Fix #5893: Relax model naming prefix filtering for custom-deployed models

- Update _matches_provider_pattern for anthropic to use 'in' (contains)
  check instead of strict startswith for 'claude' keyword, and accept
  any model starting with 'anthropic' regardless of separator character
- Add pattern-matching fallback to _infer_provider_from_model so custom
  model names without provider prefix are correctly routed
- Update _is_anthropic_model to also detect 'anthropic.' substring
- Update ANTHROPIC_PREFIXES constant for consistency
- Add regression tests for all affected methods

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

lib/crewai/src/crewai/llm.py

@@ -111,7 +111,7 @@ if LITELLM_AVAILABLE:
 
 MIN_CONTEXT: Final[int] = 1024
 MAX_CONTEXT: Final[int] = 2097152  # Current max from gemini-1.5-pro
-ANTHROPIC_PREFIXES: Final[tuple[str, str, str]] = ("anthropic/", "claude-", "claude/")
+ANTHROPIC_PREFIXES: Final[tuple[str, ...]] = ("anthropic/", "anthropic.", "claude-", "claude/")
 
 LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
     # openai
@@ -465,8 +465,9 @@ class LLM(BaseLLM):
             )
 
         if provider == "anthropic" or provider == "claude":
-            return any(
-                model_lower.startswith(prefix) for prefix in ["claude-", "anthropic."]
+            return (
+                "claude" in model_lower
+                or model_lower.startswith("anthropic")
             )
 
         if provider == "gemini" or provider == "google":
@@ -574,6 +575,19 @@ class LLM(BaseLLM):
         if model in AZURE_MODELS:
             return "azure"
 
+        # Fallback to pattern matching for models not in constants
+        provider_order = [
+            "bedrock",
+            "openai",
+            "anthropic",
+            "gemini",
+            "deepseek",
+            "dashscope",
+        ]
+        for provider in provider_order:
+            if cls._matches_provider_pattern(model, provider):
+                return provider
+
         return "openai"
 
     @classmethod
@@ -653,8 +667,8 @@ class LLM(BaseLLM):
         Returns:
             bool: True if the model is from Anthropic, False otherwise.
         """
-        anthropic_prefixes = ("anthropic/", "claude-", "claude/")
-        return any(prefix in model.lower() for prefix in anthropic_prefixes)
+        anthropic_indicators = ("anthropic/", "anthropic.", "claude-", "claude/")
+        return any(indicator in model.lower() for indicator in anthropic_indicators)
 
     def _prepare_completion_params(
         self,

lib/crewai/tests/test_llm.py

@@ -996,6 +996,81 @@ def test_validate_model_in_constants():
         is True
     )
 
+
+def test_matches_provider_pattern_custom_anthropic_names():
+    """Test that _matches_provider_pattern handles custom-deployed Anthropic model names.
+
+    Regression test for https://github.com/crewAIInc/crewAI/issues/5893
+    Users with custom-deployed models (e.g. 'anthropic--claude-...') were filtered
+    out because pattern matching only supported ["claude-", "anthropic."] prefixes.
+    """
+    # Standard patterns still work
+    assert LLM._matches_provider_pattern("claude-3-5-sonnet", "anthropic") is True
+    assert LLM._matches_provider_pattern("anthropic.claude-v2", "anthropic") is True
+
+    # Custom-deployed model names with non-standard separators (issue #5893)
+    assert LLM._matches_provider_pattern("anthropic--claude-3-5-sonnet", "anthropic") is True
+    assert LLM._matches_provider_pattern("anthropic--claude-opus-4", "anthropic") is True
+
+    # Models containing "claude" anywhere in the name
+    assert LLM._matches_provider_pattern("my-claude-model", "anthropic") is True
+    assert LLM._matches_provider_pattern("custom-claude-3-haiku", "anthropic") is True
+
+    # Models starting with "anthropic" regardless of separator
+    assert LLM._matches_provider_pattern("anthropic_custom_model", "anthropic") is True
+    assert LLM._matches_provider_pattern("anthropic-custom", "anthropic") is True
+
+    # Unrelated models should not match
+    assert LLM._matches_provider_pattern("gpt-4o", "anthropic") is False
+    assert LLM._matches_provider_pattern("gemini-2.0-flash", "anthropic") is False
+    assert LLM._matches_provider_pattern("llama-3-70b", "anthropic") is False
+
+
+def test_validate_model_in_constants_custom_anthropic_names():
+    """Test that _validate_model_in_constants accepts custom Anthropic model names.
+
+    Regression test for https://github.com/crewAIInc/crewAI/issues/5893
+    """
+    # Custom naming conventions should be accepted via pattern matching fallback
+    assert LLM._validate_model_in_constants("anthropic--claude-3-5-sonnet", "anthropic") is True
+    assert LLM._validate_model_in_constants("anthropic--claude-3-5-sonnet", "claude") is True
+    assert LLM._validate_model_in_constants("custom-claude-model", "anthropic") is True
+
+
+def test_infer_provider_custom_anthropic_names():
+    """Test that _infer_provider_from_model infers anthropic for custom model names.
+
+    Regression test for https://github.com/crewAIInc/crewAI/issues/5893
+    Models like 'anthropic--claude-...' should be inferred as anthropic, not openai.
+    """
+    assert LLM._infer_provider_from_model("anthropic--claude-3-5-sonnet") == "anthropic"
+    assert LLM._infer_provider_from_model("claude-3-5-sonnet") == "anthropic"
+
+    # Standard models should still be correctly inferred
+    assert LLM._infer_provider_from_model("gpt-4o") == "openai"
+    assert LLM._infer_provider_from_model("gemini-2.0-flash") == "gemini"
+
+
+def test_is_anthropic_model_custom_names():
+    """Test that _is_anthropic_model detects custom-deployed Anthropic models.
+
+    Regression test for https://github.com/crewAIInc/crewAI/issues/5893
+    """
+    # Standard patterns
+    assert LLM._is_anthropic_model("anthropic/claude-3-5-sonnet") is True
+    assert LLM._is_anthropic_model("claude-3-5-sonnet") is True
+
+    # Custom naming with "claude-" substring
+    assert LLM._is_anthropic_model("anthropic--claude-3-5-sonnet") is True
+
+    # Bedrock-style naming with "anthropic."
+    assert LLM._is_anthropic_model("anthropic.claude-v2") is True
+
+    # Non-anthropic models
+    assert LLM._is_anthropic_model("gpt-4o") is False
+    assert LLM._is_anthropic_model("gemini-2.0-flash") is False
+
+
 @pytest.mark.vcr(record_mode="once",decode_compressed_response=True)
 def test_usage_info_non_streaming_with_call():
     llm = LLM(model="gpt-4o-mini", is_litellm=True)