92 lines
3.6 KiB
Markdown
92 lines
3.6 KiB
Markdown
# Letta Code (Research Preview)
|
||
|
||
Letta Code is a self-improving, stateful coding agent that can learn from experience and improve with use. You can use Letta Code as a general purpose **CLI harness** to connect any Letta agent (even non-coding agents!) to your local dev environment.
|
||
|
||
Letta Code is open source and model agnostic - supporting Claude Sonnet/Opus, GPT-5, Gemini 3 Pro, GLM-4.6, and more.
|
||
|
||
**Read more about how to use Letta Code on the [official docs page](https://docs.letta.com/letta-code).**
|
||
|
||
<img width="1713" height="951" alt="letta-code" src="https://github.com/user-attachments/assets/ae546e96-368a-4a7b-9397-3963a35c8d6b" />
|
||
|
||
> [!IMPORTANT]
|
||
> Letta Code is a **research preview** in active development, and may have bugs or unexpected issues. To learn more about the roadmap and chat with the dev team, visit our [Discord](https://discord.gg/letta). Contributions welcome, join the fun.
|
||
|
||
## Get started
|
||
|
||
Requirements:
|
||
* [Node.js](https://nodejs.org/en/download) (version 18+)
|
||
* A [Letta Developer Platform](https://app.letta.com/) account (or a [self-hosted Letta server](https://docs.letta.com/letta-code/configuration#self-hosted-server))
|
||
|
||
Install the package via [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm):
|
||
```bash
|
||
npm install -g @letta-ai/letta-code
|
||
```
|
||
|
||
Then run `letta` to start Letta Code in interactive mode (see various command-line options [on the docs](https://docs.letta.com/letta-code/commands)):
|
||
```bash
|
||
letta
|
||
```
|
||
If you haven't used Letta Code before, you'll need to use OAuth to login, or set a `LETTA_API_KEY` in your environment variables.
|
||
|
||
To connect to an existing agent, use the `--agent` flag:
|
||
```bash
|
||
letta --agent [existing-agent-id]
|
||
```
|
||
|
||
You can also run Letta Code in headless mode, making it easy to integrate into scripts (see the [docs](https://docs.letta.com/letta-code/headless) for more):
|
||
```bash
|
||
letta -p "Look around this repo and write a README.md documenting it at the root level"
|
||
```
|
||
|
||
## Memory and Skill Learning
|
||
|
||
All agents in Letta are **stateful**: they maintain context forever and can self-edit their own [memory blocks](https://www.letta.com/blog/memory-blocks).
|
||
|
||
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]
|
||
```
|
||
|
||
Skills are reusable modules that teach your agent new capabilities. They’re automatically discovered from your project’s `.skills` directory and loaded into the agent’s memory at session start. The easiest way to create a skill is using the interactive skill creation mode:
|
||
```bash
|
||
> /skill
|
||
```
|
||
|
||
Read the docs to learn more about [skills and skill learning](https://docs.letta.com/letta-code/skills).
|
||
|
||
## Installing from source
|
||
|
||
First, install Bun if you don't have it yet: [https://bun.com/docs/installation](https://bun.com/docs/installation)
|
||
|
||
### Run directly from source (dev workflow)
|
||
```bash
|
||
# install deps
|
||
bun install
|
||
|
||
# run the CLI from TypeScript sources (pick up changes immediately)
|
||
bun run dev
|
||
bun run dev -- -p "Hello world" # example with args
|
||
```
|
||
|
||
### Build + link the standalone binary
|
||
```bash
|
||
# build bin/letta (includes prompts + schemas)
|
||
bun run build
|
||
|
||
# expose the binary globally (adjust to your preference)
|
||
bun link
|
||
|
||
# now you can run the compiled CLI
|
||
letta
|
||
```
|
||
Whenever you change source files, rerun `bun run build` before using the linked `letta` binary so it picks up your edits.
|
||
|
||
---
|
||
|
||
Made with 💜 in San Francisco
|