hmem vs letta
hmem: hmem is a memory system for AI agents that persists project context across sessions, devices, and AI providers. It uses a 5-level lazy loading architecture to minimize token usage while retaining full details. It supports auto-logging, background checkpoints, and cross-platform sync.; 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.
TL;DR
AI coding assistants that need to maintain project context across sessions
- 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.
Side-by-Side Comparison
hmemlettaFeatures
hmemlettaUse Cases
hmemlettaBest For
hmemlettaFAQ
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