping things lean. Developers get a simple ai agent memory framework for custom ai agent with memory github setups, dodging vector DB overhead for hundreds of entries.

Why is it gaining traction?

Unlike paid blackboxes like Mem0 or tool-heavy vector stores, this file based memory offers zero-cost, direct-editable debuggability—open any .md and tweak. Key hooks: cron-ready scripts for janitor cleanup (e.g., `python3 scripts/memory-janitor.py --dry-run`), insight compounding via LLM prompts, and layered recall (L0 abstracts first, then patterns). It draws from Stanford Generative Agents for reflection, making ai agent memory design feel lightweight yet smart for optimize ai agent memory github needs.

Who should use this?

Indie devs prototyping personal AI agents or langchain ai memory agent flows, especially those chaining with n8n workflows or building ai agent memory layer on local setups. Ideal for ai agent memory types like priority-tagged facts (P0 permanent, P1/P2 timed) in solo projects, not enterprise-scale semantic search. Suits hackers surveying ai agent memory survey options wanting file based translation memory simplicity.

Verdict

Early alpha with 15 stars and 1.0% credibility score—solid bilingual docs and templates, but no tests or broad adoption yet. Fork it for personal ai agent memory tools experimentation; skip for production until more battle-tested.

(198 words)

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