MemFuse Roadmap

This roadmap outlines our planned features and improvements for MemFuse. While we strive to deliver these features according to the timeline below, priorities may shift based on user feedback and market demands.

Q1 2025

• Performance Optimizations: Improving memory retrieval speed and accuracy
• Enhanced Privacy Controls: More granular privacy settings for multi-user environments
• Python SDK Improvements: Expanded functionality and better error handling
• Level 1 Memory Layer — Semantic and episodic memory processing
• Enhanced Website: Updated installation guides and quickstart tutorials

Q2 2025

Documentation & Examples

• Comprehensive Documentation: Complete conceptual guides, tutorials, and API reference
• Interactive Colab Notebooks: Hands-on examples demonstrating core features and integrations

Core Features

• L1 and L2 Memory Abstraction: Advanced memory organization and processing layers
• RAG Implementation: Document ingestion with intelligent chunking strategies
• Production Stack:
  • Docker containerization
  • Migration to PostgreSQL with pgvector
• Data Lineage Tracking: Comprehensive tracking of data flow and transformations

Performance & Benchmarking

• Comprehensive Benchmarking: Performance metrics using LME and MSC datasets

Integrations & Wrappers

• Memory Adapter Integrations: Support for LangChain
• Intent Analysis: Query disambiguation and understanding capabilities

Feature Requests

We prioritize our roadmap based on user feedback. If you have a feature request or suggestion, please submit it through our GitHub repository or contact our support team.

This roadmap was last updated on May 2025.