update create_agent usage due to recent changes

src/oss/langchain/agents.mdx

@@ -424,7 +424,7 @@ def dynamic_prompt(state):
         else "Provide simple, clear explanations."
     )
     return [system_msg] + state["messages"]
-agent = create_agent(model, tools, prompt=dynamic_prompt)
+agent = create_agent(model, tools, system_prompt=dynamic_prompt)
 ```
 </CodeGroup>
 :::

src/oss/langchain/rag.mdx

@@ -179,7 +179,7 @@ prompt = (
     "You have access to a tool that retrieves context from a blog post. "
     "Use the tool to help answer user queries."
 )
-agent = create_agent(llm, tools, prompt=prompt)
+agent = create_agent(llm, tools, system_prompt=prompt)
 ```
 
 ```python
@@ -608,7 +608,7 @@ prompt = (
     "You have access to a tool that retrieves context from a blog post. "
     "Use the tool to help answer user queries."
 )
-agent = create_agent(llm, tools, prompt=prompt)
+agent = create_agent(llm, tools, system_prompt=prompt)
 ```
 :::
 :::js
@@ -782,7 +782,7 @@ def prompt_with_context(state: AgentState) -> list[MessageLikeRepresentation]:
     return [{"role": "system", "content": system_message}, *list(state["messages"])]
 
 
-agent = create_agent(llm, tools=[], prompt=prompt_with_context)
+agent = create_agent(llm, tools=[], system_prompt=prompt_with_context)
 ```
 :::
 :::js

src/oss/python/integrations/tools/requests.mdx

@@ -156,13 +156,13 @@ Here is documentation on the API:
 {api_spec}
 """.format(api_spec=api_spec)
 
-agent_executor = create_agent(llm, tools, prompt=system_message)
+agent = create_agent(llm, tools, system_prompt=system_message)
 ```
 
 ```python
 example_query = "Fetch the top two posts. What are their titles?"
 
-events = agent_executor.stream(
+events = agent.stream(
     {"messages": [("user", example_query)]},
     stream_mode="values",
 )

src/oss/python/integrations/tools/sql_database.mdx

@@ -151,15 +151,15 @@ We then instantiate the agent:
 ```python
 from langchain.agents import create_agent
 
-agent_executor = create_agent(llm, toolkit.get_tools(), prompt=system_message)
+agent = create_agent(llm, toolkit.get_tools(), system_prompt=system_message)
 ```
 
 And issue it a query:
 
 ```python
 example_query = "Which country's customers spent the most?"
 
-events = agent_executor.stream(
+events = agent.stream(
     {"messages": [("user", example_query)]},
     stream_mode="values",
 )
@@ -278,7 +278,7 @@ We can also observe the agent recover from an error:
 ```python
 example_query = "Who are the top 3 best selling artists?"
 
-events = agent_executor.stream(
+events = agent.stream(
     {"messages": [("user", example_query)]},
     stream_mode="values",
 )