Letta Code

 

Letta Code is a memory-first coding harness, built on top of the Letta API. Instead of working in independent sessions, you work with a persisted agent that learns over time and is portable across models (Claude Sonnet/Opus 4.5, GPT-5.2-Codex, Gemini 3 Pro, GLM-4.7, and more).

Read more about how to use Letta Code on the official docs page.


Get started

Install the package via npm:
``bash
npm install -g @letta-ai/letta-code
`
Navigate to your project directory and run letta (see various command-line options on the docs).

Run /connect to configure your own LLM API keys (OpenAI, Anthropic, etc.), and use /model to swap models.

> [!NOTE]
> By default, Letta Code will to connect to the Letta API. Use /connect to use your own LLM API keys and coding plans (Codex, zAI, Minimax) for free. Set LETTA_BASE_URL to connect to an external Docker server.

Philosophy

Letta Code is built around long-lived agents that persist across sessions and improve with use. Rather than working in independent sessions, each session is tied to a persisted agent that learns.

Claude Code / Codex / Gemini CLI (Session-Based)
- Sessions are independent
- No learning between sessions
- Context = messages in the current session + AGENTS.md
- Relationship: Every conversation is like meeting a new contractor

Letta Code (Agent-Based)
- Same agent across sessions
- Persistent memory and learning over time
- /clear starts a new conversation (aka "thread" or "session"), but memory persists
- Relationship: Like having a coworker or mentee that learns and remembers

Agent Memory & Learning


If you’re using Letta Code for the first time, you will likely want to run the /init command to initialize the agent’s memory system:
`bash
> /init
`

Over time, the agent will update its memory as it learns. To actively guide your agents memory, you can use the /remember command:
`bash
> /remember [optional instructions on what to remember]
`
Letta Code works with skills (reusable modules that teach your agent new capabilities in a .skills directory), but additionally supports skill learning. You can ask your agent to learn a skill from it's current trajectory with the command:
`bash
> /skill [optional instructions on what skill to learn]
`

Read the docs to learn more about skills and skill learning.

Community maintained packages are available for Arch Linux users on the AUR:
`bash
yay -S letta-code # release
yay -S letta-code-git # nightly
``

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