feat(bedrock): add MiniMax provider support for AWS Bedrock (#4609)

mem0/llms/aws_bedrock.py

@@ -17,8 +17,9 @@ from mem0.memory.utils import extract_json
 logger = logging.getLogger(__name__)
 
 PROVIDERS = [
-    "ai21", "amazon", "anthropic", "cohere", "meta", "mistral", "stability", "writer", 
+    "ai21", "amazon", "anthropic", "cohere", "meta", "mistral", "stability", "writer",
-    "deepseek", "gpt-oss", "perplexity", "snowflake", "titan", "command", "j2", "llama"
+    "deepseek", "gpt-oss", "perplexity", "snowflake", "titan", "command", "j2", "llama",
+    "minimax",
 ]
 
 
@@ -120,6 +121,10 @@ class AWSBedrockLLM(LLMBase):
         """Initialize provider-specific settings and capabilities."""
         # Determine capabilities based on provider and model
         self.supports_tools = self.provider in ["anthropic", "cohere", "amazon"]
+        # MiniMax M2.x is intentionally excluded from supports_tools: tool use for MiniMax
+        # on Amazon Bedrock is only available via the bedrock-mantle (OpenAI-compatible)
+        # endpoint, not via the bedrock-runtime Converse API used by this class.
+        # See: https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-minimax-minimax-m2-5.html
         self.supports_vision = self.provider in ["anthropic", "amazon", "meta", "mistral"]
         self.supports_streaming = self.provider in ["anthropic", "cohere", "mistral", "amazon", "meta"]
 
@@ -487,7 +492,12 @@ class AWSBedrockLLM(LLMBase):
         return 2000
 
     def _build_inference_config(self) -> Dict[str, Any]:
-        """Build Converse ``inferenceConfig``. Anthropic allows only one of temperature or topP; we keep temperature and omit topP."""
+        """Build Converse ``inferenceConfig``.
+
+        Anthropic and MiniMax reasoning models reject requests that include both
+        ``temperature`` and ``topP`` simultaneously, so ``topP`` is omitted for
+        those providers even when the user has configured it.
+        """
         inference_config: Dict[str, Any] = {
             "maxTokens": self.model_config.get("max_tokens", self._default_max_tokens_for_converse()),
             "temperature": self.model_config.get("temperature", 0.1),
@@ -495,8 +505,11 @@ class AWSBedrockLLM(LLMBase):
 
         top_p = self.model_config.get("top_p")
         if top_p is not None:
-            if self.provider == "anthropic":
+            if self.provider in ("anthropic", "minimax"):
-                logger.debug("Omitting topP for Anthropic Converse (using temperature); top_p=%s", top_p)
+                # Both Anthropic and MiniMax M2.x (reasoning models) raise a
+                # ValidationException when temperature and topP are both present
+                # in inferenceConfig.  Omit topP and rely on temperature only.
+                logger.debug("Omitting topP for %s Converse (using temperature); top_p=%s", self.provider, top_p)
             else:
                 inference_config["topP"] = top_p
 
@@ -568,6 +581,39 @@ class AWSBedrockLLM(LLMBase):
             else:
                 return str(response)
 
+        elif self.provider == "minimax":
+            # MiniMax models (e.g. minimax.minimax-m2.5) use the Bedrock Converse API.
+            # M2.5 is a reasoning model whose response content array may include a
+            # `reasoningContent` block before the actual `text` block, so we iterate
+            # to find the first block that contains a "text" key.
+            # System messages must be passed via the top-level `system` parameter
+            # (not as a message with role="system") per the Converse API spec.
+            system_parts = []
+            converse_messages = []
+            for msg in messages:
+                role = msg.get("role", "user")
+                content = msg.get("content", "")
+                if not isinstance(content, str):
+                    content = str(content)
+                if role == "system":
+                    system_parts.append(content)
+                else:
+                    converse_messages.append({"role": role, "content": [{"text": content}]})
+            if not converse_messages:
+                converse_messages = [{"role": "user", "content": [{"text": ""}]}]
+            converse_params = {
+                "modelId": self.config.model,
+                "messages": converse_messages,
+                "inferenceConfig": self._build_inference_config(),
+            }
+            if system_parts:
+                converse_params["system"] = [{"text": "\n".join(system_parts)}]
+            response = self.client.converse(**converse_params)
+            for block in response["output"]["message"]["content"]:
+                if "text" in block:
+                    return block["text"]
+            return ""
+
         elif self.provider == "amazon" and "nova" in self.config.model.lower():
             # Nova models use the Converse API even without tools
             formatted_messages = self._format_messages_amazon(messages)

tests/llms/test_aws_bedrock.py

@@ -220,6 +220,25 @@ class TestBuildInferenceConfig:
         cfg = llm._build_inference_config()
         assert cfg["maxTokens"] == 2000
 
+    def test_minimax_omits_top_p_when_explicitly_set(self, mock_boto3):
+        # MiniMax M2.x (reasoning model) rejects both temperature and topP simultaneously.
+        # Even when the user explicitly configures top_p, it must be omitted.
+        llm = _make_llm(
+            "minimax.minimax-m2.5",
+            mock_boto3,
+            temperature=0.1,
+            top_p=0.9,
+        )
+        cfg = llm._build_inference_config()
+        assert "temperature" in cfg
+        assert "topP" not in cfg, "topP must be absent for MiniMax reasoning models"
+
+    def test_minimax_only_temperature_by_default(self, mock_boto3):
+        llm = _make_llm("minimax.minimax-m2.5", mock_boto3, temperature=0.1)
+        cfg = llm._build_inference_config()
+        assert cfg["temperature"] == 0.1
+        assert "topP" not in cfg
+
 
 # ---------------------------------------------------------------------------
 # generate_response — Converse API call assertions
@@ -328,3 +347,80 @@ class TestGenerateResponseConverse:
 
         _, kwargs = mock_boto3.converse.call_args
         assert "topP" not in kwargs["inferenceConfig"]
+
+
+# ---------------------------------------------------------------------------
+# MiniMax provider
+# ---------------------------------------------------------------------------
+
+class TestMiniMaxProvider:
+    """Tests for MiniMax models via Bedrock Converse API."""
+
+    def test_extract_provider(self):
+        assert extract_provider("minimax.minimax-m2.5") == "minimax"
+        assert extract_provider("minimax.minimax-m2") == "minimax"
+
+    def test_generate_response_text_only(self, mock_boto3):
+        """Standard response: single text block."""
+        mock_boto3.converse.return_value = _converse_response("Hello!")
+        llm = _make_llm("minimax.minimax-m2.5", mock_boto3)
+
+        result = llm.generate_response([{"role": "user", "content": "say hi"}])
+
+        assert result == "Hello!"
+        _, kwargs = mock_boto3.converse.call_args
+        assert kwargs["modelId"] == "minimax.minimax-m2.5"
+        assert kwargs["messages"][0]["role"] == "user"
+        assert kwargs["messages"][0]["content"][0]["text"] == "say hi"
+
+    def test_generate_response_reasoning_model(self, mock_boto3):
+        """MiniMax M2.5 is a reasoning model: reasoningContent block comes before text."""
+        reasoning_response = {
+            "output": {
+                "message": {
+                    "content": [
+                        {"reasoningContent": {"reasoningText": {"text": "Let me think..."}}},
+                        {"text": "Hello!"},
+                    ]
+                }
+            }
+        }
+        mock_boto3.converse.return_value = reasoning_response
+        llm = _make_llm("minimax.minimax-m2.5", mock_boto3)
+
+        result = llm.generate_response([{"role": "user", "content": "say hi"}])
+
+        # Must skip reasoningContent and return the actual text block
+        assert result == "Hello!"
+
+    def test_inference_config(self, mock_boto3):
+        """inferenceConfig should include maxTokens and temperature; no topP."""
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm("minimax.minimax-m2.5", mock_boto3, temperature=0.2, max_tokens=512)
+
+        llm.generate_response([{"role": "user", "content": "hi"}])
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert kwargs["inferenceConfig"]["maxTokens"] == 512
+        assert kwargs["inferenceConfig"]["temperature"] == 0.2
+        assert "topP" not in kwargs["inferenceConfig"]
+
+    def test_system_prompt_passed_correctly(self, mock_boto3):
+        """System messages must be sent via top-level `system` param, not as a message role."""
+        mock_boto3.converse.return_value = _converse_response('{"facts": ["test"]}')
+        llm = _make_llm("minimax.minimax-m2.5", mock_boto3)
+
+        llm.generate_response([
+            {"role": "system", "content": "Return JSON only."},
+            {"role": "user", "content": "Extract facts from: test"},
+        ])
+
+        _, kwargs = mock_boto3.converse.call_args
+        # system prompt must be in top-level "system" key
+        assert "system" in kwargs
+        assert kwargs["system"][0]["text"] == "Return JSON only."
+        # messages list must NOT contain a system role entry
+        for msg in kwargs["messages"]:
+            assert msg["role"] != "system"
+        # user message must be present
+        assert kwargs["messages"][0]["role"] == "user"