loryoncloud

loryoncloud / Memory-Like-A-Tree

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Memory-Like-A-Tree🌳 Memory management system for AI Agents - Let knowledge grow like a tree. Features confidence-based lifecycle, auto-decay, cross-agent search, and Obsidian sync. 🌳 AI Agent 记忆管理系统 - 让知识像树一样生长。支持置信度生命周期、自动衰减、跨 Agent 搜索、Obsidian 同步。

64
6
89% credibility
Found Mar 01, 2026 at 61 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A personal knowledge management system that organizes notes into a tree structure, rates their confidence based on usage and priority, automatically decays unused ones, extracts key insights, and integrates with task tracking.

How It Works

1
🌳 Discover the Memory Tree

You hear about a clever way to organize your notes like a living tree that grows smarter over time.

2
📦 Bring it home

You download it into your notes folder and it quietly sets up your personal knowledge garden.

3
🌱 Plant your memories

You write your learnings and experiences into a simple notes file, like adding leaves to branches.

4
✨ See it come alive

It automatically sorts, rates, and connects your notes by how useful they seem, making everything feel organized.

5
🔍 Find what you need

Whenever you search for ideas, it highlights the best matches and strengthens the ones you use often.

6
🧹 Trim the forgettable

Over time, it gently removes weak or unused notes but saves the key lessons in a special highlights area.

🏆 Harvest endless wisdom

Your knowledge stays fresh and powerful, always ready to help with tasks, like a tree that never forgets the good stuff.

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Star Growth

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AI-Generated Review

What is Memory-Like-A-Tree?

Memory-Like-A-Tree is a Python-based memory management system for AI agents that lets knowledge grow like a tree through confidence-based lifecycle tracking. It handles storage in agent workspaces, applies auto-decay to unused memories, performs cross-agent searches, and syncs extracts to Obsidian vaults. Developers get a structured way to prevent memory bloat while enabling shared knowledge retrieval across agents.

Why is it gaining traction?

Its tree-like model stands out by mimicking natural knowledge lifecycles—high-confidence memories thrive, low ones decay or archive automatically—unlike flat databases in other agent tools. Key features like CLI-driven indexing, search, and cleaning make maintenance effortless, while cross-agent recommendations boost multi-agent collaboration. Python simplicity lets you hook it into agent loops for real-time memory boosts on access.

Who should use this?

AI engineers building multi-agent systems for tasks like automation pipelines or research bots, where shared knowledge prevents redundant learning. Teams using Obsidian for notes will appreciate seamless syncs, and solo devs prototyping agent memory without vector stores. Ideal if you're tired of manual memory pruning in long-running agents.

Verdict

Try it for agent memory management if you're in early experimentation—solid Python CLIs and features deliver value fast despite 18 stars and 0.9% credibility score signaling immaturity. Polish docs and add tests to scale; it's a clever foundation for growing knowledge trees in agent fleets. (198 words)

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