#!/usr/bin/env python3 """ Graphiti MCP Server - Exposes Graphiti functionality through the Model Context Protocol (MCP) """ import argparse import asyncio import logging import os import sys from collections.abc import Callable from datetime import datetime, timezone from typing import Any, TypedDict, cast from azure.identity import DefaultAzureCredential, get_bearer_token_provider from dotenv import load_dotenv from mcp.server.fastmcp import FastMCP from openai import AsyncAzureOpenAI from pydantic import BaseModel, Field from graphiti_core import Graphiti from graphiti_core.edges import EntityEdge from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient 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.azure_openai_client import AzureOpenAILLMClient from graphiti_core.llm_client.config import LLMConfig from graphiti_core.llm_client.openai_client import OpenAIClient from graphiti_core.nodes import EpisodeType, EpisodicNode from graphiti_core.search.search_config_recipes import ( NODE_HYBRID_SEARCH_NODE_DISTANCE, NODE_HYBRID_SEARCH_RRF, ) from graphiti_core.search.search_filters import SearchFilters from graphiti_core.utils.maintenance.graph_data_operations import clear_data load_dotenv() DEFAULT_LLM_MODEL = 'gpt-4.1-mini' SMALL_LLM_MODEL = 'gpt-4.1-nano' DEFAULT_EMBEDDER_MODEL = 'text-embedding-3-small' # Semaphore limit for concurrent Graphiti operations. # Decrease this if you're experiencing 429 rate limit errors from your LLM provider. # Increase if you have high rate limits. SEMAPHORE_LIMIT = int(os.getenv('SEMAPHORE_LIMIT', 10)) class Requirement(BaseModel): """A Requirement represents a specific need, feature, or functionality that a product or service must fulfill. Always ensure an edge is created between the requirement and the project it belongs to, and clearly indicate on the edge that the requirement is a requirement. Instructions for identifying and extracting requirements: 1. Look for explicit statements of needs or necessities ("We need X", "X is required", "X must have Y") 2. Identify functional specifications that describe what the system should do 3. Pay attention to non-functional requirements like performance, security, or usability criteria 4. Extract constraints or limitations that must be adhered to 5. Focus on clear, specific, and measurable requirements rather than vague wishes 6. Capture the priority or importance if mentioned ("critical", "high priority", etc.) 7. Include any dependencies between requirements when explicitly stated 8. Preserve the original intent and scope of the requirement 9. Categorize requirements appropriately based on their domain or function """ project_name: str = Field( ..., description='The name of the project to which the requirement belongs.', ) description: str = Field( ..., description='Description of the requirement. Only use information mentioned in the context to write this description.', ) class Preference(BaseModel): """A Preference represents a user's expressed like, dislike, or preference for something. Instructions for identifying and extracting preferences: 1. Look for explicit statements of preference such as "I like/love/enjoy/prefer X" or "I don't like/hate/dislike X" 2. Pay attention to comparative statements ("I prefer X over Y") 3. Consider the emotional tone when users mention certain topics 4. Extract only preferences that are clearly expressed, not assumptions 5. Categorize the preference appropriately based on its domain (food, music, brands, etc.) 6. Include relevant qualifiers (e.g., "likes spicy food" rather than just "likes food") 7. Only extract preferences directly stated by the user, not preferences of others they mention 8. Provide a concise but specific description that captures the nature of the preference """ category: str = Field( ..., description="The category of the preference. (e.g., 'Brands', 'Food', 'Music')", ) description: str = Field( ..., description='Brief description of the preference. Only use information mentioned in the context to write this description.', ) class Procedure(BaseModel): """A Procedure informing the agent what actions to take or how to perform in certain scenarios. Procedures are typically composed of several steps. Instructions for identifying and extracting procedures: 1. Look for sequential instructions or steps ("First do X, then do Y") 2. Identify explicit directives or commands ("Always do X when Y happens") 3. Pay attention to conditional statements ("If X occurs, then do Y") 4. Extract procedures that have clear beginning and end points 5. Focus on actionable instructions rather than general information 6. Preserve the original sequence and dependencies between steps 7. Include any specified conditions or triggers for the procedure 8. Capture any stated purpose or goal of the procedure 9. Summarize complex procedures while maintaining critical details """ description: str = Field( ..., description='Brief description of the procedure. Only use information mentioned in the context to write this description.', ) ENTITY_TYPES: dict[str, BaseModel] = { 'Requirement': Requirement, # type: ignore 'Preference': Preference, # type: ignore 'Procedure': Procedure, # type: ignore } # Type definitions for API responses class ErrorResponse(TypedDict): error: str class SuccessResponse(TypedDict): message: str class NodeResult(TypedDict): uuid: str name: str summary: str labels: list[str] group_id: str created_at: str attributes: dict[str, Any] class NodeSearchResponse(TypedDict): message: str nodes: list[NodeResult] class FactSearchResponse(TypedDict): message: str facts: list[dict[str, Any]] class EpisodeSearchResponse(TypedDict): message: str episodes: list[dict[str, Any]] class StatusResponse(TypedDict): status: str message: str def create_azure_credential_token_provider() -> Callable[[], str]: credential = DefaultAzureCredential() token_provider = get_bearer_token_provider( credential, 'https://cognitiveservices.azure.com/.default' ) return token_provider # Server configuration classes # The configuration system has a hierarchy: # - GraphitiConfig is the top-level configuration # - LLMConfig handles all OpenAI/LLM related settings # - EmbedderConfig manages embedding settings # - Neo4jConfig manages database connection details # - Various other settings like group_id and feature flags # Configuration values are loaded from: # 1. Default values in the class definitions # 2. Environment variables (loaded via load_dotenv()) # 3. Command line arguments (which override environment variables) class GraphitiLLMConfig(BaseModel): """Configuration for the LLM client. Centralizes all LLM-specific configuration parameters including API keys and model selection. """ api_key: str | None = None model: str = DEFAULT_LLM_MODEL small_model: str = SMALL_LLM_MODEL temperature: float = 0.0 azure_openai_endpoint: str | None = None azure_openai_deployment_name: str | None = None azure_openai_api_version: str | None = None azure_openai_use_managed_identity: bool = False @classmethod def from_env(cls) -> 'GraphitiLLMConfig': """Create LLM configuration from environment variables.""" # Get model from environment, or use default if not set or empty model_env = os.environ.get('MODEL_NAME', '') model = model_env if model_env.strip() else DEFAULT_LLM_MODEL # Get small_model from environment, or use default if not set or empty small_model_env = os.environ.get('SMALL_MODEL_NAME', '') small_model = small_model_env if small_model_env.strip() else SMALL_LLM_MODEL azure_openai_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT', None) azure_openai_api_version = os.environ.get('AZURE_OPENAI_API_VERSION', None) azure_openai_deployment_name = os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME', None) azure_openai_use_managed_identity = ( os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true' ) if azure_openai_endpoint is None: # Setup for OpenAI API # Log if empty model was provided if model_env == '': logger.debug( f'MODEL_NAME environment variable not set, using default: {DEFAULT_LLM_MODEL}' ) elif not model_env.strip(): logger.warning( f'Empty MODEL_NAME environment variable, using default: {DEFAULT_LLM_MODEL}' ) return cls( api_key=os.environ.get('OPENAI_API_KEY'), model=model, small_model=small_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, model=model, small_model=small_model, 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.""" # Start with environment-based config config = cls.from_env() # CLI arguments override environment variables when provided if hasattr(args, 'model') and args.model: # Only use CLI model if it's not empty if args.model.strip(): config.model = args.model else: # Log that empty model was provided and default is used logger.warning(f'Empty model name provided, using default: {DEFAULT_LLM_MODEL}') if hasattr(args, 'small_model') and args.small_model: if args.small_model.strip(): config.small_model = args.small_model else: logger.warning(f'Empty small_model name provided, using default: {SMALL_LLM_MODEL}') if hasattr(args, 'temperature') and args.temperature is not None: config.temperature = args.temperature return config def create_client(self) -> LLMClient: """Create an LLM client based on this configuration. Returns: LLMClient instance """ 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 AzureOpenAILLMClient( azure_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, ), config=LLMConfig( api_key=self.api_key, model=self.model, small_model=self.small_model, temperature=self.temperature, ), ) elif self.api_key: # Use API key for authentication return AzureOpenAILLMClient( azure_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, ), config=LLMConfig( api_key=self.api_key, model=self.model, small_model=self.small_model, temperature=self.temperature, ), ) else: raise ValueError('OPENAI_API_KEY must be set when using Azure OpenAI API') if not self.api_key: raise ValueError('OPENAI_API_KEY must be set when using OpenAI API') llm_client_config = LLMConfig( api_key=self.api_key, model=self.model, small_model=self.small_model ) # Set temperature llm_client_config.temperature = self.temperature return OpenAIClient(config=llm_client_config) class GraphitiEmbedderConfig(BaseModel): """Configuration for the embedder client. Centralizes all embedding-related configuration parameters. """ model: str = DEFAULT_EMBEDDER_MODEL api_key: str | None = None azure_openai_endpoint: str | None = None azure_openai_deployment_name: str | None = None azure_openai_api_version: str | None = None azure_openai_use_managed_identity: bool = False @classmethod def from_env(cls) -> 'GraphitiEmbedderConfig': """Create embedder configuration from environment variables.""" # Get model from environment, or use default if not set or empty model_env = os.environ.get('EMBEDDER_MODEL_NAME', '') model = model_env if model_env.strip() else DEFAULT_EMBEDDER_MODEL azure_openai_endpoint = os.environ.get('AZURE_OPENAI_EMBEDDING_ENDPOINT', None) azure_openai_api_version = os.environ.get('AZURE_OPENAI_EMBEDDING_API_VERSION', None) azure_openai_deployment_name = os.environ.get( 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME', None ) azure_openai_use_managed_identity = ( os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true' ) if azure_openai_endpoint is not None: # Setup for Azure OpenAI API # Log if empty deployment name was provided azure_openai_deployment_name = os.environ.get( 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME', None ) if azure_openai_deployment_name is None: logger.error('AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable not set') raise ValueError( 'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable not set' ) if not azure_openai_use_managed_identity: # api key api_key = os.environ.get('AZURE_OPENAI_EMBEDDING_API_KEY', None) or 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, ) else: return cls( model=model, api_key=os.environ.get('OPENAI_API_KEY'), ) def create_client(self) -> EmbedderClient | 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 AzureOpenAIEmbedderClient( azure_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, ), model=self.model, ) elif self.api_key: # Use API key for authentication return AzureOpenAIEmbedderClient( azure_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, ), model=self.model, ) else: logger.error('OPENAI_API_KEY must be set when using Azure OpenAI API') return None else: # OpenAI API setup if not self.api_key: return None embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, embedding_model=self.model) return OpenAIEmbedder(config=embedder_config) class Neo4jConfig(BaseModel): """Configuration for Neo4j database connection.""" uri: str = 'bolt://localhost:7687' user: str = 'neo4j' password: str = 'password' @classmethod def from_env(cls) -> 'Neo4jConfig': """Create Neo4j configuration from environment variables.""" return cls( uri=os.environ.get('NEO4J_URI', 'bolt://localhost:7687'), user=os.environ.get('NEO4J_USER', 'neo4j'), password=os.environ.get('NEO4J_PASSWORD', 'password'), ) class GraphitiConfig(BaseModel): """Configuration for Graphiti client. Centralizes all configuration parameters for the Graphiti client. """ llm: GraphitiLLMConfig = Field(default_factory=GraphitiLLMConfig) embedder: GraphitiEmbedderConfig = Field(default_factory=GraphitiEmbedderConfig) neo4j: Neo4jConfig = Field(default_factory=Neo4jConfig) group_id: str | None = None use_custom_entities: bool = False destroy_graph: bool = False @classmethod def from_env(cls) -> 'GraphitiConfig': """Create a configuration instance from environment variables.""" return cls( llm=GraphitiLLMConfig.from_env(), embedder=GraphitiEmbedderConfig.from_env(), neo4j=Neo4jConfig.from_env(), ) @classmethod def from_cli_and_env(cls, args: argparse.Namespace) -> 'GraphitiConfig': """Create configuration from CLI arguments, falling back to environment variables.""" # Start with environment configuration config = cls.from_env() # Apply CLI overrides if args.group_id: config.group_id = args.group_id else: config.group_id = 'default' config.use_custom_entities = args.use_custom_entities config.destroy_graph = args.destroy_graph # Update LLM config using CLI args config.llm = GraphitiLLMConfig.from_cli_and_env(args) return config class MCPConfig(BaseModel): """Configuration for MCP server.""" transport: str = 'sse' # Default to SSE transport @classmethod def from_cli(cls, args: argparse.Namespace) -> 'MCPConfig': """Create MCP configuration from CLI arguments.""" return cls(transport=args.transport) # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', stream=sys.stderr, ) logger = logging.getLogger(__name__) # Create global config instance - will be properly initialized later config = GraphitiConfig() # MCP server instructions GRAPHITI_MCP_INSTRUCTIONS = """ Graphiti is a memory service for AI agents built on a knowledge graph. Graphiti performs well with dynamic data such as user interactions, changing enterprise data, and external information. Graphiti transforms information into a richly connected knowledge network, allowing you to capture relationships between concepts, entities, and information. The system organizes data as episodes (content snippets), nodes (entities), and facts (relationships between entities), creating a dynamic, queryable memory store that evolves with new information. Graphiti supports multiple data formats, including structured JSON data, enabling seamless integration with existing data pipelines and systems. Facts contain temporal metadata, allowing you to track the time of creation and whether a fact is invalid (superseded by new information). Key capabilities: 1. Add episodes (text, messages, or JSON) to the knowledge graph with the add_memory tool 2. Search for nodes (entities) in the graph using natural language queries with search_nodes 3. Find relevant facts (relationships between entities) with search_facts 4. Retrieve specific entity edges or episodes by UUID 5. Manage the knowledge graph with tools like delete_episode, delete_entity_edge, and clear_graph The server connects to a database for persistent storage and uses language models for certain operations. Each piece of information is organized by group_id, allowing you to maintain separate knowledge domains. When adding information, provide descriptive names and detailed content to improve search quality. When searching, use specific queries and consider filtering by group_id for more relevant results. For optimal performance, ensure the database is properly configured and accessible, and valid API keys are provided for any language model operations. """ # MCP server instance mcp = FastMCP( 'Graphiti Agent Memory', instructions=GRAPHITI_MCP_INSTRUCTIONS, ) # Initialize Graphiti client graphiti_client: Graphiti | None = None async def initialize_graphiti(): """Initialize the Graphiti client with the configured settings.""" global graphiti_client, config try: # Create LLM client if possible llm_client = config.llm.create_client() if not llm_client and config.use_custom_entities: # If custom entities are enabled, we must have an LLM client raise ValueError('OPENAI_API_KEY must be set when custom entities are enabled') # Validate Neo4j configuration if not config.neo4j.uri or not config.neo4j.user or not config.neo4j.password: raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set') embedder_client = config.embedder.create_client() # Initialize Graphiti client graphiti_client = Graphiti( uri=config.neo4j.uri, user=config.neo4j.user, password=config.neo4j.password, llm_client=llm_client, embedder=embedder_client, max_coroutines=SEMAPHORE_LIMIT, ) # Destroy graph if requested if config.destroy_graph: logger.info('Destroying graph...') await clear_data(graphiti_client.driver) # Initialize the graph database with Graphiti's indices await graphiti_client.build_indices_and_constraints() logger.info('Graphiti client initialized successfully') # Log configuration details for transparency if llm_client: logger.info(f'Using OpenAI model: {config.llm.model}') logger.info(f'Using temperature: {config.llm.temperature}') else: logger.info('No LLM client configured - entity extraction will be limited') logger.info(f'Using group_id: {config.group_id}') logger.info( f'Custom entity extraction: {"enabled" if config.use_custom_entities else "disabled"}' ) logger.info(f'Using concurrency limit: {SEMAPHORE_LIMIT}') except Exception as e: logger.error(f'Failed to initialize Graphiti: {str(e)}') raise def format_fact_result(edge: EntityEdge) -> dict[str, Any]: """Format an entity edge into a readable result. Since EntityEdge is a Pydantic BaseModel, we can use its built-in serialization capabilities. Args: edge: The EntityEdge to format Returns: A dictionary representation of the edge with serialized dates and excluded embeddings """ result = edge.model_dump( mode='json', exclude={ 'fact_embedding', }, ) result.get('attributes', {}).pop('fact_embedding', None) return result # Dictionary to store queues for each group_id # Each queue is a list of tasks to be processed sequentially episode_queues: dict[str, asyncio.Queue] = {} # Dictionary to track if a worker is running for each group_id queue_workers: dict[str, bool] = {} async def process_episode_queue(group_id: str): """Process episodes for a specific group_id sequentially. This function runs as a long-lived task that processes episodes from the queue one at a time. """ global queue_workers logger.info(f'Starting episode queue worker for group_id: {group_id}') queue_workers[group_id] = True try: while True: # Get the next episode processing function from the queue # This will wait if the queue is empty process_func = await episode_queues[group_id].get() try: # Process the episode await process_func() except Exception as e: logger.error(f'Error processing queued episode for group_id {group_id}: {str(e)}') finally: # Mark the task as done regardless of success/failure episode_queues[group_id].task_done() except asyncio.CancelledError: logger.info(f'Episode queue worker for group_id {group_id} was cancelled') except Exception as e: logger.error(f'Unexpected error in queue worker for group_id {group_id}: {str(e)}') finally: queue_workers[group_id] = False logger.info(f'Stopped episode queue worker for group_id: {group_id}') @mcp.tool() async def add_memory( name: str, episode_body: str, group_id: str | None = None, source: str = 'text', source_description: str = '', uuid: str | None = None, ) -> SuccessResponse | ErrorResponse: """Add an episode to memory. This is the primary way to add information to the graph. This function returns immediately and processes the episode addition in the background. Episodes for the same group_id are processed sequentially to avoid race conditions. Args: name (str): Name of the episode episode_body (str): The content of the episode to persist to memory. When source='json', this must be a properly escaped JSON string, not a raw Python dictionary. The JSON data will be automatically processed to extract entities and relationships. group_id (str, optional): A unique ID for this graph. If not provided, uses the default group_id from CLI or a generated one. source (str, optional): Source type, must be one of: - 'text': For plain text content (default) - 'json': For structured data - 'message': For conversation-style content source_description (str, optional): Description of the source uuid (str, optional): Optional UUID for the episode Examples: # Adding plain text content add_memory( name="Company News", episode_body="Acme Corp announced a new product line today.", source="text", source_description="news article", group_id="some_arbitrary_string" ) # Adding structured JSON data # NOTE: episode_body must be a properly escaped JSON string. Note the triple backslashes add_memory( name="Customer Profile", episode_body="{\\\"company\\\": {\\\"name\\\": \\\"Acme Technologies\\\"}, \\\"products\\\": [{\\\"id\\\": \\\"P001\\\", \\\"name\\\": \\\"CloudSync\\\"}, {\\\"id\\\": \\\"P002\\\", \\\"name\\\": \\\"DataMiner\\\"}]}", source="json", source_description="CRM data" ) # Adding message-style content add_memory( name="Customer Conversation", episode_body="user: What's your return policy?\nassistant: You can return items within 30 days.", source="message", source_description="chat transcript", group_id="some_arbitrary_string" ) Notes: When using source='json': - The JSON must be a properly escaped string, not a raw Python dictionary - The JSON will be automatically processed to extract entities and relationships - Complex nested structures are supported (arrays, nested objects, mixed data types), but keep nesting to a minimum - Entities will be created from appropriate JSON properties - Relationships between entities will be established based on the JSON structure """ global graphiti_client, episode_queues, queue_workers if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # Map string source to EpisodeType enum source_type = EpisodeType.text if source.lower() == 'message': source_type = EpisodeType.message elif source.lower() == 'json': source_type = EpisodeType.json # Use the provided group_id or fall back to the default from config effective_group_id = group_id if group_id is not None else config.group_id # Cast group_id to str to satisfy type checker # The Graphiti client expects a str for group_id, not Optional[str] group_id_str = str(effective_group_id) if effective_group_id is not None else '' # We've already checked that graphiti_client is not None above # This assert statement helps type checkers understand that graphiti_client is defined assert graphiti_client is not None, 'graphiti_client should not be None here' # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Define the episode processing function async def process_episode(): try: logger.info(f"Processing queued episode '{name}' for group_id: {group_id_str}") # Use all entity types if use_custom_entities is enabled, otherwise use empty dict entity_types = ENTITY_TYPES if config.use_custom_entities else {} await client.add_episode( name=name, episode_body=episode_body, source=source_type, source_description=source_description, group_id=group_id_str, # Using the string version of group_id uuid=uuid, reference_time=datetime.now(timezone.utc), entity_types=entity_types, ) logger.info(f"Episode '{name}' added successfully") logger.info(f"Episode '{name}' processed successfully") except Exception as e: error_msg = str(e) logger.error( f"Error processing episode '{name}' for group_id {group_id_str}: {error_msg}" ) # Initialize queue for this group_id if it doesn't exist if group_id_str not in episode_queues: episode_queues[group_id_str] = asyncio.Queue() # Add the episode processing function to the queue await episode_queues[group_id_str].put(process_episode) # Start a worker for this queue if one isn't already running if not queue_workers.get(group_id_str, False): asyncio.create_task(process_episode_queue(group_id_str)) # Return immediately with a success message return { 'message': f"Episode '{name}' queued for processing (position: {episode_queues[group_id_str].qsize()})" } except Exception as e: error_msg = str(e) logger.error(f'Error queuing episode task: {error_msg}') return {'error': f'Error queuing episode task: {error_msg}'} @mcp.tool() async def search_memory_nodes( query: str, group_ids: list[str] | None = None, max_nodes: int = 10, center_node_uuid: str | None = None, entity: str = '', # cursor seems to break with None ) -> NodeSearchResponse | ErrorResponse: """Search the graph memory for relevant node summaries. These contain a summary of all of a node's relationships with other nodes. Note: entity is a single entity type to filter results (permitted: "Preference", "Procedure"). Args: query: The search query group_ids: Optional list of group IDs to filter results max_nodes: Maximum number of nodes to return (default: 10) center_node_uuid: Optional UUID of a node to center the search around entity: Optional single entity type to filter results (permitted: "Preference", "Procedure") """ global graphiti_client if graphiti_client is None: return ErrorResponse(error='Graphiti client not initialized') try: # Use the provided group_ids or fall back to the default from config if none provided effective_group_ids = ( group_ids if group_ids is not None else [config.group_id] if config.group_id else [] ) # Configure the search if center_node_uuid is not None: search_config = NODE_HYBRID_SEARCH_NODE_DISTANCE.model_copy(deep=True) else: search_config = NODE_HYBRID_SEARCH_RRF.model_copy(deep=True) search_config.limit = max_nodes filters = SearchFilters() if entity != '': filters.node_labels = [entity] # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Perform the search using the _search method search_results = await client._search( query=query, config=search_config, group_ids=effective_group_ids, center_node_uuid=center_node_uuid, search_filter=filters, ) if not search_results.nodes: return NodeSearchResponse(message='No relevant nodes found', nodes=[]) # Format the node results formatted_nodes: list[NodeResult] = [ { 'uuid': node.uuid, 'name': node.name, 'summary': node.summary if hasattr(node, 'summary') else '', 'labels': node.labels if hasattr(node, 'labels') else [], 'group_id': node.group_id, 'created_at': node.created_at.isoformat(), 'attributes': node.attributes if hasattr(node, 'attributes') else {}, } for node in search_results.nodes ] return NodeSearchResponse(message='Nodes retrieved successfully', nodes=formatted_nodes) except Exception as e: error_msg = str(e) logger.error(f'Error searching nodes: {error_msg}') return ErrorResponse(error=f'Error searching nodes: {error_msg}') @mcp.tool() async def search_memory_facts( query: str, group_ids: list[str] | None = None, max_facts: int = 10, center_node_uuid: str | None = None, ) -> FactSearchResponse | ErrorResponse: """Search the graph memory for relevant facts. Args: query: The search query group_ids: Optional list of group IDs to filter results max_facts: Maximum number of facts to return (default: 10) center_node_uuid: Optional UUID of a node to center the search around """ global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # Use the provided group_ids or fall back to the default from config if none provided effective_group_ids = ( group_ids if group_ids is not None else [config.group_id] if config.group_id else [] ) # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) relevant_edges = await client.search( group_ids=effective_group_ids, query=query, num_results=max_facts, center_node_uuid=center_node_uuid, ) if not relevant_edges: return {'message': 'No relevant facts found', 'facts': []} facts = [format_fact_result(edge) for edge in relevant_edges] return {'message': 'Facts retrieved successfully', 'facts': facts} except Exception as e: error_msg = str(e) logger.error(f'Error searching facts: {error_msg}') return {'error': f'Error searching facts: {error_msg}'} @mcp.tool() async def delete_entity_edge(uuid: str) -> SuccessResponse | ErrorResponse: """Delete an entity edge from the graph memory. Args: uuid: UUID of the entity edge to delete """ global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Get the entity edge by UUID entity_edge = await EntityEdge.get_by_uuid(client.driver, uuid) # Delete the edge using its delete method await entity_edge.delete(client.driver) return {'message': f'Entity edge with UUID {uuid} deleted successfully'} except Exception as e: error_msg = str(e) logger.error(f'Error deleting entity edge: {error_msg}') return {'error': f'Error deleting entity edge: {error_msg}'} @mcp.tool() async def delete_episode(uuid: str) -> SuccessResponse | ErrorResponse: """Delete an episode from the graph memory. Args: uuid: UUID of the episode to delete """ global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Get the episodic node by UUID - EpisodicNode is already imported at the top episodic_node = await EpisodicNode.get_by_uuid(client.driver, uuid) # Delete the node using its delete method await episodic_node.delete(client.driver) return {'message': f'Episode with UUID {uuid} deleted successfully'} except Exception as e: error_msg = str(e) logger.error(f'Error deleting episode: {error_msg}') return {'error': f'Error deleting episode: {error_msg}'} @mcp.tool() async def get_entity_edge(uuid: str) -> dict[str, Any] | ErrorResponse: """Get an entity edge from the graph memory by its UUID. Args: uuid: UUID of the entity edge to retrieve """ global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Get the entity edge directly using the EntityEdge class method entity_edge = await EntityEdge.get_by_uuid(client.driver, uuid) # Use the format_fact_result function to serialize the edge # Return the Python dict directly - MCP will handle serialization return format_fact_result(entity_edge) except Exception as e: error_msg = str(e) logger.error(f'Error getting entity edge: {error_msg}') return {'error': f'Error getting entity edge: {error_msg}'} @mcp.tool() async def get_episodes( group_id: str | None = None, last_n: int = 10 ) -> list[dict[str, Any]] | EpisodeSearchResponse | ErrorResponse: """Get the most recent memory episodes for a specific group. Args: group_id: ID of the group to retrieve episodes from. If not provided, uses the default group_id. last_n: Number of most recent episodes to retrieve (default: 10) """ global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # Use the provided group_id or fall back to the default from config effective_group_id = group_id if group_id is not None else config.group_id if not isinstance(effective_group_id, str): return {'error': 'Group ID must be a string'} # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) episodes = await client.retrieve_episodes( group_ids=[effective_group_id], last_n=last_n, reference_time=datetime.now(timezone.utc) ) if not episodes: return {'message': f'No episodes found for group {effective_group_id}', 'episodes': []} # Use Pydantic's model_dump method for EpisodicNode serialization formatted_episodes = [ # Use mode='json' to handle datetime serialization episode.model_dump(mode='json') for episode in episodes ] # Return the Python list directly - MCP will handle serialization return formatted_episodes except Exception as e: error_msg = str(e) logger.error(f'Error getting episodes: {error_msg}') return {'error': f'Error getting episodes: {error_msg}'} @mcp.tool() async def clear_graph() -> SuccessResponse | ErrorResponse: """Clear all data from the graph memory and rebuild indices.""" global graphiti_client if graphiti_client is None: return {'error': 'Graphiti client not initialized'} try: # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # clear_data is already imported at the top await clear_data(client.driver) await client.build_indices_and_constraints() return {'message': 'Graph cleared successfully and indices rebuilt'} except Exception as e: error_msg = str(e) logger.error(f'Error clearing graph: {error_msg}') return {'error': f'Error clearing graph: {error_msg}'} @mcp.resource('http://graphiti/status') async def get_status() -> StatusResponse: """Get the status of the Graphiti MCP server and Neo4j connection.""" global graphiti_client if graphiti_client is None: return {'status': 'error', 'message': 'Graphiti client not initialized'} try: # We've already checked that graphiti_client is not None above assert graphiti_client is not None # Use cast to help the type checker understand that graphiti_client is not None client = cast(Graphiti, graphiti_client) # Test Neo4j connection await client.driver.verify_connectivity() return {'status': 'ok', 'message': 'Graphiti MCP server is running and connected to Neo4j'} except Exception as e: error_msg = str(e) logger.error(f'Error checking Neo4j connection: {error_msg}') return { 'status': 'error', 'message': f'Graphiti MCP server is running but Neo4j connection failed: {error_msg}', } async def initialize_server() -> MCPConfig: """Parse CLI arguments and initialize the Graphiti server configuration.""" global config parser = argparse.ArgumentParser( description='Run the Graphiti MCP server with optional LLM client' ) parser.add_argument( '--group-id', help='Namespace for the graph. This is an arbitrary string used to organize related data. ' 'If not provided, a random UUID will be generated.', ) parser.add_argument( '--transport', choices=['sse', 'stdio'], default='sse', help='Transport to use for communication with the client. (default: sse)', ) parser.add_argument( '--model', help=f'Model name to use with the LLM client. (default: {DEFAULT_LLM_MODEL})' ) parser.add_argument( '--small-model', help=f'Small model name to use with the LLM client. (default: {SMALL_LLM_MODEL})', ) parser.add_argument( '--temperature', type=float, help='Temperature setting for the LLM (0.0-2.0). Lower values make output more deterministic. (default: 0.7)', ) parser.add_argument('--destroy-graph', action='store_true', help='Destroy all Graphiti graphs') parser.add_argument( '--use-custom-entities', action='store_true', help='Enable entity extraction using the predefined ENTITY_TYPES', ) parser.add_argument( '--host', default=os.environ.get('MCP_SERVER_HOST'), help='Host to bind the MCP server to (default: MCP_SERVER_HOST environment variable)', ) args = parser.parse_args() # Build configuration from CLI arguments and environment variables config = GraphitiConfig.from_cli_and_env(args) # Log the group ID configuration if args.group_id: logger.info(f'Using provided group_id: {config.group_id}') else: logger.info(f'Generated random group_id: {config.group_id}') # Log entity extraction configuration if config.use_custom_entities: logger.info('Entity extraction enabled using predefined ENTITY_TYPES') else: logger.info('Entity extraction disabled (no custom entities will be used)') # Initialize Graphiti await initialize_graphiti() if args.host: logger.info(f'Setting MCP server host to: {args.host}') # Set MCP server host from CLI or env mcp.settings.host = args.host # Return MCP configuration return MCPConfig.from_cli(args) async def run_mcp_server(): """Run the MCP server in the current event loop.""" # Initialize the server mcp_config = await initialize_server() # Run the server with stdio transport for MCP in the same event loop logger.info(f'Starting MCP server with transport: {mcp_config.transport}') if mcp_config.transport == 'stdio': await mcp.run_stdio_async() elif mcp_config.transport == 'sse': logger.info( f'Running MCP server with SSE transport on {mcp.settings.host}:{mcp.settings.port}' ) await mcp.run_sse_async() def main(): """Main function to run the Graphiti MCP server.""" try: # Run everything in a single event loop asyncio.run(run_mcp_server()) except Exception as e: logger.error(f'Error initializing Graphiti MCP server: {str(e)}') raise if __name__ == '__main__': main()