ensure we support bedrock

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -517,7 +517,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             isinstance(first_item, dict) and "function" in first_item
         ):
             return True
-        # Anthropic-style
+        # Anthropic-style (object with attributes)
         if (
             hasattr(first_item, "type")
             and getattr(first_item, "type", None) == "tool_use"
@@ -525,6 +525,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             return True
         if hasattr(first_item, "name") and hasattr(first_item, "input"):
             return True
+        # Bedrock-style (dict with name and input keys)
+        if (
+            isinstance(first_item, dict)
+            and "name" in first_item
+            and "input" in first_item
+        ):
+            return True
         # Gemini-style
         if hasattr(first_item, "function_call") and first_item.function_call:
             return True
@@ -585,7 +592,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             func_name = tool_call.name
             func_args = tool_call.input  # Already a dict in Anthropic
         elif isinstance(tool_call, dict):
-            call_id = tool_call.get("id", f"call_{id(tool_call)}")
+            # Support OpenAI "id", Bedrock "toolUseId", or generate one
+            call_id = (
+                tool_call.get("id")
+                or tool_call.get("toolUseId")
+                or f"call_{id(tool_call)}"
+            )
             func_info = tool_call.get("function", {})
             func_name = func_info.get("name", "") or tool_call.get("name", "")
             func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -263,6 +263,13 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
             return True
         if hasattr(first_item, "name") and hasattr(first_item, "input"):
             return True
+        # Check for Bedrock-style tool call structure (dict with name and input keys)
+        if (
+            isinstance(first_item, dict)
+            and "name" in first_item
+            and "input" in first_item
+        ):
+            return True
         # Check for Gemini-style function call (Part with function_call)
         if hasattr(first_item, "function_call") and first_item.function_call:
             return True

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -330,7 +330,8 @@ class BedrockCompletion(BaseLLM):
                     cast(object, [{"text": system_message}]),
                 )
 
-            # Add tool config if present
+            # Add tool config if present or if messages contain tool content
+            # Bedrock requires toolConfig when messages have toolUse/toolResult
             if tools:
                 tool_config: ToolConfigurationTypeDef = {
                     "tools": cast(
@@ -339,6 +340,16 @@ class BedrockCompletion(BaseLLM):
                     )
                 }
                 body["toolConfig"] = tool_config
+            elif self._messages_contain_tool_content(formatted_messages):
+                # Create minimal toolConfig from tool history in messages
+                tools_from_history = self._extract_tools_from_message_history(
+                    formatted_messages
+                )
+                if tools_from_history:
+                    body["toolConfig"] = cast(
+                        "ToolConfigurationTypeDef",
+                        cast(object, {"tools": tools_from_history}),
+                    )
 
             # Add optional advanced features if configured
             if self.guardrail_config:
@@ -444,6 +455,8 @@ class BedrockCompletion(BaseLLM):
                     cast(object, [{"text": system_message}]),
                 )
 
+            # Add tool config if present or if messages contain tool content
+            # Bedrock requires toolConfig when messages have toolUse/toolResult
             if tools:
                 tool_config: ToolConfigurationTypeDef = {
                     "tools": cast(
@@ -452,6 +465,16 @@ class BedrockCompletion(BaseLLM):
                     )
                 }
                 body["toolConfig"] = tool_config
+            elif self._messages_contain_tool_content(formatted_messages):
+                # Create minimal toolConfig from tool history in messages
+                tools_from_history = self._extract_tools_from_message_history(
+                    formatted_messages
+                )
+                if tools_from_history:
+                    body["toolConfig"] = cast(
+                        "ToolConfigurationTypeDef",
+                        cast(object, {"tools": tools_from_history}),
+                    )
 
             if self.guardrail_config:
                 guardrail_config: GuardrailConfigurationTypeDef = cast(
@@ -1290,6 +1313,8 @@ class BedrockCompletion(BaseLLM):
         for message in formatted_messages:
             role = message.get("role")
             content = message.get("content", "")
+            tool_calls = message.get("tool_calls")
+            tool_call_id = message.get("tool_call_id")
 
             if role == "system":
                 # Extract system message - Converse API handles it separately
@@ -1297,9 +1322,48 @@ class BedrockCompletion(BaseLLM):
                     system_message += f"\n\n{content}"
                 else:
                     system_message = cast(str, content)
+            elif role == "assistant" and tool_calls:
+                # Convert OpenAI-style tool_calls to Bedrock toolUse format
+                bedrock_content = []
+                for tc in tool_calls:
+                    func = tc.get("function", {})
+                    tool_use_block = {
+                        "toolUse": {
+                            "toolUseId": tc.get("id", f"call_{id(tc)}"),
+                            "name": func.get("name", ""),
+                            "input": func.get("arguments", {})
+                            if isinstance(func.get("arguments"), dict)
+                            else json.loads(func.get("arguments", "{}") or "{}"),
+                        }
+                    }
+                    bedrock_content.append(tool_use_block)
+                converse_messages.append(
+                    {"role": "assistant", "content": bedrock_content}
+                )
+            elif role == "tool" and tool_call_id:
+                # Convert OpenAI-style tool response to Bedrock toolResult format
+                converse_messages.append(
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "toolResult": {
+                                    "toolUseId": tool_call_id,
+                                    "content": [
+                                        {"text": str(content) if content else ""}
+                                    ],
+                                }
+                            }
+                        ],
+                    }
+                )
             else:
                 # Convert to Converse API format with proper content structure
-                converse_messages.append({"role": role, "content": [{"text": content}]})
+                # Ensure content is not None
+                text_content = content if content else ""
+                converse_messages.append(
+                    {"role": role, "content": [{"text": text_content}]}
+                )
 
         # CRITICAL: Handle model-specific conversation requirements
         # Cohere and some other models require conversation to end with user message
@@ -1349,6 +1413,58 @@ class BedrockCompletion(BaseLLM):
 
         return converse_messages, system_message
 
+    @staticmethod
+    def _messages_contain_tool_content(messages: list[LLMMessage]) -> bool:
+        """Check if messages contain toolUse or toolResult content blocks.
+
+        Bedrock requires toolConfig when messages have tool-related content.
+        """
+        for message in messages:
+            content = message.get("content", [])
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict):
+                        if "toolUse" in block or "toolResult" in block:
+                            return True
+        return False
+
+    @staticmethod
+    def _extract_tools_from_message_history(
+        messages: list[LLMMessage],
+    ) -> list[dict[str, Any]]:
+        """Extract tool definitions from toolUse blocks in message history.
+
+        When no tools are passed but messages contain toolUse, we need to
+        recreate a minimal toolConfig to satisfy Bedrock's API requirements.
+        """
+        tools: list[dict[str, Any]] = []
+        seen_tool_names: set[str] = set()
+
+        for message in messages:
+            content = message.get("content", [])
+            if isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and "toolUse" in block:
+                        tool_use = block["toolUse"]
+                        tool_name = tool_use.get("name", "")
+                        if tool_name and tool_name not in seen_tool_names:
+                            seen_tool_names.add(tool_name)
+                            # Create a minimal tool spec from the toolUse block
+                            tool_spec: dict[str, Any] = {
+                                "toolSpec": {
+                                    "name": tool_name,
+                                    "description": f"Tool: {tool_name}",
+                                    "inputSchema": {
+                                        "json": {
+                                            "type": "object",
+                                            "properties": {},
+                                        }
+                                    },
+                                }
+                            }
+                            tools.append(tool_spec)
+        return tools
+
     @staticmethod
     def _format_tools_for_converse(
         tools: list[dict[str, Any]],

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -875,7 +875,8 @@ def extract_tool_call_info(
         call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
         return call_id, tool_call.name, tool_call.input
     if isinstance(tool_call, dict):
-        call_id = tool_call.get("id", f"call_{id(tool_call)}")
+        # Support OpenAI "id", Bedrock "toolUseId", or generate one
+        call_id = tool_call.get("id") or tool_call.get("toolUseId") or f"call_{id(tool_call)}"
         func_info = tool_call.get("function", {})
         func_name = func_info.get("name", "") or tool_call.get("name", "")
         func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -241,26 +241,26 @@ class TestGeminiNativeToolCalling:
         self, calculator_tool: CalculatorTool
     ) -> None:
         """Test Gemini agent can use native tool calling."""
-        with patch.dict(os.environ, {"GOOGLE_GENAI_USE_VERTEXAI": "true"}):
-            agent = Agent(
-                role="Math Assistant",
-                goal="Help users with mathematical calculations",
-                backstory="You are a helpful math assistant.",
-                tools=[calculator_tool],
-                llm=LLM(model="gemini/gemini-2.0-flash-exp"),
-            )
 
-            task = Task(
+        agent = Agent(
-                description="Calculate what is 15 * 8",
+            role="Math Assistant",
-                expected_output="The result of the calculation",
+            goal="Help users with mathematical calculations",
-                agent=agent,
+            backstory="You are a helpful math assistant.",
-            )
+            tools=[calculator_tool],
+            llm=LLM(model="gemini/gemini-2.0-flash-exp"),
+        )
 
-            crew = Crew(agents=[agent], tasks=[task])
+        task = Task(
-            result = crew.kickoff()
+            description="Calculate what is 15 * 8",
+            expected_output="The result of the calculation",
+            agent=agent,
+        )
 
-            assert result is not None
+        crew = Crew(agents=[agent], tasks=[task])
-            assert result.raw is not None
+        result = crew.kickoff()
+
+        assert result is not None
+        assert result.raw is not None
 
     def test_gemini_agent_kickoff_with_tools_mocked(
         self, calculator_tool: CalculatorTool
@@ -387,40 +387,45 @@ class TestBedrockNativeToolCalling:
     def mock_aws_env(self):
         """Mock AWS environment variables for tests."""
         env_vars = {
-            "AWS_ACCESS_KEY_ID": "test-key",
+        "AWS_ACCESS_KEY_ID": "test-key",
-            "AWS_SECRET_ACCESS_KEY": "test-secret",
+        "AWS_SECRET_ACCESS_KEY": "test-secret",
-            "AWS_REGION": "us-east-1",
+        "AWS_REGION": "us-east-1",
         }
-        with patch.dict(os.environ, env_vars):
+        if "AWS_ACCESS_KEY_ID" not in os.environ:
+            with patch.dict(os.environ, env_vars):
+                yield
+        else:
             yield
 
+    @pytest.mark.vcr()
     def test_bedrock_agent_kickoff_with_tools_mocked(
         self, calculator_tool: CalculatorTool
     ) -> None:
         """Test Bedrock agent kickoff with mocked LLM call."""
         llm = LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0")
 
-        with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
+        agent = Agent(
-            agent = Agent(
+            role="Math Assistant",
-                role="Math Assistant",
+            goal="Calculate math",
-                goal="Calculate math",
+            backstory="You calculate.",
-                backstory="You calculate.",
+            tools=[calculator_tool],
-                tools=[calculator_tool],
+            llm=llm,
-                llm=llm,
+            verbose=False,
-                verbose=False,
+            max_iter=5,
-            )
+        )
 
-            task = Task(
+        task = Task(
-                description="Calculate 15 * 8",
+            description="Calculate 15 * 8",
-                expected_output="Result",
+            expected_output="Result",
-                agent=agent,
+            agent=agent,
-            )
+        )
 
-            crew = Crew(agents=[agent], tasks=[task])
+        crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
+        result = crew.kickoff()
 
-            assert mock_call.called
+        assert result is not None
-            assert result is not None
+        assert result.raw is not None
+        assert "120" in str(result.raw)
 
 
 # =============================================================================

lib/crewai/tests/cassettes/agents/TestBedrockNativeToolCalling.test_bedrock_agent_kickoff_with_tools_mocked.yaml

@@ -0,0 +1,485 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Calculate
+      15 * 8\n\nThis is the expected criteria for your final answer: Result\nyou MUST
+      return the actual complete content as the final answer, not a summary.\n\nThis
+      is VERY important to you, your job depends on it!"}]}], "inferenceConfig": {"stopSequences":
+      ["\nObservation:"]}, "system": [{"text": "You are Math Assistant. You calculate.\nYour
+      personal goal is: Calculate math"}], "toolConfig": {"tools": [{"toolSpec": {"name":
+      "calculator", "description": "Perform mathematical calculations. Use this for
+      any math operations.", "inputSchema": {"json": {"properties": {"expression":
+      {"description": "Mathematical expression to evaluate", "title": "Expression",
+      "type": "string"}}, "required": ["expression"], "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '806'
+      Content-Type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      User-Agent:
+      - X-USER-AGENT-XXX
+      amz-sdk-invocation-id:
+      - AMZ-SDK-INVOCATION-ID-XXX
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - AUTHORIZATION-XXX
+      x-amz-date:
+      - X-AMZ-DATE-XXX
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse
+  response:
+    body:
+      string: '{"metrics":{"latencyMs":1540},"output":{"message":{"content":[{"text":"Here
+        is the calculation for 15 * 8:"},{"toolUse":{"input":{"expression":"15 * 8"},"name":"calculator","toolUseId":"tooluse_1OIARGnOTjiITDKGd_FgMA"}}],"role":"assistant"}},"stopReason":"tool_use","usage":{"inputTokens":417,"outputTokens":68,"serverToolUsage":{},"totalTokens":485}}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '351'
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 22 Jan 2026 21:27:56 GMT
+      x-amzn-RequestId:
+      - X-AMZN-REQUESTID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Calculate
+      15 * 8\n\nThis is the expected criteria for your final answer: Result\nyou MUST
+      return the actual complete content as the final answer, not a summary.\n\nThis
+      is VERY important to you, your job depends on it!"}]}, {"role": "assistant",
+      "content": [{"toolUse": {"toolUseId": "tooluse_1OIARGnOTjiITDKGd_FgMA", "name":
+      "calculator", "input": {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId":
+      "tooluse_1OIARGnOTjiITDKGd_FgMA", "content": [{"text": "Error executing tool:
+      CalculatorTool._run() missing 1 required positional argument: ''expression''"}]}}]},
+      {"role": "user", "content": [{"text": "Analyze the tool result. If requirements
+      are met, provide the Final Answer. Otherwise, call the next tool. Deliver only
+      the answer without meta-commentary."}]}], "inferenceConfig": {"stopSequences":
+      ["\nObservation:"]}, "system": [{"text": "You are Math Assistant. You calculate.\nYour
+      personal goal is: Calculate math"}], "toolConfig": {"tools": [{"toolSpec": {"name":
+      "calculator", "description": "Perform mathematical calculations. Use this for
+      any math operations.", "inputSchema": {"json": {"properties": {"expression":
+      {"description": "Mathematical expression to evaluate", "title": "Expression",
+      "type": "string"}}, "required": ["expression"], "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '1358'
+      Content-Type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      User-Agent:
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+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - AUTHORIZATION-XXX
+      x-amz-date:
+      - X-AMZ-DATE-XXX
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse
+  response:
+    body:
+      string: '{"metrics":{"latencyMs":1071},"output":{"message":{"content":[{"toolUse":{"input":{"expression":"15
+        * 8"},"name":"calculator","toolUseId":"tooluse_vjcn57LeQpS-pePkTvny8w"}}],"role":"assistant"}},"stopReason":"tool_use","usage":{"inputTokens":527,"outputTokens":55,"serverToolUsage":{},"totalTokens":582}}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '304'
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 22 Jan 2026 21:27:57 GMT
+      x-amzn-RequestId:
+      - X-AMZN-REQUESTID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Calculate
+      15 * 8\n\nThis is the expected criteria for your final answer: Result\nyou MUST
+      return the actual complete content as the final answer, not a summary.\n\nThis
+      is VERY important to you, your job depends on it!"}]}, {"role": "assistant",
+      "content": [{"toolUse": {"toolUseId": "tooluse_1OIARGnOTjiITDKGd_FgMA", "name":
+      "calculator", "input": {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId":
+      "tooluse_1OIARGnOTjiITDKGd_FgMA", "content": [{"text": "Error executing tool:
+      CalculatorTool._run() missing 1 required positional argument: ''expression''"}]}}]},
+      {"role": "user", "content": [{"text": "Analyze the tool result. If requirements
+      are met, provide the Final Answer. Otherwise, call the next tool. Deliver only
+      the answer without meta-commentary."}]}, {"role": "assistant", "content": [{"toolUse":
+      {"toolUseId": "tooluse_vjcn57LeQpS-pePkTvny8w", "name": "calculator", "input":
+      {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId": "tooluse_vjcn57LeQpS-pePkTvny8w",
+      "content": [{"text": "Error executing tool: CalculatorTool._run() missing 1
+      required positional argument: ''expression''"}]}}]}, {"role": "user", "content":
+      [{"text": "Analyze the tool result. If requirements are met, provide the Final
+      Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary."}]}],
+      "inferenceConfig": {"stopSequences": ["\nObservation:"]}, "system": [{"text":
+      "You are Math Assistant. You calculate.\nYour personal goal is: Calculate math"}],
+      "toolConfig": {"tools": [{"toolSpec": {"name": "calculator", "description":
+      "Perform mathematical calculations. Use this for any math operations.", "inputSchema":
+      {"json": {"properties": {"expression": {"description": "Mathematical expression
+      to evaluate", "title": "Expression", "type": "string"}}, "required": ["expression"],
+      "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '1910'
+      Content-Type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      User-Agent:
+      - X-USER-AGENT-XXX
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+      - AMZ-SDK-INVOCATION-ID-XXX
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      authorization:
+      - AUTHORIZATION-XXX
+      x-amz-date:
+      - X-AMZ-DATE-XXX
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/anthropic.claude-3-haiku-20240307-v1%3A0/converse
+  response:
+    body:
+      string: '{"metrics":{"latencyMs":927},"output":{"message":{"content":[{"toolUse":{"input":{"expression":"15
+        * 8"},"name":"calculator","toolUseId":"tooluse__4aP-hcTR4Ozp5gTlESXbg"}}],"role":"assistant"}},"stopReason":"tool_use","usage":{"inputTokens":637,"outputTokens":57,"serverToolUsage":{},"totalTokens":694}}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '303'
+      Content-Type:
+      - application/json
+      Date:
+      - Thu, 22 Jan 2026 21:27:58 GMT
+      x-amzn-RequestId:
+      - X-AMZN-REQUESTID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "\nCurrent Task: Calculate
+      15 * 8\n\nThis is the expected criteria for your final answer: Result\nyou MUST
+      return the actual complete content as the final answer, not a summary.\n\nThis
+      is VERY important to you, your job depends on it!"}]}, {"role": "assistant",
+      "content": [{"toolUse": {"toolUseId": "tooluse_1OIARGnOTjiITDKGd_FgMA", "name":
+      "calculator", "input": {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId":
+      "tooluse_1OIARGnOTjiITDKGd_FgMA", "content": [{"text": "Error executing tool:
+      CalculatorTool._run() missing 1 required positional argument: ''expression''"}]}}]},
+      {"role": "user", "content": [{"text": "Analyze the tool result. If requirements
+      are met, provide the Final Answer. Otherwise, call the next tool. Deliver only
+      the answer without meta-commentary."}]}, {"role": "assistant", "content": [{"toolUse":
+      {"toolUseId": "tooluse_vjcn57LeQpS-pePkTvny8w", "name": "calculator", "input":
+      {}}}]}, {"role": "user", "content": [{"toolResult": {"toolUseId": "tooluse_vjcn57LeQpS-pePkTvny8w",
+      "content": [{"text": "Error executing tool: CalculatorTool._run() missing 1
+      required positional argument: ''expression''"}]}}]}, {"role": "user", "content":
+      [{"text": "Analyze the tool result. If requirements are met, provide the Final
+      Answer. Otherwise, call the next tool. Deliver only the answer without meta-commentary."}]},
+      {"role": "assistant", "content": [{"toolUse": {"toolUseId": "tooluse__4aP-hcTR4Ozp5gTlESXbg",
+      "name": "calculator", "input": {}}}]}, {"role": "user", "content": [{"toolResult":
+      {"toolUseId": "tooluse__4aP-hcTR4Ozp5gTlESXbg", "content": [{"text": "Error
+      executing tool: CalculatorTool._run() missing 1 required positional argument:
+      ''expression''"}]}}]}, {"role": "user", "content": [{"text": "Analyze the tool
+      result. If requirements are met, provide the Final Answer. Otherwise, call the
+      next tool. Deliver only the answer without meta-commentary."}]}], "inferenceConfig":
+      {"stopSequences": ["\nObservation:"]}, "system": [{"text": "You are Math Assistant.
+      You calculate.\nYour personal goal is: Calculate math"}], "toolConfig": {"tools":
+      [{"toolSpec": {"name": "calculator", "description": "Perform mathematical calculations.
+      Use this for any math operations.", "inputSchema": {"json": {"properties": {"expression":
+      {"description": "Mathematical expression to evaluate", "title": "Expression",
+      "type": "string"}}, "required": ["expression"], "type": "object"}}}}]}}'
+    headers:
+      Content-Length:
+      - '2462'
+      Content-Type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+      User-Agent:
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