MemFuse: lightning-fast, open-source memory layer for LLMs.

Large Language Models (LLMs) bring the brains, but their stateless APIs create a recurring nightmare for developers: rebuilding memory solutions from the ground up, repeatedly. MemFuse attacks this problem head-on. It's an open-source memory layer, purpose-built for extreme speed and shockingly simple integration. Think of it as the dedicated memory backbone your AI applications need, compatible with virtually any LLM API or agent framework out there. And because we believe in true open source, we're making self-hosting a breeze with straightforward, no-nonsense instructions, unlike some other projects out there.

The Memory Challenge

Most LLM interactions are stateless by design - each API call exists in isolation without knowledge of previous exchanges. This creates significant challenges for developers building conversational AI applications that require context awareness and memory of past interactions.

🙌 What MemFuse Offers

MemFuse provides a dedicated memory layer that handles:

• Lightning-Fast Performance: Engineered for speed, this memory layer is so thin, you'll barely feel it, ensuring minimal latency in your application.
• Multimodal Support: Extends beyond text to handle memories from videos and audio
• Simple and Fast Integration: Designed for ease of use – often, just a few lines of code are all it takes to give your LLM a persistent memory. MemFuse integrates seamlessly with any LLM API or agent framework.
• Tackling Long-Term Memory: We're dead serious about addressing the core memory challenges highlighted in benchmarks like the LongMemEval paper, and we'll keep the community updated on our progress.

✨ Key Features

⚡️ How We're Lightning Fast

In a nutshell, MemFuse is fast because we're not just throwing data at your storage. We use a combo of:

1. Smart Buffering: Think of it like an intelligent assembly line for your data – pre-fetching what you'll need, batching writes efficiently, and caching common queries.
2. Tiered Memory: Data is organized like a VIP lounge – the most important stuff is kept closest for instant access.

This means less waiting and more doing for your AI.

🔍 Want the nitty-gritty? Click here for the technical deep dive

So, what's the secret sauce behind MemFuse's speed? No black magic, just hardcore engineering. We've architected MemFuse from the ground up for blistering throughput and minimal latency, centered around a sophisticated Buffer System and a Hierarchical Memory structure.

The Big Picture: Core Architecture

Your app talks to MemFuse via a clean API. Requests flow through a lean client and server stack, straight into our performance core: the Buffer System. This engine then intelligently juggles data with a Hierarchical Memory setup, ensuring data is organized for speed before hitting your chosen storage backends.

The Engine Room: Our Advanced Buffer System

This is where MemFuse really shifts gears. It's not just simple caching; it's a multi-stage data rocket designed to slash latency:

Here's how these key players make your app fly:

• WriteBuffer: Say goodbye to chatty storage interactions. The WriteBuffer intelligently batches incoming data, turning many small, slow writes into fewer, larger, and much faster ones. Think of it as an express lane for your data.
• SpeculativeBuffer: This is MemFuse's crystal ball. It analyzes access patterns and proactively fetches data it predicts you'll need next. Result? Data is often pre-loaded and waiting, drastically cutting down retrieval times.
• QueryBuffer: Your fast-track for reading data. It caches frequently accessed information and employs smart reranking (using techniques like Reciprocal Rank Fusion) to instantly serve up the most relevant results from various memory sources. Cache hits mean near-instant answers.

A central BufferManager orchestrates these components, ensuring data flows smoothly and efficiently.

Smart Data Tiers: Hierarchical Memory

Inspired by how both computer caches and human memory operate, MemFuse organizes data into tiers for rapid access:

• L0 (Raw Data): The freshest, most immediate data – like short-term working memory.
• L1 (Facts Extraction): Data gets processed; key facts and entities are extracted and structured.
• L2 (Knowledge Graph): Deeper connections and relationships are forged, building a rich, queryable knowledge structure for more complex reasoning.

This tiered system means MemFuse often finds what it needs in faster, closer memory layers, avoiding slower trips to main storage.

Put it all together, and MemFuse delivers a memory layer that's not just powerful, but incredibly fast, ensuring your AI applications remain responsive and agile.

Not An Agent Framework

MemFuse is intentionally focused on solving the memory challenge specifically, rather than providing a complete agent framework. This focused approach means:

1. Flexible Integration: Use MemFuse with your preferred agent framework or directly with LLM APIs
2. Specialized Optimization: Our focused approach delivers better memory capabilities
3. Lower Overhead: Only integrate the components you need

API Options

MemFuse provides two API layers to accommodate different developer needs:

• High-Level APIs: Simple, intuitive interfaces designed for ease of use to get started quickly
• Low-Level APIs: Advanced control with Mem0-compatible interfaces for existing applications

Current Status

The Python SDK is currently available, with plans to expand support to other major programming languages. Our roadmap includes JavaScript, TypeScript, Java, and Go implementations.

Getting Started

Check out our Quickstart guide to begin adding memory capabilities to your AI applications quickly and easily in minutes.