Letta

Letta (formerly MemGPT) implements the self-editing-context pattern for stateful AI agents in a usable framework. More research-flavored than Mem0; the right pick for teams that want full agent state, not just memory.

Letta's lineage matters: it's the production version of the MemGPT research from UC Berkeley, which introduced the idea of LLMs that manage their own context window — promoting important facts into core memory, archiving less-relevant context to long-term storage, and self-editing what stays in active context across turns. As a framework, Letta is genuinely different from "agent SDK + memory layer" approaches like LangChain + Mem0; the agent itself is stateful in a deeper way.

The Agent Development Environment (ADE) is the underrated feature — visual debugging of agent state, memory, and reasoning across turns. For research teams and advanced AI engineers building genuinely long-running agents, this is a real productivity win. The trade-off is learning curve: Letta requires understanding the MemGPT pattern, which is more conceptual overhead than "give my agent a memory module."

Evaluate Letta if you're building genuinely long-running stateful agents (research assistants, persistent companions, autonomous workers) and want full agent state managed natively. Use Mem0 if you just need a memory layer for an existing agent framework. Skip if your agent is short-lived or stateless — Letta is overkill.