update

Author: npentrel

scripts/generate_code_snippet_mdx.py

@@ -114,7 +114,9 @@ def _expand_to_deepagents_codegroup(
     """Wrap `content` in a CodeGroup, one tab per quickstart model variant."""
     start, end = canonical_span
     parts = [
-        _codegroup_fence(title, fence_lang, _replace_span(content, start, end, model_token))
+        _codegroup_fence(
+            title, fence_lang, _replace_span(content, start, end, model_token)
+        )
         for title, model_token in tab_definitions
     ]
     return "<CodeGroup>\n" + "\n\n".join(parts) + "\n</CodeGroup>\n"

src/code-samples/deepagents/content-builder.ts

@@ -125,7 +125,7 @@ function createContentWriter() {
   };
 
   return createDeepAgent({
-    model: "google_genai:gemini-3.1-pro-preview",
+    model: "anthropic:claude-sonnet-4-6",
     memory: ["./AGENTS.md"],
     skills: ["./skills/"],
     tools: [generateCover, generateSocialImage],

src/code-samples/langchain/nostream-tag.py

@@ -7,9 +7,9 @@
 from langgraph.graph import START, StateGraph
 
 # KEEP MODEL
-stream_model = ChatAnthropic(model_name="claude-3-haiku-20240307")
+stream_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001")
 # KEEP MODEL
-internal_model = ChatAnthropic(model_name="claude-3-haiku-20240307").with_config(
+internal_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001").with_config(
     {"tags": ["nostream"]}
 )
 

src/code-samples/langchain/nostream-tag.ts

@@ -8,10 +8,10 @@ import { StateGraph, StateSchema, START } from "@langchain/langgraph";
 import * as z from "zod";
 
 // KEEP MODEL
-const streamModel = new ChatAnthropic({ model: "claude-3-haiku-20240307" });
+const streamModel = new ChatAnthropic({ model: "claude-haiku-4-5-20251001" });
 const internalModel = new ChatAnthropic({
   // KEEP MODEL
-  model: "claude-3-haiku-20240307",
+  model: "claude-haiku-4-5-20251001",
 }).withConfig({
   tags: ["nostream"],
 });
@@ -44,7 +44,10 @@ const graph = new StateGraph(State)
   .addEdge("writeAnswer", "internal_notes")
   .compile();
 
-const stream = await graph.stream({ topic: "AI" }, { streamMode: "messages" });
+const stream = await graph.stream(
+  { topic: "AI", answer: "", notes: "" },
+  { streamMode: "messages" },
+);
 // :snippet-end:
 
 // :remove-start:

src/code-samples/package-lock.json

@@ -25,7 +25,7 @@ npm install @aws-sdk/client-bedrock-runtime langsmith
 
 </CodeGroup>
 
-This integration uses the native AWS SDKs with LangSmith's tracing capabilities. For Python, you'll use [`boto3`](https://pypi.org/project/boto3/) (the AWS SDK for Python) along with [`langsmith`](https://pypi.org/project/langsmith/) to capture traces. For JavaScript/TypeScript, you'll use [`@aws-sdk/client-bedrock-runtime`](https://www.npmjs.com/package/@aws-sdk/client-bedrock-runtime) with the [`langsmith`](https://www.npmjs.com/package/langsmith) package. Both implementations use the [Bedrock Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), which provides a unified interface for interacting with foundation models.
+This integration uses the native AWS SDKs with LangSmith's tracing capabilities. For Python, you'll use [`boto3`](https://pypi.org/project/boto3/) (the AWS SDK for Python) along with [`langsmith`](https://pypi.org/project/langsmith/) to capture traces. For JavaScript/TypeScript, you'll use [`@aws-sdk/client-bedrock-runtime`](https://www.npmjs.org/package/@aws-sdk/client-bedrock-runtime) with the [`langsmith`](https://www.npmjs.org/package/langsmith) package. Both implementations use the [Bedrock Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html), which provides a unified interface for interacting with foundation models.
 
 ## Setup
 
@@ -66,7 +66,7 @@ from langsmith import traceable
 
 # Initialize Bedrock runtime client (ensure AWS creds and region are set)
 bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
-model_id = "anthropic.claude-3-haiku-20240307-v1:0"  # Example Bedrock model ID
+model_id = "anthropic.claude-haiku-4-5-20251001-v1:0"  # Example Bedrock model ID
 
 # Decorate the model invocation function to auto-capture a trace with tags/metadata
 @traceable(tags=["aws-bedrock", "langsmith", "integration-test"],
@@ -96,7 +96,7 @@ import { BedrockRuntimeClient, ConverseCommand } from "@aws-sdk/client-bedrock-r
 import { traceable } from "langsmith";
 
 const client = new BedrockRuntimeClient({ region: "us-east-1" });
-const modelId = "anthropic.claude-3-haiku-20240307-v1:0";
+const modelId = "anthropic.claude-haiku-4-5-20251001-v1:0";
 
 // Wrap the Bedrock invocation in a traceable function with tags and metadata
 const invokeBedrock = traceable(

src/langsmith/trace-bedrock.mdx

@@ -3,9 +3,9 @@ import { ChatAnthropic } from "@langchain/anthropic";
 import { StateGraph, StateSchema, START } from "@langchain/langgraph";
 import * as z from "zod";
 
-const streamModel = new ChatAnthropic({ model: "claude-3-haiku-20240307" });
+const streamModel = new ChatAnthropic({ model: "claude-haiku-4-5-20251001" });
 const internalModel = new ChatAnthropic({
-  model: "claude-3-haiku-20240307",
+  model: "claude-haiku-4-5-20251001",
 }).withConfig({
   tags: ["nostream"],
 });

src/snippets/code-samples/nostream-tag-js.mdx

@@ -4,8 +4,8 @@ from typing import Any, TypedDict
 from langchain_anthropic import ChatAnthropic
 from langgraph.graph import START, StateGraph
 
-stream_model = ChatAnthropic(model_name="claude-3-haiku-20240307")
-internal_model = ChatAnthropic(model_name="claude-3-haiku-20240307").with_config(
+stream_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001")
+internal_model = ChatAnthropic(model_name="claude-haiku-4-5-20251001").with_config(
     {"tags": ["nostream"]}
 )