MemoGraph 🧠

A graph-based memory system for LLMs with intelligent retrieval. MemoGraph provides a powerful solution to the LLM memory problem by combining knowledge graphs, hybrid retrieval, and semantic search.

📊 Project Status: MemoGraph is production-ready! See docs/PROJECT_STATUS.md for current status and docs/FUTURE_ENHANCEMENTS.md for optional improvements.

✨ Features

• 🤖 Smart Auto-Organization Engine: Automatically extract structured information from memories using LLMs
  • Topics, subtopics, and recurring themes
  • People with roles and organizations
  • Action items with assignees and deadlines
  • Decisions, questions, and sentiment analysis
  • Risks, ideas, and timeline events
• 🏷️ AI-Powered Tag Suggestions: Automatically suggest relevant tags using semantic analysis and content structure detection
• 🔗 AI-Powered Link Suggestions: Intelligently recommend wikilinks to related notes using semantic similarity and graph analysis
• Graph-Based Memory: Navigate knowledge using bidirectional wikilinks and backlinks
• Hybrid Retrieval: Combines keyword matching, graph traversal, and optional vector embeddings
• Markdown-Native: Human-readable markdown files with YAML frontmatter
• Memory Types: Support for episodic, semantic, procedural, and fact-based memories
• Smart Indexing: Efficient caching system that only re-indexes changed files
• CLI & Python API: Use via command line or integrate into your Python applications
• Multiple LLM Providers: Works with Ollama, Claude, and OpenAI
• Context Compression: Intelligent token budgeting for optimal context windows
• Salience Scoring: Memory importance ranking for better retrieval

🚀 Quick Start

Installation

pip install memograph

Install with optional dependencies:

# For OpenAI support
pip install memograph[openai]

# For Anthropic Claude support
pip install memograph[anthropic]

# For Ollama support
pip install memograph[ollama]

# For embedding support
pip install memograph[embeddings]

# Install everything
pip install memograph[all]

Python Usage

from memograph import MemoryKernel, MemoryType

# Initialize the kernel attached to your vault path
kernel = MemoryKernel("~/my-vault")

# Ingest all notes in the vault
stats = kernel.ingest()
print(f"Indexed {stats['indexed']} memories.")

# Programmatically add a new memory
kernel.remember(
    title="Meeting Note",
    content="Decided to use BFS graph traversal for retrieval.",
    memory_type=MemoryType.EPISODIC,
    tags=["design", "retrieval"]
)

# Retrieve context for an LLM query
context = kernel.context_window(
    query="how does retrieval work?",


... [View full README on GitHub](https://github.com/Indhar01/MemoGraph#readme)