diff --git a/docs/en/concepts/llms.mdx b/docs/en/concepts/llms.mdx
index 93795549c..bfd2fedf8 100644
--- a/docs/en/concepts/llms.mdx
+++ b/docs/en/concepts/llms.mdx
@@ -283,11 +283,54 @@ In this section, you'll find detailed examples that help you select, configure,
     )
     ```
 
+    **Extended Thinking (Claude Sonnet 4 and Beyond):**
+
+    CrewAI supports Anthropic's Extended Thinking feature, which allows Claude to think through problems in a more human-like way before responding. This is particularly useful for complex reasoning, analysis, and problem-solving tasks.
+
+    ```python Code
+    from crewai import LLM
+
+    # Enable extended thinking with default settings
+    llm = LLM(
+        model="anthropic/claude-sonnet-4",
+        thinking={"type": "enabled"},
+        max_tokens=10000
+    )
+
+    # Configure thinking with budget control
+    llm = LLM(
+        model="anthropic/claude-sonnet-4",
+        thinking={
+            "type": "enabled",
+            "budget_tokens": 5000  # Limit thinking tokens
+        },
+        max_tokens=10000
+    )
+    ```
+
+    **Thinking Configuration Options:**
+    - `type`: Set to `"enabled"` to activate extended thinking mode
+    - `budget_tokens` (optional): Maximum tokens to use for thinking (helps control costs)
+
+    **Models Supporting Extended Thinking:**
+    - `claude-sonnet-4` and newer models
+    - `claude-3-7-sonnet` (with extended thinking capabilities)
+
+    **When to Use Extended Thinking:**
+    - Complex reasoning and multi-step problem solving
+    - Mathematical calculations and proofs
+    - Code analysis and debugging
+    - Strategic planning and decision making
+    - Research and analytical tasks
+
+    **Note:** Extended thinking consumes additional tokens but can significantly improve response quality for complex tasks.
+
     **Supported Environment Variables:**
     - `ANTHROPIC_API_KEY`: Your Anthropic API key (required)
 
     **Features:**
     - Native tool use support for Claude 3+ models
+    - Extended Thinking support for Claude Sonnet 4+
     - Streaming support for real-time responses
     - Automatic system message handling
     - Stop sequences for controlled output
@@ -305,6 +348,7 @@ In this section, you'll find detailed examples that help you select, configure,
 
     | Model                        | Context Window | Best For                                      |
     |------------------------------|----------------|-----------------------------------------------|
+    | claude-sonnet-4              | 200,000 tokens | Latest with extended thinking capabilities    |
     | claude-3-7-sonnet            | 200,000 tokens | Advanced reasoning and agentic tasks          |
     | claude-3-5-sonnet-20241022   | 200,000 tokens | Latest Sonnet with best performance           |
     | claude-3-5-haiku             | 200,000 tokens | Fast, compact model for quick responses       |
diff --git a/lib/crewai/src/crewai/llm.py b/lib/crewai/src/crewai/llm.py
index 8eb052683..77053deeb 100644
--- a/lib/crewai/src/crewai/llm.py
+++ b/lib/crewai/src/crewai/llm.py
@@ -67,6 +67,7 @@ if TYPE_CHECKING:
 
     from crewai.agent.core import Agent
     from crewai.llms.hooks.base import BaseInterceptor
+    from crewai.llms.providers.anthropic.completion import AnthropicThinkingConfig
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -585,6 +586,7 @@ class LLM(BaseLLM):
         reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
         stream: bool = False,
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        thinking: AnthropicThinkingConfig | dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> None:
         """Initialize LLM instance.
diff --git a/lib/crewai/src/crewai/llms/providers/anthropic/completion.py b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
index fd637adbc..723826ea7 100644
--- a/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
+++ b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
@@ -3,8 +3,9 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
+from anthropic.types import ThinkingBlock
 from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
@@ -22,8 +23,7 @@ if TYPE_CHECKING:
 
 try:
     from anthropic import Anthropic, AsyncAnthropic
-    from anthropic.types import Message
-    from anthropic.types.tool_use_block import ToolUseBlock
+    from anthropic.types import Message, TextBlock, ThinkingBlock, ToolUseBlock
     import httpx
 except ImportError:
     raise ImportError(
@@ -31,6 +31,11 @@ except ImportError:
     ) from None
 
 
+class AnthropicThinkingConfig(BaseModel):
+    type: Literal["enabled", "disabled"]
+    budget_tokens: int | None = None
+
+
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -52,6 +57,7 @@ class AnthropicCompletion(BaseLLM):
         stream: bool = False,
         client_params: dict[str, Any] | None = None,
         interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        thinking: AnthropicThinkingConfig | None = None,
         **kwargs: Any,
     ):
         """Initialize Anthropic chat completion client.
@@ -97,6 +103,10 @@ class AnthropicCompletion(BaseLLM):
         self.top_p = top_p
         self.stream = stream
         self.stop_sequences = stop_sequences or []
+        self.thinking = thinking
+        self.previous_thinking_blocks: list[ThinkingBlock] = []
+        # Model-specific settings
+        self.is_claude_3 = "claude-3" in model.lower()
         self.supports_tools = True
 
     @property
@@ -326,6 +336,12 @@ class AnthropicCompletion(BaseLLM):
         if tools and self.supports_tools:
             params["tools"] = self._convert_tools_for_interference(tools)
 
+        if self.thinking:
+            if isinstance(self.thinking, AnthropicThinkingConfig):
+                params["thinking"] = self.thinking.model_dump()
+            else:
+                params["thinking"] = self.thinking
+
         return params
 
     def _convert_tools_for_interference(
@@ -365,6 +381,34 @@ class AnthropicCompletion(BaseLLM):
 
         return anthropic_tools
 
+    def _extract_thinking_block(
+        self, content_block: Any
+    ) -> ThinkingBlock | dict[str, Any] | None:
+        """Extract and format thinking block from content block.
+
+        Args:
+            content_block: Content block from Anthropic response
+
+        Returns:
+            Dictionary with thinking block data including signature, or None if not a thinking block
+        """
+        if content_block.type == "thinking":
+            thinking_block = {
+                "type": "thinking",
+                "thinking": content_block.thinking,
+            }
+            if hasattr(content_block, "signature"):
+                thinking_block["signature"] = content_block.signature
+            return thinking_block
+        if content_block.type == "redacted_thinking":
+            redacted_block = {"type": "redacted_thinking"}
+            if hasattr(content_block, "thinking"):
+                redacted_block["thinking"] = content_block.thinking
+            if hasattr(content_block, "signature"):
+                redacted_block["signature"] = content_block.signature
+            return redacted_block
+        return None
+
     def _format_messages_for_anthropic(
         self, messages: str | list[LLMMessage]
     ) -> tuple[list[LLMMessage], str | None]:
@@ -374,6 +418,7 @@ class AnthropicCompletion(BaseLLM):
         - System messages are separate from conversation messages
         - Messages must alternate between user and assistant
         - First message must be from user
+        - When thinking is enabled, assistant messages must start with thinking blocks
 
         Args:
             messages: Input messages
@@ -398,8 +443,29 @@ class AnthropicCompletion(BaseLLM):
                     system_message = cast(str, content)
             else:
                 role_str = role if role is not None else "user"
-                content_str = content if content is not None else ""
-                formatted_messages.append({"role": role_str, "content": content_str})
+
+                if isinstance(content, list):
+                    formatted_messages.append({"role": role_str, "content": content})
+                elif (
+                    role_str == "assistant"
+                    and self.thinking
+                    and self.previous_thinking_blocks
+                ):
+                    structured_content = cast(
+                        list[dict[str, Any]],
+                        [
+                            *self.previous_thinking_blocks,
+                            {"type": "text", "text": content if content else ""},
+                        ],
+                    )
+                    formatted_messages.append(
+                        LLMMessage(role=role_str, content=structured_content)
+                    )
+                else:
+                    content_str = content if content is not None else ""
+                    formatted_messages.append(
+                        LLMMessage(role=role_str, content=content_str)
+                    )
 
         # Ensure first message is from user (Anthropic requirement)
         if not formatted_messages:
@@ -449,7 +515,6 @@ class AnthropicCompletion(BaseLLM):
             if tool_uses and tool_uses[0].name == "structured_output":
                 structured_data = tool_uses[0].input
                 structured_json = json.dumps(structured_data)
-
                 self._emit_call_completed_event(
                     response=structured_json,
                     call_type=LLMCallType.LLM_CALL,
@@ -477,15 +542,22 @@ class AnthropicCompletion(BaseLLM):
                     from_agent,
                 )
 
-        # Extract text content
         content = ""
+        thinking_blocks: list[ThinkingBlock] = []
+
         if response.content:
             for content_block in response.content:
                 if hasattr(content_block, "text"):
                     content += content_block.text
+                else:
+                    thinking_block = self._extract_thinking_block(content_block)
+                    if thinking_block:
+                        thinking_blocks.append(cast(ThinkingBlock, thinking_block))
+
+        if thinking_blocks:
+            self.previous_thinking_blocks = thinking_blocks
 
         content = self._apply_stop_words(content)
-
         self._emit_call_completed_event(
             response=content,
             call_type=LLMCallType.LLM_CALL,
@@ -540,6 +612,16 @@ class AnthropicCompletion(BaseLLM):
 
             final_message: Message = stream.get_final_message()
 
+        thinking_blocks: list[ThinkingBlock] = []
+        if final_message.content:
+            for content_block in final_message.content:
+                thinking_block = self._extract_thinking_block(content_block)
+                if thinking_block:
+                    thinking_blocks.append(cast(ThinkingBlock, thinking_block))
+
+        if thinking_blocks:
+            self.previous_thinking_blocks = thinking_blocks
+
         usage = self._extract_anthropic_token_usage(final_message)
         self._track_token_usage_internal(usage)
 
@@ -644,7 +726,26 @@ class AnthropicCompletion(BaseLLM):
         follow_up_params = params.copy()
 
         # Add Claude's tool use response to conversation
-        assistant_message = {"role": "assistant", "content": initial_response.content}
+        assistant_content: list[
+            ThinkingBlock | ToolUseBlock | TextBlock | dict[str, Any]
+        ] = []
+        for block in initial_response.content:
+            thinking_block = self._extract_thinking_block(block)
+            if thinking_block:
+                assistant_content.append(thinking_block)
+            elif block.type == "tool_use":
+                assistant_content.append(
+                    {
+                        "type": "tool_use",
+                        "id": block.id,
+                        "name": block.name,
+                        "input": block.input,
+                    }
+                )
+            elif hasattr(block, "text"):
+                assistant_content.append({"type": "text", "text": block.text})
+
+        assistant_message = {"role": "assistant", "content": assistant_content}
 
         # Add user message with tool results
         user_message = {"role": "user", "content": tool_results}
@@ -663,12 +764,20 @@ class AnthropicCompletion(BaseLLM):
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
             self._track_token_usage_internal(follow_up_usage)
 
-            # Extract final text content
             final_content = ""
+            thinking_blocks: list[ThinkingBlock] = []
+
             if final_response.content:
                 for content_block in final_response.content:
                     if hasattr(content_block, "text"):
                         final_content += content_block.text
+                    else:
+                        thinking_block = self._extract_thinking_block(content_block)
+                        if thinking_block:
+                            thinking_blocks.append(cast(ThinkingBlock, thinking_block))
+
+            if thinking_blocks:
+                self.previous_thinking_blocks = thinking_blocks
 
             final_content = self._apply_stop_words(final_content)
 
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
new file mode 100644
index 000000000..8423c518b
--- /dev/null
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
@@ -0,0 +1,220 @@
+interactions:
+- request:
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather
+      in Tokyo? Use the get_weather tool."}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"name":"get_weather","description":"Get
+      the current weather in a given location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The
+      city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"],"description":"The
+      unit of temperature"}},"required":["location"]}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '509'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.71.1
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAAwAAAP//dJBBS8NAEIX/isx5A0k0he5NhR60FRG9KLKsmzGJ3eyku7O1JeS/SyLV
+        qnic9703j5keWirRggRjdSwxCeQccnKWFEme5kU6z+cgoClBQhsqlWYXp6+rl+a8NrvIzfbuMqti
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+        j6WiEBDQbxuDihv0IGH8fql9CcPwAQAA//8DAI8uRSjwAQAA
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 08 Dec 2025 23:16:56 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '50'
+      anthropic-ratelimit-requests-remaining:
+      - '49'
+      anthropic-ratelimit-requests-reset:
+      - '2025-12-08T23:16:54Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      retry-after:
+      - '5'
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '2925'
+    status:
+      code: 200
+      message: OK
+- request:
+    body: "{\"max_tokens\":4096,\"messages\":[{\"role\":\"user\",\"content\":\"What
+      is the weather in Tokyo? Use the get_weather tool.\"},{\"role\":\"assistant\",\"content\":[{\"type\":\"tool_use\",\"id\":\"toolu_01AoUFM2koNLsFscK6xjnLPo\",\"name\":\"get_weather\",\"input\":{\"location\":\"Tokyo,
+      Japan\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"toolu_01AoUFM2koNLsFscK6xjnLPo\",\"content\":\"The
+      weather in Tokyo, Japan is sunny and 72\xB0F\"}]}],\"model\":\"claude-sonnet-4-5\",\"stream\":false,\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get
+      the current weather in a given location\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"location\":{\"type\":\"string\",\"description\":\"The
+      city and state, e.g. San Francisco, CA\"},\"unit\":{\"type\":\"string\",\"enum\":[\"celsius\",\"fahrenheit\"],\"description\":\"The
+      unit of temperature\"}},\"required\":[\"location\"]}}]}"
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
+      anthropic-version:
+      - '2023-06-01'
+      connection:
+      - keep-alive
+      content-length:
+      - '814'
+      content-type:
+      - application/json
+      host:
+      - api.anthropic.com
+      user-agent:
+      - X-USER-AGENT-XXX
+      x-stainless-arch:
+      - X-STAINLESS-ARCH-XXX
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - X-STAINLESS-OS-XXX
+      x-stainless-package-version:
+      - 0.71.1
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.13.3
+      x-stainless-timeout:
+      - NOT_GIVEN
+    method: POST
+    uri: https://api.anthropic.com/v1/messages
+  response:
+    body:
+      string: !!binary |
+        H4sIAAAAAAAAA3SQ3UoDMRBGXyXMlcKubGOrNreiiHdC70RCTIZu2t3Jmky0a9l36jP0yWSLxT+8
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+        R1IWkDC+eouaPUZQMJbtTHQwDB8AAAD//wMAkp8os98BAAA=
+    headers:
+      CF-RAY:
+      - CF-RAY-XXX
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 08 Dec 2025 23:16:59 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Robots-Tag:
+      - none
+      anthropic-organization-id:
+      - ANTHROPIC-ORGANIZATION-ID-XXX
+      anthropic-ratelimit-input-tokens-limit:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-input-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-input-tokens-reset:
+      - ANTHROPIC-RATELIMIT-INPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-output-tokens-limit:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-output-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-output-tokens-reset:
+      - ANTHROPIC-RATELIMIT-OUTPUT-TOKENS-RESET-XXX
+      anthropic-ratelimit-requests-limit:
+      - '50'
+      anthropic-ratelimit-requests-remaining:
+      - '49'
+      anthropic-ratelimit-requests-reset:
+      - '2025-12-08T23:16:58Z'
+      anthropic-ratelimit-tokens-limit:
+      - ANTHROPIC-RATELIMIT-TOKENS-LIMIT-XXX
+      anthropic-ratelimit-tokens-remaining:
+      - ANTHROPIC-RATELIMIT-TOKENS-REMAINING-XXX
+      anthropic-ratelimit-tokens-reset:
+      - ANTHROPIC-RATELIMIT-TOKENS-RESET-XXX
+      cf-cache-status:
+      - DYNAMIC
+      request-id:
+      - REQUEST-ID-XXX
+      retry-after:
+      - '1'
+      strict-transport-security:
+      - STS-XXX
+      x-envoy-upstream-service-time:
+      - '2878'
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
index 8759062f9..4f286b9aa 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
@@ -8,8 +8,6 @@ interactions:
     headers:
       Accept:
       - '*/*'
-      Accept-Encoding:
-      - gzip, deflate, zstd
       Connection:
       - keep-alive
       Content-Length:
@@ -17,9 +15,11 @@ interactions:
       Content-Type:
       - application/json
       User-Agent:
-      - CrewAI-CLI/1.3.0
+      - X-USER-AGENT-XXX
       X-Crewai-Version:
       - 1.3.0
+      accept-encoding:
+      - ACCEPT-ENCODING-XXX
     method: POST
     uri: https://app.crewai.com/crewai_plus/api/v1/tracing/batches
   response:
@@ -37,61 +37,31 @@ interactions:
       cache-control:
       - no-store
       content-security-policy:
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diff --git a/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml b/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml
new file mode 100644
index 000000000..3b5b70ad5
--- /dev/null
+++ b/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml
@@ -0,0 +1,222 @@
+interactions:
+- request:
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diff --git a/lib/crewai/tests/llms/anthropic/test_anthropic.py b/lib/crewai/tests/llms/anthropic/test_anthropic.py
index 340aeb642..03cd772a1 100644
--- a/lib/crewai/tests/llms/anthropic/test_anthropic.py
+++ b/lib/crewai/tests/llms/anthropic/test_anthropic.py
@@ -12,8 +12,11 @@ from crewai.task import Task
 
 @pytest.fixture(autouse=True)
 def mock_anthropic_api_key():
-    """Automatically mock ANTHROPIC_API_KEY for all tests in this module."""
-    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+    """Automatically mock ANTHROPIC_API_KEY for all tests in this module if not already set."""
+    if "ANTHROPIC_API_KEY" not in os.environ:
+        with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+            yield
+    else:
         yield
 
 
@@ -63,6 +66,7 @@ def test_anthropic_tool_use_conversation_flow():
     with patch.object(completion.client.messages, 'create') as mock_create:
         # Mock initial response with tool use - need to properly mock ToolUseBlock
         mock_tool_use = Mock(spec=ToolUseBlock)
+        mock_tool_use.type = "tool_use"
         mock_tool_use.id = "tool_123"
         mock_tool_use.name = "get_weather"
         mock_tool_use.input = {"location": "San Francisco"}
@@ -75,6 +79,7 @@ def test_anthropic_tool_use_conversation_flow():
 
         # Mock final response after tool result - properly mock text content
         mock_text_block = Mock()
+        mock_text_block.type = "text"
         # Set the text attribute as a string, not another Mock
         mock_text_block.configure_mock(text="Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature.")
 
@@ -698,3 +703,167 @@ def test_anthropic_stop_sequences_sent_to_api():
     assert result is not None
     assert isinstance(result, str)
     assert len(result) > 0
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_thinking():
+    """Test that thinking is properly handled and thinking params are passed to messages.create"""
+    from unittest.mock import patch
+    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+    llm = LLM(
+        model="anthropic/claude-sonnet-4-5",
+        thinking={"type": "enabled", "budget_tokens": 5000},
+        max_tokens=10000
+    )
+
+    assert isinstance(llm, AnthropicCompletion)
+
+    original_create = llm.client.messages.create
+    captured_params = {}
+
+    def capture_and_call(**kwargs):
+        captured_params.update(kwargs)
+        return original_create(**kwargs)
+
+    with patch.object(llm.client.messages, 'create', side_effect=capture_and_call):
+        result = llm.call("What is the weather in Tokyo?")
+
+        assert result is not None
+        assert isinstance(result, str)
+        assert len(result) > 0
+
+        assert "thinking" in captured_params
+        assert captured_params["thinking"] == {"type": "enabled", "budget_tokens": 5000}
+
+        assert captured_params["model"] == "claude-sonnet-4-5"
+        assert captured_params["max_tokens"] == 10000
+        assert "messages" in captured_params
+        assert len(captured_params["messages"]) > 0
+
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_thinking_blocks_preserved_across_turns():
+    """Test that thinking blocks are stored and included in subsequent API calls across turns"""
+    from unittest.mock import patch
+    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
+
+    llm = LLM(
+        model="anthropic/claude-sonnet-4-5",
+        thinking={"type": "enabled", "budget_tokens": 5000},
+        max_tokens=10000
+    )
+
+    assert isinstance(llm, AnthropicCompletion)
+
+    # Capture all messages.create calls to verify thinking blocks are included
+    original_create = llm.client.messages.create
+    captured_calls = []
+
+    def capture_and_call(**kwargs):
+        captured_calls.append(kwargs)
+        return original_create(**kwargs)
+
+    with patch.object(llm.client.messages, 'create', side_effect=capture_and_call):
+        # First call - establishes context and generates thinking blocks
+        messages = [{"role": "user", "content": "What is 2+2?"}]
+        first_result = llm.call(messages)
+
+        # Verify first call completed
+        assert first_result is not None
+        assert isinstance(first_result, str)
+        assert len(first_result) > 0
+
+        # Verify thinking blocks were stored after first response
+        assert len(llm.previous_thinking_blocks) > 0, "No thinking blocks stored after first call"
+        first_thinking = llm.previous_thinking_blocks[0]
+        assert first_thinking["type"] == "thinking"
+        assert "thinking" in first_thinking
+        assert "signature" in first_thinking
+
+        # Store the thinking block content for comparison
+        stored_thinking_content = first_thinking["thinking"]
+        stored_signature = first_thinking["signature"]
+
+        # Second call - should include thinking blocks from first call
+        messages.append({"role": "assistant", "content": first_result})
+        messages.append({"role": "user", "content": "Now what is 3+3?"})
+        second_result = llm.call(messages)
+
+        # Verify second call completed
+        assert second_result is not None
+        assert isinstance(second_result, str)
+
+        # Verify at least 2 API calls were made
+        assert len(captured_calls) >= 2, f"Expected at least 2 API calls, got {len(captured_calls)}"
+
+        # Verify second call includes thinking blocks in assistant message
+        second_call_messages = captured_calls[1]["messages"]
+
+        # Should have: user message + assistant message (with thinking blocks) + follow-up user message
+        assert len(second_call_messages) >= 2
+
+        # Find the assistant message in the second call
+        assistant_message = None
+        for msg in second_call_messages:
+            if msg["role"] == "assistant" and isinstance(msg.get("content"), list):
+                assistant_message = msg
+                break
+
+        assert assistant_message is not None, "Assistant message with list content not found in second call"
+        assert isinstance(assistant_message["content"], list)
+
+        # Verify thinking block is included in assistant message content
+        thinking_found = False
+        for block in assistant_message["content"]:
+            if isinstance(block, dict) and block.get("type") == "thinking":
+                thinking_found = True
+                assert "thinking" in block
+                assert "signature" in block
+                # Verify it matches what was stored from the first call
+                assert block["thinking"] == stored_thinking_content
+                assert block["signature"] == stored_signature
+                break
+
+        assert thinking_found, "Thinking block not found in assistant message content in second call"
+
+@pytest.mark.vcr(filter_headers=["authorization", "x-api-key"])
+def test_anthropic_function_calling():
+    """Test that function calling is properly handled"""
+    llm = LLM(model="anthropic/claude-sonnet-4-5")
+
+    def get_weather(location: str) -> str:
+        return f"The weather in {location} is sunny and 72°F"
+
+    tools = [
+        {
+            "name": "get_weather",
+            "description": "Get the current weather in a given location",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA"
+                    },
+                    "unit": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The unit of temperature"
+                    }
+                },
+                "required": ["location"]
+            }
+        }
+    ]
+
+    result = llm.call(
+        "What is the weather in Tokyo? Use the get_weather tool.",
+        tools=tools,
+        available_functions={"get_weather": get_weather}
+    )
+
+    assert result is not None
+    assert isinstance(result, str)
+    assert len(result) > 0
+    # Verify the response includes information about Tokyo's weather
+    assert "tokyo" in result.lower() or "72" in result