feature: add support for custom model provider

src/mcp_agent/core/agent_types.py

@@ -34,7 +34,7 @@ class AgentConfig:
     default_request_params: RequestParams | None = None
     human_input: bool = False
     agent_type: str = AgentType.BASIC.value
-
+    provider: str | None = None
     def __post_init__(self) -> None:
         """Ensure default_request_params exists with proper history setting"""
 

src/mcp_agent/core/direct_decorators.py

@@ -90,6 +90,7 @@ def _decorator_impl(
     use_history: bool = True,
     request_params: RequestParams | None = None,
     human_input: bool = False,
+    provider: Optional[str] = None,
     **extra_kwargs,
 ) -> Callable[[AgentCallable[P, R]], DecoratedAgentProtocol[P, R]]:
     """
@@ -132,6 +133,7 @@ def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             model=model,
             use_history=use_history,
             human_input=human_input,
+            provider=provider,
         )
 
         # Update request params if provided
@@ -174,6 +176,7 @@ def agent(
     use_history: bool = True,
     request_params: RequestParams | None = None,
     human_input: bool = False,
+    provider: Optional[str] = None,
 ) -> Callable[[AgentCallable[P, R]], DecoratedAgentProtocol[P, R]]:
     """
     Decorator to create and register a standard agent with type-safe signature.
@@ -203,6 +206,7 @@ def agent(
         use_history=use_history,
         request_params=request_params,
         human_input=human_input,
+        provider=provider,
     )
 
 
@@ -218,6 +222,7 @@ def orchestrator(
     human_input: bool = False,
     plan_type: Literal["full", "iterative"] = "full",
     max_iterations: int = 30,
+    provider: Optional[str] = None,
 ) -> Callable[[AgentCallable[P, R]], DecoratedOrchestratorProtocol[P, R]]:
     """
     Decorator to create and register an orchestrator agent with type-safe signature.
@@ -232,7 +237,7 @@ def orchestrator(
         human_input: Whether to enable human input capabilities
         plan_type: Planning approach - "full" or "iterative"
         max_iterations: Maximum number of planning iterations
-
+        provider: Provider name
     Returns:
         A decorator that registers the orchestrator with proper type annotations
     """
@@ -259,6 +264,7 @@ def orchestrator(
             child_agents=agents,
             plan_type=plan_type,
             max_iterations=max_iterations,
+            provider=provider,
         ),
     )
 
@@ -274,6 +280,7 @@ def router(
     use_history: bool = False,
     request_params: RequestParams | None = None,
     human_input: bool = False,
+    provider: Optional[str] = None,
 ) -> Callable[[AgentCallable[P, R]], DecoratedRouterProtocol[P, R]]:
     """
     Decorator to create and register a router agent with type-safe signature.
@@ -308,6 +315,7 @@ def router(
             request_params=request_params,
             human_input=human_input,
             router_agents=agents,
+            provider=provider,
         ),
     )
 

src/mcp_agent/core/direct_factory.py

@@ -28,7 +28,7 @@
 T = TypeVar("T")  # For generic types
 
 # Type for model factory functions
-ModelFactoryFn = Callable[[Optional[str], Optional[RequestParams]], Callable[[], Any]]
+ModelFactoryFn = Callable[[Optional[str], Optional[RequestParams],Optional[str]], Callable[[], Any]]
 
 
 logger = get_logger(__name__)
@@ -54,6 +54,8 @@ def get_model_factory(
     request_params: Optional[RequestParams] = None,
     default_model: Optional[str] = None,
     cli_model: Optional[str] = None,
+    provider: Optional[str] = None,
+    cli_provider: Optional[str] = None,
 ) -> Callable:
     """
     Get model factory using specified or default model.
@@ -71,14 +73,23 @@ def get_model_factory(
     """
     # Config has lowest precedence
     model_spec = default_model or context.config.default_model
-
+    provider_spec = None
+
     # Command line override has next precedence
     if cli_model:
         model_spec = cli_model
+
+    # Provider from command line has next precedence
+    if cli_provider:
+        provider_spec = cli_provider
 
     # Model from decorator has highest precedence
     if model:
         model_spec = model
+
+    # Provider from decorator has highest precedence
+    if provider:
+        provider_spec = provider
 
     # Update or create request_params with the final model choice
     if request_params:
@@ -87,7 +98,7 @@ def get_model_factory(
         request_params = RequestParams(model=model_spec)
 
     # Let model factory handle the model string parsing and setup
-    return ModelFactory.create_factory(model_spec, request_params=request_params)
+    return ModelFactory.create_factory(model_spec, request_params=request_params,provider_name=provider_spec)
 
 
 async def create_agents_by_type(
@@ -147,7 +158,7 @@ def model_factory_func(model=None, request_params=None):
                 await agent.initialize()
 
                 # Attach LLM to the agent
-                llm_factory = model_factory_func(model=config.model)
+                llm_factory = model_factory_func(model=config.model,provider=config.provider)
                 await agent.attach_llm(llm_factory, request_params=config.default_request_params)
                 result_agents[name] = agent
 
@@ -180,7 +191,7 @@ def model_factory_func(model=None, request_params=None):
                 await orchestrator.initialize()
 
                 # Attach LLM to the orchestrator
-                llm_factory = model_factory_func(model=config.model)
+                llm_factory = model_factory_func(model=config.model,provider=config.provider)
                 await orchestrator.attach_llm(
                     llm_factory, request_params=config.default_request_params
                 )
@@ -241,7 +252,7 @@ def model_factory_func(model=None, request_params=None):
                 await router.initialize()
 
                 # Attach LLM to the router
-                llm_factory = model_factory_func(model=config.model)
+                llm_factory = model_factory_func(model=config.model,provider=config.provider)
                 await router.attach_llm(llm_factory, request_params=config.default_request_params)
                 result_agents[name] = router
 

src/mcp_agent/core/fastagent.py

@@ -129,6 +129,11 @@ def __init__(
             default="0.0.0.0",
             help="Host address to bind to when running as a server with SSE transport",
         )
+        parser.add_argument(
+            "--provider",
+            default=None,
+            help="Provider to use for custom models",
+        )
 
         if ignore_unknown_args:
             known_args, _ = parser.parse_known_args()
@@ -241,12 +246,14 @@ async def run(self):
                 validate_workflow_references(self.agents)
 
                 # Get a model factory function
-                def model_factory_func(model=None, request_params=None):
+                def model_factory_func(model=None, request_params=None, provider=None):
                     return get_model_factory(
                         self.context,
                         model=model,
+                        provider=provider,
                         request_params=request_params,
                         cli_model=self.args.model if hasattr(self, "args") else None,
+                        cli_provider=self.args.provider if hasattr(self, "args") else None,
                     )
 
                 # Create all agents in dependency order
@@ -445,6 +452,10 @@ async def start_server(
         self.args.model = None
         if hasattr(original_args, "model"):
             self.args.model = original_args.model
+
+        self.args.provider = None
+        if hasattr(original_args, "provider"):
+            self.args.provider = original_args.provider
 
         # Run the application, which will detect the server flag and start server mode
         async with self.run():

src/mcp_agent/llm/model_factory.py

@@ -137,16 +137,24 @@ class ModelFactory:
     }
 
     @classmethod
-    def parse_model_string(cls, model_string: str) -> ModelConfig:
+    def parse_model_string(cls, model_string: str,provider_name:Optional[str]=None) -> ModelConfig:
         """Parse a model string into a ModelConfig object"""
+
+        provider = None
+        reasoning_effort = None
+
+        # Support for custom models
+        if provider_name is not None:
+            provider = cls.PROVIDER_MAP.get(provider_name.lower())
+            if provider:
+                return ModelConfig(provider=provider, model_name=model_string, reasoning_effort=None)
+
         # Check if model string is an alias
         model_string = cls.MODEL_ALIASES.get(model_string, model_string)
         parts = model_string.split(".")
 
         # Start with all parts as the model name
         model_parts = parts.copy()
-        provider = None
-        reasoning_effort = None
 
         # Check last part for reasoning effort
         if len(parts) > 1 and parts[-1].lower() in cls.EFFORT_MAP:
@@ -175,7 +183,7 @@ def parse_model_string(cls, model_string: str) -> ModelConfig:
 
     @classmethod
     def create_factory(
-        cls, model_string: str, request_params: Optional[RequestParams] = None
+        cls, model_string: str, request_params: Optional[RequestParams] = None,provider_name:Optional[str]=None
     ) -> Callable[..., AugmentedLLMProtocol]:
         """
         Creates a factory function that follows the attach_llm protocol.
@@ -188,7 +196,7 @@ def create_factory(
             A callable that takes an agent parameter and returns an LLM instance
         """
         # Parse configuration up front
-        config = cls.parse_model_string(model_string)
+        config = cls.parse_model_string(model_string,provider_name)
         if config.model_name in cls.MODEL_SPECIFIC_CLASSES:
             llm_class = cls.MODEL_SPECIFIC_CLASSES[config.model_name]
         else: