chore: Update README.md (letta-ai#2215)

README.md

@@ -9,9 +9,21 @@
 <div align="center">
 <h1>Letta (previously MemGPT)</h1>
 
+**☄️ New release: Letta Agent Development Environment (_read more [here](#-access-the-letta-ade-agent-development-environment)_) ☄️**
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="assets/example_ade_screenshot.png">
+    <source media="(prefers-color-scheme: light)" srcset="assets/example_ade_screenshot_light.png">
+    <img alt="Letta logo" src="assets/example_ade_screenshot.png" width="800">
+  </picture>
+</p>
+
+---
+
 <h3>
 
-[Homepage](https://letta.com) // [Documentation](https://docs.letta.com) // [Letta Cloud](https://forms.letta.com/early-access)
+[Homepage](https://letta.com) // [Documentation](https://docs.letta.com) // [ADE](https://app.letta.com) // [Letta Cloud](https://forms.letta.com/early-access)
 
 </h3>
 
@@ -23,24 +35,181 @@
 
 [![Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-silver?style=flat-square)](LICENSE)
 [![Release](https://img.shields.io/github/v/release/cpacker/MemGPT?style=flat-square&label=Release&color=limegreen)](https://github.com/cpacker/MemGPT/releases)
+[![Docker](https://img.shields.io/docker/v/letta/letta?style=flat-square&logo=docker&label=Docker&color=0db7ed)](https://hub.docker.com/r/letta/letta)
 [![GitHub](https://img.shields.io/github/stars/cpacker/MemGPT?style=flat-square&logo=github&label=Stars&color=gold)](https://github.com/cpacker/MemGPT)
 
 <a href="https://trendshift.io/repositories/3612" target="_blank"><img src="https://trendshift.io/api/badge/repositories/3612" alt="cpacker%2FMemGPT | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
 </div>
 
-> [!NOTE]
+> [!IMPORTANT]
 > **Looking for MemGPT?** You're in the right place!
 >
-> The MemGPT package and Docker image have been renamed to `letta` to clarify the distinction between MemGPT agents and the API server / runtime that runs LLM agents as *services*.
->
-> You use the **Letta _framework_** to create **MemGPT _agents_**. Read more about the relationship between MemGPT and Letta [here](https://www.letta.com/blog/memgpt-and-letta).
+> The MemGPT package and Docker image have been renamed to `letta` to clarify the distinction between MemGPT *agents* and the Letta API *server* / *runtime* that runs LLM agents as *services*. Read more about the relationship between MemGPT and Letta [here](https://www.letta.com/blog/memgpt-and-letta).
+
+---
 
 ## ⚡ Quickstart
 
-The two main ways to install Letta are through **pypi** (`pip`) or via **Docker**:
-* **`pip`** (guide below) - the easiest way to try Letta, will default to using SQLite and ChromaDB for the database backends
-* **Docker** (guide [here](https://docs.letta.com/install#run-letta-with-docker)) - recommended for production settings, will default to using Postgres (+ pgvector) for the database backend
+_The recommended way to use Letta is to run use Docker. To install Docker, see [Docker's installation guide](https://docs.docker.com/get-docker/). For issues with installing Docker, see [Docker's troubleshooting guide](https://docs.docker.com/desktop/troubleshoot-and-support/troubleshoot/). You can also install Letta using `pip` (see guide [below](#-quickstart-pip))._
+
+### 🌖 Run the Letta server
+
+> [!NOTE]
+> Letta agents live inside the Letta server, which persists them to a database. You can interact with the Letta agents inside your Letta server via the [REST API](https://docs.letta.com/api-reference) + Python / Typescript SDKs, and the [Agent Development Environment](https://app.letta.com) (a graphical interface).
+
+The Letta server can be connected to various LLM API backends ([OpenAI](https://docs.letta.com/models/openai), [Anthropic](https://docs.letta.com/models/anthropic), [vLLM](https://docs.letta.com/models/vllm), [Ollama](https://docs.letta.com/models/ollama), etc.). To enable access to these LLM API providers, set the appropriate environment variables when you use `docker run`:
+```sh
+# replace `~/.letta/.persist/pgdata` with wherever you want to store your agent data
+docker run \
+  -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
+  -p 8283:8283 \
+  -e OPENAI_API_KEY="your_openai_api_key" \
+  letta/letta:latest
+```
+
+If you have many different LLM API keys, you can also set up a `.env` file instead and pass that to `docker run`:
+```sh
+# using a .env file instead of passing environment variables
+docker run \
+  -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
+  -p 8283:8283 \
+  --env-file .env \
+  letta/letta:latest
+```
+
+Once the Letta server is running, you can access it via port `8283` (e.g. sending REST API requests to `http://localhost:8283/v1`). You can also connect your server to the Letta ADE to access and manage your agents in a web interface.
+
+### 👾 Access the [Letta ADE (Agent Development Environment)](https://app.letta.com)
+
+> [!NOTE]
+> The Letta ADE is a graphical user interface for creating, deploying, interacting and observing with your Letta agents.
+>
+> For example, if you're running a Letta server to power an end-user application (such as a customer support chatbot), you can use the ADE to test, debug, and observe the agents in your server. You can also use the ADE as a general chat interface to interacting with your Letta agents.
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="assets/example_ade_screenshot.png">
+    <source media="(prefers-color-scheme: light)" srcset="assets/example_ade_screenshot_light.png">
+    <img alt="Letta logo" src="assets/example_ade_screenshot.png" width="800">
+  </picture>
+</p>
+
+The ADE can connect to self-hosted Letta servers (e.g. a Letta server running on your laptop), as well as the Letta Cloud service. When connected to a self-hosted / private server, the ADE uses the Letta REST API to communicate with your server.
+
+#### 🖥️ Connecting the ADE to your local Letta server
+To connect the ADE with your local Letta server, simply:
+1. Start your Letta server (`docker run ...`)
+2. Visit [https://app.letta.com](https://app.letta.com) and you will see "Local server" as an option in the left panel
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="assets/example_ade_screenshot_agents.png">
+    <source media="(prefers-color-scheme: light)" srcset="assets/example_ade_screenshot_agents_light.png">
+    <img alt="Letta logo" src="assets/example_ade_screenshot_agents.png" width="800">
+  </picture>
+</p>
+
+🔐 To password protect your server, include `SECURE=true` and `LETTA_SERVER_PASSWORD=yourpassword` in your `docker run` command:
+```sh
+# If LETTA_SERVER_PASSWORD isn't set, the server will autogenerate a password
+docker run \
+  -v ~/.letta/.persist/pgdata:/var/lib/postgresql/data \
+  -p 8283:8283 \
+  --env-file .env \
+  -e SECURE=true \
+  -e LETTA_SERVER_PASSWORD=yourpassword \
+  letta/letta:latest
+```
+
+#### 🌐 Connecting the ADE to an external (self-hosted) Letta server
+If your Letta server isn't running on `localhost` (for example, you deployed it on an external service like EC2):
+1. Click "Add remote server"
+2. Enter your desired server name, the IP address of the server, and the server password (if set)
+
+---
+
+## 🧑‍🚀 Frequently asked questions (FAQ)
+
+> _"Do I need to install Docker to use Letta?"_
+
+No, you can install Letta using `pip` (via `pip install -U letta`), as well as from source (via `poetry install`). See instructions below.
+
+> _"How do I use the ADE locally?"_
+
+To connect the ADE to your local Letta server, simply run your Letta server (make sure you can access `localhost:8283`) and go to [https://app.letta.com](https://app.letta.com). If you would like to use the old version of the ADE (that runs on `localhost`), downgrade to Letta version `<=0.5.0`.
+
+> _"If I connect the ADE to my local server, does my agent data get uploaded to letta.com?"_
+
+No, the data in your Letta server database stays on your machine. The Letta ADE web application simply connects to your local Letta server (via the REST API) and provides a graphical interface on top of it to visualize your local Letta data in your browser's local state.
+
+> _"Do I have to use your ADE? Can I build my own?"_
+
+The ADE is built on top of the (fully open source) Letta server and Letta Agents API. You can build your own application like the ADE on top of the REST API (view the documention [here](https://docs.letta.com/api-reference)).
+
+> _"Can I interact with Letta agents via the CLI?"_
+
+The recommended way to use Letta is via the REST API and ADE, however you can also access your agents via the CLI.
+
+<details>
+<summary>View instructions for running the Letta CLI</summary>
+
+You can chat with your agents via the Letta CLI tool (`letta run`). If you have a Letta Docker container running, you can use `docker exec` to run the Letta CLI inside the container:
+```sh
+# replace `<letta_container_id>` with the ID of your Letta container, found via `docker ps`
+docker exec -it <letta_container_id> letta run
+```
+
+You can also use `docker ps` within the command to automatically find the ID of your Letta container:
+```
+docker exec -it $(docker ps -q -f ancestor=letta/letta) letta run
+```
+
+In the CLI tool, you'll be able to create new agents, or load existing agents:
+```
+🧬 Creating new agent...
+? Select LLM model: letta-free [type=openai] [ip=https://inference.memgpt.ai]
+? Select embedding model: letta-free [type=hugging-face] [ip=https://embeddings.memgpt.ai]
+->  🤖 Using persona profile: 'sam_pov'
+->  🧑 Using human profile: 'basic'
+->  🛠️  8 tools: send_message, pause_heartbeats, conversation_search, conversation_search_date, archival_memory_insert, archival_memory_search, core_memory_append, core_memory_replace
+
+🎉 Created new agent 'InspiringSpinach'
+
+Hit enter to begin (will request first Letta message)
+
+💭 User engaged. Recognize as Chad. Acknowledge presence and establish connection. Explore interests and engage in meaningful discourse.
+🤖 Hello, Chad. It's nice to meet you. I am keen on learning and exploring new ideas and concepts. Is there a specific topic you'd like to discuss today?
+
+> Enter your message: my name is Brad, not Chad...
+
+💭 A discrepancy in identity. Reflect this change in core memory. Apologize for the oversight, and rectify the error.
+⚡🧠 [function] updating memory with core_memory_replace
+         First name: Chad
+        → First name: Brad
+💭 Apologize for the mistake in recognizing his name, and begin the conversation anew, addressing him by the correct name this time.
+🤖 I apologize for the oversight, Brad. Let's restart. It's a pleasure to meet you, Brad. Is there a specific topic you'd like to discuss today?
+
+> Enter your message:
+```
+</details>
+
+---
+
+## ⚡ Quickstart (pip)
+
+> [!WARNING]
+> **Database migrations are not officially support with `SQLite`**
+>
+> When you install Letta with `pip`, the default database backend is `SQLite` (you can still use an external `postgres` service with your `pip` install of Letta by setting `LETTA_PG_URI`).
+>
+> We do not officially support migrations between Letta versions with `SQLite` backends, only `postgres`. If you would like to keep your agent data across multiple Letta versions we highly recommend using the Docker install method which is the easiest way to use `postgres` with Letta.
+
+<details>
+
+<summary>View instructions for installing with pip</summary>
+
+You can also install Letta with `pip`, will default to using `SQLite` for the database backends (whereas Docker will default to using `postgres`).
 
 ### Step 1 - Install Letta using `pip`
 ```sh
@@ -104,11 +273,9 @@ INFO:     Waiting for application startup.
 INFO:     Application startup complete.
 INFO:     Uvicorn running on http://localhost:8283 (Press CTRL+C to quit)
 ```
+</details>
 
-When you start the Letta API server, the ADE (Agent Development Environment) will be available on `http://localhost:8283`:
-<img alt="Screenshot of the Letta ADE (Agent Development Environment)" src="assets/letta_ade_screenshot.png" width="1600">
-
-In Letta, all agents are stored/persisted in the same database, so the agents you create in the CLI are accessible via the API and ADE, and vice versa. Check out the [quickstart guide on our docs](https://docs.letta.com/quickstart) for a tutorial where you create an agent in the Letta CLI and message the same agent via the Letta API.
+---
 
 ## 🤗 How to contribute