wip

README.md

@@ -151,12 +151,6 @@ For a complete working example, see the [Quickstart Example](./examples/quicksta
 
 The example is fully documented with clear explanations of each functionality and includes a comprehensive README with setup instructions and next steps.
 
-## Graph Service
-
-The `server` directory contains an API service for interacting with the Graphiti API. It is built using FastAPI.
-
-Please see the [server README](./server/README.md) for more information.
-
 ## MCP Server
 
 The `mcp_server` directory contains a Model Context Protocol (MCP) server implementation for Graphiti. This server allows AI assistants to interact with Graphiti's knowledge graph capabilities through the MCP protocol.
@@ -173,6 +167,12 @@ The MCP server can be deployed using Docker with Neo4j, making it easy to integr
 
 For detailed setup instructions and usage examples, see the [MCP server README](./mcp_server/README.md).
 
+## Graph Service
+
+The `server` directory contains an API service for interacting with the Graphiti API. It is built using FastAPI.
+
+Please see the [server README](./server/README.md) for more information.
+
 ## Optional Environment Variables
 
 In addition to the Neo4j and OpenAi-compatible credentials, Graphiti also has a few optional environment variables.

graphiti_core/llm_client/openai_client.py

@@ -19,7 +19,7 @@ import typing
 from typing import ClassVar
 
 import openai
-from openai import AsyncOpenAI
+from openai import AsyncAzureOpenAI, AsyncOpenAI
 from openai.types.chat import ChatCompletionMessageParam
 from pydantic import BaseModel
 
@@ -61,7 +61,7 @@ class OpenAIClient(LLMClient):
         self,
         config: LLMConfig | None = None,
         cache: bool = False,
-        client: typing.Any = None,
+        client: AsyncOpenAI | AsyncAzureOpenAI | None = None,
         max_tokens: int = DEFAULT_MAX_TOKENS,
     ):
         """

graphiti_core/utils/maintenance/community_operations.py

@@ -101,9 +101,7 @@ def label_propagation(projection: dict[str, list[Neighbor]]) -> list[list[str]]:
             ]
 
             community_lst.sort(reverse=True)
-            candidate_rank, community_candidate = (
+            candidate_rank, community_candidate = community_lst[0] if community_lst else (0, -1)
-                community_lst[0] if community_lst else (0, -1)
-            )
             if community_candidate != -1 and candidate_rank > 1:
                 new_community = community_candidate
             else:

mcp_server/README.md

@@ -78,6 +78,7 @@ uv run graphiti_mcp_server.py --model gpt-4.1-mini --transport sse
 Available arguments:
 
 - `--model`: Specify the model name to use with the LLM client
+- `--model-provider <openai|azure_openai|anthropic>`: Select the LLM provider for primary operations (default: `openai`). Affects `--model` and requires the corresponding API key/Azure environment variables. **Embeddings always use OpenAI/Azure OpenAI regardless of this setting.**
 - `--transport`: Choose the transport method (sse or stdio, default: sse)
 - `--group-id`: Set a namespace for the graph (optional)
 - `--destroy-graph`: Destroy all Graphiti graphs (use with caution)
@@ -94,25 +95,25 @@ Before running the Docker Compose setup, you need to configure the environment v
 
 1. **Using a .env file** (recommended):
 
-    - Copy the provided `.env.example` file to create a `.env` file:
+   - Copy the provided `.env.example` file to create a `.env` file:
-      ```bash
+     ```bash
-      cp .env.example .env
+     cp .env.example .env
-      ```
+     ```
-    - Edit the `.env` file to set your OpenAI API key and other configuration options:
+   - Edit the `.env` file to set your OpenAI API key and other configuration options:
-      ```
+     ```
-      # Required for LLM operations
+     # Required for LLM operations
-      OPENAI_API_KEY=your_openai_api_key_here
+     OPENAI_API_KEY=your_openai_api_key_here
-      MODEL_NAME=gpt-4.1-mini
+     MODEL_NAME=gpt-4.1-mini
-      # Optional: OPENAI_BASE_URL only needed for non-standard OpenAI endpoints
+     # Optional: OPENAI_BASE_URL only needed for non-standard OpenAI endpoints
-      # OPENAI_BASE_URL=https://api.openai.com/v1
+     # OPENAI_BASE_URL=https://api.openai.com/v1
-      ```
+     ```
-    - The Docker Compose setup is configured to use this file if it exists (it's optional)
+   - The Docker Compose setup is configured to use this file if it exists (it's optional)
 
 2. **Using environment variables directly**:
-    - You can also set the environment variables when running the Docker Compose command:
+   - You can also set the environment variables when running the Docker Compose command:
-      ```bash
+     ```bash
-      OPENAI_API_KEY=your_key MODEL_NAME=gpt-4.1-mini docker compose up
+     OPENAI_API_KEY=your_key MODEL_NAME=gpt-4.1-mini docker compose up
-      ```
+     ```
 
 #### Neo4j Configuration
 
@@ -283,3 +284,58 @@ capabilities.
 ## License
 
 This project is licensed under the same license as the Graphiti project.
+
+### Using Anthropic Models
+
+To use an Anthropic model (like Claude) as the primary LLM:
+
+1.  Ensure the `anthropic` package is installed:
+    ```bash
+    # Install the server with anthropic support
+    uv pip install -e .[anthropic]
+    ```
+2.  Set the `ANTHROPIC_API_KEY` environment variable:
+    ```bash
+    export ANTHROPIC_API_KEY='your-anthropic-api-key'
+    ```
+3.  Set the `OPENAI_API_KEY` environment variable (or Azure equivalents). This is still required for the embedding model, which currently uses OpenAI or Azure OpenAI.
+    ```bash
+    export OPENAI_API_KEY='your-openai-api-key'
+    ```
+4.  Run the server specifying Anthropic as the provider and optionally the model name:
+    ```bash
+    uv run graphiti_mcp_server.py --model-provider anthropic --model claude-3-7-sonnet-latest
+    ```
+    - If `--model` is not specified, it will use the default specified in the code (currently OpenAI's default, which might not be ideal - specify an Anthropic model explicitly).
+    - Features requiring LLM interaction (like custom entity extraction with `--use-custom-entities`) will use the configured Anthropic model.
+    - Embeddings will still use OpenAI/Azure services.
+    - **Note:** Reranking (`OpenAIRerankerClient`) is currently incompatible with the Anthropic provider and will cause an error during initialization if attempted.
+
+### Using Azure OpenAI Models
+
+To use Azure OpenAI as the primary LLM provider:
+
+1.  Set the required Azure OpenAI environment variables:
+    ```bash
+    export AZURE_OPENAI_ENDPOINT='your-azure-endpoint'
+    export AZURE_OPENAI_DEPLOYMENT_NAME='your-llm-deployment-name' # Deployment name for the LLM model
+    export AZURE_OPENAI_API_VERSION='your-api-version'
+    # Set if using API Key auth (omit if using Managed Identity)
+    export OPENAI_API_KEY='your-azure-openai-api-key'
+    # Set if using Managed Identity auth (omit if using API Key)
+    # export AZURE_OPENAI_USE_MANAGED_IDENTITY=true
+    ```
+2.  Ensure the embedding configuration is also correct. If using Azure for embeddings too, set:
+    ```bash
+    export AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME='your-embedding-deployment-name'
+    export AZURE_OPENAI_EMBEDDING_API_VERSION='your-embedding-api-version' # Often same as LLM version
+    # OPENAI_API_KEY is reused if needed; AZURE_OPENAI_ENDPOINT is reused.
+    ```
+    If using standard OpenAI for embeddings, just ensure `OPENAI_API_KEY` is set.
+3.  Run the server specifying Azure OpenAI as the provider and optionally the model name (which should match your Azure deployment):
+    ```bash
+    uv run graphiti_mcp_server.py --model-provider azure_openai --model your-llm-deployment-name
+    ```
+    - The `--model` argument here should generally match the `AZURE_OPENAI_DEPLOYMENT_NAME`.
+    - Features requiring LLM interaction will use the configured Azure OpenAI service.
+    - Embeddings will use either Azure OpenAI or standard OpenAI based on the presence of `AZURE_OPENAI_ENDPOINT` and the embedding-specific deployment/version variables.

mcp_server/graphiti_mcp_server.py

@@ -26,6 +26,7 @@ from graphiti_core.edges import EntityEdge
 from graphiti_core.embedder.client import EmbedderClient
 from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
 from graphiti_core.llm_client import LLMClient
+from graphiti_core.llm_client.anthropic_client import AnthropicClient
 from graphiti_core.llm_client.config import LLMConfig
 from graphiti_core.llm_client.openai_client import OpenAIClient
 from graphiti_core.nodes import EpisodeType, EpisodicNode
@@ -186,7 +187,9 @@ class GraphitiLLMConfig(BaseModel):
     Centralizes all LLM-specific configuration parameters including API keys and model selection.
     """
 
+    model_provider: str = 'openai'
     api_key: str | None = None
+    anthropic_api_key: str | None = None
     model: str = DEFAULT_LLM_MODEL
     temperature: float = 0.0
     azure_openai_endpoint: str | None = None
@@ -196,75 +199,114 @@ class GraphitiLLMConfig(BaseModel):
 
     @classmethod
     def from_env(cls) -> 'GraphitiLLMConfig':
-        """Create LLM configuration from environment variables."""
+        """Create LLM configuration from environment variables.
-        # Get model from environment, or use default if not set or empty
+        Loads potential keys and default settings. Provider-specific logic is handled in from_cli_and_env.
+        """
+        # Load all potentially relevant keys and settings from environment
+        model_provider = os.environ.get('MODEL_PROVIDER', 'openai').lower()
         model_env = os.environ.get('MODEL_NAME', '')
         model = model_env if model_env.strip() else DEFAULT_LLM_MODEL
-
+        openai_api_key = os.environ.get('OPENAI_API_KEY')
-        azure_openai_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT', None)
+        anthropic_api_key = os.environ.get('ANTHROPIC_API_KEY')
-        azure_openai_api_version = os.environ.get('AZURE_OPENAI_API_VERSION', None)
+        azure_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT')
-        azure_openai_deployment_name = os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME', None)
+        azure_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME')
-        azure_openai_use_managed_identity = (
+        azure_api_version = os.environ.get('AZURE_OPENAI_API_VERSION')
+        azure_use_managed_identity = (
             os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true'
         )
+        temperature = float(os.environ.get('LLM_TEMPERATURE', '0.0'))
 
-        if azure_openai_endpoint is None:
+        # Return a config with all loaded env vars, provider logic deferred
-            # Setup for OpenAI API
+        return cls(
-            # Log if empty model was provided
+            model_provider=model_provider,  # Initial provider from env
-            if model_env == '':
+            api_key=openai_api_key,
-                logger.debug(
+            anthropic_api_key=anthropic_api_key,
-                    f'MODEL_NAME environment variable not set, using default: {DEFAULT_LLM_MODEL}'
+            model=model,
-                )
+            temperature=temperature,
-            elif not model_env.strip():
+            azure_openai_endpoint=azure_endpoint,
-                logger.warning(
+            azure_openai_deployment_name=azure_deployment,
-                    f'Empty MODEL_NAME environment variable, using default: {DEFAULT_LLM_MODEL}'
+            azure_openai_api_version=azure_api_version,
-                )
+            azure_openai_use_managed_identity=azure_use_managed_identity,
-
+        )
-            return cls(
-                api_key=os.environ.get('OPENAI_API_KEY'),
-                model=model,
-                temperature=float(os.environ.get('LLM_TEMPERATURE', '0.0')),
-            )
-        else:
-            # Setup for Azure OpenAI API
-            # Log if empty deployment name was provided
-            if azure_openai_deployment_name is None:
-                logger.error('AZURE_OPENAI_DEPLOYMENT_NAME environment variable not set')
-
-                raise ValueError('AZURE_OPENAI_DEPLOYMENT_NAME environment variable not set')
-            if not azure_openai_use_managed_identity:
-                # api key
-                api_key = os.environ.get('OPENAI_API_KEY', None)
-            else:
-                # Managed identity
-                api_key = None
-
-            return cls(
-                azure_openai_use_managed_identity=azure_openai_use_managed_identity,
-                azure_openai_endpoint=azure_openai_endpoint,
-                api_key=api_key,
-                azure_openai_api_version=azure_openai_api_version,
-                azure_openai_deployment_name=azure_openai_deployment_name,
-                temperature=float(os.environ.get('LLM_TEMPERATURE', '0.0')),
-            )
 
     @classmethod
     def from_cli_and_env(cls, args: argparse.Namespace) -> 'GraphitiLLMConfig':
-        """Create LLM configuration from CLI arguments, falling back to environment variables."""
+        """Create LLM configuration from CLI arguments, falling back to environment variables.
-        # Start with environment-based config
+        Determines final provider and loads settings accordingly.
-        config = cls.from_env()
+        """
+        # 1. Determine the final model provider (CLI > Env Var > Default)
+        final_model_provider = 'openai'  # Default
+        if 'MODEL_PROVIDER' in os.environ:
+            final_model_provider = os.environ['MODEL_PROVIDER'].lower()
+        if hasattr(args, 'model_provider') and args.model_provider:
+            final_model_provider = args.model_provider.lower()
 
-        # CLI arguments override environment variables when provided
+        # 2. Determine final model name (CLI > Env Var > Default)
-        if hasattr(args, 'model') and args.model:
+        final_model = DEFAULT_LLM_MODEL  # Default
-            # Only use CLI model if it's not empty
+        if 'MODEL_NAME' in os.environ and os.environ['MODEL_NAME'].strip():
-            if args.model.strip():
+            final_model = os.environ['MODEL_NAME']
-                config.model = args.model
+        if hasattr(args, 'model') and args.model and args.model.strip():
-            else:
+            final_model = args.model
-                # Log that empty model was provided and default is used
+        elif final_model_provider != 'openai' and final_model == DEFAULT_LLM_MODEL:
-                logger.warning(f'Empty model name provided, using default: {DEFAULT_LLM_MODEL}')
+            # Warn if using non-openai provider but haven't specified a model
+            logger.warning(
+                f"Model provider is '{final_model_provider}' but no model specified. "
+                f"Using default '{DEFAULT_LLM_MODEL}', which might be incorrect."
+            )
 
+        # 3. Determine final temperature (CLI > Env Var > Default)
+        final_temperature = 0.0  # Default
+        if 'LLM_TEMPERATURE' in os.environ:
+            try:
+                final_temperature = float(os.environ['LLM_TEMPERATURE'])
+            except ValueError:
+                logger.warning('Invalid LLM_TEMPERATURE in env, using default 0.0')
         if hasattr(args, 'temperature') and args.temperature is not None:
-            config.temperature = args.temperature
+            final_temperature = args.temperature
+
+        # 4. Load relevant API keys and Azure settings from environment
+        openai_api_key = os.environ.get('OPENAI_API_KEY')
+        anthropic_api_key = os.environ.get('ANTHROPIC_API_KEY')
+        azure_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT')
+        azure_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME')
+        azure_api_version = os.environ.get('AZURE_OPENAI_API_VERSION')
+        azure_use_managed_identity = (
+            os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true'
+        )
+
+        # 5. Construct the final config object based on the provider
+        config_params = {
+            'model_provider': final_model_provider,
+            'model': final_model,
+            'temperature': final_temperature,
+        }
+
+        if final_model_provider == 'anthropic':
+            config_params['anthropic_api_key'] = anthropic_api_key
+        elif final_model_provider == 'azure_openai':
+            config_params['azure_openai_endpoint'] = azure_endpoint
+            config_params['azure_openai_deployment_name'] = azure_deployment
+            config_params['azure_openai_api_version'] = azure_api_version
+            config_params['azure_openai_use_managed_identity'] = azure_use_managed_identity
+            if not azure_use_managed_identity:
+                config_params['api_key'] = (
+                    openai_api_key  # Azure uses OPENAI_API_KEY var for key auth
+                )
+        else:  # Default to openai
+            config_params['api_key'] = openai_api_key
+
+        # Log the Anthropic key if relevant, for debugging
+        if final_model_provider == 'anthropic':
+            key_snippet = (
+                f'{anthropic_api_key[:5]}...{anthropic_api_key[-4:]}'
+                if anthropic_api_key
+                else 'None'
+            )
+            logger.info(
+                f'Anthropic provider selected. Read ANTHROPIC_API_KEY from env: {key_snippet}'
+            )
+
+        config = cls(**config_params)
 
         return config
 
@@ -275,47 +317,161 @@ class GraphitiLLMConfig(BaseModel):
             LLMClient instance if able, None otherwise
         """
 
-        if self.azure_openai_endpoint is not None:
+        # --- Anthropic Client ---
-            # Azure OpenAI API setup
+        if self.model_provider == 'anthropic':
+            logger.info(f'Using Anthropic model: {self.model}')
+            if not self.anthropic_api_key:
+                logger.error('ANTHROPIC_API_KEY must be set when model_provider is anthropic')
+                return None
+            anthropic_config = LLMConfig(
+                api_key=self.anthropic_api_key, model=self.model, temperature=self.temperature
+            )
+            return AnthropicClient(config=anthropic_config)
+
+        # --- Azure OpenAI Client ---
+        elif self.model_provider == 'azure':
+            logger.info(f'Attempting to use Azure OpenAI model: {self.model}')
+
+            # Check required Azure configuration
+            if not self.azure_openai_endpoint:
+                logger.error('AZURE_OPENAI_ENDPOINT must be set when model_provider is azure')
+                return None
+            if not self.azure_openai_deployment_name:
+                logger.error(
+                    'AZURE_OPENAI_DEPLOYMENT_NAME must be set when model_provider is azure'
+                )
+                return None
+            if not self.azure_openai_api_version:
+                logger.error('AZURE_OPENAI_API_VERSION must be set when model_provider is azure')
+                return None
+            if not self.azure_openai_use_managed_identity and not self.api_key:
+                logger.error(
+                    'OPENAI_API_KEY must be set for Azure OpenAI when not using managed identity'
+                )
+                return None
+
+            llm_client: AsyncAzureOpenAI | None = None
             if self.azure_openai_use_managed_identity:
                 # Use managed identity for authentication
                 token_provider = create_azure_credential_token_provider()
-                return AsyncAzureOpenAI(
+                llm_client = AsyncAzureOpenAI(
                     azure_endpoint=self.azure_openai_endpoint,
                     azure_deployment=self.azure_openai_deployment_name,
                     api_version=self.azure_openai_api_version,
                     azure_ad_token_provider=token_provider,
                 )
-            elif self.api_key:
+            elif self.api_key:  # We already checked api_key exists if needed
                 # Use API key for authentication
-                return AsyncAzureOpenAI(
+                llm_client = AsyncAzureOpenAI(
                     azure_endpoint=self.azure_openai_endpoint,
                     azure_deployment=self.azure_openai_deployment_name,
                     api_version=self.azure_openai_api_version,
                     api_key=self.api_key,
                 )
-            else:
-                logger.error('OPENAI_API_KEY must be set when using Azure OpenAI API')
-                return None
 
-        if not self.api_key:
+            # Wrap the Azure client in OpenAIClient
+            if llm_client:
+                # Assuming OpenAIClient can accept an initialized client
+                return OpenAIClient(client=llm_client)  # type: ignore
             return None
 
-        llm_client_config = LLMConfig(api_key=self.api_key, model=self.model)
+        # --- Standard OpenAI Client ---
+        elif self.model_provider == 'openai':
+            logger.info(f'Using standard OpenAI model: {self.model}')
+            if not self.api_key:
+                logger.error('OPENAI_API_KEY must be set when model_provider is openai')
+                return None
 
-        # Set temperature
+            llm_client_config = LLMConfig(api_key=self.api_key, model=self.model)
-        llm_client_config.temperature = self.temperature
 
-        return OpenAIClient(config=llm_client_config)
+            # Set temperature
+            llm_client_config.temperature = self.temperature
+
+            return OpenAIClient(config=llm_client_config)
+        else:
+            logger.error(f'Unknown model provider configured: {self.model_provider}')
+            return None
 
     def create_cross_encoder_client(self) -> CrossEncoderClient | None:
         """Create a cross-encoder client based on this configuration."""
-        if self.azure_openai_endpoint is not None:
+        # Note: OpenAIRerankerClient currently only supports OpenAI models.
-            client = self.create_client()
+        # If Anthropic is selected as the main LLM, reranking might not function correctly
-            return OpenAIRerankerClient(client=client)
+        # or may require a separate OpenAI configuration specifically for reranking.
-        else:
+        if self.model_provider == 'anthropic':
+            logger.warning(
+                'Anthropic selected as LLM provider. Cross-Encoder (reranking) might not work as expected '
+                'as it currently relies on OpenAI models. Using Anthropic client for reranking attempt.'
+            )
+
+        # Determine the client needed based on LLM provider config
+        reranker_azure_client: AsyncAzureOpenAI | None = None
+
+        if self.model_provider == 'azure_openai':
+            # Check required Azure configuration for reranker
+            if not all(
+                [
+                    self.azure_openai_endpoint,
+                    self.azure_openai_deployment_name,
+                    self.azure_openai_api_version,
+                ]
+            ):
+                logger.error('Azure config (endpoint, deployment, version) missing for reranker')
+                return None
+            if not self.azure_openai_use_managed_identity and not self.api_key:
+                logger.error('OPENAI_API_KEY needed for Azure reranker without managed identity')
+                return None
+
+            # Create the raw Azure client again for OpenAIRerankerClient
+            if self.azure_openai_use_managed_identity:
+                token_provider = create_azure_credential_token_provider()
+                reranker_azure_client = AsyncAzureOpenAI(
+                    azure_endpoint=self.azure_openai_endpoint,  # type: ignore[arg-type]
+                    azure_deployment=self.azure_openai_deployment_name,  # Use LLM deployment name
+                    api_version=self.azure_openai_api_version,  # Use LLM API version
+                    azure_ad_token_provider=token_provider,
+                )
+            elif self.api_key:
+                reranker_azure_client = AsyncAzureOpenAI(
+                    azure_endpoint=self.azure_openai_endpoint,  # type: ignore[arg-type]
+                    azure_deployment=self.azure_openai_deployment_name,  # Use LLM deployment name
+                    api_version=self.azure_openai_api_version,  # Use LLM API version
+                    api_key=self.api_key,
+                )
+        elif self.model_provider == 'openai':
+            if not self.api_key:
+                logger.error('OPENAI_API_KEY needed for OpenAI reranker')
+                return None
+            # Standard OpenAI config for reranker
             llm_client_config = LLMConfig(api_key=self.api_key, model=self.model)
+            # OpenAIRerankerClient expects a config or a raw client.
+            # Passing config is consistent with how OpenAIClient itself can be initialized.
             return OpenAIRerankerClient(config=llm_client_config)
+        elif self.model_provider == 'anthropic':
+            # Attempt to use Anthropic client for reranking, though likely incompatible
+            if not self.anthropic_api_key:
+                logger.error('ANTHROPIC_API_KEY needed for Anthropic reranker attempt')
+                return None
+            # Pass config, OpenAIRerankerClient might handle it or raise error
+            # This might require changes in graphiti-core's OpenAIRerankerClient
+            # return OpenAIRerankerClient(config=anthropic_config) # Assuming it might work?
+            # Raise an error as OpenAIRerankerClient does not support Anthropic
+            raise ValueError(
+                'Cannot create CrossEncoderClient (reranker): OpenAIRerankerClient currently only supports '
+                'OpenAI or Azure OpenAI models. Anthropic was selected as the model_provider.'
+            )
+        else:
+            logger.error(f"Unsupported model provider '{self.model_provider}' for CrossEncoder")
+            return None
+
+        # Pass the raw Azure client instance if created
+        if reranker_azure_client:
+            return OpenAIRerankerClient(client=reranker_azure_client)
+        else:
+            # This case should ideally not be reached if provider is Azure due to checks above
+            logger.error(
+                'Failed to create Azure client for CrossEncoder despite provider being Azure'
+            )
+            return None
 
 
 class GraphitiEmbedderConfig(BaseModel):
@@ -381,12 +537,13 @@ class GraphitiEmbedderConfig(BaseModel):
             )
 
     def create_client(self) -> EmbedderClient | None:
+        embedder_client: AsyncAzureOpenAI | None = None
         if self.azure_openai_endpoint is not None:
             # Azure OpenAI API setup
             if self.azure_openai_use_managed_identity:
                 # Use managed identity for authentication
                 token_provider = create_azure_credential_token_provider()
-                return AsyncAzureOpenAI(
+                embedder_client = AsyncAzureOpenAI(
                     azure_endpoint=self.azure_openai_endpoint,
                     azure_deployment=self.azure_openai_deployment_name,
                     api_version=self.azure_openai_api_version,
@@ -394,7 +551,7 @@ class GraphitiEmbedderConfig(BaseModel):
                 )
             elif self.api_key:
                 # Use API key for authentication
-                return AsyncAzureOpenAI(
+                embedder_client = AsyncAzureOpenAI(
                     azure_endpoint=self.azure_openai_endpoint,
                     azure_deployment=self.azure_openai_deployment_name,
                     api_version=self.azure_openai_api_version,
@@ -403,12 +560,17 @@ class GraphitiEmbedderConfig(BaseModel):
             else:
                 logger.error('OPENAI_API_KEY must be set when using Azure OpenAI API')
                 return None
+            # Wrap the Azure client in OpenAIEmbedder
+            if embedder_client:
+                # Assuming OpenAIEmbedder can accept an initialized client
+                return OpenAIEmbedder(client=embedder_client)  # type: ignore
+            return None
         else:
             # OpenAI API setup
             if not self.api_key:
                 return None
 
-            embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, model=self.model)
+            embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, embedding_model=self.model)
 
             return OpenAIEmbedder(config=embedder_config)
 
@@ -541,11 +703,37 @@ async def initialize_graphiti():
     global graphiti_client, config
 
     try:
+        # --- Critical Check: Embedder API Key ---
+        # Embeddings always use OpenAI/Azure, so we need the key regardless of LLM provider
+        # Check if using standard OpenAI embeddings and API key is missing
+        if not config.embedder.azure_openai_endpoint and not config.embedder.api_key:
+            raise ValueError(
+                'OPENAI_API_KEY must be set for embeddings (via OPENAI_API_KEY env var), '
+                'even if using a different LLM provider like Anthropic.'
+            )
+        # Check if using Azure embeddings and relevant Azure config/key is missing
+        elif (
+            config.embedder.azure_openai_endpoint
+            and not config.embedder.azure_openai_use_managed_identity
+            and not config.embedder.api_key
+        ):
+            raise ValueError(
+                'OPENAI_API_KEY must be set for Azure embeddings (via OPENAI_API_KEY env var) '
+                'if not using managed identity.'
+            )
+
         # Create LLM client if possible
         llm_client = config.llm.create_client()
-        if not llm_client and config.use_custom_entities:
+        # Check if custom entities require an LLM and if the selected LLM provider is configured
-            # If custom entities are enabled, we must have an LLM client
+        if config.use_custom_entities and not llm_client:
-            raise ValueError('OPENAI_API_KEY must be set when custom entities are enabled')
+            missing_key = (
+                'ANTHROPIC_API_KEY'
+                if config.llm.model_provider == 'anthropic'
+                else 'OPENAI_API_KEY or Azure equivalent'
+            )
+            raise ValueError(
+                f"{missing_key} must be set when custom entities are enabled and model provider is '{config.llm.model_provider}'."
+            )
 
         # Validate Neo4j configuration
         if not config.neo4j.uri or not config.neo4j.user or not config.neo4j.password:
@@ -1133,6 +1321,12 @@ async def initialize_server() -> MCPConfig:
         default='sse',
         help='Transport to use for communication with the client. (default: sse)',
     )
+    parser.add_argument(
+        '--model-provider',
+        choices=['openai', 'anthropic', 'azure_openai'],
+        default='openai',
+        help='Which LLM provider to use (openai, azure_openai, anthropic). Affects --model and API key usage. (default: openai)',
+    )
     parser.add_argument(
         '--model', help=f'Model name to use with the LLM client. (default: {DEFAULT_LLM_MODEL})'
     )
@@ -1160,6 +1354,7 @@ async def initialize_server() -> MCPConfig:
         logger.info(f'Generated random group_id: {config.group_id}')
 
     # Log entity extraction configuration
+    logger.info(f'Using LLM Provider: {config.llm.model_provider}')
     if config.use_custom_entities:
         logger.info('Entity extraction enabled using predefined ENTITY_TYPES')
     else:

mcp_server/pyproject.toml

@@ -10,3 +10,6 @@ dependencies = [
     "graphiti-core>=0.8.2",
     "azure-identity>=1.21.0",
 ]
+
+[project.optional-dependencies]
+anthropic = ["anthropic>=0.49.0"]

mcp_server/uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.10"
 
 [[package]]
@@ -11,6 +10,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "anthropic"
+version = "0.50.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/40/85/4dd9f80da0727c56d7e7f7c627cb724edd9e6df062df6ecc0e90f06e6dbb/anthropic-0.50.0.tar.gz", hash = "sha256:42175ec04ce4ff2fa37cd436710206aadff546ee99d70d974699f59b49adc66f", size = 213021 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/35/ae/975f97ad5581a9e187a3717e21d79d6c7ad6be926fee9aa8a15b3d9f8f37/anthropic-0.50.0-py3-none-any.whl", hash = "sha256:defbd79327ca2fa61fd7b9eb2f1627dfb1f69c25d49288c52e167ddb84574f80", size = 245291 },
+]
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -188,7 +205,7 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
 wheels = [
@@ -276,12 +293,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/02/cc/b7e31358aac6ed1ef2bb790a9746ac2c69bcb3c8588b41616914eb106eaf/exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b", size = 16453 },
 ]
 
+[[package]]
+name = "graph-service"
+version = "1.0.0.7"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/68/c94b57ddc6bd38b50a3f7357793f8c6a0e9d039af02dadac02b50b05746c/graph_service-1.0.0.7-py3-none-any.whl", hash = "sha256:d34cd38529ff0704b83fa4e8692b4f8fcf1524dce7110f89d4ee33423eeff0bd", size = 6392 },
+]
+
 [[package]]
 name = "graphiti-core"
-version = "0.8.2"
+version = "0.10.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "diskcache" },
+    { name = "graph-service" },
     { name = "neo4j" },
     { name = "numpy" },
     { name = "openai" },
@@ -289,9 +315,9 @@ dependencies = [
     { name = "python-dotenv" },
     { name = "tenacity" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b7/29/f0d74adc687514a226a164340a7fa9254f9cffa2ff7ab353fc1edf4016cb/graphiti_core-0.8.2.tar.gz", hash = "sha256:477a9172728b92ba1875222f8d9c6337d9fa465363b75afd67db9a159c39d379", size = 62464 }
+sdist = { url = "https://files.pythonhosted.org/packages/37/18/64852bf053aaf9ac3e4ab1a594c704be5b226971d3d2d25732a8531054aa/graphiti_core-0.10.5.tar.gz", hash = "sha256:f118c3d7a504fa2dcd17ca8795c0eed77ad8ff787e35fbe73c94ec059e2ec543", size = 67525 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/19/4b/d2065544beb24402bb41197b2adf7ed7af287453c82290d1ca757532f6d3/graphiti_core-0.8.2-py3-none-any.whl", hash = "sha256:ce992f9f3dee168d3ca442e0d0fd685a1a66dee4df94c47909037295aceac45e", size = 95052 },
+    { url = "https://files.pythonhosted.org/packages/ea/5a/d01664ea93abf1836a4d86987c3946c72e4cb5092a31611cad3e77858d73/graphiti_core-0.10.5-py3-none-any.whl", hash = "sha256:73172818ce696c0ab3ece827099522924e61a918d6f80e665927a0724a13d941", size = 105809 },
 ]
 
 [[package]]
@@ -305,15 +331,15 @@ wheels = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.7"
+version = "1.0.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "certifi" },
     { name = "h11" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6a/41/d7d0a89eb493922c37d343b607bc1b5da7f5be7e383740b4753ad8943e90/httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c", size = 85196 }
+sdist = { url = "https://files.pythonhosted.org/packages/9f/45/ad3e1b4d448f22c0cff4f5692f5ed0666658578e358b8d58a19846048059/httpcore-1.0.8.tar.gz", hash = "sha256:86e94505ed24ea06514883fd44d2bc02d90e77e7979c8eb71b90f41d364a1bad", size = 85385 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/f5/72347bc88306acb359581ac4d52f23c0ef445b57157adedb9aee0cd689d2/httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd", size = 78551 },
+    { url = "https://files.pythonhosted.org/packages/18/8d/f052b1e336bb2c1fc7ed1aaed898aa570c0b61a09707b108979d9fc6e308/httpcore-1.0.8-py3-none-any.whl", hash = "sha256:5254cf149bcb5f75e9d1b2b9f729ea4a4b883d1ad7379fc632b727cec23674be", size = 78732 },
 ]
 
 [[package]]
@@ -410,7 +436,7 @@ wheels = [
 
 [[package]]
 name = "mcp"
-version = "1.5.0"
+version = "1.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -422,9 +448,9 @@ dependencies = [
     { name = "starlette" },
     { name = "uvicorn" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6d/c9/c55764824e893fdebe777ac7223200986a275c3191dba9169f8eb6d7c978/mcp-1.5.0.tar.gz", hash = "sha256:5b2766c05e68e01a2034875e250139839498c61792163a7b221fc170c12f5aa9", size = 159128 }
+sdist = { url = "https://files.pythonhosted.org/packages/95/d2/f587cb965a56e992634bebc8611c5b579af912b74e04eb9164bd49527d21/mcp-1.6.0.tar.gz", hash = "sha256:d9324876de2c5637369f43161cd71eebfd803df5a95e46225cab8d280e366723", size = 200031 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c1/d1/3ff566ecf322077d861f1a68a1ff025cad337417bd66ad22a7c6f7dfcfaf/mcp-1.5.0-py3-none-any.whl", hash = "sha256:51c3f35ce93cb702f7513c12406bbea9665ef75a08db909200b07da9db641527", size = 73734 },
+    { url = "https://files.pythonhosted.org/packages/10/30/20a7f33b0b884a9d14dd3aa94ff1ac9da1479fe2ad66dd9e2736075d2506/mcp-1.6.0-py3-none-any.whl", hash = "sha256:7bd24c6ea042dbec44c754f100984d186620d8b841ec30f1b19eda9b93a634d0", size = 76077 },
 ]
 
 [[package]]
@@ -438,8 +464,14 @@ dependencies = [
     { name = "openai" },
 ]
 
+[package.optional-dependencies]
+anthropic = [
+    { name = "anthropic" },
+]
+
 [package.metadata]
 requires-dist = [
+    { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.49.0" },
     { name = "azure-identity", specifier = ">=1.21.0" },
     { name = "graphiti-core", specifier = ">=0.8.2" },
     { name = "mcp", specifier = ">=1.5.0" },
@@ -486,69 +518,69 @@ wheels = [
 
 [[package]]
 name = "numpy"
-version = "2.2.4"
+version = "2.2.5"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e1/78/31103410a57bc2c2b93a3597340a8119588571f6a4539067546cb9a0bfac/numpy-2.2.4.tar.gz", hash = "sha256:9ba03692a45d3eef66559efe1d1096c4b9b75c0986b5dff5530c378fb8331d4f", size = 20270701 }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/b2/ce4b867d8cd9c0ee84938ae1e6a6f7926ebf928c9090d036fc3c6a04f946/numpy-2.2.5.tar.gz", hash = "sha256:a9c0d994680cd991b1cb772e8b297340085466a6fe964bc9d4e80f5e2f43c291", size = 20273920 }
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