Cachly vs MemGPT:
Which AI Memory Is Right for Developers?

What is MemGPT / Letta?

MemGPT — now rebranded as Letta — is a research project turned open-source framework for building stateful AI agents. The core idea: wrap a standard LLM with a custom memory management loop, giving it the ability to read from and write to tiered memory stores (in-context, archival, recall) dynamically during a conversation.

It's a powerful research tool. If you are building a custom AI agent from scratch — a customer support bot with long conversations, a personal AI that needs to remember facts about you — MemGPT gives you low-level control over the memory system. The tradeoff: you're also responsible for building, hosting, and maintaining the entire agent loop.

What is cachly?

cachly is the persistent memory layer for AI coding assistants. Rather than replacing your AI tool, cachly extends the tools you already use — Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, Zed — via the Model Context Protocol (MCP). No custom LLM loop. No agent architecture to build. You run one command and your existing tools gain permanent memory.

The core of cachly is a Causal Knowledge Graph (CKG): a semantically-indexed, causally-linked graph of everything you and your team have learned — from sessions, from git commits, from code reviews. 121 MCP tools let your AI assistants read from and write to this graph in milliseconds.

The key architectural difference

Head-to-head comparison

When MemGPT / Letta is the right choice

MemGPT shines when you are building a new AI agent from the ground up and need fine-grained control over the memory architecture. Research projects, custom long-running agents for internal tools, or LLM apps where you want to own every part of the memory stack — these are MemGPT's home territory.

It is not designed for developers who want to make their existing AI coding tools smarter. If your daily workflow involves opening Cursor or Claude Code and writing software, MemGPT gives you an agent framework you will spend weeks configuring when what you needed was a two-minute memory upgrade for your IDE.

When cachly is the right choice

cachly is built for the developer using AI-assisted coding tools who is tired of re-explaining the same context every session. If you reach for Claude Code, Cursor, GitHub Copilot, or Windsurf every day, cachly adds persistent memory to those tools exactly as they are — no new architecture, no custom agents, no ongoing maintenance.

The three things cachly does that MemGPT cannot

1. Git-native learning. brain_from_git reads your entire commit history and extracts lessons automatically. Every bug fix, every revert, every meaningful commit becomes a lesson in your brain. MemGPT has no concept of a git repository.

2. Causal root-cause analysis. When something breaks, causal_trace traverses the causal edges in your knowledge graph to find not just what broke, but why — and what previously fixed the same root cause. MemGPT stores facts; cachly stores causality.

3. Pre-deploy failure prediction. brain_predict analyzes your brain before a deploy and warns you about patterns that historically precede incidents in your codebase. MemGPT has no concept of a deployment or a production incident.

The TL;DR