dory vs letta
dory: Dory is a local-first memory daemon that gives every AI agent the same brain. It uses Markdown as source of truth and SQLite as a disposable sidecar, providing a narrow API for agents to read and write shared memory. This eliminates the need to re-explain context across different tools like Claude, Codex, opencode, etc.; 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
Persistent memory across different coding agents (Claude, Codex, etc.)
- 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
dorylettaFeatures
dorylettaUse Cases
dorylettaBest For
dorylettaFAQ
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