letta vs mex
letta: Letta is a powerful platform for building stateful AI agents equipped with advanced memory, enabling them to learn and self-improve over time. It provides both a command-line interface for local agent execution and a comprehensive API with Python and TypeScript SDKs for seamless integration into applications.; mex: mex provides persistent, navigable project memory for AI coding agents, allowing every session to start with the right context instead of a cold prompt. It structures agent memory through a markdown scaffold, offers CLI tools for drift detection against the real codebase, and synchronizes context to keep agents aligned and reduce token usage.
TL;DR
- You want a long-lived agent that remembers past work and rewrites its own memory and context over time.
- You want one agent reachable from CLI, desktop, browser, mobile and messaging apps, with subagents, schedules and hooks.
- You want memory you can inspect: the README says context is tracked in git and can sync to your own repository.
- You only need to add memory to an agent you already use through MCP; a small MCP memory server is a lighter fit.
- You rely on older Letta tutorials or the V1 API server: the project restructured, and the V1 server is retired.
- You want everything to work without signing in: Letta Cloud is the default backend and some features need it.
- Your AGENTS.md or CLAUDE.md has grown too big and you want a small anchor plus routed context files.
- You want memory as plain markdown in the repo, reviewable in pull requests and diffable.
- You want drift checks that run without AI tokens and flag missing paths, stale files and broken scripts.
- You want recall across unrelated projects or conversations; mex lives inside one repository.
- You have a small project: another set of files to maintain may cost more than it saves.
- You expect the AST code-graph feature today: the README marks it as an unreleased preview.
Side-by-Side Comparison
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