Update README with pypi

Author: alexzhang13

README.md

@@ -35,17 +35,27 @@ This repository provides an extensible inference engine for using RLMs around st
 > [!NOTE]
 > This repository contains inference code for RLMs with support for various sandbox environments. Open-source contributions are welcome. This repository is maintained by the authors of the paper from the MIT OASYS lab.
 
-<!-- ## Installation
-```
-pip install rlm
-```
-To install the latest from `main`:
+## Quick Setup
+```bash
+pip install rlms
 ```
-pip install git+https://github.com/alexzhang13/rlm.git
+
+The default RLM client uses a REPL environment that runs on the host process through Python `exec` calls. It uses the same virtual environment as the host process (i.e. it will have access to the same dependencies), but with some limitations in its available global modules. As an example, we can call RLM completions using GPT-5-nano:
+```python
+from rlm import RLM
+
+rlm = RLM(
+    backend="openai",
+    backend_kwargs={"model_name": "gpt-5-nano"},
+    verbose=True,  # For printing to console with rich, disabled by default.
+)
+
+print(rlm.completion("Print me the first 100 powers of two, each on a newline.").response)
 ```
-``` -->
 
-## Quick Setup
+<details>
+<summary>Manual Setup</summary>
+
 Set up the dependencies with `uv` (or your virtual environment of choice):
 ```bash
 curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -63,18 +73,7 @@ To run a quick test, the following will run an RLM query with the OpenAI client
 make quickstart
 ```
 
-The default RLM client uses a REPL environment that runs on the host process through Python `exec` calls. It uses the same virtual environment as the host process (i.e. it will have access to the same dependencies), but with some limitations in its available global modules. As an example, we can call RLM completions using GPT-5-nano:
-```python
-from rlm import RLM
-
-rlm = RLM(
-    backend="openai",
-    backend_kwargs={"model_name": "gpt-5-nano"},
-    verbose=True,  # For printing to console with rich, disabled by default.
-)
-
-print(rlm.completion("Print me the first 100 powers of two, each on a newline.").response)
-```
+</details>
 
 ## REPL Environments
 We support two types of REPL environments -- isolated, and non-isolated. Non-isolated environments (default) run code execution on the same machine as the RLM (e.g. through `exec`), which is pretty reasonable for some local low-risk tasks, like simple benchmarking, but can be problematic if the prompts or tool calls can interact with malicious users. Fully isolated environments used Cloud-based sandboxes (e.g. Prime Sandboxes, [Modal Sandboxes](https://modal.com/docs/guide/sandboxes)) to run code generated by the RLM, ensuring completely isolation from the host process. Environments can be added, but we natively support the following: `local` (default), `modal`, `prime`.