graphiti
graphiti copied to clipboard
getzep
Question: How to setup the graphiti to use my OpenRouter and Voyage?
What I am trying to do:
I am trying to use Graphiti with the OpenRouter as a base URL using OpenAIGenericClient and with the Voyage for the embeddings using VoyageAIEmbedder and VoyageAIEmbedderConfig
What I have done so far:
I've created the following script based on the examples from Alternative LLM Providers
Code:
import os
from dotenv import load_dotenv
from graphiti_core import Graphiti
from graphiti_core.llm_client import LLMConfig, OpenAIGenericClient
from graphiti_core.embedder.voyage import VoyageAIEmbedder, VoyageAIEmbedderConfig
# Load environment variables from .env file
load_dotenv()
# Get OpenAI configuration from environment variables
api_key = os.getenv("OPENAI_API_KEY")
model = os.getenv("OPENAI_API_MODEL")
azure_endpoint = os.getenv("BASE_URL")
temperature = float(os.getenv("OPENAI_TEMPERATURE", "0.7")) # Default to 0.7 if not specified
small_model = os.getenv("OPENAI_SMALL_MODEL")
# Configure OpenAI client
openai_client = LLMConfig(
api_key=api_key,
model=model,
base_url=azure_endpoint,
temperature=temperature,
small_model=small_model
)
# Get Neo4j configuration from environment variables
neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
neo4j_user = os.getenv("NEO4J_USER", "neo4j")
neo4j_password = os.getenv("NEO4J_PASSWORD", "password")
# Get Voyage AI configuration from environment variables
voyage_embedding_model = os.getenv("VOYAGE_EMBEDDING_MODEL")
voyage_api_key = os.getenv("VOYAGE_API_KEY")
# Initialize Graphiti with all configurations
graphiti = Graphiti(
neo4j_uri,
neo4j_user,
neo4j_password,
llm_client=OpenAIGenericClient(
client=openai_client
),
embedder=VoyageAIEmbedder(
config=VoyageAIEmbedderConfig(
embedding_model=voyage_embedding_model,
api_key=voyage_api_key
),
),
)
I've also created an .env file with all of those needed keys.
The problem:
The problem that I have is that from the documentation, I don't quite understand how I should use my script if I want to use it with the docker-compose as an MCP server. I've searched everywhere in the documentation and I cond't find anywhere an example of this.
I did understand that in order to run the Graphiti MCP, I have to do the following steps:
- git clone https://github.com/getzep/graphiti
- cd graphiti
- cd mcp_server
- docker compose run
But how exactly can I have it run my configuration instead of the default one?
UPDATE:
The only solution I've found to fix this problem it was to change the code from the graphiti_mcp_server.py to use my config:
#!/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 dotenv import load_dotenv
from graphiti_core.llm_client import LLMClient
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field
from graphiti_core import Graphiti
from graphiti_core.cross_encoder.client import CrossEncoderClient
from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
from graphiti_core.edges import EntityEdge
from graphiti_core.embedder.client import EmbedderClient
#from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
from graphiti_core.embedder.voyage import VoyageAIEmbedder, VoyageAIEmbedderConfig
#from graphiti_core.llm_client.openai_client import OpenAIClient
from graphiti_core.llm_client.openai_generic_client import LLMConfig, OpenAIGenericClient
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'
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
# 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
@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
# 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')),
)
@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 | None:
"""Create an LLM client based on this configuration.
Returns:
LLMClient instance if able, None otherwise
"""
llm_client_config = LLMConfig(
api_key=os.environ.get('OPENAI_API_KEY', ''),
model=os.environ.get('OPENAI_API_MODEL', ''),
small_model=os.environ.get('OPENAI_SMALL_MODEL', ''),
temperature=os.environ.get('OPENAI_TEMPERATURE', ''),
base_url=os.environ.get('BASE_URL', '')
)
# Set temperature
llm_client_config.temperature = self.temperature
return OpenAIGenericClient(config=llm_client_config)
def create_cross_encoder_client(self) -> CrossEncoderClient | None:
"""Create a cross-encoder client based on this configuration."""
llm_client_config = LLMConfig(
api_key=self.api_key, model=self.model, small_model=self.small_model
)
return OpenAIRerankerClient(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
@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
return cls(
model=model,
api_key=os.environ.get('OPENAI_API_KEY'),
)
def create_client(self) -> EmbedderClient | None:
# OpenAI API setup
if not self.api_key:
return None
voyage_key = os.environ.get('VOYAGE_API_KEY', None)
embedder_config = VoyageAIEmbedderConfig(api_key=voyage_key, embedding_model=self.model)
return VoyageAIEmbedder(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_episode 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()
cross_encoder_client = config.llm.create_cross_encoder_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,
cross_encoder=cross_encoder_client,
)
# 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"}'
)
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
"""
return edge.model_dump(
mode='json',
exclude={
'fact_embedding',
},
)
# 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_episode(
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_episode(
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_episode(
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',
)
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()
# 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()
New problem:
But I am not sure if this is because of those changes or it is an entirely new issue, but I get the following error message inside the logs of the docker-compose when I am trying to add a new episode:
graphiti-mcp-1 | 2025-05-22 13:36:16,884 - __main__ - ERROR - Error processing episode 'AppwriteDatabaseRow Implementation' for group_id default: {code: Neo.ClientError.Statement.TypeError} {message: Property values can only be of primitive types or arrays thereof. Encountered: Map{}.}
The full log can be seen here
Regarding support for OpenRouter and Voyage: As you've discovered, the MCP server does not yet support OpenAIGenericClient and the Voyage embedder.
What model are you using with OpenRouter? The inference results don't appear to in the correct schema for saving to Neo4j. We've found this happens often with smaller models. See the warning here: https://help.getzep.com/graphiti/graphiti/installation#openai-compatible-llm-providers
Regarding the model that I used, it is Google Gemini 2.5 Flash (the new version). I've thought that this has been supported with the interface via OpenAIGenericClient, and also the Voyage, which are going to be supported. Now it makes sense.
So, in theory, if I change the Voyage to the OpenAI as it is said here:
Note that even when using Anthropic for LLM inference, OpenAI is still required for embedding functionality. Make sure to set both OPENAI_API_KEY and ANTHROPIC_API_KEY environment variables when using Anthropic.
It should work, right?
@Vildnex Is this still relevant? Please confirm within 14 days or this issue will be closed.
![claude[bot] avatar](https://avatars.githubusercontent.com/in/1236702?v=4 "Published by a pub.dev verified publisher"){kind=small}
@Vildnex Is this still an issue? Please confirm within 14 days or this issue will be closed.
@Vildnex Is this still an issue? Please confirm within 14 days or this issue will be closed.
@Vildnex Is this still relevant? Please confirm within 14 days or this issue will be closed.