feat: Add support for Chutes API integration

- Introduced CHUTES_API_TOKEN in .env.example files for configuration.
- Updated README.md in quickstart example to include Chutes API setup.
- Created quickstart_chutes.py example for demonstrating Chutes integration.
- Added ChutesEmbedder and ChutesClient classes for embedding and LLM functionalities.
- Updated LLM and embedder provider configurations to support Chutes.
- Enhanced factories to include Chutes client and embedder.
- Modified schema.py to define Chutes provider configuration.
- Updated mcp_server configuration files to integrate Chutes API.
- Added necessary dependencies in pyproject.toml for Chutes support.

.env.example

@@ -1,4 +1,5 @@
 OPENAI_API_KEY=
+CHUTES_API_TOKEN=
 
 # Neo4j database connection
 NEO4J_URI=

examples/opentelemetry/.env.example

@@ -1,2 +1,3 @@
 # OpenAI API key (required for LLM inference and embeddings)
 OPENAI_API_KEY=your_api_key_here
+CHUTES_API_TOKEN=your_api_key_here

examples/quickstart/README.md

@@ -35,6 +35,7 @@ pip install graphiti-core
 ```bash
 # Required for LLM and embedding
 export OPENAI_API_KEY=your_openai_api_key
+export CHUTES_API_TOKEN=your_chutes_api_key
 
 # Optional Neo4j connection parameters (defaults shown)
 export NEO4J_URI=bolt://localhost:7687
@@ -67,6 +68,9 @@ python quickstart_falkordb.py
 
 # For Amazon Neptune
 python quickstart_neptune.py
+
+# For Chutes
+python quickstart_chutes.py
 ```
 
 ## What This Example Demonstrates

examples/quickstart/quickstart_chutes.py

@@ -0,0 +1,157 @@
+"""
+Copyright 2025, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import asyncio
+import json
+import logging
+import os
+from datetime import datetime, timezone
+from logging import INFO
+
+from dotenv import load_dotenv
+
+from graphiti_core import Graphiti
+from graphiti_core.embedder.chutes import ChutesEmbedder
+from graphiti_core.llm_client.chutes_client import ChutesClient
+from graphiti_core.nodes import EpisodeType
+
+#################################################
+# CONFIGURATION
+#################################################
+# Set up logging and environment variables for
+# connecting to Neo4j database
+#################################################
+
+# Configure logging
+logging.basicConfig(
+    level=INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S',
+)
+logger = logging.getLogger(__name__)
+
+load_dotenv()
+
+# Neo4j connection parameters
+# Make sure Neo4j Desktop is running with a local DBMS started
+neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
+neo4j_user = os.environ.get('NEO4J_USER', 'neo4j')
+neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password')
+chutes_api_key = os.environ.get('CHUTES_API_TOKEN')
+
+if not neo4j_uri or not neo4j_user or not neo4j_password:
+    raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set')
+
+if not chutes_api_key:
+    raise ValueError('CHUTES_API_TOKEN must be set')
+
+
+async def main():
+    #################################################
+    # INITIALIZATION
+    #################################################
+    # Connect to Neo4j and set up Graphiti indices
+    # This is required before using other Graphiti
+    # functionality
+    #################################################
+
+    # Initialize Graphiti with Neo4j connection and Chutes provider
+    graphiti = Graphiti(
+        neo4j_uri,
+        neo4j_user,
+        neo4j_password,
+        llm_client=ChutesClient(),
+        embedder=ChutesEmbedder(),
+    )
+
+    try:
+        #################################################
+        # ADDING EPISODES
+        #################################################
+        # Episodes are the primary units of information
+        # in Graphiti. They can be text or structured JSON
+        # and are automatically processed to extract entities
+        # and relationships.
+        #################################################
+
+        # Example: Add Episodes
+        # Episodes list containing both text and JSON episodes
+        episodes = [
+            {
+                'content': 'Kamala Harris is the Attorney General of California. She was previously '
+                'the district attorney for San Francisco.',
+                'type': EpisodeType.text,
+                'description': 'podcast transcript',
+            },
+            {
+                'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017',
+                'type': EpisodeType.text,
+                'description': 'podcast transcript',
+            },
+        ]
+
+        # Add episodes to the graph
+        for i, episode in enumerate(episodes):
+            await graphiti.add_episode(
+                name=f'Freakonomics Radio {i}',
+                episode_body=episode['content']
+                if isinstance(episode['content'], str)
+                else json.dumps(episode['content']),
+                source=episode['type'],
+                source_description=episode['description'],
+                reference_time=datetime.now(timezone.utc),
+            )
+            print(f'Added episode: Freakonomics Radio {i} ({episode["type"].value})')
+
+        #################################################
+        # BASIC SEARCH
+        #################################################
+        # The simplest way to retrieve relationships (edges)
+        # from Graphiti is using the search method, which
+        # performs a hybrid search combining semantic
+        # similarity and BM25 text retrieval.
+        #################################################
+
+        # Perform a hybrid search combining semantic similarity and BM25 retrieval
+        print("\nSearching for: 'Who was the California Attorney General?'")
+        results = await graphiti.search('Who was the California Attorney General?')
+
+        # Print search results
+        print('\nSearch Results:')
+        for result in results:
+            print(f'UUID: {result.uuid}')
+            print(f'Fact: {result.fact}')
+            if hasattr(result, 'valid_at') and result.valid_at:
+                print(f'Valid from: {result.valid_at}')
+            if hasattr(result, 'invalid_at') and result.invalid_at:
+                print(f'Valid until: {result.invalid_at}')
+            print('---')
+
+    finally:
+        #################################################
+        # CLEANUP
+        #################################################
+        # Always close the connection to Neo4j when
+        # finished to properly release resources
+        #################################################
+
+        # Close the connection
+        await graphiti.close()
+        print('\nConnection closed')
+
+
+if __name__ == '__main__':
+    asyncio.run(main())

graphiti_core/embedder/__init__.py

@@ -1,8 +1,11 @@
 from .client import EmbedderClient
 from .openai import OpenAIEmbedder, OpenAIEmbedderConfig
+from .chutes import ChutesEmbedder, ChutesEmbedderConfig
 
 __all__ = [
     'EmbedderClient',
     'OpenAIEmbedder',
     'OpenAIEmbedderConfig',
+    'ChutesEmbedder',
+    'ChutesEmbedderConfig',
 ]

graphiti_core/embedder/chutes.py

@@ -0,0 +1,65 @@
+"""
+Copyright 2024, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import os
+from collections.abc import Iterable
+
+from openai import AsyncOpenAI
+from openai.types import EmbeddingModel
+
+from .client import EmbedderClient, EmbedderConfig
+
+DEFAULT_EMBEDDING_MODEL = "qwen-3-8b"
+
+
+class ChutesEmbedderConfig(EmbedderConfig):
+    embedding_model: EmbeddingModel | str = DEFAULT_EMBEDDING_MODEL
+    api_key: str | None = os.environ.get("CHUTES_API_TOKEN")
+    base_url: str | None = "https://chutes-qwen-qwen3-embedding-8b.chutes.ai/v1"
+
+
+class ChutesEmbedder(EmbedderClient):
+    """
+    Chutes Embedder Client
+    """
+
+    def __init__(
+        self,
+        config: ChutesEmbedderConfig | None = None,
+        client: AsyncOpenAI | None = None,
+    ):
+        if config is None:
+            config = ChutesEmbedderConfig()
+        self.config = config
+
+        if client is not None:
+            self.client = client
+        else:
+            self.client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+
+    async def create(
+        self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
+    ) -> list[float]:
+        result = await self.client.embeddings.create(
+            input=input_data, model=None
+        )
+        return result.data[0].embedding[: self.config.embedding_dim]
+
+    async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
+        result = await self.client.embeddings.create(
+            input=input_data_list, model=None
+        )
+        return [embedding.embedding[: self.config.embedding_dim] for embedding in result.data]

graphiti_core/llm_client/__init__.py

@@ -18,5 +18,6 @@ from .client import LLMClient
 from .config import LLMConfig
 from .errors import RateLimitError
 from .openai_client import OpenAIClient
+from .chutes_client import ChutesClient
 
-__all__ = ['LLMClient', 'OpenAIClient', 'LLMConfig', 'RateLimitError']
+__all__ = ['LLMClient', 'OpenAIClient', 'LLMConfig', 'RateLimitError', "ChutesClient"]

graphiti_core/llm_client/chutes_client.py

@@ -0,0 +1,90 @@
+"""
+Copyright 2024, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUTHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import os
+import typing
+
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam
+from pydantic import BaseModel
+
+from .config import DEFAULT_MAX_TOKENS, LLMConfig
+from .openai_base_client import DEFAULT_REASONING, DEFAULT_VERBOSITY, BaseOpenAIClient
+
+
+class ChutesClient(BaseOpenAIClient):
+    """
+    ChutesClient is a client class for interacting with Chutes's language models.
+    """
+
+    def __init__(
+        self,
+        config: LLMConfig | None = None,
+        cache: bool = False,
+        client: typing.Any = None,
+        max_tokens: int = DEFAULT_MAX_TOKENS,
+        reasoning: str = DEFAULT_REASONING,
+        verbosity: str = DEFAULT_VERBOSITY,
+    ):
+        """
+        Initialize the ChutesClient with the provided configuration, cache setting, and client.
+        """
+        super().__init__(config, cache, max_tokens, reasoning, verbosity)
+
+        if config is None:
+            config = LLMConfig()
+
+        if client is None:
+            self.client = AsyncOpenAI(
+                api_key=config.api_key,
+                base_url="https://llm.chutes.ai/v1",
+            )
+        else:
+            self.client = client
+
+    async def _create_structured_completion(
+        self,
+        model: str,
+        messages: list[ChatCompletionMessageParam],
+        temperature: float | None,
+        max_tokens: int,
+        response_model: type[BaseModel],
+        reasoning: str | None = None,
+        verbosity: str | None = None,
+    ):
+        """Create a structured completion using Chutes's API."""
+        return await self._create_completion(
+            model, messages, temperature, max_tokens, response_model
+        )
+
+    async def _create_completion(
+        self,
+        model: str,
+        messages: list[ChatCompletionMessageParam],
+        temperature: float | None,
+        max_tokens: int,
+        response_model: type[BaseModel] | None = None,
+        reasoning: str | None = None,
+        verbosity: str | None = None,
+    ):
+        """Create a regular completion with JSON format."""
+        return await self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            response_format={"type": "json_object"} if response_model else None,
+        )

graphiti_core/llm_client/client.py

@@ -223,6 +223,8 @@ class LLMClient(ABC):
             return 'gemini'
         elif 'groq' in class_name:
             return 'groq'
+        elif 'chutes' in class_name:
+            return 'chutes'
         else:
             return 'unknown'
 

graphiti_core/llm_client/config.py

@@ -25,6 +25,11 @@ class ModelSize(Enum):
     medium = 'medium'
 
 
+class LLMProvider(str, Enum):
+    OPENAI = "openai"
+    CHUTES = "chutes"
+
+
 class LLMConfig:
     """
     Configuration class for the Language Learning Model (LLM).
@@ -42,6 +47,7 @@ class LLMConfig:
         temperature: float = DEFAULT_TEMPERATURE,
         max_tokens: int = DEFAULT_MAX_TOKENS,
         small_model: str | None = None,
+        provider: LLMProvider = LLMProvider.OPENAI,
     ):
         """
         Initialize the LLMConfig with the provided parameters.
@@ -59,6 +65,8 @@ class LLMConfig:
 
                 small_model (str, optional): The specific LLM model to use for generating responses of simpler prompts.
                                                                 Defaults to "gpt-4.1-nano".
+                provider (LLMProvider, optional): The LLM provider to use.
+                                                                Defaults to LLMProvider.OPENAI.
         """
         self.base_url = base_url
         self.api_key = api_key
@@ -66,3 +74,4 @@ class LLMConfig:
         self.small_model = small_model
         self.temperature = temperature
         self.max_tokens = max_tokens
+        self.provider = provider

graphiti_core/llm_client/openai_base_client.py

@@ -115,14 +115,21 @@ class BaseOpenAIClient(LLMClient):
 
     def _handle_structured_response(self, response: Any) -> dict[str, Any]:
         """Handle structured response parsing and validation."""
-        response_object = response.output_text
-
-        if response_object:
-            return json.loads(response_object)
-        elif response_object.refusal:
-            raise RefusalError(response_object.refusal)
+        # Check if the response has the beta output_text attribute
+        if hasattr(response, 'output_text') and response.output_text is not None:
+            response_content = response.output_text
+        elif hasattr(response, 'choices') and response.choices:
+            # Fallback to standard OpenAI chat completion content
+            response_content = response.choices[0].message.content
         else:
-            raise Exception(f'Invalid response from LLM: {response_object.model_dump()}')
+            raise Exception(f'Invalid response from LLM: {response}')
+
+        if response_content:
+            return json.loads(response_content)
+        elif hasattr(response, 'refusal') and response.refusal:
+            raise RefusalError(response.refusal)
+        else:
+            raise Exception(f'Invalid response from LLM: {response}')
 
     def _handle_json_response(self, response: Any) -> dict[str, Any]:
         """Handle JSON response parsing."""

graphiti_core/prompts/dedupe_nodes.py

@@ -180,6 +180,14 @@ def nodes(context: dict[str, Any]) -> list[Message]:
         - Only use idx values that appear in EXISTING ENTITIES.
         - Set duplicate_idx to the smallest idx you collected for that entity, or -1 if duplicates is empty.
         - Never fabricate entities or indices.
+
+        Response Format:
+        Your response must be a JSON object with a single key: "entity_resolutions".
+        The value of "entity_resolutions" must be a list of JSON objects, where each object has the following keys:
+        - "id": The integer id from the ENTITIES list.
+        - "name": The best full name for the entity.
+        - "duplicate_idx": The index of the best duplicate match from EXISTING ENTITIES, or -1 if none.
+        - "duplicates": A sorted list of all duplicate indices from EXISTING ENTITIES.
         """,
         ),
     ]

graphiti_core/prompts/extract_edges.py

@@ -112,6 +112,16 @@ You may use information from the PREVIOUS MESSAGES only to disambiguate referenc
 
 {context['custom_prompt']}
 
+# RESPONSE FORMAT
+Your response must be a JSON object with a single key: "edges".
+The value of "edges" must be a list of JSON objects, where each object has the following keys:
+- "relation_type": A SCREAMING_SNAKE_CASE string representing the fact type (e.g., FOUNDED, WORKS_AT).
+- "source_entity_id": The ID of the source entity from the ENTITIES list.
+- "target_entity_id": The ID of the target entity from the ENTITIES list.
+- "fact": A natural language description of the relationship.
+- "valid_at": The start time of the relationship in ISO 8601 format (or null).
+- "invalid_at": The end time of the relationship in ISO 8601 format (or null).
+
 # EXTRACTION RULES
 
 1. **Entity ID Validation**: `source_entity_id` and `target_entity_id` must use only the `id` values from the ENTITIES list provided above.

graphiti_core/prompts/extract_nodes.py

@@ -125,6 +125,12 @@ reference entities. Only extract distinct entities from the CURRENT MESSAGE. Don
    - Be **explicit and unambiguous** in naming entities (e.g., use full names when available).
 
 {context['custom_prompt']}
+
+Response Format:
+Your response must be a JSON object with a single key: "extracted_entities".
+The value of "extracted_entities" must be a list of JSON objects, where each object has the following keys:
+- "name": The name of the extracted entity.
+- "entity_type_id": The ID of the classified entity type.
 """
     return [
         Message(role='system', content=sys_prompt),
@@ -154,6 +160,12 @@ Given the above source description and JSON, extract relevant entities from the
 For each entity extracted, also determine its entity type based on the provided ENTITY TYPES and their descriptions.
 Indicate the classified entity type by providing its entity_type_id.
 
+Response Format:
+Your response must be a JSON object with a single key: "extracted_entities".
+The value of "extracted_entities" must be a list of JSON objects, where each object has the following keys:
+- "name": The name of the extracted entity.
+- "entity_type_id": The ID of the classified entity type.
+
 Guidelines:
 1. Extract all entities that the JSON represents. This will often be something like a "name" or "user" field
 2. Extract all entities mentioned in all other properties throughout the JSON structure
@@ -184,6 +196,12 @@ Indicate the classified entity type by providing its entity_type_id.
 
 {context['custom_prompt']}
 
+Response Format:
+Your response must be a JSON object with a single key: "extracted_entities".
+The value of "extracted_entities" must be a list of JSON objects, where each object has the following keys:
+- "name": The name of the extracted entity.
+- "entity_type_id": The ID of the classified entity type.
+
 Guidelines:
 1. Extract significant entities, concepts, or actors mentioned in the conversation.
 2. Avoid creating nodes for relationships or actions.

mcp_server/.env.example

@@ -2,13 +2,13 @@
 
 # Neo4j Database Configuration
 # These settings are used to connect to your Neo4j database
-NEO4J_URI=bolt://localhost:7687
+NEO4J_URI=neo4j+s://6a25b5ba.databases.neo4j.io
 NEO4J_USER=neo4j
-NEO4J_PASSWORD=demodemo
+NEO4J_PASSWORD=YuU8ypE1yzjVbn5LPaNkLcotsLL4aAOILGIQtSG2KAY
 
 # OpenAI API Configuration
 # Required for LLM operations
-OPENAI_API_KEY=your_openai_api_key_here
+OPENAI_API_KEY=dummy-key
 MODEL_NAME=gpt-4.1-mini
 
 # Optional: Only needed for non-standard OpenAI endpoints
@@ -47,3 +47,6 @@ SEMAPHORE_LIMIT=10
 # AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
 # AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=text-embedding-3-large-deployment
 # AZURE_OPENAI_USE_MANAGED_IDENTITY=false
+
+# Chutes AI Configuration
+CHUTES_API_KEY=

mcp_server/config/config.yaml

@@ -11,8 +11,8 @@ server:
   port: 8000
   
 llm:
-  provider: "openai"  # Options: openai, azure_openai, anthropic, gemini, groq
-  model: "gpt-5-mini"
+  provider: "chutes"  # Options: openai, azure_openai, anthropic, gemini, groq, chutes
+  model: "moonshotai/Kimi-K2-Instruct-0905"
   max_tokens: 4096
   
   providers:
@@ -42,9 +42,14 @@ llm:
       api_key: ${GROQ_API_KEY}
       api_url: ${GROQ_API_URL:https://api.groq.com/openai/v1}
 
+    chutes:
+      api_key: ${CHUTES_API_KEY}
+      api_url: ${CHUTES_API_URL:https://llm.chutes.ai/v1}
+      embedding_url: ${CHUTES_EMBEDDING_URL:https://chutes-qwen-qwen3-embedding-8b.chutes.ai/v1}
+
 embedder:
-  provider: "openai"  # Options: openai, azure_openai, gemini, voyage
-  model: "text-embedding-3-small"
+  provider: "chutes"  # Options: openai, azure_openai, gemini, voyage, chutes
+  model: "Qwen/Qwen2-57B-A14B-Instruct"
   dimensions: 1536
   
   providers:
@@ -70,8 +75,13 @@ embedder:
       api_url: ${VOYAGE_API_URL:https://api.voyageai.com/v1}
       model: "voyage-3"
 
+    chutes:
+      api_key: ${CHUTES_API_KEY}
+      api_url: ${CHUTES_API_URL:https://llm.chutes.ai/v1}
+      embedding_url: ${CHUTES_EMBEDDING_URL:https://chutes-qwen-qwen3-embedding-8b.chutes.ai/v1}
+
 database:
-  provider: "falkordb"  # Default: falkordb. Options: neo4j, falkordb
+  provider: "neo4j"  # Default: falkordb. Options: neo4j, falkordb
 
   providers:
     falkordb:
@@ -88,7 +98,7 @@ database:
 
 graphiti:
   group_id: ${GRAPHITI_GROUP_ID:main}
-  episode_id_prefix: ${EPISODE_ID_PREFIX:}
+  episode_id_prefix: ${EPISODE_ID_PREFIX:ep}
   user_id: ${USER_ID:mcp_user}
   entity_types:
     - name: "Preference"

mcp_server/pyproject.toml

@@ -5,7 +5,7 @@ description = "Graphiti MCP Server"
 readme = "README.md"
 requires-python = ">=3.10,<4"
 dependencies = [
-    "mcp>=1.9.4",
+    "mcp[cli]>=1.9.4",
     "openai>=1.91.0",
     "graphiti-core[falkordb]>=0.23.1",
     "pydantic-settings>=2.0.0",

mcp_server/src/config/schema.py

@@ -125,6 +125,14 @@ class GroqProviderConfig(BaseModel):
     api_url: str = 'https://api.groq.com/openai/v1'
 
 
+class ChutesProviderConfig(BaseModel):
+    """Chutes provider configuration."""
+
+    api_key: str | None = None
+    api_url: str = 'https://llm.chutes.ai/v1'
+    embedding_url: str = 'https://chutes-qwen-qwen3-embedding-8b.chutes.ai/v1'
+
+
 class VoyageProviderConfig(BaseModel):
     """Voyage AI provider configuration."""
 
@@ -141,6 +149,7 @@ class LLMProvidersConfig(BaseModel):
     anthropic: AnthropicProviderConfig | None = None
     gemini: GeminiProviderConfig | None = None
     groq: GroqProviderConfig | None = None
+    chutes: ChutesProviderConfig | None = None
 
 
 class LLMConfig(BaseModel):
@@ -162,6 +171,7 @@ class EmbedderProvidersConfig(BaseModel):
     azure_openai: AzureOpenAIProviderConfig | None = None
     gemini: GeminiProviderConfig | None = None
     voyage: VoyageProviderConfig | None = None
+    chutes: ChutesProviderConfig | None = None
 
 
 class EmbedderConfig(BaseModel):

mcp_server/src/graphiti_mcp_server.py

@@ -752,7 +752,6 @@ async def get_status() -> StatusResponse:
             message=f'Graphiti MCP server is running but database connection failed: {error_msg}',
         )
 
-
 @mcp.custom_route('/health', methods=['GET'])
 async def health_check(request) -> JSONResponse:
     """Health check endpoint for Docker and load balancers."""
@@ -796,12 +795,12 @@ async def initialize_server() -> ServerConfig:
     # Provider selection arguments
     parser.add_argument(
         '--llm-provider',
-        choices=['openai', 'azure_openai', 'anthropic', 'gemini', 'groq'],
+        choices=['openai', 'azure_openai', 'anthropic', 'gemini', 'groq', 'chutes'],
         help='LLM provider to use',
     )
     parser.add_argument(
         '--embedder-provider',
-        choices=['openai', 'azure_openai', 'gemini', 'voyage'],
+        choices=['openai', 'azure_openai', 'gemini', 'voyage', 'chutes'],
         help='Embedder provider to use',
     )
     parser.add_argument(

mcp_server/src/services/chutes_client.py

@@ -0,0 +1,6 @@
+"""Chutes clients for LLM and Embedder."""
+
+from graphiti_core.llm_client.chutes_client import ChutesClient as ChutesLLMClient
+from graphiti_core.embedder.chutes import ChutesEmbedder as ChutesEmbedderClient
+
+__all__ = ["ChutesLLMClient", "ChutesEmbedderClient"]

mcp_server/src/services/factories.py

@@ -43,6 +43,13 @@ try:
 except ImportError:
     HAS_VOYAGE_EMBEDDER = False
 
+try:
+    from services.chutes_client import ChutesEmbedderClient
+
+    HAS_CHUTES_EMBEDDER = True
+except ImportError:
+    HAS_CHUTES_EMBEDDER = False
+
 try:
     from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
 
@@ -70,6 +77,12 @@ try:
     HAS_GROQ = True
 except ImportError:
     HAS_GROQ = False
+try:
+    from services.chutes_client import ChutesLLMClient
+
+    HAS_CHUTES_LLM = True
+except ImportError:
+    HAS_CHUTES_LLM = False
 from utils.utils import create_azure_credential_token_provider
 
 
@@ -246,6 +259,27 @@ class LLMClientFactory:
                 )
                 return GroqClient(config=llm_config)
 
+            case 'chutes':
+                if not HAS_CHUTES_LLM:
+                    raise ValueError(
+                        'Chutes LLM client not available in current graphiti-core version'
+                    )
+                if not config.providers.chutes:
+                    raise ValueError('Chutes provider configuration not found')
+
+                api_key = config.providers.chutes.api_key
+                _validate_api_key('Chutes', api_key, logger)
+
+                llm_config = GraphitiLLMConfig(
+                    api_key=api_key,
+                    base_url=config.providers.chutes.api_url,
+                    model=config.model,
+                    small_model=config.model,
+                    temperature=config.temperature,
+                    max_tokens=config.max_tokens,
+                )
+                return ChutesLLMClient(config=llm_config)
+
             case _:
                 raise ValueError(f'Unsupported LLM provider: {provider}')
 
@@ -356,6 +390,27 @@ class EmbedderFactory:
                 )
                 return VoyageAIEmbedder(config=voyage_config)
 
+            case 'chutes':
+                if not HAS_CHUTES_EMBEDDER:
+                    raise ValueError(
+                        'Chutes embedder not available in current graphiti-core version'
+                    )
+                if not config.providers.chutes:
+                    raise ValueError('Chutes provider configuration not found')
+
+                api_key = config.providers.chutes.api_key
+                _validate_api_key('Chutes Embedder', api_key, logger)
+
+                from graphiti_core.embedder.chutes import ChutesEmbedderConfig
+
+                embedder_config = ChutesEmbedderConfig(
+                    api_key=api_key,
+                    base_url=config.providers.chutes.embedding_url,
+                    embedding_model=config.model,
+                    embedding_dim=config.dimensions,
+                )
+                return ChutesEmbedderClient(config=embedder_config)
+
             case _:
                 raise ValueError(f'Unsupported Embedder provider: {provider}')
 

mcp_server/uv.lock

@@ -994,6 +994,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/79/ed/7a48189bdad850cfd47df671204c31779dd190de6bc681f169d4535f852e/langsmith-0.4.16-py3-none-any.whl", hash = "sha256:9ba95ed09b057dfe227e882f5446e1824bfc9f2c89de542ee6f0f8d90ab953a7", size = 375761, upload-time = "2025-08-22T15:45:14.82Z" },
 ]
 
+[[package]]
+name = "markdown-it-py"
+version = "4.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mdurl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" },
+]
+
 [[package]]
 name = "markupsafe"
 version = "3.0.2"
@@ -1072,13 +1084,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/97/fc/80e655c955137393c443842ffcc4feccab5b12fa7cb8de9ced90f90e6998/mcp-1.9.4-py3-none-any.whl", hash = "sha256:7fcf36b62936adb8e63f89346bccca1268eeca9bf6dfb562ee10b1dfbda9dac0", size = 130232, upload-time = "2025-06-12T08:20:28.551Z" },
 ]
 
+[package.optional-dependencies]
+cli = [
+    { name = "python-dotenv" },
+    { name = "typer" },
+]
+
 [[package]]
 name = "mcp-server"
 version = "1.0.1"
 source = { virtual = "." }
 dependencies = [
     { name = "graphiti-core", extra = ["falkordb"] },
-    { name = "mcp" },
+    { name = "mcp", extra = ["cli"] },
     { name = "openai" },
     { name = "pydantic-settings" },
     { name = "pyyaml" },
@@ -1117,7 +1135,7 @@ requires-dist = [
     { name = "google-genai", marker = "extra == 'providers'", specifier = ">=1.8.0" },
     { name = "graphiti-core", extras = ["falkordb"], editable = "../" },
     { name = "groq", marker = "extra == 'providers'", specifier = ">=0.2.0" },
-    { name = "mcp", specifier = ">=1.9.4" },
+    { name = "mcp", extras = ["cli"], specifier = ">=1.9.4" },
     { name = "openai", specifier = ">=1.91.0" },
     { name = "pydantic-settings", specifier = ">=2.0.0" },
     { name = "pyyaml", specifier = ">=6.0" },
@@ -1140,6 +1158,15 @@ dev = [
     { name = "ruff", specifier = ">=0.7.1" },
 ]
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729, upload-time = "2022-08-14T12:40:10.846Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" },
+]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -2283,6 +2310,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3f/51/d4db610ef29373b879047326cbf6fa98b6c1969d6f6dc423279de2b1be2c/requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06", size = 54481, upload-time = "2023-05-01T04:11:28.427Z" },
 ]
 
+[[package]]
+name = "rich"
+version = "14.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/d2/8920e102050a0de7bfabeb4c4614a49248cf8d5d7a8d01885fbb24dc767a/rich-14.2.0.tar.gz", hash = "sha256:73ff50c7c0c1c77c8243079283f4edb376f0f6442433aecb8ce7e6d0b92d1fe4", size = 219990, upload-time = "2025-10-09T14:16:53.064Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/7a/b0178788f8dc6cafce37a212c99565fa1fe7872c70c6c9c1e1a372d9d88f/rich-14.2.0-py3-none-any.whl", hash = "sha256:76bc51fe2e57d2b1be1f96c524b890b816e334ab4c1e45888799bfaab0021edd", size = 243393, upload-time = "2025-10-09T14:16:51.245Z" },
+]
+
 [[package]]
 name = "rsa"
 version = "4.9.1"
@@ -2466,6 +2506,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922", size = 1201486, upload-time = "2025-05-27T00:56:49.664Z" },
 ]
 
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310, upload-time = "2023-10-24T04:13:40.426Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" },
+]
+
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -2702,6 +2751,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/20/63/8cb444ad5cdb25d999b7d647abac25af0ee37d292afc009940c05b82dda0/triton-3.4.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7936b18a3499ed62059414d7df563e6c163c5e16c3773678a3ee3d417865035d", size = 155659780, upload-time = "2025-07-30T19:58:51.171Z" },
 ]
 
+[[package]]
+name = "typer"
+version = "0.20.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "rich" },
+    { name = "shellingham" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8f/28/7c85c8032b91dbe79725b6f17d2fffc595dff06a35c7a30a37bef73a1ab4/typer-0.20.0.tar.gz", hash = "sha256:1aaf6494031793e4876fb0bacfa6a912b551cf43c1e63c800df8b1a866720c37", size = 106492, upload-time = "2025-10-20T17:03:49.445Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/78/64/7713ffe4b5983314e9d436a90d5bd4f63b6054e2aca783a3cfc44cb95bbf/typer-0.20.0-py3-none-any.whl", hash = "sha256:5b463df6793ec1dca6213a3cf4c0f03bc6e322ac5e16e13ddd622a889489784a", size = 47028, upload-time = "2025-10-20T17:03:47.617Z" },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.14.0"

server/.env.example

@@ -1,4 +1,5 @@
 OPENAI_API_KEY=
+CHUTES_API_TOKEN=
 NEO4J_PORT=7687
 # Only used if not running a neo4j container in docker
 NEO4J_URI=bolt://localhost:7687