chore: use latest chat model names (#1203)

- use latest chat model in examples to highlight support
- drop prefixed `openai:` and `anthropic:` where possible due to dynamic
routing
- standardize on full model name instead of aliases (for easier future
updates)

Author: mdrxy

src/langsmith/define-target-function.mdx

@@ -87,7 +87,7 @@ const target = async(inputs) => {
 ```python Python (LangChain)
 from langchain.chat_models import init_chat_model
 
-model = init_chat_model("openai:gpt-4o-mini")
+model = init_chat_model("gpt-4o-mini")
 
 def target(inputs: dict) -> dict:
   # This assumes your dataset has inputs with a `messages` key

src/langsmith/evaluate-complex-agent.mdx

@@ -322,7 +322,7 @@ def lookup_album( ...
 def lookup_artist( ...
 
 # Agent model
-qa_llm = init_chat_model("claude-3-5-sonnet-latest")
+qa_llm = init_chat_model("claude-sonnet-4-5-20250929")
 # The prebuilt ReACT agent only expects State to have a 'messages' key, so the
 # state we defined for the refund agent can also be passed to our lookup agent.
 qa_graph = create_agent(qa_llm, tools=[lookup_track, lookup_artist, lookup_album])
@@ -1260,7 +1260,7 @@ def lookup_artist(
 
 
 # Agent model
-qa_llm = init_chat_model("claude-3-5-sonnet-latest")
+qa_llm = init_chat_model("claude-sonnet-4-5-20250929")
 # The prebuilt ReACT agent only expects State to have a 'messages' key, so the
 # state we defined for the refund agent can also be passed to our lookup agent.
 qa_graph = create_agent(qa_llm, [lookup_track, lookup_artist, lookup_album])

src/langsmith/evaluate-graph.mdx

@@ -42,7 +42,7 @@ def search(query: str) -> str:
 
 tools = [search]
 tool_node = ToolNode(tools)
-model = init_chat_model("claude-3-5-sonnet-latest").bind_tools(tools)
+model = init_chat_model("claude-sonnet-4-5-20250929").bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: State) -> Literal["tools", END]:
@@ -282,7 +282,7 @@ def search(query: str) -> str:
 
 tools = [search]
 tool_node = ToolNode(tools)
-model = init_chat_model("claude-3-5-sonnet-latest").bind_tools(tools)
+model = init_chat_model("claude-sonnet-4-5-20250929").bind_tools(tools)
 
 # Define the function that determines whether to continue or not
 def should_continue(state: State) -> Literal["tools", END]:

src/langsmith/generative-ui-react.mdx

@@ -367,7 +367,7 @@ Then you can push updates to the UI component by calling `ui.push()` / `push_ui_
 
 
     async def writer_node(state: AgentState):
-        model = ChatAnthropic(model="claude-3-5-sonnet-latest")
+        model = ChatAnthropic(model="claude-sonnet-4-5-20250929")
         message: AIMessage = await model.bind_tools(
             tools=[CreateTextDocument],
             tool_choice={"type": "tool", "name": "CreateTextDocument"},
@@ -432,7 +432,7 @@ Then you can push updates to the UI component by calling `ui.push()` / `push_ui_
     ): Promise<typeof AgentState.Update> {
       const ui = typedUi<typeof ComponentMap>(config);
 
-      const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest" });
+      const model = new ChatAnthropic({ model: "claude-sonnet-4-5-20250929" });
       const message = await model
         .bindTools(
           [

src/langsmith/human-in-the-loop-time-travel.mdx

@@ -27,7 +27,7 @@ To time travel using the LangGraph Server API (via the LangGraph SDK):
         joke: NotRequired[str]
 
     model = init_chat_model(
-        "anthropic:claude-sonnet-4-5",
+        "claude-sonnet-4-5-20250929",
         temperature=0,
     )
 

src/langsmith/log-llm-trace.mdx

@@ -397,7 +397,7 @@ def chat_model(inputs: dict) -> dict:
 When using a custom model, it is recommended to also provide the following `metadata` fields to identify the model when viewing traces and when filtering.
 
 * `ls_provider`: The provider of the model, eg "openai", "anthropic", etc.
-* `ls_model_name`: The name of the model, eg "gpt-4o-mini", "claude-3-opus-20240307", etc.
+* `ls_model_name`: The name of the model, eg "gpt-4o-mini", "claude-3-opus-20240229", etc.
 
 <CodeGroup>
 

src/langsmith/manage-prompts-programmatically.mdx

@@ -349,7 +349,7 @@ const { messages, system } = convertPromptToAnthropic(formattedPrompt);
 
 const anthropicClient = new Anthropic();
 const anthropicResponse = await anthropicClient.messages.create({
-  model: "claude-3-haiku-20240307",
+  model: "claude-haiku-4-5-20251001",
   system,
   messages,
   max_tokens: 1024,

src/langsmith/observability-studio.mdx

@@ -78,8 +78,8 @@ class Configuration(BaseModel):
 
     model: Annotated[
         Literal[
-            "anthropic/claude-sonnet-4-5",
-            "anthropic/claude-3-5-haiku-latest",
+            "anthropic/claude-sonnet-4-5-20250929",
+            "anthropic/claude-haiku-4-5-20251001",
             "openai/o1",
             "openai/gpt-4o-mini",
             "openai/o1-mini",

src/langsmith/server-mcp.mdx

@@ -154,7 +154,7 @@ Use an MCP-compliant client to connect to the LangGraph server. The following ex
                 tools = await load_mcp_tools(session)
 
                 # Create and run a react agent with the tools
-                agent = create_agent("openai:gpt-4.1", tools)
+                agent = create_agent("gpt-4.1", tools)
 
                 # Invoke the agent with a message
                 agent_response = await agent.ainvoke({"messages": "What can the finance agent do for me?"})

src/langsmith/streaming.mdx

@@ -649,7 +649,7 @@ The streamed output from [`messages-tuple` mode](#supported-stream-modes) is a t
         topic: str
         joke: str = ""
 
-    model = init_chat_model(model="openai:gpt-4o-mini")
+    model = init_chat_model(model="gpt-4o-mini")
 
     def call_model(state: MyState):
         """Call the LLM to generate a joke about a topic"""