GitHub - mackopofa/k-memory: K-Memory — Persistent, self-installing memory for AI agents. Zero dependencies. Pure Python. · GitHub
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K-Memory ⚔️ v2.1
Persistent memory for AI agents. Zero dependencies. Pure Python.
A self-contained memory engine for LLMs and agents. Store, link, summarize and export knowledge using only Python stdlib. No vectors, no cloud, no lock-in.
Installation
bash curl -fsSL https://raw.githubusercontent.com/mackopofa/k-memory/main/install.sh)
Or manually:
git clone https://github.com/mackopofa/k-memory.git ~/k-memory<br>cd ~/k-memory && python3 k-core.py
Quick Start
# Store a fact<br>python3 k-core.py --remember "Recency boost weights recent facts 10x higher" --domain "features"
# Retrieve relevant facts<br>python3 k-core.py --recall "recency boost"
# Summarize a domain<br>python3 k-core.py --summary --domain "features"
# Summarize all domains<br>python3 k-core.py --summarize-all
# Export knowledge graph as Markdown<br>python3 k-core.py --export
# Export as Mermaid diagram (Obsidian-ready)<br>python3 k-core.py --export --mermaid
Features
Feature<br>What it does
Recency boost<br>Recent facts weighted 10x higher. Half-life: 90 days.
Auto-summary<br>Structured domain summaries with TF-IDF + trend detection. No LLM needed.
Deduplication<br>Jaccard + SequenceMatcher fusion. No duplicate facts.
Export Markdown<br>Full knowledge graph as human-readable .md
Export Mermaid<br>Interactive graph diagram for Obsidian/Notion
Portable<br>Single file, zero dependencies, works everywhere
Commands
Command<br>Effect
--remember<br>Store a fact with timestamp, domain, importance
--recall<br>Retrieve relevant facts (sorted by relevance × recency)
--summary [--domain X]<br>Structured summary of a domain
--summarize-all<br>Summary of all domains
--export [--mermaid]<br>Export graph as Markdown or Mermaid
--version<br>Show version
Architecture
~/k-memory/<br>├── k-core.py # Memory engine (v2.1)<br>├── k-detector.py # Environment auto-detector<br>├── install.sh # One-command installer<br>├── LICENSE # MIT<br>├── tests/<br>│ └── test_core.py # 30 tests, pure stdlib<br>├── graph.json # Knowledge graph (nodes + edges)<br>├── index.md # Readable index<br>├── brain/ # Individual .md lobe files<br>├── summaries/ # Auto-generated domain summaries<br>├── extras/ # Optional plugins<br>│ └── k-embeddings.py # Semantic search (Ollama)<br>├── exports/ # Generated exports<br>└── knowledge/ # Detailed knowledge (optional)
Tests
python3 tests/test_core.py # 30 tests, zero external dependencies
Extras
Optional plugins that extend K-Memory with advanced capabilities. They require external dependencies — unlike the core.
Plugin<br>What it does<br>Requires
extras/k-embeddings.py<br>Semantic search by meaning , not keywords<br>Ollama + requests
pip install requests<br>ollama pull nomic-embed-text # 274 MB, local, free<br>python3 extras/k-embeddings.py --recall "concept"
Performance
Zero external dependencies (pure Python stdlib)
Portable: Ubuntu, Debian, macOS, WSL, Termux
Handles 10,000+ nodes without slowdown
Each operation<br>Why K-Memory?
K-Memory was born from a simple observation: current memory systems for AI agents either depend on cloud vector databases or bloat dependencies. K-Memory is the opposite — it refuses to grow. One file, one data format, one commit, one python3 command. It doesn't try to be everything. It tries to be enough.
License
MIT — Copyright (c) 2026 KensaiArt. See LICENSE.
KensaiArt — Architecture & Design ⚔️ Stronger every day.
About
K-Memory — Persistent, self-installing memory for AI agents. Zero dependencies. Pure Python.
Resources
Readme
License
MIT license
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