diff --git a/docs/docs/agents/models.md b/docs/docs/agents/models.md
index 5657d24d0..acd242aa7 100644
--- a/docs/docs/agents/models.md
+++ b/docs/docs/agents/models.md
@@ -145,6 +145,76 @@ const agent = createReactAgent({
 
 :::
 
+:::python
+
+### Dynamic model selection
+
+Pass a callable function to `create_react_agent` to dynamically select the model at runtime. This is useful for scenarios where you want to choose a model based on user input, configuration settings, or other runtime conditions.
+
+The selector function must return a chat model. If you're using tools, you must bind the tools to the model within the selector function.
+
+  ```python
+from dataclasses import dataclass
+from typing import Literal
+from langchain.chat_models import init_chat_model
+from langchain_core.language_models import BaseChatModel
+from langchain_core.tools import tool
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.runtime import Runtime
+
+@tool
+def weather() -> str:
+    """Returns the current weather conditions."""
+    return "It's nice and sunny."
+
+
+# Define the runtime context
+@dataclass
+class CustomContext:
+    provider: Literal["anthropic", "openai"]
+
+# Initialize models
+openai_model = init_chat_model("openai:gpt-4o")
+anthropic_model = init_chat_model("anthropic:claude-sonnet-4-20250514")
+
+
+# Selector function for model choice
+def select_model(state: AgentState, runtime: Runtime[CustomContext]) -> BaseChatModel:
+    if runtime.context.provider == "anthropic":
+        model = anthropic_model
+    elif runtime.context.provider == "openai":
+        model = openai_model
+    else:
+        raise ValueError(f"Unsupported provider: {runtime.context.provider}")
+
+    # With dynamic model selection, you must bind tools explicitly
+    return model.bind_tools([weather])
+
+
+# Create agent with dynamic model selection
+agent = create_react_agent(select_model, tools=[weather])
+
+# Invoke with context to select model
+output = agent.invoke(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "Which model is handling this?",
+            }
+        ]
+    },
+    context=CustomContext(provider="openai"),
+)
+
+print(output["messages"][-1].text())
+```
+
+!!! version-added "New in LangGraph v0.6"
+
+:::
+
 ## Advanced model configuration
 
 ### Disable streaming
diff --git a/docs/docs/how-tos/tool-calling.md b/docs/docs/how-tos/tool-calling.md
index d8f6e29e5..750497759 100644
--- a/docs/docs/how-tos/tool-calling.md
+++ b/docs/docs/how-tos/tool-calling.md
@@ -172,6 +172,82 @@ await agent.invoke({
 
 :::
 
+:::python
+
+### Dynamically select tools
+
+Configure tool availability at runtime based on context:
+
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+
+from langgraph.prebuilt import create_react_agent
+from langgraph.prebuilt.chat_agent_executor import AgentState
+from langgraph.runtime import Runtime
+
+
+@dataclass
+class CustomContext:
+    tools: list[Literal["weather", "compass"]]
+
+
+@tool
+def weather() -> str:
+    """Returns the current weather conditions."""
+    return "It's nice and sunny."
+
+
+@tool
+def compass() -> str:
+    """Returns the direction the user is facing."""
+    return "North"
+
+model = init_chat_model("anthropic:claude-sonnet-4-20250514")
+
+# highlight-next-line
+def configure_model(state: AgentState, runtime: Runtime[CustomContext]):
+    """Configure the model with tools based on runtime context."""
+    selected_tools = [
+        tool
+        for tool in [weather, compass]
+        if tool.name in runtime.context.tools
+    ]
+    return model.bind_tools(selected_tools)
+
+
+agent = create_react_agent(
+    # Dynamically configure the model with tools based on runtime context
+    # highlight-next-line
+    configure_model,
+    # Initialize with all tools available
+    # highlight-next-line
+    tools=[weather, compass]
+)
+
+output = agent.invoke(
+    {
+        "messages": [
+            {
+                "role": "user",
+                "content": "Who are you and what tools do you have access to?",
+            }
+        ]
+    },
+    # highlight-next-line
+    context=CustomContext(tools=["weather"]),  # Only enable the weather tool
+)
+
+print(output["messages"][-1].text())
+```
+
+!!! version-added "New in langgraph>=0.6"
+
+:::
+
 ## Use in a workflow
 
 If you are writing a custom workflow, you will need to: