I keep seeing people pit MemGPT against SPR, usually along the lines of “Don’t use MemGPT, use SPR instead!” This misses the point; they’re different tools designed for different tasks.
MemGPT extends the context window of large language models. Think of the context window as the model’s working memory: the information it can actively use. When analyzing a long document or maintaining a complex conversation, MemGPT helps track important details even when they occurred far back in the interaction.
Sparse Priming Representation, or SPR, takes a different approach. It compresses information into concise, token-efficient primers. This lets the model work with more knowledge while reading fewer tokens. SPR excels at packing complex ideas into compact representations that can be efficiently unpacked later.
Used together, MemGPT and SPR complement each other. MemGPT manages the working memory, while SPR handles efficient storage and retrieval. The combination helps models maintain both broad context and deep understanding throughout long interactions.
Pass up the sensationalist clickbait and spend some time understanding how they can work in concert. Each solves a different part of the memory challenge in large language models. Used properly, they make each other better.