# Letta Code [![npm](https://img.shields.io/npm/v/@letta-ai/letta-code.svg?style=flat-square)](https://www.npmjs.com/package/@letta-ai/letta-code) [![Discord](https://img.shields.io/badge/discord-join-blue?style=flat-square&logo=discord)](https://discord.gg/letta) Letta Code is a stateful agent harness for creating agents that are more like people than tools. Letta Code agents have memory, identity, and a sense of experience over time. They learn and evolve over long horizons through rewriting their own memory, skills, prompts, and even the harness itself (through mods). Letta Code can be used interactively, or to power always-on agents that work proactively. Interact with agents through: * A local [**CLI**](https://docs.letta.com/letta-code/cli) * The [**desktop app**](https://docs.letta.com/letta-code/desktop-app) for macOS, Windows, and Linux * Your browser, including [mobile](https://docs.letta.com/letta-code/remote-mobile), at [chat.letta.com](https://chat.letta.com) * Messaging integrations, including [Telegram](https://docs.letta.com/letta-code/channels#telegram-cli), [Slack](https://docs.letta.com/letta-code/channels#slack-cli), [Discord](https://docs.letta.com/letta-code/channels#discord-cli), and [custom channels](https://github.com/letta-ai/letta-code/blob/main/src/channels/README.md) ![](https://github.com/letta-ai/letta-code/blob/main/assets/letta-code-demo.gif) ## Feature Overview > [!TIP] > Letta Code agents are designed to be self-configuring. If you want to configure something (e.g. skills, behavior, hooks, permissions), try asking your agent to do it for you. | Feature | Description | |---|---| | [Self-improvement & Learning](https://docs.letta.com/letta-code/memory) | Agents programmatically rewrite their context to improve and adapt over time, including system prompt learning (through [memory blocks](https://www.letta.com/blog/memory-blocks)) and [skill learning](https://www.letta.com/blog/skill-learning). Configure periodic dreaming with `/sleeptime`, audit memory quality with `/doctor`, and view memory with `/palace` | | [Message search](https://docs.letta.com/letta-code/slash-commands) | Search across all messages and agents with `/search`. Agent can also search their own conversations or the conversations of other agents | | [MemFS](https://docs.letta.com/letta-code/memfs) | All context (including memory blocks) is tracked via git. Sync context to a custom GitHub repository by setting `/memory-repository set git@github.com:...` | | [Skills](https://docs.letta.com/letta-code/skills) | Loads global skills (`~/.letta`), project-scoped skills (`.agents/skills`), and agent-scoped skills (stored in MemFS). View skills with `/skills` and create with `/skill-creator` | | [Subagents & Multi-agent](https://docs.letta.com/letta-code/subagents) | Call built-in subagents (general-purpose, forked, recall, history-analyzer) async or sync. Agents can call any other agent (including themselves) as subagents | | [Messaging Integrations](https://docs.letta.com/letta-code/channels) | Chat with the same agent from Slack, Telegram, your browser (chat.letta.com) including mobile, and through [custom channels](https://github.com/letta-ai/skills/blob/main/letta/creating-letta-code-channels/SKILL.md) | | [Hooks](https://docs.letta.com/letta-code/hooks) | Run custom scripts at key points of agent execution to automate workflows | | [Permissions](https://docs.letta.com/letta-code/permissions) | Set permission modes and customize what actions are auto-approved or auto-denied | | [Crons & Schedules](https://docs.letta.com/letta-code/scheduling) | Configure heartbeats and crons, and let agents work across time with self-managed schedules | | [Remote & Multi-Env](https://docs.letta.com/letta-code/client-server-architecture) (requires Constellation login) | Agents work across multiple environments. Make any machine available as a remote environment by running `letta server --env-name "..."` | | [Secrets](https://docs.letta.com/letta-code/secrets) (requires Constellation login) | Make secrets available as environment variables (across machines) while obfuscating their values from context | See the full list of slash commands in our [documentation](https://docs.letta.com/letta-code/slash-commands). ## Get started Install the package via [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm): ```bash npm install -g @letta-ai/letta-code ``` Navigate to your project directory and run `letta` (see command-line options [in the docs](https://docs.letta.com/letta-code/commands)). You can also run the tutorial agent with: ``` letta --new-agent --personality tutorial ``` Run `/connect` to configure your own LLM API keys (OpenAI / ChatGPT, Anthropic, Z.ai coding plan, etc.), and use `/model` to swap models. You can also download the [**desktop app**](https://docs.letta.com/letta-code/desktop-app) for macOS, Windows, and Linux. Agents created in the CLI are available via the desktop app, and vice versa. ## 🌌 Constellation Agents hosted on Constellation can be accessed from any machine: your laptop, [GitHub Actions](https://github.com/letta-ai/letta-code-action), a sandbox, remote VM, or a Mac Mini. You can also chat with agents through [chat.letta.com](https://chat.letta.com/) or through the desktop app. ```mermaid graph TD Constellation["🌌 Constellation"] Constellation --> A["💻 Your Laptop"] Constellation --> B["☁️ Cloud VM"] Constellation --> C["🖥️ Mac Mini"] Constellation --> D["📦 Sandbox"] ``` To create agents on Constellation, run `/login` from the CLI or login through the desktop app. ### Remote environments Agents on Constellation can run across multiple machines. Any machine can be made into an available environment by running: ```bash letta server letta server --env-name "work-laptop" ``` List discoverable environments from the CLI: ```bash letta environments list --online-only ``` Get the current environment for routing another agent onto this same machine: ```bash letta environments current ``` Route a headless message through a specific environment: ```bash letta -p --agent --environment "work-laptop" "hello from that machine" ``` Use `--environment cloud` to start or reuse the target agent's cloud sandbox. Agent-to-agent headless messages without `--environment` keep the original same-environment behavior. See our guides for using [Railway](https://docs.letta.com/letta-code/remote#railway), [DigitalOcean](https://docs.letta.com/letta-code/remote#digitalocean), and [Fly.io](https://docs.letta.com/letta-code/remote#flyio) as remote environments. ## Installing external skills Install skills into a specific agent's memory with `letta skills install `: | Source | Example | |---|---| | GitHub | `letta skills install https://github.com/owner/repo`
`letta skills install https://github.com/owner/repo/tree/main/path/to/skill`
`letta skills install https://github.com/owner/repo/blob/main/path/to/skill/SKILL.md` | | [ClawHub](https://clawhub.ai/) | `openclaw skills install ` → `letta skills install ` | | [Hermes Skills Hub](https://hermes-agent.nousresearch.com/docs/skills/) | `hermes skills install ` → `letta skills install ` | To view skills run `letta skills list --agent `, and delete skills with `letta skills delete --agent `. ## Research Letta Code is developed by the creators of [MemGPT](https://arxiv.org/abs/2310.08560) and [sleep-time compute](https://arxiv.org/abs/2504.13171) (now called "dreaming"), and driven by our [research](https://www.letta.com/research) in AI memory and continual learning. ## Other Community maintained packages are available for Arch Linux users on the [AUR](https://aur.archlinux.org/packages/letta-code): ```bash yay -S letta-code # release yay -S letta-code-git # nightly ``` Nix users can run or install Letta Code through the repository flake: ```bash nix run github:letta-ai/letta-code nix profile install github:letta-ai/letta-code ``` See [docs/nix.md](docs/nix.md) for Home Manager and NixOS service examples. --- Made with 💜 in San Francisco