feat(cli): update model options to include latest models

- Update OpenAI models: add gpt-4-turbo, o1, o3, o3-mini, o4-mini
- Update Anthropic models: add claude-sonnet-4-5, claude-3-7-sonnet, claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022
- Update Groq models: add llama-3.3-70b-versatile, llama-3.2 variants, mixtral-8x7b-32768
- Update Ollama models: add llama3.2, llama3.3, deepseek-r1
- Add tests to verify latest models are included for major providers

Fixes #4317

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

docs/en/concepts/knowledge.mdx

@@ -25,35 +25,6 @@ For file-based Knowledge Sources, make sure to place your files in a `knowledge`
 Also, use relative paths from the `knowledge` directory when creating the source.
 </Tip>
 
-### Skills.md for Crews (CrewBase)
-
-When using the `@CrewBase` decorator and project layout (e.g. from `crewai create crew`), you can add **Skills.md** files as crew-level knowledge sources. Place one markdown file per skill under:
-
-```
-src/<project>/.agents/<skill_name>/Skills.md
-```
-
-Each `Skills.md` is loaded as a knowledge source so the crew can query it via RAG at runtime. Use `get_skills_knowledge_sources()` when building your crew:
-
-```python
-from crewai.project import CrewBase, agent, crew, task
-
-@CrewBase
-class MyCrew():
-    # ...
-
-    @crew
-    def crew(self) -> Crew:
-        return Crew(
-            agents=self.agents,
-            tasks=self.tasks,
-            process=Process.sequential,
-            knowledge_sources=self.get_skills_knowledge_sources(),
-        )
-```
-
-You can combine skills with other knowledge sources: `knowledge_sources=[*self.get_skills_knowledge_sources(), other_source]`. If the `.agents` directory is missing or has no `Skills.md` files, `get_skills_knowledge_sources()` returns an empty list. To use a different directory than `.agents`, set the class attribute `skills_directory = "my_skills"`. Skills.md support requires the `docling` package (`uv add docling`).
-
 ### Vector store (RAG) client configuration
 
 CrewAI exposes a provider-neutral RAG client abstraction for vector stores. The default provider is ChromaDB, and Qdrant is supported as well. You can switch providers using configuration utilities.

lib/crewai/src/crewai/cli/constants.py

@@ -129,20 +129,25 @@ PROVIDERS = [
 
 MODELS = {
     "openai": [
-        "gpt-4",
-        "gpt-4.1",
-        "gpt-4.1-mini-2025-04-14",
-        "gpt-4.1-nano-2025-04-14",
         "gpt-4o",
         "gpt-4o-mini",
+        "gpt-4-turbo",
+        "gpt-4.1",
+        "gpt-4.1-mini",
+        "gpt-4.1-nano",
+        "o1",
         "o1-mini",
         "o1-preview",
+        "o3",
+        "o3-mini",
+        "o4-mini",
     ],
     "anthropic": [
-        "claude-3-5-sonnet-20240620",
-        "claude-3-sonnet-20240229",
+        "claude-sonnet-4-5-20250514",
+        "claude-3-7-sonnet-20250219",
+        "claude-3-5-sonnet-20241022",
+        "claude-3-5-haiku-20241022",
         "claude-3-opus-20240229",
-        "claude-3-haiku-20240307",
     ],
     "gemini": [
         "gemini/gemini-3-pro-preview",
@@ -230,13 +235,15 @@ MODELS = {
         "nvidia_nim/baichuan-inc/baichuan2-13b-chat",
     ],
     "groq": [
+        "groq/llama-3.3-70b-versatile",
+        "groq/llama-3.3-70b-specdec",
         "groq/llama-3.1-8b-instant",
-        "groq/llama-3.1-70b-versatile",
-        "groq/llama-3.1-405b-reasoning",
+        "groq/llama-3.2-3b-preview",
+        "groq/llama-3.2-1b-preview",
+        "groq/mixtral-8x7b-32768",
         "groq/gemma2-9b-it",
-        "groq/gemma-7b-it",
     ],
-    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
+    "ollama": ["ollama/llama3.2", "ollama/llama3.3", "ollama/mixtral", "ollama/deepseek-r1"],
     "watson": [
         "watsonx/meta-llama/llama-3-1-70b-instruct",
         "watsonx/meta-llama/llama-3-1-8b-instruct",

lib/crewai/src/crewai/cli/create_crew.py

@@ -110,9 +110,6 @@ def create_folder_structure(name, parent_folder=None):
         (folder_path / "src" / folder_name).mkdir(parents=True)
         (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
         (folder_path / "src" / folder_name / "config").mkdir(parents=True)
-        (folder_path / "src" / folder_name / ".agents" / "research").mkdir(
-            parents=True
-        )
 
     return folder_path, folder_name, class_name
 
@@ -157,13 +154,6 @@ def copy_template_files(folder_path, name, class_name, parent_folder):
             dst_file = src_folder / file_name
             copy_template(src_file, dst_file, name, class_name, folder_path.name)
 
-        # Copy Skills.md from .agents/research template
-        skills_src = templates_dir / ".agents" / "research" / "Skills.md"
-        skills_dst = src_folder / ".agents" / "research" / "Skills.md"
-        if skills_src.exists():
-            skills_dst.parent.mkdir(parents=True, exist_ok=True)
-            copy_template(skills_src, skills_dst, name, class_name, folder_path.name)
-
 
 def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
     folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)

lib/crewai/src/crewai/cli/templates/crew/crew.py

@@ -54,18 +54,11 @@ class {{crew_name}}():
         """Creates the {{crew_name}} crew"""
         # To learn how to add knowledge sources to your crew, check out the documentation:
         # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-        # Skills.md files under .agents/<skill_name>/ are loaded via get_skills_knowledge_sources()
 
-        skills_sources = (
-            self.get_skills_knowledge_sources()
-            if hasattr(self, "get_skills_knowledge_sources")
-            else []
-        )
         return Crew(
             agents=self.agents, # Automatically created by the @agent decorator
             tasks=self.tasks, # Automatically created by the @task decorator
             process=Process.sequential,
             verbose=True,
-            knowledge_sources=skills_sources,
             # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
         )

lib/crewai/src/crewai/project/crew_base.py

@@ -178,57 +178,6 @@ def _set_mcp_params(cls: type[CrewClass]) -> None:
     cls.mcp_connect_timeout = getattr(cls, "mcp_connect_timeout", 30)
 
 
-def _set_skills_params(cls: type[CrewClass]) -> None:
-    """Set the skills directory path for the crew class.
-
-    Args:
-        cls: Crew class to configure.
-    """
-    cls.skills_directory = getattr(cls, "skills_directory", ".agents")
-
-
-def get_skills_knowledge_sources(self: CrewInstance) -> list[Any]:
-    """Discover Skills.md files under .agents/<skill_name>/ and return them as knowledge sources.
-
-    Looks for src/<project>/.agents/<skill_name>/Skills.md (relative to the crew class
-    base_directory). Each found file is wrapped in a CrewDoclingSource so the crew can
-    query it via RAG. Requires the docling package; if not installed, returns an empty list.
-
-    Returns:
-        List of knowledge sources (CrewDoclingSource instances), or empty list if
-        .agents is missing, has no Skills.md files, or docling is not installed.
-    """
-    skills_dir_name = getattr(self, "skills_directory", ".agents")
-    skills_dir = self.base_directory / skills_dir_name
-    if not skills_dir.exists() or not skills_dir.is_dir():
-        return []
-
-    try:
-        from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-        from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
-    except ImportError:
-        logging.warning(
-            "Skills.md support requires the docling package. "
-            "Install it with: uv add docling"
-        )
-        return []
-
-    sources: list[Any] = []
-    for subdir in sorted(skills_dir.iterdir()):
-        if not subdir.is_dir():
-            continue
-        skills_md = subdir / "Skills.md"
-        if skills_md.exists():
-            try:
-                source = CrewDoclingSource(file_paths=[skills_md])
-                sources.append(source)
-            except Exception as e:
-                logging.warning(
-                    f"Could not create knowledge source for {skills_md}: {e}"
-                )
-    return sources
-
-
 def _is_string_list(value: list[str] | list[BaseTool]) -> TypeGuard[list[str]]:
     """Type guard to check if list contains strings rather than BaseTool instances.
 
@@ -782,7 +731,6 @@ _CLASS_SETUP_FUNCTIONS: tuple[Callable[[type[CrewClass]], None], ...] = (
     _set_base_directory,
     _set_config_paths,
     _set_mcp_params,
-    _set_skills_params,
 )
 
 _METHODS_TO_INJECT = (
@@ -791,7 +739,6 @@ _METHODS_TO_INJECT = (
     _load_config,
     load_configurations,
     staticmethod(load_yaml),
-    get_skills_knowledge_sources,
     map_all_agent_variables,
     _map_agent_variables,
     map_all_task_variables,

lib/crewai/src/crewai/project/wrappers.py

@@ -81,10 +81,8 @@ class CrewInstance(Protocol):
     tasks_config: dict[str, Any]
     mcp_server_params: Any
     mcp_connect_timeout: int
-    skills_directory: str
 
     def load_configurations(self) -> None: ...
-    def get_skills_knowledge_sources(self) -> list[Any]: ...
     def map_all_agent_variables(self) -> None: ...
     def map_all_task_variables(self) -> None: ...
     def close_mcp_server(self, instance: Self, outputs: CrewOutput) -> CrewOutput: ...
@@ -124,10 +122,8 @@ class CrewClass(Protocol):
     original_tasks_config_path: str
     mcp_server_params: Any
     mcp_connect_timeout: int
-    skills_directory: str
     close_mcp_server: Callable[..., Any]
     get_mcp_tools: Callable[..., list[BaseTool]]
-    get_skills_knowledge_sources: Callable[..., list[Any]]
     _load_config: Callable[..., dict[str, Any]]
     load_configurations: Callable[..., None]
     load_yaml: Callable[..., dict[str, Any]]

lib/crewai/src/crewai/utilities/llm_utils.py

@@ -148,10 +148,7 @@ def _llm_via_environment_or_fallback() -> LLM | None:
         "AWS_SECRET_ACCESS_KEY",
         "AWS_REGION_NAME",
     ]
-    if "/" in model_name:
-        set_provider = model_name.partition("/")[0]
-    else:
-        set_provider = LLM._infer_provider_from_model(model_name)
+    set_provider = model_name.partition("/")[0] if "/" in model_name else "openai"
 
     if set_provider in ENV_VARS:
         env_vars_for_provider = ENV_VARS[set_provider]

lib/crewai/tests/cli/test_constants.py

@@ -18,3 +18,68 @@ def test_huggingface_models():
     """Test that Huggingface models are properly configured."""
     assert "huggingface" in MODELS
     assert len(MODELS["huggingface"]) > 0
+
+
+def test_openai_models_include_latest():
+    """Test that OpenAI models include the latest models."""
+    assert "openai" in MODELS
+    openai_models = MODELS["openai"]
+    assert len(openai_models) > 0
+    assert "gpt-4o" in openai_models
+    assert "gpt-4o-mini" in openai_models
+    assert "o1" in openai_models
+    assert "o3" in openai_models
+    assert "o3-mini" in openai_models
+
+
+def test_anthropic_models_include_latest():
+    """Test that Anthropic models include the latest Claude models."""
+    assert "anthropic" in MODELS
+    anthropic_models = MODELS["anthropic"]
+    assert len(anthropic_models) > 0
+    assert "claude-3-7-sonnet-20250219" in anthropic_models
+    assert "claude-3-5-sonnet-20241022" in anthropic_models
+    assert "claude-3-5-haiku-20241022" in anthropic_models
+
+
+def test_groq_models_include_latest():
+    """Test that Groq models include the latest Llama models."""
+    assert "groq" in MODELS
+    groq_models = MODELS["groq"]
+    assert len(groq_models) > 0
+    assert "groq/llama-3.3-70b-versatile" in groq_models
+
+
+def test_ollama_models_include_latest():
+    """Test that Ollama models include the latest models."""
+    assert "ollama" in MODELS
+    ollama_models = MODELS["ollama"]
+    assert len(ollama_models) > 0
+    assert "ollama/llama3.2" in ollama_models
+    assert "ollama/llama3.3" in ollama_models
+
+
+def test_all_providers_have_models():
+    """Test that all providers in PROVIDERS have corresponding models in MODELS."""
+    providers_with_models = [
+        "openai",
+        "anthropic",
+        "gemini",
+        "nvidia_nim",
+        "groq",
+        "ollama",
+        "watson",
+        "bedrock",
+        "huggingface",
+        "sambanova",
+    ]
+    for provider in providers_with_models:
+        assert provider in MODELS, f"Provider {provider} should have models defined"
+        assert len(MODELS[provider]) > 0, f"Provider {provider} should have at least one model"
+
+
+def test_all_providers_have_env_vars_or_defaults():
+    """Test that all providers have environment variable configurations."""
+    for provider in PROVIDERS:
+        if provider in ENV_VARS:
+            assert len(ENV_VARS[provider]) > 0, f"Provider {provider} should have env var config"

lib/crewai/tests/test_project.py

@@ -1,5 +1,3 @@
-import tempfile
-from pathlib import Path
 from typing import Any, ClassVar
 from unittest.mock import Mock, patch
 
@@ -384,93 +382,3 @@ def test_internal_crew_with_mcp():
     adapter_mock.assert_called_once_with(
         {"host": "localhost", "port": 8000}, connect_timeout=120
     )
-
-
-def test_get_skills_knowledge_sources_discovery():
-    """get_skills_knowledge_sources discovers .agents/<skill_name>/Skills.md and returns sources."""
-
-    @CrewBase
-    class SkillsCrew:
-        agents_config = "nonexistent/agents.yaml"
-        tasks_config = "nonexistent/tasks.yaml"
-        agents: list[BaseAgent]
-        tasks: list[Task]
-
-        @agent
-        def researcher(self):
-            return Agent(
-                role="Researcher",
-                goal="Research",
-                backstory="Expert researcher",
-            )
-
-        @task
-        def research_task(self):
-            return Task(
-                description="Research", expected_output="Report", agent=self.researcher()
-            )
-
-        @crew
-        def crew(self):
-            return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
-
-    with tempfile.TemporaryDirectory() as tmp:
-        base = Path(tmp)
-        (base / ".agents" / "skill_a").mkdir(parents=True)
-        (base / ".agents" / "skill_b").mkdir(parents=True)
-        (base / ".agents" / "skill_a" / "Skills.md").write_text("# Skill A")
-        (base / ".agents" / "skill_b" / "Skills.md").write_text("# Skill B")
-
-        crew_instance = SkillsCrew()
-        crew_instance.base_directory = base
-        sources = crew_instance.get_skills_knowledge_sources()
-
-    # With docling installed we get 2 sources; without it we get []
-    if len(sources) == 2:
-        paths = []
-        for s in sources:
-            paths.extend(getattr(s, "file_paths", []) or getattr(s, "safe_file_paths", []))
-        path_strs = {str(Path(p).resolve()) for p in paths}
-        expected_a = str((base / ".agents" / "skill_a" / "Skills.md").resolve())
-        expected_b = str((base / ".agents" / "skill_b" / "Skills.md").resolve())
-        assert expected_a in path_strs
-        assert expected_b in path_strs
-    else:
-        assert len(sources) == 0
-
-
-def test_get_skills_knowledge_sources_missing_dir_returns_empty():
-    """get_skills_knowledge_sources returns [] when .agents does not exist."""
-
-    @CrewBase
-    class NoSkillsCrew:
-        agents_config = "nonexistent/agents.yaml"
-        tasks_config = "nonexistent/tasks.yaml"
-        agents: list[BaseAgent]
-        tasks: list[Task]
-
-        @agent
-        def researcher(self):
-            return Agent(
-                role="Researcher",
-                goal="Research",
-                backstory="Expert researcher",
-            )
-
-        @task
-        def research_task(self):
-            return Task(
-                description="Research", expected_output="Report", agent=self.researcher()
-            )
-
-        @crew
-        def crew(self):
-            return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
-
-    with tempfile.TemporaryDirectory() as tmp:
-        base = Path(tmp)
-        crew_instance = NoSkillsCrew()
-        crew_instance.base_directory = base
-        sources = crew_instance.get_skills_knowledge_sources()
-
-    assert sources == []

pyproject.toml

@@ -149,7 +149,6 @@ members = [
     "lib/crewai-tools",
     "lib/devtools",
     "lib/crewai-files",
-    "testing_agents",
 ]