diff --git a/nemoguardrails/actions/llm/utils.py b/nemoguardrails/actions/llm/utils.py
index 5b1ef3d2ce..73e46df7ae 100644
--- a/nemoguardrails/actions/llm/utils.py
+++ b/nemoguardrails/actions/llm/utils.py
@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 import logging
 import re
 from typing import TYPE_CHECKING, Any, Dict, List, NoReturn, Optional, Union, cast
@@ -134,6 +135,10 @@ async def _stream_llm_call(
 
         llm_response_metadata_var.set(accumulated_provider_metadata or None)
 
+        if tool_calls:
+            payload = json.dumps({"tool_calls": [tc.to_dict() for tc in tool_calls]})
+            await handler.push_chunk(payload)
+
         await handler.finish()
 
         llm_call_info = llm_call_info_var.get()
@@ -532,10 +537,10 @@ def get_colang_history(
                 history += f"# The result was {return_value}\n"
             elif event["type"] == "mask_prev_user_message":
                 utterance_to_replace = get_last_user_utterance(events[:idx])
-                # We replace the last user utterance that led to jailbreak rail trigger with a placeholder text
-                split_history = history.rsplit(utterance_to_replace, 1)
-                placeholder_text = "<<<This text is hidden because the assistant should not talk about this.>>>"
-                history = placeholder_text.join(split_history)
+                if utterance_to_replace:
+                    split_history = history.rsplit(utterance_to_replace, 1)
+                    placeholder_text = "<<<This text is hidden because the assistant should not talk about this.>>>"
+                    history = placeholder_text.join(split_history)
 
     elif colang_version == "2.x":
         new_history: List[str] = []
diff --git a/nemoguardrails/llm/anthropic_utils.py b/nemoguardrails/llm/anthropic_utils.py
new file mode 100644
index 0000000000..de05a97266
--- /dev/null
+++ b/nemoguardrails/llm/anthropic_utils.py
@@ -0,0 +1,200 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Conversion utilities between Anthropic Messages API and NeMo ChatMessage formats.
+
+Used by both the server endpoint (messages.py) and the native Anthropic model
+adapter (anthropic_chat.py).
+"""
+
+import json
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from nemoguardrails.types import ChatMessage, Role, ToolCall, ToolCallFunction
+
+
+def anthropic_content_to_text(content: Union[str, List[Dict[str, Any]]]) -> str:
+    if isinstance(content, str):
+        return content
+    parts = []
+    for block in content:
+        if isinstance(block, str):
+            parts.append(block)
+        elif isinstance(block, dict):
+            if block.get("type") == "text":
+                parts.append(block.get("text", ""))
+    return "\n".join(parts) if parts else ""
+
+
+def anthropic_tool_use_to_openai(content_blocks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    tool_calls = []
+    for block in content_blocks:
+        if isinstance(block, dict) and block.get("type") == "tool_use":
+            args = block.get("input", {})
+            tool_calls.append(
+                {
+                    "id": block.get("id", ""),
+                    "type": "function",
+                    "function": {
+                        "name": block.get("name", ""),
+                        "arguments": json.dumps(args) if isinstance(args, dict) else str(args),
+                    },
+                }
+            )
+    return tool_calls
+
+
+def anthropic_to_nemo_messages(
+    system: Optional[Union[str, List[Dict[str, Any]]]] = None,
+    messages: Optional[List[Dict[str, Any]]] = None,
+) -> List[ChatMessage]:
+    result: List[ChatMessage] = []
+
+    if system:
+        system_text = anthropic_content_to_text(system) if not isinstance(system, str) else system
+        result.append(ChatMessage(role=Role.SYSTEM, content=system_text))
+
+    for msg in messages or []:
+        role_str = msg.get("role", "user")
+        content = msg.get("content", "")
+
+        if role_str == "user":
+            if isinstance(content, list):
+                tool_results = [b for b in content if isinstance(b, dict) and b.get("type") == "tool_result"]
+                if tool_results:
+                    text = anthropic_content_to_text(content)
+                    if text:
+                        result.append(ChatMessage(role=Role.USER, content=text))
+                    for tr in tool_results:
+                        tr_content = tr.get("content", "")
+                        tr_text = (
+                            anthropic_content_to_text(tr_content)
+                            if isinstance(tr_content, list)
+                            else str(tr_content)
+                            if tr_content
+                            else ""
+                        )
+                        result.append(
+                            ChatMessage(role=Role.TOOL, content=tr_text, tool_call_id=tr.get("tool_use_id", ""))
+                        )
+                else:
+                    text = anthropic_content_to_text(content)
+                    result.append(ChatMessage(role=Role.USER, content=text))
+            else:
+                text = anthropic_content_to_text(content)
+                result.append(ChatMessage(role=Role.USER, content=text))
+
+        elif role_str == "assistant":
+            if isinstance(content, list):
+                text_parts = []
+                tool_calls = []
+                thinking_blocks = []
+                for block in content:
+                    if isinstance(block, dict):
+                        if block.get("type") == "text":
+                            text_parts.append(block.get("text", ""))
+                        elif block.get("type") == "tool_use":
+                            args = block.get("input", {})
+                            tool_calls.append(
+                                ToolCall(
+                                    id=block.get("id", ""),
+                                    type="function",
+                                    function=ToolCallFunction(
+                                        name=block.get("name", ""),
+                                        arguments=args if isinstance(args, dict) else {},
+                                    ),
+                                )
+                            )
+                        elif block.get("type") in ("thinking", "redacted_thinking"):
+                            thinking_blocks.append(block)
+                result.append(
+                    ChatMessage(
+                        role=Role.ASSISTANT,
+                        content="\n".join(text_parts) if text_parts else "",
+                        tool_calls=tool_calls if tool_calls else None,
+                        reasoning=thinking_blocks if thinking_blocks else None,
+                    )
+                )
+            else:
+                result.append(ChatMessage(role=Role.ASSISTANT, content=str(content) if content else ""))
+
+        elif role_str == "tool_result":
+            tool_use_id = msg.get("tool_use_id", "")
+            text = anthropic_content_to_text(content)
+            result.append(ChatMessage(role=Role.TOOL, content=text, tool_call_id=tool_use_id))
+
+    return result
+
+
+def nemo_to_anthropic_messages(
+    messages: List[ChatMessage],
+) -> Tuple[Optional[str], List[Dict[str, Any]]]:
+    system_text: Optional[str] = None
+    anthropic_messages: List[Dict[str, Any]] = []
+
+    for msg in messages:
+        if msg.role == Role.SYSTEM:
+            system_text = msg.content if isinstance(msg.content, str) else anthropic_content_to_text(msg.content or "")
+            continue
+
+        if msg.role == Role.USER:
+            content: Union[str, List[Dict[str, Any]]]
+            if isinstance(msg.content, list):
+                content = msg.content
+            else:
+                content = str(msg.content) if msg.content else ""
+            anthropic_messages.append({"role": "user", "content": content})
+
+        elif msg.role == Role.ASSISTANT:
+            blocks: List[Dict[str, Any]] = []
+            if msg.reasoning:
+                if isinstance(msg.reasoning, list):
+                    blocks.extend(msg.reasoning)
+                elif isinstance(msg.reasoning, str):
+                    blocks.append({"type": "thinking", "thinking": msg.reasoning})
+            if msg.content:
+                text = str(msg.content) if isinstance(msg.content, str) else anthropic_content_to_text(msg.content)
+                if text:
+                    blocks.append({"type": "text", "text": text})
+            if msg.tool_calls:
+                for tc in msg.tool_calls:
+                    blocks.append(
+                        {
+                            "type": "tool_use",
+                            "id": tc.id,
+                            "name": tc.function.name,
+                            "input": tc.function.arguments,
+                        }
+                    )
+            anthropic_messages.append(
+                {"role": "assistant", "content": blocks if blocks else [{"type": "text", "text": ""}]}
+            )
+
+        elif msg.role == Role.TOOL:
+            content_blocks: List[Dict[str, Any]] = [{"type": "text", "text": str(msg.content) if msg.content else ""}]
+            anthropic_messages.append(
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "tool_result",
+                            "tool_use_id": msg.tool_call_id or "",
+                            "content": content_blocks,
+                        }
+                    ],
+                }
+            )
+
+    return system_text, anthropic_messages
diff --git a/nemoguardrails/llm/clients/anthropic.py b/nemoguardrails/llm/clients/anthropic.py
new file mode 100644
index 0000000000..9b39555f3a
--- /dev/null
+++ b/nemoguardrails/llm/clients/anthropic.py
@@ -0,0 +1,222 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import logging
+from typing import Any, AsyncGenerator, Dict, List, Optional
+
+import httpx
+
+from nemoguardrails.exceptions import (
+    LLMConnectionError,
+    LLMResponseValidationError,
+    LLMTimeoutError,
+)
+from nemoguardrails.llm.clients._errors import raise_for_status
+from nemoguardrails.llm.clients._sse import SSEDecoder
+from nemoguardrails.llm.clients.base import BaseClient, HTTPResponse
+
+log = logging.getLogger(__name__)
+
+
+class AnthropicClient(BaseClient):
+    _ROUTE = "/messages"
+
+    def __init__(
+        self,
+        base_url: str,
+        api_key: Optional[str] = None,
+        *,
+        anthropic_version: str = "2023-06-01",
+        **kwargs: Any,
+    ):
+        custom_headers = dict(kwargs.pop("custom_headers", None) or {})
+        if api_key:
+            custom_headers.setdefault("x-api-key", api_key)
+        custom_headers.setdefault("anthropic-version", anthropic_version)
+
+        super().__init__(base_url=base_url, api_key=None, custom_headers=custom_headers, **kwargs)
+
+    @property
+    def provider_url(self) -> Optional[str]:
+        return self._base_url
+
+    def _build_payload(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        *,
+        max_tokens: int = 4096,
+        system: Optional[str] = None,
+        stop_sequences: Optional[List[str]] = None,
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Dict[str, Any]:
+        payload: Dict[str, Any] = {
+            "model": model,
+            "messages": messages,
+            "max_tokens": max_tokens,
+        }
+        if system:
+            payload["system"] = system
+        if stop_sequences:
+            payload["stop_sequences"] = stop_sequences
+        if stream:
+            payload["stream"] = True
+        payload.update(kwargs)
+        return payload
+
+    async def create_message(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        *,
+        max_tokens: int = 4096,
+        system: Optional[str] = None,
+        stop_sequences: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> HTTPResponse:
+        payload = self._build_payload(
+            model, messages, max_tokens=max_tokens, system=system, stop_sequences=stop_sequences, **kwargs
+        )
+        return await self._apost(self._ROUTE, payload)
+
+    async def stream_message(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        *,
+        max_tokens: int = 4096,
+        system: Optional[str] = None,
+        stop_sequences: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> AsyncGenerator[HTTPResponse, None]:
+        payload = self._build_payload(
+            model,
+            messages,
+            max_tokens=max_tokens,
+            system=system,
+            stop_sequences=stop_sequences,
+            stream=True,
+            **kwargs,
+        )
+        gen = self._apost_stream_anthropic(self._ROUTE, payload)
+        try:
+            async for chunk in gen:
+                yield chunk
+        finally:
+            await gen.aclose()
+
+    async def _apost_stream_anthropic(self, path: str, payload: Dict[str, Any]) -> AsyncGenerator[HTTPResponse, None]:
+        retries_remaining = self._max_retries
+        retries_attempted = 0
+        ctx = self._error_context()
+
+        while True:
+            first_yielded = False
+            try:
+                async with self._client.stream(
+                    "POST",
+                    f"{self._base_url}{path}",
+                    json=payload,
+                    headers=self._build_headers(),
+                    params=self._custom_query or None,
+                ) as response:
+                    if self._should_retry(response.status_code, response.headers) and retries_remaining > 0:
+                        await response.aread()
+                        await self._sleep_for_retry(retries_attempted, response.headers)
+                        retries_remaining -= 1
+                        retries_attempted += 1
+                        continue
+
+                    if response.status_code >= 400:
+                        await response.aread()
+                        raise_for_status(response.status_code, response.text, response.headers, ctx)
+
+                    response_headers = dict(response.headers)
+                    response_status = response.status_code
+                    decoder = SSEDecoder()
+                    async for line in response.aiter_lines():
+                        sse = decoder.decode(line)
+                        if sse is None:
+                            continue
+
+                        if sse.event == "message_stop":
+                            return
+
+                        if sse.event == "ping":
+                            continue
+
+                        try:
+                            parsed = sse.json()
+                        except json.JSONDecodeError as err:
+                            raise LLMResponseValidationError(
+                                f"Malformed SSE chunk: {sse.data[:200]!r}: {err}",
+                                response_data=None,
+                                **ctx.as_kwargs(),
+                            ) from err
+
+                        if isinstance(parsed, dict) and parsed.get("type") == "error":
+                            error_msg = parsed.get("error", {})
+                            raise LLMResponseValidationError(
+                                f"Anthropic stream error: {error_msg}",
+                                response_data=parsed,
+                                **ctx.as_kwargs(),
+                            )
+
+                        parsed["_event_type"] = sse.event
+                        first_yielded = True
+                        yield HTTPResponse(body=parsed, headers=response_headers, status_code=response_status)
+
+                    sse = decoder.decode("")
+                    if sse is not None and sse.event != "message_stop":
+                        try:
+                            parsed = sse.json()
+                        except json.JSONDecodeError as err:
+                            raise LLMResponseValidationError(
+                                f"Malformed trailing SSE chunk: {sse.data[:200]!r}: {err}",
+                                response_data=None,
+                                **ctx.as_kwargs(),
+                            ) from err
+                        parsed["_event_type"] = sse.event
+                        yield HTTPResponse(body=parsed, headers=response_headers, status_code=response_status)
+                    return
+
+            except httpx.TimeoutException as err:
+                if first_yielded or retries_remaining <= 0:
+                    raise LLMTimeoutError(0, f"Request timed out: {err}", **ctx.as_kwargs()) from err
+                await self._sleep_for_retry(retries_attempted)
+                retries_remaining -= 1
+                retries_attempted += 1
+                continue
+            except httpx.NetworkError as err:
+                if first_yielded or retries_remaining <= 0:
+                    raise LLMConnectionError(0, f"Connection error: {err}", **ctx.as_kwargs()) from err
+                await self._sleep_for_retry(retries_attempted)
+                retries_remaining -= 1
+                retries_attempted += 1
+                continue
+            except RuntimeError as err:
+                from nemoguardrails.llm.clients.base import _is_stale_loop_error
+
+                if not _is_stale_loop_error(err):
+                    raise
+                if first_yielded or retries_remaining <= 0:
+                    raise LLMConnectionError(0, f"Stale event loop: {err}", **ctx.as_kwargs()) from err
+                log.warning("Retrying after stale event loop binding: %s", err)
+                await self._sleep_for_retry(retries_attempted)
+                retries_remaining -= 1
+                retries_attempted += 1
+                continue
diff --git a/nemoguardrails/llm/frameworks/default.py b/nemoguardrails/llm/frameworks/default.py
index bce44d3249..ac0c9f7cb8 100644
--- a/nemoguardrails/llm/frameworks/default.py
+++ b/nemoguardrails/llm/frameworks/default.py
@@ -18,8 +18,11 @@
 import os
 from typing import Any, Dict, List, Optional
 
+from nemoguardrails.llm.clients.anthropic import AnthropicClient
+from nemoguardrails.llm.clients.base import BaseClient
 from nemoguardrails.llm.clients.openai_compatible import OpenAICompatibleClient
 from nemoguardrails.llm.constants import AZURE_PROVIDERS
+from nemoguardrails.llm.models.anthropic_chat import AnthropicChatModel
 from nemoguardrails.llm.models.openai_chat import OpenAIChatModel
 from nemoguardrails.types import LLMModel
 
@@ -30,6 +33,7 @@
     "nim": "https://integrate.api.nvidia.com/v1",
     "nvidia_ai_endpoints": "https://integrate.api.nvidia.com/v1",
     "ollama": "http://localhost:11434/v1",
+    "anthropic": "https://api.anthropic.com/v1",
 }
 
 _API_KEY_ENV_VARS: Dict[str, str] = {
@@ -38,6 +42,7 @@
     "nvidia_ai_endpoints": "NVIDIA_API_KEY",
     "azure": "AZURE_OPENAI_API_KEY",
     "azure_openai": "AZURE_OPENAI_API_KEY",
+    "anthropic": "ANTHROPIC_API_KEY",
 }
 
 _UNSET: Any = object()
@@ -65,7 +70,7 @@ def _resolve_api_key(provider_name: str) -> Optional[str]:
 class DefaultFramework:
     def __init__(self):
         self._providers: Dict[str, Any] = {}
-        self._clients: Dict[tuple, OpenAICompatibleClient] = {}
+        self._clients: Dict[tuple, BaseClient] = {}
 
     def _get_or_create_client(
         self,
@@ -76,7 +81,7 @@ def _get_or_create_client(
         max_retries: Optional[int],
         default_headers: Optional[Dict[str, str]],
         default_query: Optional[Dict[str, Any]],
-    ) -> OpenAICompatibleClient:
+    ) -> BaseClient:
         key = (
             base_url,
             api_key or "",
@@ -113,6 +118,9 @@ def create_model(
         if provider_name in self._providers:
             return self._providers[provider_name](model=model_name, **kwargs)
 
+        if provider_name == "anthropic":
+            return self._create_anthropic_model(model_name, provider_name, kwargs)
+
         if provider_name in AZURE_PROVIDERS:
             self._prepare_azure_kwargs(provider_name, kwargs)
 
@@ -134,9 +142,53 @@ def create_model(
         client = self._get_or_create_client(
             base_url, api_key, timeout, connect_timeout, max_retries, default_headers, default_query
         )
+        assert isinstance(client, OpenAICompatibleClient)
 
         return OpenAIChatModel(client=client, model=model_name, provider_name=provider_name, **kwargs)
 
+    def _create_anthropic_model(
+        self, model_name: str, provider_name: str, kwargs: Dict[str, Any]
+    ) -> AnthropicChatModel:
+        base_url = kwargs.pop("base_url", None) or _resolve_base_url(provider_name)
+
+        api_key = kwargs.pop("api_key", _UNSET)
+        if api_key is _UNSET:
+            api_key = _resolve_api_key(provider_name)
+
+        timeout = kwargs.pop("timeout", None)
+        connect_timeout = kwargs.pop("connect_timeout", None)
+        max_retries = kwargs.pop("max_retries", None)
+        default_headers = kwargs.pop("default_headers", None)
+        default_query = kwargs.pop("default_query", None)
+        anthropic_version = kwargs.pop("anthropic_version", "2023-06-01")
+
+        pool_key = (
+            base_url,
+            api_key or "",
+            timeout,
+            connect_timeout,
+            max_retries,
+            json.dumps(default_headers or {}, sort_keys=True, default=str),
+            json.dumps(default_query or {}, sort_keys=True, default=str),
+        )
+        if pool_key not in self._clients:
+            client_kwargs: Dict[str, Any] = {"anthropic_version": anthropic_version}
+            if timeout is not None:
+                client_kwargs["timeout"] = timeout
+            if connect_timeout is not None:
+                client_kwargs["connect_timeout"] = connect_timeout
+            if max_retries is not None:
+                client_kwargs["max_retries"] = max_retries
+            if default_headers is not None:
+                client_kwargs["custom_headers"] = default_headers
+            if default_query is not None:
+                client_kwargs["custom_query"] = default_query
+            self._clients[pool_key] = AnthropicClient(base_url=base_url, api_key=api_key, **client_kwargs)
+
+        client = self._clients[pool_key]
+        assert isinstance(client, AnthropicClient)
+        return AnthropicChatModel(client=client, model=model_name, provider_name=provider_name, **kwargs)
+
     def _prepare_azure_kwargs(self, provider_name: str, kwargs: Dict[str, Any]) -> None:
         """Reshape kwargs in place for the Azure preset.
 
diff --git a/nemoguardrails/llm/models/anthropic_chat.py b/nemoguardrails/llm/models/anthropic_chat.py
new file mode 100644
index 0000000000..572480b869
--- /dev/null
+++ b/nemoguardrails/llm/models/anthropic_chat.py
@@ -0,0 +1,286 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+from typing import Any, AsyncIterator, Dict, List, Optional, Union
+
+from nemoguardrails.exceptions import LLMClientError, LLMResponseValidationError
+from nemoguardrails.llm.anthropic_utils import nemo_to_anthropic_messages
+from nemoguardrails.llm.clients.anthropic import AnthropicClient
+from nemoguardrails.llm.clients.base import HTTPResponse
+from nemoguardrails.types import (
+    ChatMessage,
+    FinishReason,
+    LLMResponse,
+    LLMResponseChunk,
+    ToolCall,
+    ToolCallFunction,
+    UsageInfo,
+)
+
+_FINISH_REASON_MAP: Dict[str, FinishReason] = {
+    "end_turn": "stop",
+    "max_tokens": "length",
+    "tool_use": "tool_calls",
+    "stop_sequence": "stop",
+}
+
+
+class AnthropicChatModel:
+    def __init__(
+        self,
+        client: AnthropicClient,
+        model: str,
+        *,
+        provider_name: Optional[str] = None,
+        **kwargs: Any,
+    ):
+        self._client = client
+        self._model = model
+        self._provider_name = provider_name or "anthropic"
+        self._default_kwargs = kwargs
+
+    @property
+    def model_name(self) -> str:
+        return self._model
+
+    @property
+    def provider_name(self) -> str:
+        return self._provider_name
+
+    @property
+    def provider_url(self) -> Optional[str]:
+        return self._client.provider_url
+
+    def _enrich(self, exc: LLMClientError) -> LLMClientError:
+        exc.provider_name = self._provider_name
+        exc.model_name = self._model
+        exc.base_url = self._client.provider_url
+        return exc
+
+    def _prepare_params(self, stop: Optional[List[str]], kwargs: Dict[str, Any]) -> Dict[str, Any]:
+        merged = {**self._default_kwargs, **kwargs}
+        if stop is not None:
+            merged["stop_sequences"] = stop
+        return merged
+
+    def _to_messages(self, prompt: Union[str, List[ChatMessage]]) -> tuple[Optional[str], List[Dict[str, Any]]]:
+        if isinstance(prompt, str):
+            return None, [{"role": "user", "content": prompt}]
+        return nemo_to_anthropic_messages(prompt)
+
+    async def generate_async(
+        self,
+        prompt: Union[str, List[ChatMessage]],
+        *,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        system, messages = self._to_messages(prompt)
+        params = self._prepare_params(stop, kwargs)
+        try:
+            response = await self._client.create_message(self._model, messages, system=system, **params)
+        except LLMClientError as exc:
+            raise self._enrich(exc)
+        return self._parse_response(response)
+
+    async def stream_async(
+        self,
+        prompt: Union[str, List[ChatMessage]],
+        *,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[LLMResponseChunk]:
+        system, messages = self._to_messages(prompt)
+        params = self._prepare_params(stop, kwargs)
+
+        tool_call_acc: Dict[int, Dict[str, Any]] = {}
+        current_block_idx = -1
+        current_block_type: Optional[str] = None
+
+        gen = self._client.stream_message(self._model, messages, system=system, **params)
+        try:
+            async for chunk_response in gen:
+                body = chunk_response.body
+                event_type = body.pop("_event_type", "")
+
+                if event_type == "message_start":
+                    continue
+
+                if event_type == "content_block_start":
+                    current_block_idx = body.get("index", current_block_idx + 1)
+                    block = body.get("content_block", {})
+                    current_block_type = block.get("type")
+                    if current_block_type == "tool_use":
+                        tool_call_acc[current_block_idx] = {
+                            "id": block.get("id", ""),
+                            "name": block.get("name", ""),
+                            "arguments_buffer": "",
+                        }
+                    continue
+
+                if event_type == "content_block_stop":
+                    current_block_type = None
+                    continue
+
+                if event_type == "content_block_delta":
+                    delta = body.get("delta", {})
+                    delta_type = delta.get("type")
+
+                    if delta_type == "text_delta":
+                        yield LLMResponseChunk(delta_content=delta.get("text"))
+                        continue
+
+                    if delta_type == "thinking_delta":
+                        yield LLMResponseChunk(delta_reasoning=delta.get("thinking"))
+                        continue
+
+                    if delta_type == "input_json_delta":
+                        partial_json = delta.get("partial_json", "")
+                        if current_block_idx in tool_call_acc and partial_json:
+                            tool_call_acc[current_block_idx]["arguments_buffer"] += partial_json
+                        continue
+
+                if event_type == "message_delta":
+                    delta = body.get("delta", {})
+                    raw_finish = delta.get("stop_reason")
+                    finish_reason = _FINISH_REASON_MAP.get(raw_finish, "other") if raw_finish else None
+
+                    usage = None
+                    raw_usage = body.get("usage")
+                    if raw_usage:
+                        usage = UsageInfo(
+                            input_tokens=raw_usage.get("input_tokens", 0),
+                            output_tokens=raw_usage.get("output_tokens", 0),
+                            total_tokens=(raw_usage.get("input_tokens", 0) + raw_usage.get("output_tokens", 0)),
+                        )
+
+                    chunk = LLMResponseChunk(finish_reason=finish_reason, usage=usage)
+                    if finish_reason == "tool_calls" and tool_call_acc:
+                        chunk.delta_tool_calls = self._finalize_tool_calls(tool_call_acc)
+                    yield chunk
+                    continue
+
+        except LLMClientError as exc:
+            raise self._enrich(exc)
+        finally:
+            await gen.aclose()
+
+    @staticmethod
+    def _finalize_tool_calls(acc: Dict[int, Dict[str, Any]]) -> List[ToolCall]:
+        result = []
+        for idx in sorted(acc.keys()):
+            entry = acc[idx]
+            raw_args = entry["arguments_buffer"]
+            try:
+                args_dict = json.loads(raw_args) if raw_args else {}
+            except json.JSONDecodeError:
+                args_dict = {}
+            result.append(
+                ToolCall(
+                    id=entry["id"],
+                    type="function",
+                    function=ToolCallFunction(
+                        name=entry["name"],
+                        arguments=args_dict,
+                    ),
+                )
+            )
+        return result
+
+    def _parse_response(self, response: HTTPResponse) -> LLMResponse:
+        data = response.body
+        self._validate_response(data)
+
+        content_blocks = data.get("content", [])
+        text_parts = []
+        reasoning_parts = []
+        thinking_blocks = []
+        tool_calls = []
+
+        for block in content_blocks:
+            block_type = block.get("type")
+            if block_type == "text":
+                text_parts.append(block.get("text", ""))
+            elif block_type == "thinking":
+                reasoning_parts.append(block.get("thinking", ""))
+                thinking_blocks.append(block)
+            elif block_type == "redacted_thinking":
+                thinking_blocks.append(block)
+            elif block_type == "tool_use":
+                args = block.get("input", {})
+                tool_calls.append(
+                    ToolCall(
+                        id=block.get("id", ""),
+                        type="function",
+                        function=ToolCallFunction(
+                            name=block.get("name", ""),
+                            arguments=args if isinstance(args, dict) else {},
+                        ),
+                    )
+                )
+
+        raw_finish = data.get("stop_reason")
+        finish_reason = _FINISH_REASON_MAP.get(raw_finish, "other") if raw_finish else None
+
+        usage = None
+        raw_usage = data.get("usage")
+        if raw_usage:
+            input_tokens = raw_usage.get("input_tokens", 0)
+            output_tokens = raw_usage.get("output_tokens", 0)
+            usage = UsageInfo(
+                input_tokens=input_tokens,
+                output_tokens=output_tokens,
+                total_tokens=input_tokens + output_tokens,
+                cached_tokens=raw_usage.get("cache_read_input_tokens"),
+            )
+
+        provider_metadata: Dict[str, Any] = {}
+        if response.headers:
+            provider_metadata["response_headers"] = dict(response.headers)
+        if thinking_blocks:
+            provider_metadata["thinking_blocks"] = thinking_blocks
+
+        return LLMResponse(
+            content="\n".join(text_parts) if text_parts else "",
+            reasoning="\n".join(reasoning_parts) if reasoning_parts else None,
+            tool_calls=tool_calls if tool_calls else None,
+            model=data.get("model"),
+            finish_reason=finish_reason,
+            request_id=data.get("id"),
+            usage=usage,
+            provider_metadata=provider_metadata or None,
+        )
+
+    def _validate_response(self, data: Any) -> None:
+        ctx = dict(
+            model_name=self.model_name,
+            provider_name=self.provider_name,
+            base_url=self.provider_url,
+        )
+        if not isinstance(data, dict):
+            raise LLMResponseValidationError(
+                f"Expected dict response, got {type(data).__name__}", response_data=None, **ctx
+            )
+        if data.get("type") == "error":
+            error = data.get("error", {})
+            raise LLMResponseValidationError(
+                f"Anthropic error: {error.get('message', str(error))}", response_data=data, **ctx
+            )
+        content = data.get("content")
+        if not isinstance(content, list):
+            raise LLMResponseValidationError(
+                f"Missing or invalid 'content' in response: {list(data.keys())}", response_data=data, **ctx
+            )
diff --git a/nemoguardrails/llm/models/openai_chat.py b/nemoguardrails/llm/models/openai_chat.py
index 5d3a381399..a212303a7a 100644
--- a/nemoguardrails/llm/models/openai_chat.py
+++ b/nemoguardrails/llm/models/openai_chat.py
@@ -219,7 +219,7 @@ def _parse_response(self, response: HTTPResponse) -> LLMResponse:
         choice = data["choices"][0]
 
         content = message.get("content") or ""
-        reasoning = message.get("reasoning_content")
+        reasoning = message.get("reasoning_content") or message.get("reasoning")
 
         tool_calls = None
         raw_tool_calls = message.get("tool_calls")
@@ -287,7 +287,7 @@ def _parse_chunk(self, response: HTTPResponse) -> Optional[LLMResponseChunk]:
         delta = choice.get("delta", {})
 
         content = delta.get("content")
-        reasoning = delta.get("reasoning_content")
+        reasoning = delta.get("reasoning_content") or delta.get("reasoning")
         raw_finish = choice.get("finish_reason")
         finish_reason = _FINISH_REASON_MAP.get(raw_finish, "other") if raw_finish else None
 
diff --git a/nemoguardrails/rails/llm/llmrails.py b/nemoguardrails/rails/llm/llmrails.py
index 3c59e88cb8..ea821ba1e9 100644
--- a/nemoguardrails/rails/llm/llmrails.py
+++ b/nemoguardrails/rails/llm/llmrails.py
@@ -1179,7 +1179,6 @@ async def generate_async(
         # If there is a streaming handler, we make sure we close it now
         streaming_handler = streaming_handler_var.get()
         if streaming_handler:
-            # print("Closing the stream handler explicitly")
             await streaming_handler.push_chunk(END_OF_STREAM)
 
         # IF tracing is enabled we need to set GenerationLog attrs
diff --git a/nemoguardrails/server/api.py b/nemoguardrails/server/api.py
index 002fca70ae..848efcdadc 100644
--- a/nemoguardrails/server/api.py
+++ b/nemoguardrails/server/api.py
@@ -353,8 +353,12 @@ def _inject_model(config: RailsConfig, model_name: str) -> RailsConfig:
     """Inject the request's model into a RailsConfig using env-based engine/base_url."""
     engine = os.environ.get("MAIN_MODEL_ENGINE")
     if not engine:
-        engine = "openai"
-        log.warning("MAIN_MODEL_ENGINE not set, defaulting to 'openai'. ")
+        existing_main = next((m for m in config.models if m.type == "main"), None)
+        if existing_main:
+            engine = existing_main.engine
+        else:
+            engine = "openai"
+            log.warning("MAIN_MODEL_ENGINE not set, defaulting to 'openai'. ")
     parameters = {}
     base_url = os.environ.get("MAIN_MODEL_BASE_URL")
     if base_url:
@@ -874,6 +878,13 @@ class GuardrailsConfigurationError(Exception):
 # register_exception(app)
 
 
+try:
+    from nemoguardrails.server.messages import router as messages_router
+
+    app.include_router(messages_router)
+except ImportError:
+    log.debug("Anthropic Messages API endpoints not available (anthropic package not installed)")
+
 if not app.disable_chat_ui and mount_chainlit is not None:
     chainlit_app_path = os.path.join(os.path.dirname(__file__), "app.py")
     mount_chainlit(app=app, target=chainlit_app_path, path="/chat")
diff --git a/nemoguardrails/server/messages.py b/nemoguardrails/server/messages.py
new file mode 100644
index 0000000000..867ee8fcf7
--- /dev/null
+++ b/nemoguardrails/server/messages.py
@@ -0,0 +1,438 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Anthropic Messages API endpoints for the NeMo Guardrails server.
+
+Provides /v1/messages (guardrailed inference proxy) and /v1/messages/checks
+(guardrail-only analysis without proxying inference).
+"""
+
+import asyncio
+import json
+import logging
+import uuid
+from typing import Any, Dict, List, Optional
+
+from fastapi import APIRouter, HTTPException, Request
+from starlette.responses import StreamingResponse
+
+from nemoguardrails import LLMRails, RailsConfig
+from nemoguardrails.llm.anthropic_utils import (
+    anthropic_content_to_text,
+    anthropic_to_nemo_messages,
+    anthropic_tool_use_to_openai,
+)
+from nemoguardrails.rails.llm.options import GenerationResponse, RailStatus
+from nemoguardrails.server.api import (
+    _get_rails,
+    _inject_model,
+    api_request_headers,
+    registered_loggers,
+)
+from nemoguardrails.server.schemas.anthropic import (
+    AnthropicMessagesRequest,
+    GuardrailsMessagesCheckRequest,
+    GuardrailsMessagesRequest,
+)
+from nemoguardrails.server.schemas.openai import GuardrailCheckResponse
+
+log = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+# =============================================================================
+# Anthropic format helpers
+# =============================================================================
+
+
+def _build_anthropic_message_response(
+    content: str,
+    model: str,
+    *,
+    stop_reason: str = "end_turn",
+    input_tokens: int = 0,
+    output_tokens: int = 0,
+    thinking_blocks: Optional[List[Dict[str, Any]]] = None,
+    tool_calls: Optional[List[Dict[str, Any]]] = None,
+    guardrails_data: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    content_blocks: List[Dict[str, Any]] = []
+    if thinking_blocks:
+        content_blocks.extend(thinking_blocks)
+    if content:
+        content_blocks.append({"type": "text", "text": content})
+    if tool_calls:
+        for tc in tool_calls:
+            func = tc.get("function", {})
+            content_blocks.append(
+                {
+                    "type": "tool_use",
+                    "id": tc.get("id", f"toolu_{uuid.uuid4().hex[:24]}"),
+                    "name": func.get("name", ""),
+                    "input": func.get("arguments", {}),
+                }
+            )
+    if not content_blocks:
+        content_blocks.append({"type": "text", "text": ""})
+
+    if tool_calls:
+        stop_reason = "tool_use"
+
+    response: Dict[str, Any] = {
+        "id": f"msg_{uuid.uuid4().hex[:24]}",
+        "type": "message",
+        "role": "assistant",
+        "content": content_blocks,
+        "model": model,
+        "stop_reason": stop_reason,
+        "stop_sequence": None,
+        "usage": {
+            "input_tokens": input_tokens,
+            "output_tokens": output_tokens,
+        },
+    }
+    if guardrails_data is not None:
+        response["guardrails"] = guardrails_data
+    return response
+
+
+def _build_error_message_response(
+    model: str,
+    error_message: str,
+) -> Dict[str, Any]:
+    return _build_anthropic_message_response(
+        content=error_message,
+        model=model,
+        stop_reason="end_turn",
+        guardrails_data={"error": error_message},
+    )
+
+
+async def _format_anthropic_sse(
+    stream_iterator,
+    model: str,
+) -> Any:
+    msg_id = f"msg_{uuid.uuid4().hex[:24]}"
+
+    yield f"event: message_start\ndata: {json.dumps({'type': 'message_start', 'message': {'id': msg_id, 'type': 'message', 'role': 'assistant', 'content': [], 'model': model, 'stop_reason': None, 'stop_sequence': None, 'usage': {'input_tokens': 0, 'output_tokens': 0}}})}\n\n"
+
+    yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}})}\n\n"
+
+    total_output_tokens = 0
+    tool_calls_data: Optional[List[Dict[str, Any]]] = None
+
+    async for chunk in stream_iterator:
+        if isinstance(chunk, str):
+            try:
+                parsed = json.loads(chunk)
+            except (json.JSONDecodeError, ValueError):
+                parsed = None
+            if parsed and isinstance(parsed, dict) and "tool_calls" in parsed:
+                tool_calls_data = parsed["tool_calls"]
+                continue
+            text = chunk
+        elif isinstance(chunk, dict):
+            if "tool_calls" in chunk:
+                tool_calls_data = chunk["tool_calls"]
+                continue
+            text = chunk.get("content", chunk.get("text", ""))
+        else:
+            text = str(chunk)
+
+        if text:
+            total_output_tokens += max(1, len(text) // 4)
+            yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': text}})}\n\n"
+
+    yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n"
+
+    if tool_calls_data:
+        for i, tc in enumerate(tool_calls_data):
+            block_index = i + 1
+            func = tc.get("function", {})
+            tool_id = tc.get("id", f"toolu_{uuid.uuid4().hex[:24]}")
+            tool_name = func.get("name", "")
+            raw_args = func.get("arguments", {})
+            if isinstance(raw_args, str):
+                try:
+                    args_dict = json.loads(raw_args)
+                except (json.JSONDecodeError, ValueError):
+                    args_dict = {}
+            else:
+                args_dict = raw_args
+            args_json = json.dumps(args_dict)
+
+            yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': block_index, 'content_block': {'type': 'tool_use', 'id': tool_id, 'name': tool_name, 'input': {}}})}\n\n"
+
+            yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': block_index, 'delta': {'type': 'input_json_delta', 'partial_json': args_json}})}\n\n"
+
+            yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': block_index})}\n\n"
+
+            total_output_tokens += max(1, len(args_json) // 4)
+
+    stop_reason = "tool_use" if tool_calls_data else "end_turn"
+    yield f"event: message_delta\ndata: {json.dumps({'type': 'message_delta', 'delta': {'stop_reason': stop_reason, 'stop_sequence': None}, 'usage': {'output_tokens': total_output_tokens}})}\n\n"
+
+    yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n"
+
+
+# =============================================================================
+# Message conversion for checks
+# =============================================================================
+
+
+def _anthropic_messages_to_check_format(
+    system: Optional[Any],
+    messages: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    result: List[Dict[str, Any]] = []
+
+    if system:
+        text = anthropic_content_to_text(system) if not isinstance(system, str) else system
+        result.append({"role": "system", "content": text})
+
+    for msg in messages:
+        role = msg.get("role", "user")
+        content = msg.get("content", "")
+
+        if role == "user":
+            result.append({"role": "user", "content": anthropic_content_to_text(content)})
+
+        elif role == "assistant":
+            if isinstance(content, list):
+                tool_calls = anthropic_tool_use_to_openai(content)
+                text = anthropic_content_to_text(content)
+                entry: Dict[str, Any] = {"role": "assistant", "content": text}
+                if tool_calls:
+                    entry["tool_calls"] = tool_calls
+                result.append(entry)
+            else:
+                result.append({"role": "assistant", "content": str(content) if content else ""})
+
+        elif role == "tool_result":
+            result.append(
+                {
+                    "role": "tool",
+                    "content": anthropic_content_to_text(content),
+                    "tool_call_id": msg.get("tool_use_id", ""),
+                }
+            )
+
+    return result
+
+
+# =============================================================================
+# /v1/messages endpoint
+# =============================================================================
+
+
+@router.post("/v1/messages")
+async def messages_endpoint(body: GuardrailsMessagesRequest, request: Request):
+    """Anthropic Messages API compatible endpoint with guardrails.
+
+    Accepts Anthropic-format requests, applies guardrails, and returns
+    Anthropic-format responses (both streaming and non-streaming).
+    """
+    log.info("Got messages request for config %s", body.guardrails.config_id)
+    for logger in registered_loggers:
+        asyncio.get_event_loop().create_task(logger({"endpoint": "/v1/messages", "body": body.model_dump_json()}))
+
+    api_request_headers.set(request.headers)
+
+    config_ids = body.guardrails.config_ids
+    if not config_ids:
+        if request.app.default_config_id:
+            config_ids = [request.app.default_config_id]
+        else:
+            raise HTTPException(
+                status_code=422,
+                detail="No guardrails config_id provided and server has no default configuration",
+            )
+
+    try:
+        llm_rails = await _get_rails(config_ids, model_name=body.model)
+    except ValueError as ex:
+        log.exception(ex)
+        return _build_error_message_response(
+            model=body.model,
+            error_message=f"Could not load the {config_ids} guardrails configuration.",
+        )
+
+    try:
+        nemo_messages = anthropic_to_nemo_messages(body.system, body.messages)
+        messages_dicts = [m.to_dict() for m in nemo_messages]
+
+        if body.guardrails.context:
+            messages_dicts.insert(0, {"role": "context", "content": body.guardrails.context})
+
+        generation_options = body.guardrails.options
+
+        if generation_options.llm_params is None:
+            generation_options.llm_params = {}
+
+        # the following fields are all superseded by NeMo Guardrails in some way, and so shouldn't be auto-forwarded
+        _FIELDS_NOT_FORWARDED = {"messages", "model", "system", "stream"}
+        for field in AnthropicMessagesRequest.model_fields:
+            if field in _FIELDS_NOT_FORWARDED:
+                continue
+            value = getattr(body, field)
+            if value is not None:
+                generation_options.llm_params[field] = value
+
+        if body.stream:
+            stream_iterator = llm_rails.stream_async(
+                messages=messages_dicts,
+                options=generation_options,
+                state=body.guardrails.state,
+            )
+
+            return StreamingResponse(
+                _format_anthropic_sse(stream_iterator, model=body.model),
+                media_type="text/event-stream",
+            )
+        else:
+            res = await llm_rails.generate_async(
+                messages=messages_dicts,
+                options=generation_options,
+                state=body.guardrails.state,
+            )
+
+            if isinstance(res, GenerationResponse):
+                if isinstance(res.response, str):
+                    content = res.response
+                elif res.response:
+                    content = res.response[0].get("content", "")
+                else:
+                    content = ""
+                guardrails_data = None
+                if res.log:
+                    guardrails_data = {
+                        "log": res.log.model_dump() if hasattr(res.log, "model_dump") else {},
+                    }
+
+                thinking_blocks = None
+                if res.llm_metadata and "thinking_blocks" in res.llm_metadata:
+                    thinking_blocks = res.llm_metadata["thinking_blocks"]
+                elif res.reasoning_content:
+                    thinking_blocks = [{"type": "thinking", "thinking": res.reasoning_content}]
+
+                return _build_anthropic_message_response(
+                    content=content,
+                    model=body.model,
+                    thinking_blocks=thinking_blocks,
+                    tool_calls=res.tool_calls,
+                    guardrails_data=guardrails_data,
+                )
+            else:
+                content = ""
+                if isinstance(res, dict):
+                    content = res.get("content", "")
+                elif isinstance(res, str):
+                    content = res
+                return _build_anthropic_message_response(
+                    content=content,
+                    model=body.model,
+                )
+
+    except HTTPException:
+        raise
+    except Exception as ex:
+        log.exception(ex)
+        return _build_error_message_response(
+            model=body.model,
+            error_message="Internal server error",
+        )
+
+
+# =============================================================================
+# /v1/messages/checks endpoint
+# =============================================================================
+
+
+def _map_rail_status(status: RailStatus) -> str:
+    return status.value
+
+
+@router.post(
+    "/v1/messages/checks",
+    response_model=GuardrailCheckResponse,
+    response_model_exclude_none=True,
+)
+async def messages_check_endpoint(body: GuardrailsMessagesCheckRequest, request: Request):
+    """Check Anthropic Messages against guardrails without generating LLM responses.
+
+    Mirrors /v1/checks but accepts Anthropic Messages API format requests.
+    Returns the same GuardrailCheckResponse schema.
+    """
+    api_request_headers.set(request.headers)
+
+    check_messages = _anthropic_messages_to_check_format(body.system, body.messages)
+
+    if not check_messages:
+        raise HTTPException(status_code=422, detail="messages must be non-empty")
+
+    config_ids = None
+    config = body.guardrails.config
+
+    if isinstance(config, dict):
+        try:
+            rails_config = RailsConfig.from_content(config=config)
+            if body.model:
+                rails_config = _inject_model(rails_config, body.model)
+            llm_rails = LLMRails(config=rails_config, verbose=True)
+        except Exception as ex:
+            log.exception(ex)
+            raise HTTPException(status_code=422, detail=f"Invalid inline config: {ex}")
+    else:
+        if isinstance(config, str):
+            config_ids = [config]
+        elif body.guardrails.config_ids:
+            config_ids = list(body.guardrails.config_ids)
+        elif request.app.default_config_id:
+            config_ids = [request.app.default_config_id]
+        else:
+            raise HTTPException(
+                status_code=422,
+                detail="No guardrails config_id provided and server has no default configuration",
+            )
+        try:
+            llm_rails = await _get_rails(config_ids, model_name=body.model)
+        except ValueError as ex:
+            log.exception(ex)
+            raise HTTPException(status_code=422, detail=str(ex))
+
+    if llm_rails.config.colang_version != "1.0":
+        raise HTTPException(
+            status_code=422,
+            detail="check_async does not support Colang 2.0 configurations.",
+        )
+
+    try:
+        if body.guardrails.context:
+            check_messages.insert(0, {"role": "context", "content": body.guardrails.context})
+
+        result = await llm_rails.check_async(messages=check_messages)
+
+        return GuardrailCheckResponse(
+            status=_map_rail_status(result.status),
+            content=result.content,
+            rail=result.rail,
+        )
+
+    except HTTPException:
+        raise
+    except Exception as ex:
+        log.exception(ex)
+        raise HTTPException(status_code=500, detail="Internal server error")
diff --git a/nemoguardrails/server/schemas/anthropic.py b/nemoguardrails/server/schemas/anthropic.py
new file mode 100644
index 0000000000..2316c628ea
--- /dev/null
+++ b/nemoguardrails/server/schemas/anthropic.py
@@ -0,0 +1,135 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Anthropic Messages API schema definitions for the NeMo Guardrails server."""
+
+from typing import Any, Dict, List, Optional, Union
+
+from anthropic.types import Message
+from pydantic import BaseModel, Field
+
+from nemoguardrails.server.schemas.openai import (
+    GuardrailCheckDataInput,
+    GuardrailsDataInput,
+    GuardrailsDataOutput,
+)
+
+
+class AnthropicMessagesRequest(BaseModel):
+    """Anthropic Messages API request parameters."""
+
+    model: str = Field(
+        ...,
+        description="The model to use (e.g. 'claude-opus-4-8').",
+    )
+    max_tokens: int = Field(
+        ...,
+        description="The maximum number of tokens to generate.",
+    )
+    messages: List[Dict[str, Any]] = Field(
+        ...,
+        description="Input messages. Each message has a role and content.",
+    )
+    system: Optional[Union[str, List[Dict[str, Any]]]] = Field(
+        default=None,
+        description="System prompt (string or array of content blocks).",
+    )
+    stream: Optional[bool] = Field(
+        default=False,
+        description="Whether to stream the response using SSE.",
+    )
+    temperature: Optional[float] = Field(
+        default=None,
+        description="Sampling temperature (0.0 to 1.0).",
+    )
+    top_p: Optional[float] = Field(
+        default=None,
+        description="Nucleus sampling parameter.",
+    )
+    top_k: Optional[int] = Field(
+        default=None,
+        description="Top-k sampling parameter.",
+    )
+    stop_sequences: Optional[List[str]] = Field(
+        default=None,
+        description="Custom text sequences that cause the model to stop generating.",
+    )
+    metadata: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Request metadata (e.g. user_id for abuse detection).",
+    )
+    tools: Optional[List[Dict[str, Any]]] = Field(
+        default=None,
+        description="Tool definitions available to the model.",
+    )
+    tool_choice: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="How the model should use the provided tools.",
+    )
+    thinking: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Extended thinking configuration.",
+    )
+    cache_control: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Cache control for prompt caching.",
+    )
+    container: Optional[str] = Field(
+        default=None,
+        description="Container ID for sandboxed execution.",
+    )
+    inference_geo: Optional[str] = Field(
+        default=None,
+        description="Geographic constraint for inference.",
+    )
+    output_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Output format configuration (e.g. structured output).",
+    )
+    service_tier: Optional[str] = Field(
+        default=None,
+        description="Service tier selection ('auto' or 'standard_only').",
+    )
+    user_profile_id: Optional[str] = Field(
+        default=None,
+        description="User profile ID for personalization.",
+    )
+
+
+class GuardrailsMessagesRequest(AnthropicMessagesRequest):
+    """Anthropic Messages request with NeMo Guardrails extensions."""
+
+    guardrails: GuardrailsDataInput = Field(
+        default_factory=GuardrailsDataInput,
+        description="Guardrails specific options for the request.",
+    )
+
+
+class GuardrailsMessagesCheckRequest(AnthropicMessagesRequest):
+    """Anthropic Messages request for the checks endpoint."""
+
+    guardrails: GuardrailCheckDataInput = Field(
+        default_factory=GuardrailCheckDataInput,
+        description="Guardrails specific options for the check request.",
+    )
+
+
+class GuardrailsMessagesResponse(Message):
+    """Anthropic Messages API response with NeMo-Guardrails extensions."""
+
+    guardrails: Optional[GuardrailsDataOutput] = Field(
+        default=None,
+        description="Guardrails specific output data.",
+    )
diff --git a/nemoguardrails/types.py b/nemoguardrails/types.py
index 4ff3d61889..459575ae78 100644
--- a/nemoguardrails/types.py
+++ b/nemoguardrails/types.py
@@ -87,7 +87,7 @@ class UsageInfo:
 FinishReason = Literal["stop", "length", "tool_calls", "content_filter", "error", "other"]
 
 
-_STANDARD_MESSAGE_KEYS = {"role", "content", "tool_calls", "tool_call_id", "name", "provider_metadata"}
+_STANDARD_MESSAGE_KEYS = {"role", "content", "tool_calls", "tool_call_id", "name", "reasoning", "provider_metadata"}
 
 _ROLE_ALIASES = {
     "bot": Role.ASSISTANT,
@@ -107,6 +107,7 @@ class ChatMessage:
     tool_calls: Optional[List[ToolCall]] = None
     tool_call_id: Optional[str] = None
     name: Optional[str] = None
+    reasoning: Optional[Union[str, List[Dict[str, Any]]]] = None
     provider_metadata: Dict[str, Any] = field(default_factory=dict)
 
     @classmethod
@@ -140,6 +141,9 @@ def to_dict(self) -> Dict[str, Any]:
         if self.name is not None:
             payload["name"] = self.name
 
+        if self.reasoning is not None:
+            payload["reasoning"] = self.reasoning
+
         if self.provider_metadata:
             payload["provider_metadata"] = self.provider_metadata
 
@@ -212,6 +216,7 @@ def from_dict(cls, d: Dict[str, Any]) -> "ChatMessage":
             tool_calls=tool_calls,
             tool_call_id=d.get("tool_call_id"),
             name=d.get("name"),
+            reasoning=d.get("reasoning"),
             provider_metadata=provider_metadata,
         )
 
diff --git a/pyproject.toml b/pyproject.toml
index b7dcc39d07..53f296c726 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,7 +47,7 @@ dependencies = [
 
 tracing = ["opentelemetry-api (>=1.27.0,<2.0.0)", "aiofiles (>=24.1.0)"]
 
-server = ["aiofiles (>=24.1.0)", "openai (>=1.0.0,<3.0.0)", "fastapi (>=0.103.0)", "starlette (>=0.49.1)", "uvicorn (>=0.23)", "watchdog (>=3.0.0)"]
+server = ["aiofiles (>=24.1.0)", "openai (>=1.0.0,<3.0.0)", "anthropic (>=0.40.0,<1.0.0)", "fastapi (>=0.103.0)", "starlette (>=0.49.1)", "uvicorn (>=0.23)", "watchdog (>=3.0.0)"]
 
 sdd = [
   "presidio-analyzer (>=2.2) ; python_version<'3.13'",
@@ -84,6 +84,7 @@ all = [
   "numpy (>=1.26) ; python_version>='3.12' and python_version<'3.13'",
   "numpy (>=2.1.0) ; python_version>='3.13'",
   "openai (>=1.0.0,<3.0.0)",
+  "anthropic (>=0.40.0,<1.0.0)",
   "google-cloud-language (>=2.14.0)",
   "opentelemetry-api (>=1.27.0,<2.0.0)",
   "aiofiles (>=24.1.0)",
@@ -117,6 +118,7 @@ test_integration = [
   "opentelemetry-sdk>=1.34.1,<2.0.0",
   "fast-langdetect>=1.0.0",
   "openai>=1.0.0,<3.0.0",
+  "anthropic>=0.40.0,<1.0.0",
   "fastapi>=0.103.0",
   "uvicorn>=0.23",
   "starlette>=0.49.1",
diff --git a/tests/llm/clients/test_client_config.py b/tests/llm/clients/test_client_config.py
index 4f3c48c6dc..cf3626edd7 100644
--- a/tests/llm/clients/test_client_config.py
+++ b/tests/llm/clients/test_client_config.py
@@ -747,9 +747,9 @@ async def test_unknown_provider_error_lists_both_fix_paths(self):
         fw = DefaultFramework()
         try:
             with pytest.raises(ValueError) as excinfo:
-                fw.create_model("claude-3-5-sonnet-latest", "anthropic", {})
+                fw.create_model("some-model", "unknown_provider_xyz", {})
             message = str(excinfo.value)
-            assert "anthropic" in message
+            assert "unknown_provider_xyz" in message
             assert "parameters.base_url" in message
             assert "NEMOGUARDRAILS_LLM_FRAMEWORK=langchain" in message
             assert "langchain-<provider>" in message
diff --git a/tests/server/test_anthropic_param_forwarding.py b/tests/server/test_anthropic_param_forwarding.py
new file mode 100644
index 0000000000..15b5bece20
--- /dev/null
+++ b/tests/server/test_anthropic_param_forwarding.py
@@ -0,0 +1,225 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Verify that every LLM-relevant field in AnthropicMessagesRequest reaches the
+Anthropic model client's outgoing HTTP payload.
+
+The test mocks the AnthropicClient at the HTTP layer and inspects the payload
+dict that would be sent to the upstream provider. This catches cases where the
+messages endpoint or model adapter silently drops a parameter.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+pytest.importorskip("anthropic")
+
+from nemoguardrails.llm.clients.anthropic import AnthropicClient  # noqa: E402
+from nemoguardrails.llm.clients.base import HTTPResponse  # noqa: E402
+from nemoguardrails.llm.models.anthropic_chat import AnthropicChatModel  # noqa: E402
+from nemoguardrails.server.schemas.anthropic import AnthropicMessagesRequest  # noqa: E402
+from nemoguardrails.types import ChatMessage, Role  # noqa: E402
+
+FIELDS_NOT_FORWARDED_TO_LLM = {
+    "messages",
+    "model",
+    "system",
+    "stream",
+}
+
+FIELD_RENAMES = {
+    "stop_sequences": "stop_sequences",
+}
+
+ANTHROPIC_RESPONSE = HTTPResponse(
+    body={
+        "id": "msg_test",
+        "type": "message",
+        "role": "assistant",
+        "content": [{"type": "text", "text": "Hello"}],
+        "model": "test-model",
+        "stop_reason": "end_turn",
+        "usage": {"input_tokens": 10, "output_tokens": 5},
+    },
+    headers={},
+    status_code=200,
+)
+
+REQUEST_WITH_ALL_FIELDS = {
+    "model": "test-model",
+    "max_tokens": 256,
+    "messages": [{"role": "user", "content": "Hi"}],
+    "system": "You are helpful.",
+    "stream": False,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "top_k": 40,
+    "stop_sequences": ["STOP"],
+    "metadata": {"user_id": "test"},
+    "tools": [
+        {
+            "name": "get_weather",
+            "description": "Get weather",
+            "input_schema": {"type": "object", "properties": {}},
+        }
+    ],
+    "tool_choice": {"type": "auto"},
+    "thinking": {"type": "adaptive"},
+    "cache_control": {"type": "ephemeral"},
+    "container": "container-123",
+    "inference_geo": "us",
+    "output_config": {"format": {"type": "json"}},
+    "service_tier": "auto",
+    "user_profile_id": "user-abc-123",
+}
+
+
+def _forwardable_fields() -> set[str]:
+    """Return AnthropicMessagesRequest fields that should be forwarded to the LLM."""
+    return set(AnthropicMessagesRequest.model_fields.keys()) - FIELDS_NOT_FORWARDED_TO_LLM
+
+
+@pytest.mark.asyncio
+async def test_all_params_reach_client_payload():
+    """Every forwardable request field must appear in the client's outgoing payload."""
+    captured_payload = {}
+
+    client = AnthropicClient(base_url="http://test.local/v1", api_key="fake")
+    model = AnthropicChatModel(client=client, model="test-model")
+
+    original_build = client._build_payload
+
+    def capturing_build(*args, **kwargs):
+        result = original_build(*args, **kwargs)
+        captured_payload.update(result)
+        return result
+
+    with (
+        patch.object(client, "_build_payload", side_effect=capturing_build),
+        patch.object(client, "_apost", new_callable=AsyncMock, return_value=ANTHROPIC_RESPONSE),
+    ):
+        body = AnthropicMessagesRequest(**REQUEST_WITH_ALL_FIELDS)
+
+        params = {}
+        for field in _forwardable_fields():
+            value = getattr(body, field)
+            if value is not None:
+                wire_name = FIELD_RENAMES.get(field, field)
+                params[wire_name] = value
+
+        await model.generate_async(
+            [ChatMessage(role=Role.USER, content="Hi")],
+            **params,
+        )
+
+    forwardable = _forwardable_fields()
+    missing = []
+    for field in sorted(forwardable):
+        wire_name = FIELD_RENAMES.get(field, field)
+        if wire_name not in captured_payload:
+            missing.append(field)
+
+    assert not missing, (
+        f"These AnthropicMessagesRequest fields were not forwarded to the client payload: {missing}. "
+        f"Update the messages endpoint or model adapter to forward them."
+    )
+
+
+@pytest.mark.asyncio
+async def test_values_match_request():
+    """Forwarded values must match what was in the original request."""
+    captured_payload = {}
+
+    client = AnthropicClient(base_url="http://test.local/v1", api_key="fake")
+    model = AnthropicChatModel(client=client, model="test-model")
+
+    original_build = client._build_payload
+
+    def capturing_build(*args, **kwargs):
+        result = original_build(*args, **kwargs)
+        captured_payload.update(result)
+        return result
+
+    with (
+        patch.object(client, "_build_payload", side_effect=capturing_build),
+        patch.object(client, "_apost", new_callable=AsyncMock, return_value=ANTHROPIC_RESPONSE),
+    ):
+        body = AnthropicMessagesRequest(**REQUEST_WITH_ALL_FIELDS)
+
+        params = {}
+        for field in _forwardable_fields():
+            value = getattr(body, field)
+            if value is not None:
+                wire_name = FIELD_RENAMES.get(field, field)
+                params[wire_name] = value
+
+        await model.generate_async(
+            [ChatMessage(role=Role.USER, content="Hi")],
+            **params,
+        )
+
+    for field in _forwardable_fields():
+        wire_name = FIELD_RENAMES.get(field, field)
+        expected = REQUEST_WITH_ALL_FIELDS[field]
+        assert captured_payload[wire_name] == expected, (
+            f"Payload value for '{wire_name}' was {captured_payload.get(wire_name)!r}, expected {expected!r}"
+        )
+
+
+@pytest.mark.asyncio
+async def test_system_prompt_forwarded():
+    """The system field must be forwarded as a top-level param, not inside messages."""
+    captured_payload = {}
+
+    client = AnthropicClient(base_url="http://test.local/v1", api_key="fake")
+    model = AnthropicChatModel(client=client, model="test-model")
+
+    original_build = client._build_payload
+
+    def capturing_build(*args, **kwargs):
+        result = original_build(*args, **kwargs)
+        captured_payload.update(result)
+        return result
+
+    with (
+        patch.object(client, "_build_payload", side_effect=capturing_build),
+        patch.object(client, "_apost", new_callable=AsyncMock, return_value=ANTHROPIC_RESPONSE),
+    ):
+        messages = [
+            ChatMessage(role=Role.SYSTEM, content="You are helpful."),
+            ChatMessage(role=Role.USER, content="Hi"),
+        ]
+
+        await model.generate_async(messages, max_tokens=100)
+
+    assert captured_payload.get("system") == "You are helpful."
+    for msg in captured_payload["messages"]:
+        assert msg["role"] != "system", "System messages should not appear in the messages list"
+
+
+def test_request_with_all_fields_covers_forwardable():
+    """Ensure REQUEST_WITH_ALL_FIELDS includes every forwardable field.
+
+    This test fails when a new field is added to AnthropicMessagesRequest but
+    not to the test fixture, preventing silent gaps in coverage.
+    """
+    forwardable = _forwardable_fields()
+    all_test_fields = set(REQUEST_WITH_ALL_FIELDS.keys()) - FIELDS_NOT_FORWARDED_TO_LLM
+    missing_from_fixture = forwardable - all_test_fields
+    assert not missing_from_fixture, (
+        f"REQUEST_WITH_ALL_FIELDS is missing forwardable fields: {missing_from_fixture}. "
+        f"Add them to the test fixture or to FIELDS_NOT_FORWARDED_TO_LLM."
+    )
diff --git a/tests/server/test_anthropic_schema_compliance.py b/tests/server/test_anthropic_schema_compliance.py
new file mode 100644
index 0000000000..df66192bf1
--- /dev/null
+++ b/tests/server/test_anthropic_schema_compliance.py
@@ -0,0 +1,84 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Verify AnthropicMessagesRequest stays in sync with the anthropic SDK.
+
+The request schema is defined as an explicit Pydantic model for readability.
+This test introspects the SDK's MessageCreateParams TypedDict and checks that
+every field the SDK considers required is present in our model, and that we
+haven't invented fields that don't exist upstream.
+"""
+
+import typing
+
+import pytest
+
+anthropic_types = pytest.importorskip("anthropic.types")
+
+from anthropic.types import MessageCreateParams  # noqa: E402
+from anthropic.types import message_create_params as mcp_module  # noqa: E402
+
+from nemoguardrails.server.schemas.anthropic import AnthropicMessagesRequest  # noqa: E402
+
+GUARDRAILS_ONLY_FIELDS = {"guardrails"}
+
+
+def _get_sdk_fields() -> tuple[set[str], set[str]]:
+    """Return (required, all) field names from the SDK's MessageCreateParams."""
+    td = MessageCreateParams.__args__[0]
+    resolved = typing.get_type_hints(td, localns=vars(mcp_module), include_extras=True)
+
+    required = set()
+    for name, annotation in resolved.items():
+        origin = typing.get_origin(annotation)
+        if origin is typing.Required:
+            required.add(name)
+        elif name in td.__required_keys__:
+            required.add(name)
+
+    return required, set(resolved.keys())
+
+
+def test_all_sdk_required_fields_present():
+    """Every field the SDK marks as Required must appear in our schema."""
+    required, _ = _get_sdk_fields()
+    our_fields = set(AnthropicMessagesRequest.model_fields.keys())
+    missing = required - our_fields - GUARDRAILS_ONLY_FIELDS
+    assert not missing, (
+        f"AnthropicMessagesRequest is missing required SDK fields: {missing}. "
+        f"Add them to nemoguardrails/server/schemas/anthropic.py."
+    )
+
+
+def test_no_invented_fields():
+    """Our schema should not contain fields that don't exist in the SDK."""
+    _, sdk_fields = _get_sdk_fields()
+    our_fields = set(AnthropicMessagesRequest.model_fields.keys())
+    invented = our_fields - sdk_fields - GUARDRAILS_ONLY_FIELDS
+    assert not invented, (
+        f"AnthropicMessagesRequest has fields not in the SDK: {invented}. "
+        f"Remove them or add to GUARDRAILS_ONLY_FIELDS if intentional."
+    )
+
+
+def test_all_sdk_fields_present():
+    """Every SDK field must appear in our schema — we forward everything to the model."""
+    _, sdk_fields = _get_sdk_fields()
+    our_fields = set(AnthropicMessagesRequest.model_fields.keys())
+    missing = sdk_fields - our_fields - GUARDRAILS_ONLY_FIELDS
+    assert not missing, (
+        f"AnthropicMessagesRequest is missing SDK fields: {missing}. "
+        f"Add them to nemoguardrails/server/schemas/anthropic.py so they are forwarded to the model."
+    )
diff --git a/tests/server/test_anthropic_streaming_tool_calls.py b/tests/server/test_anthropic_streaming_tool_calls.py
new file mode 100644
index 0000000000..5077821908
--- /dev/null
+++ b/tests/server/test_anthropic_streaming_tool_calls.py
@@ -0,0 +1,308 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Verify that tool calls propagate through the streaming path to Anthropic SSE.
+
+Tests two layers:
+1. _format_anthropic_sse correctly converts a tool_calls JSON chunk into
+   Anthropic-format SSE events (content_block_start/delta/stop for tool_use).
+2. The streaming handler receives tool calls extracted from Colang pipeline
+   events (BotToolCalls) before END_OF_STREAM, matching the code path in
+   LLMRails.generate_async.
+"""
+
+import json
+
+import pytest
+
+from nemoguardrails.server.messages import _format_anthropic_sse
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+SAMPLE_TOOL_CALLS = [
+    {
+        "id": "toolu_abc123",
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "arguments": json.dumps({"location": "Seattle"}),
+        },
+    }
+]
+
+
+async def _collect_sse_events(async_gen):
+    """Collect all SSE events from an async generator, parsed into dicts."""
+    events = []
+    async for raw in async_gen:
+        for line in raw.strip().split("\n"):
+            if line.startswith("data: "):
+                events.append(json.loads(line[len("data: ") :]))
+    return events
+
+
+async def _async_iter_from_list(items):
+    """Turn a plain list into an async iterator."""
+    for item in items:
+        yield item
+
+
+# ---------------------------------------------------------------------------
+# 1. _format_anthropic_sse: tool call chunk handling
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_sse_formatter_text_only():
+    """Text-only stream produces text content block and end_turn stop reason."""
+    chunks = ["Hello", " world"]
+    events = await _collect_sse_events(_format_anthropic_sse(_async_iter_from_list(chunks), model="test-model"))
+
+    text_deltas = [
+        e for e in events if e.get("type") == "content_block_delta" and e.get("delta", {}).get("type") == "text_delta"
+    ]
+    assert len(text_deltas) == 2
+    assert text_deltas[0]["delta"]["text"] == "Hello"
+    assert text_deltas[1]["delta"]["text"] == " world"
+
+    msg_delta = [e for e in events if e.get("type") == "message_delta"][0]
+    assert msg_delta["delta"]["stop_reason"] == "end_turn"
+
+
+@pytest.mark.asyncio
+async def test_sse_formatter_tool_calls_from_json_string():
+    """A JSON-encoded tool_calls chunk produces Anthropic tool_use SSE blocks."""
+    tool_calls_chunk = json.dumps({"tool_calls": SAMPLE_TOOL_CALLS})
+    chunks = ["Some text", tool_calls_chunk]
+
+    events = await _collect_sse_events(_format_anthropic_sse(_async_iter_from_list(chunks), model="test-model"))
+
+    tool_starts = [
+        e
+        for e in events
+        if e.get("type") == "content_block_start" and e.get("content_block", {}).get("type") == "tool_use"
+    ]
+    assert len(tool_starts) == 1
+    assert tool_starts[0]["content_block"]["name"] == "get_weather"
+    assert tool_starts[0]["content_block"]["id"] == "toolu_abc123"
+
+    tool_deltas = [
+        e
+        for e in events
+        if e.get("type") == "content_block_delta" and e.get("delta", {}).get("type") == "input_json_delta"
+    ]
+    assert len(tool_deltas) == 1
+    parsed_args = json.loads(tool_deltas[0]["delta"]["partial_json"])
+    assert parsed_args == {"location": "Seattle"}
+
+    msg_delta = [e for e in events if e.get("type") == "message_delta"][0]
+    assert msg_delta["delta"]["stop_reason"] == "tool_use"
+
+
+@pytest.mark.asyncio
+async def test_sse_formatter_tool_calls_from_dict():
+    """A dict chunk with tool_calls key produces Anthropic tool_use SSE blocks."""
+    chunks = [{"tool_calls": SAMPLE_TOOL_CALLS}]
+
+    events = await _collect_sse_events(_format_anthropic_sse(_async_iter_from_list(chunks), model="test-model"))
+
+    tool_starts = [
+        e
+        for e in events
+        if e.get("type") == "content_block_start" and e.get("content_block", {}).get("type") == "tool_use"
+    ]
+    assert len(tool_starts) == 1
+    assert tool_starts[0]["content_block"]["name"] == "get_weather"
+
+    msg_delta = [e for e in events if e.get("type") == "message_delta"][0]
+    assert msg_delta["delta"]["stop_reason"] == "tool_use"
+
+
+@pytest.mark.asyncio
+async def test_sse_formatter_multiple_tool_calls():
+    """Multiple tool calls get separate content blocks with correct indices."""
+    multi_tool_calls = [
+        {
+            "id": "toolu_1",
+            "type": "function",
+            "function": {"name": "get_weather", "arguments": json.dumps({"city": "NYC"})},
+        },
+        {
+            "id": "toolu_2",
+            "type": "function",
+            "function": {"name": "get_time", "arguments": json.dumps({"timezone": "EST"})},
+        },
+    ]
+    chunks = ["Hi", json.dumps({"tool_calls": multi_tool_calls})]
+
+    events = await _collect_sse_events(_format_anthropic_sse(_async_iter_from_list(chunks), model="test-model"))
+
+    tool_starts = [
+        e
+        for e in events
+        if e.get("type") == "content_block_start" and e.get("content_block", {}).get("type") == "tool_use"
+    ]
+    assert len(tool_starts) == 2
+    assert tool_starts[0]["index"] == 1
+    assert tool_starts[0]["content_block"]["name"] == "get_weather"
+    assert tool_starts[1]["index"] == 2
+    assert tool_starts[1]["content_block"]["name"] == "get_time"
+
+
+# ---------------------------------------------------------------------------
+# 2. _stream_llm_call: tool call propagation through streaming handler
+#
+# In the real flow, _stream_llm_call (actions/llm/utils.py) accumulates
+# ToolCall objects from streaming chunks, serializes them as a JSON chunk
+# via handler.push_chunk(), then calls handler.finish() which pushes
+# END_OF_STREAM. These tests replicate that exact sequence.
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_streaming_handler_receives_tool_calls():
+    """Simulates _stream_llm_call pushing tool calls before finish().
+
+    This mirrors the exact code path at utils.py:135-141.
+    """
+    from nemoguardrails.streaming import StreamingHandler
+    from nemoguardrails.types import ToolCall, ToolCallFunction
+
+    handler = StreamingHandler()
+
+    tool_calls = [
+        ToolCall(
+            id="call_123",
+            type="function",
+            function=ToolCallFunction(name="get_weather", arguments={"location": "Seattle"}),
+        )
+    ]
+
+    await handler.push_chunk("Hello from LLM")
+
+    payload = json.dumps({"tool_calls": [tc.to_dict() for tc in tool_calls]})
+    await handler.push_chunk(payload)
+    await handler.finish()
+
+    chunks = []
+    async for chunk in handler:
+        chunks.append(chunk)
+
+    text_chunks = [c for c in chunks if isinstance(c, str) and not c.startswith("{")]
+    assert any("Hello from LLM" in c for c in text_chunks)
+
+    tool_call_chunks = []
+    for c in chunks:
+        if isinstance(c, str):
+            try:
+                parsed = json.loads(c)
+                if isinstance(parsed, dict) and "tool_calls" in parsed:
+                    tool_call_chunks.append(parsed)
+            except (json.JSONDecodeError, ValueError):
+                pass
+
+    assert len(tool_call_chunks) == 1, f"Expected 1 tool_calls chunk, got {len(tool_call_chunks)}. All chunks: {chunks}"
+    assert tool_call_chunks[0]["tool_calls"][0]["function"]["name"] == "get_weather"
+
+
+@pytest.mark.asyncio
+async def test_streaming_handler_no_tool_calls_when_none():
+    """When the LLM returns no tool calls, no tool_calls chunk appears."""
+    from nemoguardrails.streaming import StreamingHandler
+
+    handler = StreamingHandler()
+
+    await handler.push_chunk("Just text")
+    await handler.finish()
+
+    chunks = []
+    async for chunk in handler:
+        chunks.append(chunk)
+
+    for c in chunks:
+        if isinstance(c, str):
+            try:
+                parsed = json.loads(c)
+                assert "tool_calls" not in parsed, "Unexpected tool_calls chunk in text-only stream"
+            except (json.JSONDecodeError, ValueError):
+                pass
+
+
+@pytest.mark.asyncio
+async def test_end_to_end_sse_with_tool_calls():
+    """Full pipeline: _stream_llm_call pushes tool calls to streaming handler,
+    then _format_anthropic_sse converts to complete Anthropic SSE sequence."""
+    from nemoguardrails.streaming import StreamingHandler
+    from nemoguardrails.types import ToolCall, ToolCallFunction
+
+    handler = StreamingHandler()
+
+    tool_calls = [
+        ToolCall(
+            id="toolu_weather1",
+            type="function",
+            function=ToolCallFunction(
+                name="get_weather",
+                arguments={"location": "Seattle", "units": "celsius"},
+            ),
+        )
+    ]
+
+    await handler.push_chunk("Here's the weather")
+    payload = json.dumps({"tool_calls": [tc.to_dict() for tc in tool_calls]})
+    await handler.push_chunk(payload)
+    await handler.finish()
+
+    sse_events = await _collect_sse_events(_format_anthropic_sse(handler, model="test-model"))
+
+    msg_start = [e for e in sse_events if e.get("type") == "message_start"]
+    assert len(msg_start) == 1
+    assert msg_start[0]["message"]["model"] == "test-model"
+
+    text_deltas = [
+        e
+        for e in sse_events
+        if e.get("type") == "content_block_delta" and e.get("delta", {}).get("type") == "text_delta"
+    ]
+    assert len(text_deltas) >= 1
+    full_text = "".join(d["delta"]["text"] for d in text_deltas)
+    assert "weather" in full_text
+
+    tool_starts = [
+        e
+        for e in sse_events
+        if e.get("type") == "content_block_start" and e.get("content_block", {}).get("type") == "tool_use"
+    ]
+    assert len(tool_starts) == 1
+    assert tool_starts[0]["content_block"]["name"] == "get_weather"
+    assert tool_starts[0]["content_block"]["id"] == "toolu_weather1"
+
+    tool_deltas = [
+        e
+        for e in sse_events
+        if e.get("type") == "content_block_delta" and e.get("delta", {}).get("type") == "input_json_delta"
+    ]
+    assert len(tool_deltas) == 1
+    parsed_args = json.loads(tool_deltas[0]["delta"]["partial_json"])
+    assert parsed_args["location"] == "Seattle"
+    assert parsed_args["units"] == "celsius"
+
+    msg_delta = [e for e in sse_events if e.get("type") == "message_delta"][0]
+    assert msg_delta["delta"]["stop_reason"] == "tool_use"
+
+    msg_stop = [e for e in sse_events if e.get("type") == "message_stop"]
+    assert len(msg_stop) == 1
diff --git a/tests/test_anthropic_utils.py b/tests/test_anthropic_utils.py
new file mode 100644
index 0000000000..ef015d75a9
--- /dev/null
+++ b/tests/test_anthropic_utils.py
@@ -0,0 +1,194 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for Anthropic ↔ NeMo ChatMessage conversion utilities.
+
+Covers thinking/redacted_thinking block preservation through the round-trip.
+"""
+
+from nemoguardrails.llm.anthropic_utils import (
+    anthropic_to_nemo_messages,
+    nemo_to_anthropic_messages,
+)
+from nemoguardrails.types import ChatMessage, Role, ToolCall, ToolCallFunction
+
+THINKING_BLOCK = {
+    "type": "thinking",
+    "thinking": "Let me reason about this...",
+    "signature": "abc123sig",
+}
+
+REDACTED_THINKING_BLOCK = {
+    "type": "redacted_thinking",
+    "data": "opaque-redacted-data",
+}
+
+
+def test_anthropic_to_nemo_preserves_thinking_blocks():
+    messages = [
+        {
+            "role": "assistant",
+            "content": [
+                THINKING_BLOCK,
+                {"type": "text", "text": "The answer is 42."},
+            ],
+        }
+    ]
+    result = anthropic_to_nemo_messages(None, messages)
+
+    assert len(result) == 1
+    msg = result[0]
+    assert msg.role == Role.ASSISTANT
+    assert msg.content == "The answer is 42."
+    assert msg.reasoning == [THINKING_BLOCK]
+
+
+def test_anthropic_to_nemo_preserves_redacted_thinking():
+    messages = [
+        {
+            "role": "assistant",
+            "content": [
+                REDACTED_THINKING_BLOCK,
+                {"type": "text", "text": "Here is my answer."},
+            ],
+        }
+    ]
+    result = anthropic_to_nemo_messages(None, messages)
+
+    msg = result[0]
+    assert msg.reasoning == [REDACTED_THINKING_BLOCK]
+
+
+def test_anthropic_to_nemo_preserves_mixed_thinking():
+    messages = [
+        {
+            "role": "assistant",
+            "content": [
+                THINKING_BLOCK,
+                REDACTED_THINKING_BLOCK,
+                {"type": "text", "text": "Response."},
+            ],
+        }
+    ]
+    result = anthropic_to_nemo_messages(None, messages)
+
+    msg = result[0]
+    assert msg.reasoning == [THINKING_BLOCK, REDACTED_THINKING_BLOCK]
+
+
+def test_anthropic_to_nemo_no_thinking_yields_none():
+    messages = [
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "No thinking here."}],
+        }
+    ]
+    result = anthropic_to_nemo_messages(None, messages)
+    assert result[0].reasoning is None
+
+
+def test_nemo_to_anthropic_reconstructs_thinking_blocks():
+    msg = ChatMessage(
+        role=Role.ASSISTANT,
+        content="The answer is 42.",
+        reasoning=[THINKING_BLOCK],
+    )
+    system, messages = nemo_to_anthropic_messages([msg])
+
+    assert system is None
+    assert len(messages) == 1
+    content = messages[0]["content"]
+    assert content[0] == THINKING_BLOCK
+    assert content[1] == {"type": "text", "text": "The answer is 42."}
+
+
+def test_nemo_to_anthropic_reconstructs_redacted_thinking():
+    msg = ChatMessage(
+        role=Role.ASSISTANT,
+        content="Answer.",
+        reasoning=[REDACTED_THINKING_BLOCK],
+    )
+    _, messages = nemo_to_anthropic_messages([msg])
+
+    content = messages[0]["content"]
+    assert content[0] == REDACTED_THINKING_BLOCK
+    assert content[1] == {"type": "text", "text": "Answer."}
+
+
+def test_nemo_to_anthropic_string_reasoning_creates_thinking_block():
+    msg = ChatMessage(
+        role=Role.ASSISTANT,
+        content="Result.",
+        reasoning="I thought about it.",
+    )
+    _, messages = nemo_to_anthropic_messages([msg])
+
+    content = messages[0]["content"]
+    assert content[0] == {"type": "thinking", "thinking": "I thought about it."}
+    assert content[1] == {"type": "text", "text": "Result."}
+
+
+def test_nemo_to_anthropic_no_reasoning_no_thinking_block():
+    msg = ChatMessage(role=Role.ASSISTANT, content="Plain response.")
+    _, messages = nemo_to_anthropic_messages([msg])
+
+    content = messages[0]["content"]
+    assert len(content) == 1
+    assert content[0]["type"] == "text"
+
+
+def test_thinking_block_round_trip():
+    original_messages = [
+        {
+            "role": "assistant",
+            "content": [
+                THINKING_BLOCK,
+                REDACTED_THINKING_BLOCK,
+                {"type": "text", "text": "My answer."},
+            ],
+        }
+    ]
+
+    nemo_msgs = anthropic_to_nemo_messages(None, original_messages)
+    _, reconstructed = nemo_to_anthropic_messages(nemo_msgs)
+
+    content = reconstructed[0]["content"]
+    assert content[0] == THINKING_BLOCK
+    assert content[1] == REDACTED_THINKING_BLOCK
+    assert content[2] == {"type": "text", "text": "My answer."}
+
+
+def test_thinking_blocks_before_tool_use_in_output():
+    msg = ChatMessage(
+        role=Role.ASSISTANT,
+        content="",
+        reasoning=[THINKING_BLOCK],
+        tool_calls=[
+            ToolCall(
+                id="call_1",
+                type="function",
+                function=ToolCallFunction(
+                    name="get_weather",
+                    arguments={"city": "London"},
+                ),
+            )
+        ],
+    )
+    _, messages = nemo_to_anthropic_messages([msg])
+
+    content = messages[0]["content"]
+    assert content[0] == THINKING_BLOCK
+    assert content[-1]["type"] == "tool_use"
+    assert content[-1]["name"] == "get_weather"
diff --git a/uv.lock b/uv.lock
index 5cc63cb17d..4547052e8e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -8,31 +8,27 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
-[options]
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+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668 },
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 [package.optional-dependencies]
@@ -718,7 +714,7 @@ toml = [
 
 [[package]]
 name = "cryptography"
-version = "46.0.7"
+version = "48.0.1"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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@@ -726,45 +722,45 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
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 [[package]]
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