diff --git a/src/google/adk/models/anthropic_llm.py b/src/google/adk/models/anthropic_llm.py
index a14c767f23..e8b91fe651 100644
--- a/src/google/adk/models/anthropic_llm.py
+++ b/src/google/adk/models/anthropic_llm.py
@@ -36,6 +36,7 @@
 from anthropic import AsyncAnthropicVertex
 from anthropic import NOT_GIVEN
 from anthropic import NotGiven
+from anthropic import RateLimitError
 from anthropic import types as anthropic_types
 from google.genai import types
 from pydantic import BaseModel
@@ -53,6 +54,28 @@
 logger = logging.getLogger("google_adk." + __name__)
 
 
+_RATE_LIMIT_POSSIBLE_FIX_MESSAGE = """
+On how to mitigate this issue, please refer to:
+
+https://docs.anthropic.com/en/api/errors#http-errors
+"""
+
+
+class _AnthropicRateLimitError(RateLimitError):  # type: ignore[misc]
+  """Represents a rate limit error received from Anthropic."""
+
+  def __init__(self, rate_limit_error: RateLimitError):
+    super().__init__(
+        str(rate_limit_error),
+        response=rate_limit_error.response,
+        body=getattr(rate_limit_error, "body", None),
+    )
+
+  def __str__(self) -> str:
+    base_message = super().__str__()
+    return f"{_RATE_LIMIT_POSSIBLE_FIX_MESSAGE}\n\n{base_message}"
+
+
 @dataclasses.dataclass
 class _ToolUseAccumulator:
   """Accumulates streamed tool_use content block data."""
@@ -402,21 +425,24 @@ async def generate_content_async(
         else NOT_GIVEN
     )
 
-    if not stream:
-      message = await self._anthropic_client.messages.create(
-          model=model_to_use,
-          system=llm_request.config.system_instruction,
-          messages=messages,
-          tools=tools,
-          tool_choice=tool_choice,
-          max_tokens=self.max_tokens,
-      )
-      yield message_to_generate_content_response(message)
-    else:
-      async for response in self._generate_content_streaming(
-          llm_request, messages, tools, tool_choice
-      ):
-        yield response
+    try:
+      if not stream:
+        message = await self._anthropic_client.messages.create(
+            model=model_to_use,
+            system=llm_request.config.system_instruction,
+            messages=messages,
+            tools=tools,
+            tool_choice=tool_choice,
+            max_tokens=self.max_tokens,
+        )
+        yield message_to_generate_content_response(message)
+      else:
+        async for response in self._generate_content_streaming(
+            llm_request, messages, tools, tool_choice
+        ):
+          yield response
+    except RateLimitError as rate_limit_error:
+      raise _AnthropicRateLimitError(rate_limit_error) from rate_limit_error
 
   async def _generate_content_streaming(
       self,
diff --git a/tests/unittests/models/test_anthropic_llm.py b/tests/unittests/models/test_anthropic_llm.py
index fb44d5c8e7..86702bb50d 100644
--- a/tests/unittests/models/test_anthropic_llm.py
+++ b/tests/unittests/models/test_anthropic_llm.py
@@ -20,9 +20,11 @@
 from unittest.mock import AsyncMock
 from unittest.mock import MagicMock
 
+from anthropic import RateLimitError
 from anthropic import types as anthropic_types
 from google.adk import version as adk_version
 from google.adk.models import anthropic_llm
+from google.adk.models.anthropic_llm import _AnthropicRateLimitError
 from google.adk.models.anthropic_llm import AnthropicLlm
 from google.adk.models.anthropic_llm import Claude
 from google.adk.models.anthropic_llm import content_to_message_param
@@ -34,6 +36,7 @@
 from google.genai import version as genai_version
 from google.genai.types import Content
 from google.genai.types import Part
+import httpx
 import pytest
 
 
@@ -1350,3 +1353,52 @@ async def test_non_streaming_does_not_pass_stream_param():
   mock_client.messages.create.assert_called_once()
   _, kwargs = mock_client.messages.create.call_args
   assert "stream" not in kwargs
+
+
+def _make_rate_limit_error() -> RateLimitError:
+  request = httpx.Request("POST", "https://api.anthropic.com/v1/messages")
+  response = httpx.Response(429, request=request)
+  return RateLimitError(
+      "rate limited",
+      response=response,
+      body={"type": "error", "error": {"type": "rate_limit_error"}},
+  )
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_wraps_anthropic_rate_limit_error():
+  llm = AnthropicLlm(model="claude-sonnet-4-20250514")
+  mock_client = MagicMock()
+  mock_client.messages.create = AsyncMock(side_effect=_make_rate_limit_error())
+
+  llm_request = LlmRequest(
+      model="claude-sonnet-4-20250514",
+      contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
+      config=types.GenerateContentConfig(system_instruction="Test"),
+  )
+
+  with mock.patch.object(llm, "_anthropic_client", mock_client):
+    with pytest.raises(_AnthropicRateLimitError) as excinfo:
+      _ = [r async for r in llm.generate_content_async(llm_request)]
+
+  assert "docs.anthropic.com/en/api/errors#http-errors" in str(excinfo.value)
+  assert "rate limited" in str(excinfo.value)
+
+
+@pytest.mark.asyncio
+async def test_streaming_wraps_anthropic_rate_limit_error():
+  llm = AnthropicLlm(model="claude-sonnet-4-20250514")
+  mock_client = MagicMock()
+  mock_client.messages.create = AsyncMock(side_effect=_make_rate_limit_error())
+
+  llm_request = LlmRequest(
+      model="claude-sonnet-4-20250514",
+      contents=[Content(role="user", parts=[Part.from_text(text="Hi")])],
+      config=types.GenerateContentConfig(system_instruction="Test"),
+  )
+
+  with mock.patch.object(llm, "_anthropic_client", mock_client):
+    with pytest.raises(_AnthropicRateLimitError):
+      _ = [
+          r async for r in llm.generate_content_async(llm_request, stream=True)
+      ]