letta vs engram
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.; engram: Engram is a local identity layer for AI tools that stores your profile, preferences, lessons learned, and key decisions as local JSON files. It exposes them through MCP so every AI tool reads the same context, persisting across sessions, tool updates, and switches. 100% local and open source.
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.
Developers: Maintain quality standards, architecture decisions, and lessons across projects.
Side-by-Side Comparison
lettaengramFeatures
lettaengramUse Cases
lettaengramBest For
lettaengramFAQ
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