e2b details

docs/source/en/guided_tour.md

@@ -24,7 +24,7 @@ In this guided visit, you will learn how to build an agent, how to run it, and h
 To initialize a minimal agent, you need at least these two arguments:
 
 - An text-generation model to power your agent - because the agent is different from a simple LLM, it is a system that uses a LLM as its engine.
-- A list of tools from which the agent pick tools to execute
+- A list of tools - tools the agent can use to solve the task.
 
 For your model, you can use any of these options:
 - [`TransformersModel`] takes a pre-initialized `transformers` pipeline to run inference on your local machine using `transformers`.
@@ -69,7 +69,7 @@ This gives you at the end of the agent run:
 ```text
 'Hugging Face – Blog'
 ```
-The execution will stop at any code trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent. You can also use E2B code executor instead of a local Python interpreter by passing `use_e2b_executor=True` upon agent initialization.
+The execution will stop at any code trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent. You can also use [E2B code executor](https://e2b.dev/docs#what-is-e2-b) instead of a local Python interpreter by first [setting the `E2B_API_KEY` environment variable](https://e2b.dev/dashboard?tab=keys) and then passing `use_e2b_executor=True` upon agent initialization.
 
 > [!WARNING]
 > The LLM can generate arbitrary code that will then be executed: do not add any unsafe imports!