feat: add MiniMax provider support (default: MiniMax-M3)

gui_agents/s2/core/engine.py

@@ -321,7 +321,7 @@ def __init__(
         model=None,
         api_version=None,
         rate_limit=-1,
-        **kwargs
+        **kwargs,
     ):
         assert model is not None, "model must be provided"
         self.model = model
@@ -390,7 +390,7 @@ def generate(
         top_p=0.8,
         repetition_penalty=1.05,
         max_new_tokens=512,
-        **kwargs
+        **kwargs,
     ):
         api_key = self.api_key or os.getenv("vLLM_API_KEY")
         if api_key is None:
@@ -483,3 +483,73 @@ def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
             .choices[0]
             .message.content
         )
+
+
+class LMMEngineMiniMax(LMMEngine):
+    _UNSUPPORTED_PARAMS = frozenset(
+        [
+            "top_k",
+            "stop_sequences",
+            "service_tier",
+            "mcp_servers",
+            "context_management",
+            "container",
+        ]
+    )
+
+    def __init__(
+        self,
+        base_url=None,
+        api_key=None,
+        model=None,
+        temperature=None,
+        **kwargs,
+    ):
+        assert model is not None, "model must be provided"
+        self.model = model
+        self.base_url = base_url
+        self.api_key = api_key
+        self.llm_client = None
+        self.temperature = temperature
+
+    @backoff.on_exception(
+        backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60
+    )
+    def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
+        api_key = self.api_key or os.getenv("MINIMAX_API_KEY")
+        if api_key is None:
+            raise ValueError(
+                "An API Key needs to be provided in either the api_key parameter or as an environment variable named MINIMAX_API_KEY"
+            )
+        base_url = (
+            self.base_url
+            or os.getenv("MINIMAX_BASE_URL")
+            or "https://api.minimax.io/anthropic"
+        )
+        if not self.llm_client:
+            from anthropic import Anthropic as _Anthropic
+
+            self.llm_client = _Anthropic(api_key=api_key, base_url=base_url)
+        # Use instance temperature if set, otherwise use generate argument
+        temp = self.temperature if self.temperature is not None else temperature
+        # MiniMax temperature must be in (0.0, 1.0]; clamp to 1.0 if not positive
+        if temp <= 0.0:
+            temp = 1.0
+        # Filter out parameters not supported by MiniMax
+        filtered_kwargs = {
+            k: v for k, v in kwargs.items() if k not in self._UNSUPPORTED_PARAMS
+        }
+        system_text = messages[0]["content"][0]["text"]
+        conversation = messages[1:]
+        return (
+            self.llm_client.messages.create(
+                model=self.model,
+                system=system_text,
+                messages=conversation,
+                max_tokens=max_new_tokens if max_new_tokens else 4096,
+                temperature=temp,
+                **filtered_kwargs,
+            )
+            .content[0]
+            .text
+        )

gui_agents/s2/core/mllm.py

@@ -11,6 +11,7 @@
     LMMEngineParasail,
     LMMEnginevLLM,
     LMMEngineGemini,
+    LMMEngineMiniMax,
 )
 
 
@@ -35,6 +36,8 @@ def __init__(self, engine_params=None, system_prompt=None, engine=None):
                     self.engine = LMMEngineOpenRouter(**engine_params)
                 elif engine_type == "parasail":
                     self.engine = LMMEngineParasail(**engine_params)
+                elif engine_type == "minimax":
+                    self.engine = LMMEngineMiniMax(**engine_params)
                 else:
                     raise ValueError("engine_type is not supported")
             else:
@@ -180,8 +183,8 @@ def add_message(
 
             self.messages.append(message)
 
-        # For API-style inference from Anthropic
-        elif isinstance(self.engine, LMMEngineAnthropic):
+        # For API-style inference from Anthropic or MiniMax (Anthropic-compatible)
+        elif isinstance(self.engine, (LMMEngineAnthropic, LMMEngineMiniMax)):
             # infer role from previous message
             if role != "user":
                 if self.messages[-1]["role"] == "system":

gui_agents/s2_5/core/engine.py

@@ -439,3 +439,73 @@ def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
             .choices[0]
             .message.content
         )
+
+
+class LMMEngineMiniMax(LMMEngine):
+    _UNSUPPORTED_PARAMS = frozenset(
+        [
+            "top_k",
+            "stop_sequences",
+            "service_tier",
+            "mcp_servers",
+            "context_management",
+            "container",
+        ]
+    )
+
+    def __init__(
+        self,
+        base_url=None,
+        api_key=None,
+        model=None,
+        temperature=None,
+        **kwargs,
+    ):
+        assert model is not None, "model must be provided"
+        self.model = model
+        self.base_url = base_url
+        self.api_key = api_key
+        self.llm_client = None
+        self.temperature = temperature
+
+    @backoff.on_exception(
+        backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60
+    )
+    def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
+        api_key = self.api_key or os.getenv("MINIMAX_API_KEY")
+        if api_key is None:
+            raise ValueError(
+                "An API Key needs to be provided in either the api_key parameter or as an environment variable named MINIMAX_API_KEY"
+            )
+        base_url = (
+            self.base_url
+            or os.getenv("MINIMAX_BASE_URL")
+            or "https://api.minimax.io/anthropic"
+        )
+        if not self.llm_client:
+            from anthropic import Anthropic as _Anthropic
+
+            self.llm_client = _Anthropic(api_key=api_key, base_url=base_url)
+        # Use instance temperature if set, otherwise use generate argument
+        temp = self.temperature if self.temperature is not None else temperature
+        # MiniMax temperature must be in (0.0, 1.0]; clamp to 1.0 if not positive
+        if temp <= 0.0:
+            temp = 1.0
+        # Filter out parameters not supported by MiniMax
+        filtered_kwargs = {
+            k: v for k, v in kwargs.items() if k not in self._UNSUPPORTED_PARAMS
+        }
+        system_text = messages[0]["content"][0]["text"]
+        conversation = messages[1:]
+        return (
+            self.llm_client.messages.create(
+                model=self.model,
+                system=system_text,
+                messages=conversation,
+                max_tokens=max_new_tokens if max_new_tokens else 4096,
+                temperature=temp,
+                **filtered_kwargs,
+            )
+            .content[0]
+            .text
+        )

gui_agents/s2_5/core/mllm.py

@@ -11,6 +11,7 @@
     LMMEngineParasail,
     LMMEnginevLLM,
     LMMEngineGemini,
+    LMMEngineMiniMax,
 )
 
 
@@ -35,6 +36,8 @@ def __init__(self, engine_params=None, system_prompt=None, engine=None):
                     self.engine = LMMEngineOpenRouter(**engine_params)
                 elif engine_type == "parasail":
                     self.engine = LMMEngineParasail(**engine_params)
+                elif engine_type == "minimax":
+                    self.engine = LMMEngineMiniMax(**engine_params)
                 else:
                     raise ValueError("engine_type is not supported")
             else:
@@ -180,8 +183,8 @@ def add_message(
 
             self.messages.append(message)
 
-        # For API-style inference from Anthropic
-        elif isinstance(self.engine, LMMEngineAnthropic):
+        # For API-style inference from Anthropic or MiniMax (Anthropic-compatible)
+        elif isinstance(self.engine, (LMMEngineAnthropic, LMMEngineMiniMax)):
             # infer role from previous message
             if role != "user":
                 if self.messages[-1]["role"] == "system":

gui_agents/s3/core/engine.py

@@ -443,3 +443,73 @@ def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
             .choices[0]
             .message.content
         )
+
+
+class LMMEngineMiniMax(LMMEngine):
+    _UNSUPPORTED_PARAMS = frozenset(
+        [
+            "top_k",
+            "stop_sequences",
+            "service_tier",
+            "mcp_servers",
+            "context_management",
+            "container",
+        ]
+    )
+
+    def __init__(
+        self,
+        base_url=None,
+        api_key=None,
+        model=None,
+        temperature=None,
+        **kwargs,
+    ):
+        assert model is not None, "model must be provided"
+        self.model = model
+        self.base_url = base_url
+        self.api_key = api_key
+        self.llm_client = None
+        self.temperature = temperature
+
+    @backoff.on_exception(
+        backoff.expo, (APIConnectionError, APIError, RateLimitError), max_time=60
+    )
+    def generate(self, messages, temperature=0.0, max_new_tokens=None, **kwargs):
+        api_key = self.api_key or os.getenv("MINIMAX_API_KEY")
+        if api_key is None:
+            raise ValueError(
+                "An API Key needs to be provided in either the api_key parameter or as an environment variable named MINIMAX_API_KEY"
+            )
+        base_url = (
+            self.base_url
+            or os.getenv("MINIMAX_BASE_URL")
+            or "https://api.minimax.io/anthropic"
+        )
+        if not self.llm_client:
+            from anthropic import Anthropic as _Anthropic
+
+            self.llm_client = _Anthropic(api_key=api_key, base_url=base_url)
+        # Use instance temperature if set, otherwise use generate argument
+        temp = self.temperature if self.temperature is not None else temperature
+        # MiniMax temperature must be in (0.0, 1.0]; clamp to 1.0 if not positive
+        if temp <= 0.0:
+            temp = 1.0
+        # Filter out parameters not supported by MiniMax
+        filtered_kwargs = {
+            k: v for k, v in kwargs.items() if k not in self._UNSUPPORTED_PARAMS
+        }
+        system_text = messages[0]["content"][0]["text"]
+        conversation = messages[1:]
+        return (
+            self.llm_client.messages.create(
+                model=self.model,
+                system=system_text,
+                messages=conversation,
+                max_tokens=max_new_tokens if max_new_tokens else 4096,
+                temperature=temp,
+                **filtered_kwargs,
+            )
+            .content[0]
+            .text
+        )

gui_agents/s3/core/mllm.py

@@ -11,6 +11,7 @@
     LMMEngineParasail,
     LMMEnginevLLM,
     LMMEngineGemini,
+    LMMEngineMiniMax,
 )
 
 
@@ -35,6 +36,8 @@ def __init__(self, engine_params=None, system_prompt=None, engine=None):
                     self.engine = LMMEngineOpenRouter(**engine_params)
                 elif engine_type == "parasail":
                     self.engine = LMMEngineParasail(**engine_params)
+                elif engine_type == "minimax":
+                    self.engine = LMMEngineMiniMax(**engine_params)
                 else:
                     raise ValueError(f"engine_type '{engine_type}' is not supported")
             else:
@@ -180,8 +183,8 @@ def add_message(
 
             self.messages.append(message)
 
-        # For API-style inference from Anthropic
-        elif isinstance(self.engine, LMMEngineAnthropic):
+        # For API-style inference from Anthropic or MiniMax (Anthropic-compatible)
+        elif isinstance(self.engine, (LMMEngineAnthropic, LMMEngineMiniMax)):
             # infer role from previous message
             if role != "user":
                 if self.messages[-1]["role"] == "system":

models.md

@@ -1,4 +1,4 @@
-We support the following APIs for MLLM inference: OpenAI, Anthropic, Gemini, Azure OpenAI, vLLM for local models, and Open Router. To use these APIs, you need to set the corresponding environment variables:
+We support the following APIs for MLLM inference: OpenAI, Anthropic, Gemini, Azure OpenAI, vLLM for local models, Open Router, and MiniMax. To use these APIs, you need to set the corresponding environment variables:
 
 1. OpenAI
 
@@ -55,6 +55,14 @@ agent = AgentS2_5(
 )
 ```
 
+7. MiniMax
+
+```
+export MINIMAX_API_KEY=<YOUR_API_KEY>
+```
+
+Supported models: `MiniMax-M3` (default, 512K context, 128K max output, image input support), `MiniMax-M2.7`, `MiniMax-M2.7-highspeed`
+
 To use the underlying Multimodal Agent (LMMAgent) which wraps LLMs with message handling functionality, you can use the following code snippet:
 
 ```python