diff --git a/docs/docs/agents/context.md b/docs/docs/agents/context.md
index b246b6dce..94bb27616 100644
--- a/docs/docs/agents/context.md
+++ b/docs/docs/agents/context.md
@@ -18,29 +18,20 @@ LangGraph provides **three** primary ways to manage context:
 
 ### Runtime Context
 
-!!! note "`config['configurable']` -> `runtime.context`"
-
-    In LangGraph < v0.6, static runtime context was passed via the `config['configurable']` key, paired with a `config_schema` argument
-    to `StateGraph` or `Pregel`. This is now deprecated and will be removed in v2.0.
-
-    As of LangGraph v0.6, the `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
-
-!!! warning "Context is an overloaded term"
-
-    In the world of LLMs, "context" is quite the overloaded term.
-
-    There are two main types of context that you will encounter:
-
-    1. Local context: data and dependencies your code needs to run. This might be helpful for tools, node invocations,
-    conditional branching, etc.
-    2. LLM context: often talked about regarding the "context window" of an LLM. This is the data the LLM sees when generating a response.
-    The field of "context engineering" refers to the practice of optimizing the content of the context window to improve the LLM's performance.
-
-    The context discussed in this section is the local context. As a developer, you might use the local context to eventually optimize
-    the LLM context (ex: use a user_id to fetch a user's name and information from a database to populate the context window with relevant memories).
-
 Runtime context is for immutable data like user metadata, tools, db connections, etc. Use this when you have values that don't change mid-run.
 
+!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces config['configurable']"
+
+    The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
+
+!!! note "'Context' is an overloaded term"
+
+    Runtime context refers to local context: data and dependencies your code needs to run.
+    It does not refer to the LLM context, which is the data passed into the LLM's prompt.
+
+    You likely want to use the local context to optimize the llm's context window. For example, you
+    could use a user_id to fetch a user's name and information from a database to populate the context window with relevant memories.
+
 Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose:
 
 ```python