Letta Code
A stateful agent harness for long-lived agents that retain memory, identity, and learned context.
What does this agent do, and when should you use it?
Letta Code is a stateful agent harness for building agents intended to evolve over time rather than operate as isolated chats. The same agent can be reached through a local CLI, desktop apps for macOS, Windows, and Linux, chat.letta.com, and Telegram, Slack, Discord, or custom channels. Its documented controls cover memory rewriting, skills, prompts, mods, subagents, hooks, schedules, and message search. With Letta Cloud, agent state and conversations are stored in the cloud while the harness can run on connected machines or cloud environments. This makes it most relevant when continuity across sessions, interfaces, or execution hosts matters.
After you run letta in a project directory, /connect configures your OpenAI, ChatGPT, Anthropic, or Z.ai coding plan API keys, and /model switches models. MemFS tracks context, including memory blocks, through Git; /memory-repository set [email protected]:... syncs that context to a custom GitHub repository. The harness exposes /search for messages and agents, /skills for skills, /sleeptime for periodic dreaming, /doctor for memory-quality audits, and /palace for viewing memory. A machine can be registered with letta server --env-name "...", discovered with letta environments list --online-only, and targeted with letta -p --agent <agent-id> --environment "..." "...".
- A developer building a project assistant that must retain context and revise its memory or skills over many sessions.
- A user who wants to continue working with one agent while moving between a terminal, desktop app, browser, and Slack.
- An automation owner who needs an agent to operate over time through heartbeats, crons, or periodic dreaming.
- An engineering team routing the same agent between a laptop, cloud VM, Mac Mini, or managed sandbox.
- A developer who wants Git-tracked agent context and the option to sync it to a custom GitHub repository.
What are this agent's strengths and limitations?
- It treats memory blocks, skills, prompts, and mods as long-lived state that agents can rewrite, rather than limiting adaptation to a single conversation.
- One agent is documented as reachable through CLI, desktop, browser, mobile browser, and multiple messaging channels.
- MemFS tracks all context through Git and can sync it to a custom GitHub repository.
- Built-in subagents, hooks, search, and self-managed schedules provide concrete building blocks for long-running workflows.
- You must configure your own LLM API keys for the named providers; provider pricing and quota behavior are not documented here.
- Letta Cloud, remote multi-environment operation, and secrets require a Letta sign-in, so those workflows are not purely local.
- Permissions, hooks, skills, and autonomous schedules require operational configuration and governance by the adopter.
- The supplied documentation does not specify CLI runtime prerequisites by operating system, resource requirements, or production failure-recovery behavior.
How do you install or deploy this agent?
Install with npm install -g @letta-ai/letta-code. In a project directory, run letta; for the tutorial agent, run letta --new-agent --personality tutorial. Run /connect to configure your LLM API keys, then choose a model with /model. For Letta Cloud, remote environments, or secrets, run /login in the CLI or sign in through the desktop app.
How do you use this agent?
Start letta in the relevant project directory and interact with the agent. Use /skills to inspect loaded global, project-scoped, or agent-scoped skills; install an external skill with letta skills install <skill> and list a specific agent's skills with letta skills list --agent <agent-id>. To use a remote host, run letta server --env-name "work-laptop" there, list available hosts with letta environments list --online-only, then send work using --environment "work-laptop".