refactor: Improve Mistral LLM implementation based on feedback

- Add MISTRAL_IDENTIFIERS constant
- Use deepcopy for message copying
- Add type annotations
- Improve test organization and add edge cases
- Add error handling and logging

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/llm.py

@@ -21,6 +21,8 @@ from typing import (
 from dotenv import load_dotenv
 from pydantic import BaseModel
 
+logger = logging.getLogger(__name__)
+
 from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
 
 with warnings.catch_warnings():
@@ -133,6 +135,9 @@ def suppress_warnings():
 
 
 class LLM:
+    # Constants for model identification
+    MISTRAL_IDENTIFIERS = {'mistral', 'mixtral'}
+
     def __init__(
         self,
         model: str,
@@ -392,9 +397,11 @@ class LLM:
         Returns:
             List of formatted messages according to provider requirements.
             For Anthropic models, ensures first message has 'user' role.
+            For Mistral models, converts 'assistant' roles to 'user' roles.
 
         Raises:
             TypeError: If messages is None or contains invalid message format.
+            Exception: If message formatting fails for any provider-specific reason.
         """
         if messages is None:
             raise TypeError("Messages cannot be None")
@@ -406,6 +413,19 @@ class LLM:
                     "Invalid message format. Each message must be a dict with 'role' and 'content' keys"
                 )
 
+        # Handle Mistral role requirements
+        if any(identifier in self.model.lower() for identifier in self.MISTRAL_IDENTIFIERS):
+            try:
+                from copy import deepcopy
+                messages_copy = deepcopy(messages)
+                for message in messages_copy:
+                    if message.get("role") == "assistant":
+                        message["role"] = "user"
+                return messages_copy
+            except Exception as e:
+                logger.error(f"Error formatting messages for Mistral: {str(e)}")
+                raise
+
         if not self.is_anthropic:
             return messages
 

tests/cassettes/test_mistral_with_tools.yaml

@@ -0,0 +1,76 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "Use the dummy tool with param
+      ''test''"}], "model": "mistral-large-latest", "stop": [], "tools": [{"type":
+      "function", "function": {"name": "dummy_tool", "description": "A simple test
+      tool.", "parameters": {"type": "object", "properties": {"param": {"type": "string",
+      "description": "A test parameter"}}, "required": ["param"]}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '372'
+      content-type:
+      - application/json
+      host:
+      - api.mistral.ai
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - x64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - Linux
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.7
+    method: POST
+    uri: https://api.mistral.ai/v1/chat/completions
+  response:
+    content: "{\n  \"message\":\"Unauthorized\",\n  \"request_id\":\"96ca5615d43f134988d0fc4b1ded1455\"\n}"
+    headers:
+      CF-RAY:
+      - 9158bb5adad376f1-SEA
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '81'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Fri, 21 Feb 2025 18:17:12 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=MGDKyTo6P8HCsRCn9L6BcLQuWlHhR_Oyx0OAG2lNook-1740161832-1.0.1.1-4TQjjEAQkY4UdlzBET20v1w7G87AU38G8amFRICHPql3I0aHI5pV3Bez0qKp6f3cBT351xkaHyInoOA6FeoJqQ;
+        path=/; expires=Fri, 21-Feb-25 18:47:12 GMT; domain=.mistral.ai; HttpOnly;
+        Secure; SameSite=None
+      access-control-allow-origin:
+      - '*'
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      www-authenticate:
+      - Key
+      x-kong-request-id:
+      - 96ca5615d43f134988d0fc4b1ded1455
+      x-kong-response-latency:
+      - '0'
+    http_version: HTTP/1.1
+    status_code: 401
+version: 1

tests/llm_test.py

@@ -13,6 +13,84 @@ from crewai.utilities.token_counter_callback import TokenCalcHandler
 
 
 # TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.mistral
+class TestMistralLLM:
+    """Test suite for Mistral LLM functionality."""
+    
+    @pytest.fixture
+    def mistral_llm(self):
+        """Fixture providing a Mistral LLM instance."""
+        return LLM(model="mistral/mistral-large-latest")
+    
+    def test_mistral_role_handling(self, mistral_llm):
+        """
+        Verify that roles are handled correctly in various scenarios:
+        - Assistant roles are converted to user roles
+        - Original messages remain unchanged
+        - System messages are preserved
+        """
+        messages = [
+            {"role": "system", "content": "System message"},
+            {"role": "user", "content": "Test message"},
+            {"role": "assistant", "content": "Assistant response"}
+        ]
+        
+        formatted_messages = mistral_llm._format_messages_for_provider(messages)
+        
+        # Verify role conversions
+        assert any(msg["role"] == "user" for msg in formatted_messages if msg["content"] == "Assistant response")
+        assert not any(msg["role"] == "assistant" for msg in formatted_messages)
+        assert any(msg["role"] == "system" for msg in formatted_messages)
+        
+        # Original messages should not be modified
+        assert any(msg["role"] == "assistant" for msg in messages)
+    
+    def test_mistral_empty_messages(self, mistral_llm):
+        """Test handling of empty message list."""
+        messages = []
+        formatted_messages = mistral_llm._format_messages_for_provider(messages)
+        assert formatted_messages == []
+    
+    def test_mistral_multiple_assistant_messages(self, mistral_llm):
+        """Test handling of multiple consecutive assistant messages."""
+        messages = [
+            {"role": "user", "content": "User 1"},
+            {"role": "assistant", "content": "Assistant 1"},
+            {"role": "assistant", "content": "Assistant 2"},
+            {"role": "user", "content": "User 2"}
+        ]
+        
+        formatted_messages = mistral_llm._format_messages_for_provider(messages)
+        
+        # All assistant messages should be converted to user
+        assert all(msg["role"] == "user" for msg in formatted_messages 
+                  if msg["content"] in ["Assistant 1", "Assistant 2"])
+        
+        # Original messages should not be modified
+        assert len([msg for msg in messages if msg["role"] == "assistant"]) == 2
+
+
+def test_mistral_role_handling():
+    """Test that Mistral LLM correctly handles role requirements."""
+    llm = LLM(model="mistral/mistral-large-latest")
+    messages = [
+        {"role": "system", "content": "System message"},
+        {"role": "user", "content": "User message"},
+        {"role": "assistant", "content": "Assistant message"}
+    ]
+    
+    # Get the formatted messages
+    formatted_messages = llm._format_messages_for_provider(messages)
+    
+    # Verify that assistant role was changed to user for Mistral
+    assert any(msg["role"] == "user" for msg in formatted_messages if msg["content"] == "Assistant message")
+    assert not any(msg["role"] == "assistant" for msg in formatted_messages)
+    
+    # Original messages should not be modified
+    assert any(msg["role"] == "assistant" for msg in messages)
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_callback_replacement():
     llm1 = LLM(model="gpt-4o-mini")