feat(model): support OpenAI compatible provider

.env.example

@@ -33,8 +33,9 @@ AGENT_DEBUG_MODE=false
 # Model Provider Settings
 # ============================================
 # For more details, see the documentation in the /doc directory.
-# To access full functionality, make sure to include an embedding service.
+# To access full functionality, make sure to include an embedding-capable provider.
 # You can configure multiple providers below.
+# The primary setup uses OpenRouter, along with another provider that supports embeddings.
 
 # Get your API key from: https://openrouter.ai/
 # Note: OpenRouter does not currently support embedding or reranker models.
@@ -53,6 +54,10 @@ SILICONFLOW_API_KEY=
 # Get your API key from: https://platform.openai.com/api-keys
 OPENAI_API_KEY=
 
+# You can set any OpenAI-compatitble API KEY, but you neeed to configure python/configs/providers/openai-compatible.yaml manully.
+OPENAI_COMPATIBLE_API_KEY=
+OPENAI_COMPATIBLE_BASE_URL=
+
 
 # ============================================
 # Research Agent Configurations

python/configs/config.yaml

@@ -34,6 +34,10 @@ models:
     openai:
       config_file: "providers/openai.yaml"
       api_key_env: "OPENAI_API_KEY"
+
+    openai-compatible:
+      config_file: "providers/openai-compatible.yaml"
+      api_key_env: "OPENAI_COMPATIBLE_API_KEY"
 
 # Agent Configuration
 agents:

python/configs/providers/openai-compatible.yaml

@@ -0,0 +1,72 @@
+# ============================================
+# OpenAI Compatible API Provider Configuration
+# ============================================
+# This configuration supports any OpenAI-compatible API including:
+
+#
+# Usage:
+#   1. Set OPENAI_API_BASE environment variable to your API endpoint
+#   2. Set OPENAI_API_KEY if your service requires authentication
+#   3. Set PRIMARY_PROVIDER=openai-compatible
+#
+# Examples:
+#
+#   # vLLM on remote server
+#   export OPENAI_API_BASE=http://your-vllm-server:8000/v1
+#   export PRIMARY_PROVIDER=openai-compatible
+#
+#   # Together.ai
+#   export OPENAI_API_BASE=https://api.together.xyz/v1
+#   export OPENAI_API_KEY=your-together-api-key
+#   export PRIMARY_PROVIDER=openai-compatible
+
+name: "OpenAI-Compatible"
+provider_type: "openai-compatible"
+
+enabled: true
+
+# Connection Configuration
+connection:
+  # Use environment variable with fallback to local Ollama
+  base_url: "${OPENAI_COMPATIBLE_BASE_URL:http://localhost:11434/v1}"
+  api_key_env: "OPENAI_COMPATIBLE_API_KEY"
+
+# Default model if agents.yaml specified
+# Example: qwen3-max
+default_model: "qwen3-max"
+
+# Model Parameters Defaults
+defaults:
+  temperature: 0.7
+  max_tokens: 4096
+
+# Available Models (examples - adjust based on your provider model id)
+models:
+  - id: "qwen3-max"
+    name: "Qwen3 Max"
+    context_length: 256000
+    description: "Qwen3 Max model"
+
+
+# ============================================
+# Embedding Models Configuration
+# ============================================
+# Note: Embedding support depends on your provider
+
+# Example: text-embedding-v4
+embedding:
+  # Default embedding model
+  default_model: "text-embedding-v4"
+
+  # Default parameters
+  defaults:
+    dimensions: 2048
+    encoding_format: "float"
+
+  # Available embedding models
+  models:
+    - id: "text-embedding-v4"
+      name: "Text Embedding V4"
+      dimensions: 2048
+      max_input: 8192
+      description: "Text Embedding V4 model"

python/valuecell/adapters/models/factory.py

@@ -272,6 +272,7 @@ def create_model(self, model_id: Optional[str] = None, **kwargs):
         return OpenAIChat(
             id=model_id,
             api_key=self.config.api_key,
+            base_url=self.config.base_url,
             temperature=params.get("temperature"),
             max_tokens=params.get("max_tokens"),
             top_p=params.get("top_p"),
@@ -302,13 +303,83 @@ def create_embedder(self, model_id: Optional[str] = None, **kwargs):
         return OpenAIEmbedder(
             id=model_id,
             api_key=self.config.api_key,
+            base_url=self.config.base_url,
             dimensions=int(params.get("dimensions", 1536))
             if params.get("dimensions")
             else None,
             encoding_format=params.get("encoding_format", "float"),
         )
 
 
+class OpenAICompatibleProvider(ModelProvider):
+    """OpenAI-compatible model provider with role compatibility handling
+
+    This provider handles OpenAI-compatible APIs (like DashScope, vLLM, etc.) that may not
+    support newer OpenAI features like the 'developer' role. It wraps the OpenAIChat model
+    and converts incompatible roles to compatible ones.
+    """
+
+    def create_model(self, model_id: Optional[str] = None, **kwargs):
+        """Create OpenAI-compatible model via agno with role compatibility"""
+        try:
+            from agno.models.openai import OpenAILike
+        except ImportError:
+            raise ImportError(
+                "agno package not installed. Install with: pip install agno"
+            )
+
+        model_id = model_id or self.config.default_model
+        params = {**self.config.parameters, **kwargs}
+
+        logger.info(
+            f"Creating OpenAI-compatible model: {model_id} (base_url: {self.config.base_url})"
+        )
+
+        # Create the base OpenAILike model
+        return OpenAILike(
+            id=model_id,
+            api_key=self.config.api_key,
+            base_url=self.config.base_url,
+            temperature=params.get("temperature"),
+            max_tokens=params.get("max_tokens"),
+            top_p=params.get("top_p"),
+            frequency_penalty=params.get("frequency_penalty"),
+            presence_penalty=params.get("presence_penalty"),
+        )
+
+    def create_embedder(self, model_id: Optional[str] = None, **kwargs):
+        """Create embedder via OpenAI-compatible API"""
+        try:
+            from agno.knowledge.embedder.openai import OpenAIEmbedder
+        except ImportError:
+            raise ImportError("agno package not installed")
+
+        # Use provided model_id or default embedding model
+        model_id = model_id or self.config.default_embedding_model
+
+        if not model_id:
+            raise ValueError(
+                f"No embedding model specified for provider '{self.config.name}'"
+            )
+
+        # Merge parameters: provider embedding defaults < kwargs
+        params = {**self.config.embedding_parameters, **kwargs}
+
+        logger.info(f"Creating OpenAI-compatible embedder: {model_id}")
+
+        return OpenAIEmbedder(
+            id=model_id,
+            api_key=self.config.api_key,
+            base_url=self.config.base_url,
+            dimensions=int(params.get("dimensions", 1024)),
+            encoding_format=params.get("encoding_format"),
+        )
+
+    def is_available(self) -> bool:
+        """Check if provider is available (needs both API key and base URL)"""
+        return bool(self.config.api_key and self.config.base_url)
+
+
 class ModelFactory:
     """
     Factory for creating model instances with provider abstraction
@@ -327,6 +398,7 @@ class ModelFactory:
         "azure": AzureProvider,
         "siliconflow": SiliconFlowProvider,
         "openai": OpenAIProvider,
+        "openai-compatible": OpenAICompatibleProvider,
     }
 
     def __init__(self, config_manager: Optional[ConfigManager] = None):

python/valuecell/config/manager.py

@@ -133,6 +133,7 @@ def primary_provider(self) -> str:
                     "siliconflow",
                     "google",
                     "openai",
+                    "openai-compatible",
                 ]
 
                 for preferred in preferred_order: