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Repository Overview (README excerpt)
Crawler viewLetta 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: Navigate to your project directory and run (see various command-line options on the docs). Run to configure your own LLM API keys (OpenAI, Anthropic, etc.), and use to swap models. > [!NOTE] > By default, Letta Code will to connect to the Letta API. Use to use your own LLM API keys and coding plans (Codex, zAI, Minimax) for free. Set 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 + • Relationship: Every conversation is like meeting a new contractor **Letta Code** (Agent-Based) • Same agent across sessions • Persistent memory and learning over time • 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 command to initialize the agent’s memory system: Over time, the agent will update its memory as it learns. To actively guide your agents memory, you can use the command: Letta Code works with skills (reusable modules that teach your agent new capabilities in a directory), but additionally supports skill learning. You can ask your agent to learn a skill from its current trajectory with the command: Read the docs to learn more about skills and skill learning. Community maintained packages are available for Arch Linux users on the AUR: --- Made with 💜 in San Francisco