Update mcp_server/.env

fix: Replace non-existent model names and handle Neo4j index race condition

- Replace hardcoded non-existent models (gpt-5-mini, gpt-5-nano, gpt-4.1)
  with valid gpt-4o-mini across config files and default client
- Add exception handling for EquivalentSchemaRuleAlreadyExists in
  Neo4j index creation to prevent race condition failures on startup

.github/workflows/daily_issue_maintenance.yml

@@ -1,123 +0,0 @@
-name: Daily Issue Maintenance
-on:
-  schedule:
-    - cron: "0 0 * * *" # Every day at midnight
-  workflow_dispatch: # Manual trigger option
-
-jobs:
-  find-legacy-duplicates:
-    runs-on: ubuntu-latest
-    if: github.event_name == 'workflow_dispatch'
-    permissions:
-      contents: read
-      issues: write
-      id-token: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          prompt: |
-            REPO: ${{ github.repository }}
-
-            Find potential duplicate issues in the repository:
-
-            1. Use `gh issue list --state open --limit 1000 --json number,title,body,createdAt` to get all open issues
-            2. For each issue, search for potential duplicates using `gh search issues` with keywords from the title and body
-            3. Compare issues to identify true duplicates using these criteria:
-               - Same bug or error being reported
-               - Same feature request (even if worded differently)
-               - Same question being asked
-               - Issues describing the same root problem
-
-            For each duplicate found:
-            - Add a comment linking to the original issue
-            - Apply the "duplicate" label using `gh issue edit`
-            - Be polite and explain why it's a duplicate
-
-            Focus on finding true duplicates, not just similar issues.
-
-          claude_args: |
-            --allowedTools "Bash(gh issue:*),Bash(gh search:*)"
-            --model claude-sonnet-4-5-20250929
-
-  check-stale-issues:
-    runs-on: ubuntu-latest
-    if: github.event_name == 'schedule'
-    permissions:
-      contents: read
-      issues: write
-      id-token: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          prompt: |
-            REPO: ${{ github.repository }}
-
-            Review stale issues and request confirmation:
-
-            1. Use `gh issue list --state open --limit 1000 --json number,title,updatedAt,comments` to get all open issues
-            2. Identify issues that are:
-               - Older than 60 days (based on updatedAt)
-               - Have no comments with "stale-check" label
-               - Are not labeled as "enhancement" or "documentation"
-            3. For each stale issue:
-               - Add a polite comment asking the issue originator if this is still relevant
-               - Apply a "stale-check" label to track that we've asked
-               - Use format: "@{author} Is this still an issue? Please confirm within 14 days or this issue will be closed."
-
-            Use:
-            - `gh issue view` to check issue details and labels
-            - `gh issue comment` to add comments
-            - `gh issue edit` to add the "stale-check" label
-
-          claude_args: |
-            --allowedTools "Bash(gh issue:*)"
-            --model claude-sonnet-4-5-20250929
-
-  close-unconfirmed-issues:
-    runs-on: ubuntu-latest
-    if: github.event_name == 'schedule'
-    needs: check-stale-issues
-    permissions:
-      contents: read
-      issues: write
-      id-token: write
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          prompt: |
-            REPO: ${{ github.repository }}
-
-            Close unconfirmed stale issues:
-
-            1. Use `gh issue list --state open --label "stale-check" --limit 1000 --json number,title,comments,updatedAt` to get issues with stale-check label
-            2. For each issue, check if:
-               - The "stale-check" comment was added 14+ days ago
-               - There has been no response from the issue author or activity since the comment
-            3. For issues meeting the criteria:
-               - Add a polite closing comment
-               - Close the issue using `gh issue close`
-               - Use format: "Closing due to inactivity. Feel free to reopen if this is still relevant."
-
-            Use:
-            - `gh issue view` to check issue comments and activity
-            - `gh issue comment` to add closing comment
-            - `gh issue close` to close the issue
-
-          claude_args: |
-            --allowedTools "Bash(gh issue:*)"
-            --model claude-sonnet-4-5-20250929

.github/workflows/issue-triage.yml

@@ -1,141 +0,0 @@
-name: Issue Triage and Deduplication
-on:
-  issues:
-    types: [opened]
-
-jobs:
-  triage:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    permissions:
-      contents: read
-      issues: write
-      id-token: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Run Claude Code for Issue Triage
-        uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          allowed_non_write_users: "*"
-          github_token: ${{ secrets.GITHUB_TOKEN }}
-          prompt: |
-            You're an issue triage assistant for GitHub issues. Your task is to analyze the issue and select appropriate labels from the provided list.
-
-            IMPORTANT: Don't post any comments or messages to the issue. Your only action should be to apply labels. DO NOT check for duplicates - that's handled by a separate job.
-
-            Issue Information:
-            - REPO: ${{ github.repository }}
-            - ISSUE_NUMBER: ${{ github.event.issue.number }}
-
-            TASK OVERVIEW:
-
-            1. First, fetch the list of labels available in this repository by running: `gh label list`. Run exactly this command with nothing else.
-
-            2. Next, use gh commands to get context about the issue:
-               - Use `gh issue view ${{ github.event.issue.number }}` to retrieve the current issue's details
-               - Use `gh search issues` to find similar issues that might provide context for proper categorization
-               - You have access to these Bash commands:
-                 - Bash(gh label list:*) - to get available labels
-                 - Bash(gh issue view:*) - to view issue details
-                 - Bash(gh issue edit:*) - to apply labels to the issue
-                 - Bash(gh search:*) - to search for similar issues
-
-            3. Analyze the issue content, considering:
-               - The issue title and description
-               - The type of issue (bug report, feature request, question, etc.)
-               - Technical areas mentioned
-               - Database mentions (neo4j, falkordb, neptune, etc.)
-               - LLM providers mentioned (openai, anthropic, gemini, groq, etc.)
-               - Components affected (embeddings, search, prompts, server, mcp, etc.)
-
-            4. Select appropriate labels from the available labels list:
-               - Choose labels that accurately reflect the issue's nature
-               - Be specific but comprehensive
-               - Add database-specific labels if mentioned: neo4j, falkordb, neptune
-               - Add component labels if applicable
-               - DO NOT add priority labels (P1, P2, P3)
-               - DO NOT add duplicate label - that's handled by the deduplication job
-
-            5. Apply the selected labels:
-               - Use `gh issue edit ${{ github.event.issue.number }} --add-label "label1,label2,label3"` to apply your selected labels
-               - DO NOT post any comments explaining your decision
-               - DO NOT communicate directly with users
-               - If no labels are clearly applicable, do not apply any labels
-
-            IMPORTANT GUIDELINES:
-            - Be thorough in your analysis
-            - Only select labels from the provided list
-            - DO NOT post any comments to the issue
-            - Your ONLY action should be to apply labels using gh issue edit
-            - It's okay to not add any labels if none are clearly applicable
-            - DO NOT check for duplicates
-
-          claude_args: |
-            --allowedTools "Bash(gh label list:*),Bash(gh issue view:*),Bash(gh issue edit:*),Bash(gh search:*)"
-            --model claude-sonnet-4-5-20250929
-
-  deduplicate:
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    needs: triage
-    permissions:
-      contents: read
-      issues: write
-      id-token: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      - name: Check for duplicate issues
-        uses: anthropics/claude-code-action@v1
-        with:
-          allowed_non_write_users: "*"
-          prompt: |
-            Analyze this new issue and check if it's a duplicate of existing issues in the repository.
-
-            Issue: #${{ github.event.issue.number }}
-            Repository: ${{ github.repository }}
-
-            Your task:
-            1. Use mcp__github__get_issue to get details of the current issue (#${{ github.event.issue.number }})
-            2. Search for similar existing OPEN issues using mcp__github__search_issues with relevant keywords from the issue title and body
-            3. Compare the new issue with existing ones to identify potential duplicates
-
-            Criteria for duplicates:
-            - Same bug or error being reported
-            - Same feature request (even if worded differently)
-            - Same question being asked
-            - Issues describing the same root problem
-
-            If you find duplicates:
-            - Add a comment on the new issue linking to the original issue(s)
-            - Apply the "duplicate" label to the new issue
-            - Be polite and explain why it's a duplicate
-            - Suggest the user follow the original issue for updates
-
-            If it's NOT a duplicate:
-            - Don't add any comments
-            - Don't modify labels
-
-            Use these tools:
-            - mcp__github__get_issue: Get issue details
-            - mcp__github__search_issues: Search for similar issues (use state:open)
-            - mcp__github__list_issues: List recent issues if needed
-            - mcp__github__create_issue_comment: Add a comment if duplicate found
-            - mcp__github__update_issue: Add "duplicate" label
-
-            Be thorough but efficient. Focus on finding true duplicates, not just similar issues.
-
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          claude_args: |
-            --allowedTools "mcp__github__get_issue,mcp__github__search_issues,mcp__github__list_issues,mcp__github__create_issue_comment,mcp__github__update_issue,mcp__github__get_issue_comments"
-            --model claude-sonnet-4-5-20250929

README.md

@@ -479,7 +479,7 @@ graphiti = Graphiti(
     cross_encoder=GeminiRerankerClient(
         config=LLMConfig(
             api_key=api_key,
-            model="gemini-2.5-flash-lite-preview-06-17"
+            model="gemini-2.5-flash-lite"
         )
     )
 )
@@ -487,7 +487,7 @@ graphiti = Graphiti(
 # Now you can use Graphiti with Google Gemini for all components
 ```
 
-The Gemini reranker uses the `gemini-2.5-flash-lite-preview-06-17` model by default, which is optimized for
+The Gemini reranker uses the `gemini-2.5-flash-lite` model by default, which is optimized for
 cost-effective and low-latency classification tasks. It uses the same boolean classification approach as the OpenAI
 reranker, leveraging Gemini's log probabilities feature to rank passage relevance.
 

Zep-CLA.md

@@ -34,7 +34,7 @@ You are not expected to provide support for Your Contributions, except to the ex
 
 ## Third-Party Submissions
 
-Should You wish to submit work that is not Your original creation, You may submit it to Zep separately from any Contribution, identifying the complete details of its source and of any license or other restriction (including, but not limited to, related patents, trademarks, and license agreements) of which you are personally aware, and conspicuously marking the work as "Submitted on behalf of a third-party: [named here]".
+Should You wish to submit work that is not Your original creation, You may submit it to Zep separately from any Contribution, identifying the complete details of its source and of any license or other restriction (including, but not limited to, related patents, trademarks, and license agreements) of which you are personally aware, and conspicuously marking the work as "Submitted on behalf of a third party: [named here]".
 
 ## Notifications
 

examples/azure-openai/azure_openai_neo4j.py

@@ -40,8 +40,8 @@ from graphiti_core.nodes import EpisodeType
 # Configure logging
 logging.basicConfig(
     level=INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S',
 )
 logger = logging.getLogger(__name__)
 
@@ -49,20 +49,20 @@ 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")
+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')
 
 # Azure OpenAI connection parameters
-azure_endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT")
-azure_api_key = os.environ.get("AZURE_OPENAI_API_KEY")
-azure_deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT", "gpt-4.1")
+azure_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT')
+azure_api_key = os.environ.get('AZURE_OPENAI_API_KEY')
+azure_deployment = os.environ.get('AZURE_OPENAI_DEPLOYMENT', 'gpt-4.1')
 azure_embedding_deployment = os.environ.get(
-    "AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding-3-small"
+    'AZURE_OPENAI_EMBEDDING_DEPLOYMENT', 'text-embedding-3-small'
 )
 
 if not azure_endpoint or not azure_api_key:
-    raise ValueError("AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY must be set")
+    raise ValueError('AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY must be set')
 
 
 async def main():
@@ -76,7 +76,7 @@ async def main():
 
     # Initialize Azure OpenAI client
     azure_client = AsyncOpenAI(
-        base_url=f"{azure_endpoint}/openai/v1/",
+        base_url=f'{azure_endpoint}/openai/v1/',
         api_key=azure_api_key,
     )
 
@@ -112,40 +112,40 @@ async def main():
         # 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': '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",
+                'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017',
+                'type': EpisodeType.text,
+                'description': 'podcast transcript',
             },
             {
-                "content": {
-                    "name": "Gavin Newsom",
-                    "position": "Governor",
-                    "state": "California",
-                    "previous_role": "Lieutenant Governor",
-                    "previous_location": "San Francisco",
+                'content': {
+                    'name': 'Gavin Newsom',
+                    'position': 'Governor',
+                    'state': 'California',
+                    'previous_role': 'Lieutenant Governor',
+                    'previous_location': 'San Francisco',
                 },
-                "type": EpisodeType.json,
-                "description": "podcast metadata",
+                'type': EpisodeType.json,
+                'description': 'podcast metadata',
             },
         ]
 
         # Add episodes to the graph
         for i, episode in enumerate(episodes):
             await graphiti.add_episode(
-                name=f"California Politics {i}",
+                name=f'California Politics {i}',
                 episode_body=(
-                    episode["content"]
-                    if isinstance(episode["content"], str)
-                    else json.dumps(episode["content"])
+                    episode['content']
+                    if isinstance(episode['content'], str)
+                    else json.dumps(episode['content'])
                 ),
-                source=episode["type"],
-                source_description=episode["description"],
+                source=episode['type'],
+                source_description=episode['description'],
                 reference_time=datetime.now(timezone.utc),
             )
             print(f'Added episode: California Politics {i} ({episode["type"].value})')
@@ -161,18 +161,18 @@ async def main():
 
         # 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?")
+        results = await graphiti.search('Who was the California Attorney General?')
 
         # Print search results
-        print("\nSearch 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("---")
+            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('---')
 
         #################################################
         # CENTER NODE SEARCH
@@ -187,26 +187,26 @@ async def main():
             # Get the source node UUID from the top result
             center_node_uuid = results[0].source_node_uuid
 
-            print("\nReranking search results based on graph distance:")
-            print(f"Using center node UUID: {center_node_uuid}")
+            print('\nReranking search results based on graph distance:')
+            print(f'Using center node UUID: {center_node_uuid}')
 
             reranked_results = await graphiti.search(
-                "Who was the California Attorney General?",
+                'Who was the California Attorney General?',
                 center_node_uuid=center_node_uuid,
             )
 
             # Print reranked search results
-            print("\nReranked Search Results:")
+            print('\nReranked Search Results:')
             for result in reranked_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("---")
+                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('---')
         else:
-            print("No results found in the initial search to use as center node.")
+            print('No results found in the initial search to use as center node.')
 
     finally:
         #################################################
@@ -218,8 +218,8 @@ async def main():
 
         # Close the connection
         await graphiti.close()
-        print("\nConnection closed")
+        print('\nConnection closed')
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     asyncio.run(main())

examples/langgraph-agent/agent.ipynb

@@ -540,7 +540,7 @@
     "submit_button = widgets.Button(description='Send')\n",
     "submit_button.on_click(on_submit)\n",
     "\n",
-    "conversation_output.append_stdout('Asssistant: Hello, how can I help you find shoes today?')\n",
+    "conversation_output.append_stdout('Assistant: Hello, how can I help you find shoes today?')\n",
     "\n",
     "display(widgets.VBox([input_box, submit_button, conversation_output]))"
    ]

graphiti_core/cross_encoder/gemini_reranker_client.py

@@ -37,7 +37,7 @@ else:
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_MODEL = 'gemini-2.5-flash-lite-preview-06-17'
+DEFAULT_MODEL = 'gemini-2.5-flash-lite'
 
 
 class GeminiRerankerClient(CrossEncoderClient):

graphiti_core/driver/kuzu_driver.py

@@ -134,7 +134,7 @@ class KuzuDriver(GraphDriver):
         return KuzuDriverSession(self)
 
     async def close(self):
-        # Do not explicity close the connection, instead rely on GC.
+        # Do not explicitly close the connection, instead rely on GC.
         pass
 
     def delete_all_indexes(self, database_: str):

graphiti_core/driver/neo4j_driver.py

@@ -18,7 +18,9 @@ import logging
 from collections.abc import Coroutine
 from typing import Any
 
+import neo4j.exceptions
 from neo4j import AsyncGraphDatabase, EagerResult
+from neo4j.exceptions import ClientError
 from typing_extensions import LiteralString
 
 from graphiti_core.driver.driver import GraphDriver, GraphDriverSession, GraphProvider
@@ -70,6 +72,15 @@ class Neo4jDriver(GraphDriver):
 
         try:
             result = await self.client.execute_query(cypher_query_, parameters_=params, **kwargs)
+        except neo4j.exceptions.ClientError as e:
+            # Handle race condition when creating indices/constraints in parallel
+            # Neo4j 5.26+ may throw EquivalentSchemaRuleAlreadyExists even with IF NOT EXISTS
+            if 'EquivalentSchemaRuleAlreadyExists' in str(e):
+                logger.info(f'Index or constraint already exists, continuing: {cypher_query_}')
+                # Return empty result to indicate success (index exists)
+                return EagerResult([], None, None)  # type: ignore
+            logger.error(f'Error executing Neo4j query: {e}\n{cypher_query_}\n{params}')
+            raise
         except Exception as e:
             logger.error(f'Error executing Neo4j query: {e}\n{cypher_query_}\n{params}')
             raise
@@ -88,6 +99,21 @@ class Neo4jDriver(GraphDriver):
             'CALL db.indexes() YIELD name DROP INDEX name',
         )
 
+    async def _execute_index_query(self, query: LiteralString) -> EagerResult | None:
+        """Execute an index creation query, ignoring 'index already exists' errors.
+
+        Neo4j can raise EquivalentSchemaRuleAlreadyExists when concurrent CREATE INDEX
+        IF NOT EXISTS queries race, even though the index exists. This is safe to ignore.
+        """
+        try:
+            return await self.execute_query(query)
+        except ClientError as e:
+            # Ignore "equivalent index already exists" error (race condition with IF NOT EXISTS)
+            if 'EquivalentSchemaRuleAlreadyExists' in str(e):
+                logger.debug(f'Index already exists (concurrent creation): {query[:50]}...')
+                return None
+            raise
+
     async def build_indices_and_constraints(self, delete_existing: bool = False):
         if delete_existing:
             await self.delete_all_indexes()
@@ -98,15 +124,8 @@ class Neo4jDriver(GraphDriver):
 
         index_queries: list[LiteralString] = range_indices + fulltext_indices
 
-        await semaphore_gather(
-            *[
-                self.execute_query(
-                    query,
-                )
-                for query in index_queries
-            ]
-        )
-    
+        await semaphore_gather(*[self._execute_index_query(query) for query in index_queries])
+
     async def health_check(self) -> None:
         """Check Neo4j connectivity by running the driver's verify_connectivity method."""
         try:

graphiti_core/embedder/azure_openai.py

@@ -33,7 +33,7 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
     def __init__(
         self,
         azure_client: AsyncAzureOpenAI | AsyncOpenAI,
-        model: str = "text-embedding-3-small",
+        model: str = 'text-embedding-3-small',
     ):
         self.azure_client = azure_client
         self.model = model
@@ -44,22 +44,18 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
             # Handle different input types
             if isinstance(input_data, str):
                 text_input = [input_data]
-            elif isinstance(input_data, list) and all(
-                isinstance(item, str) for item in input_data
-            ):
+            elif isinstance(input_data, list) and all(isinstance(item, str) for item in input_data):
                 text_input = input_data
             else:
                 # Convert to string list for other types
                 text_input = [str(input_data)]
 
-            response = await self.azure_client.embeddings.create(
-                model=self.model, input=text_input
-            )
+            response = await self.azure_client.embeddings.create(model=self.model, input=text_input)
 
             # Return the first embedding as a list of floats
             return response.data[0].embedding
         except Exception as e:
-            logger.error(f"Error in Azure OpenAI embedding: {e}")
+            logger.error(f'Error in Azure OpenAI embedding: {e}')
             raise
 
     async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
@@ -71,5 +67,5 @@ class AzureOpenAIEmbedderClient(EmbedderClient):
 
             return [embedding.embedding for embedding in response.data]
         except Exception as e:
-            logger.error(f"Error in Azure OpenAI batch embedding: {e}")
+            logger.error(f'Error in Azure OpenAI batch embedding: {e}')
             raise

graphiti_core/llm_client/anthropic_client.py

@@ -47,6 +47,9 @@ else:
 logger = logging.getLogger(__name__)
 
 AnthropicModel = Literal[
+    'claude-sonnet-4-5-latest',
+    'claude-sonnet-4-5-20250929',
+    'claude-haiku-4-5-latest',
     'claude-3-7-sonnet-latest',
     'claude-3-7-sonnet-20250219',
     'claude-3-5-haiku-latest',
@@ -62,7 +65,7 @@ AnthropicModel = Literal[
     'claude-2.0',
 ]
 
-DEFAULT_MODEL: AnthropicModel = 'claude-3-7-sonnet-latest'
+DEFAULT_MODEL: AnthropicModel = 'claude-haiku-4-5-latest'
 
 # Maximum output tokens for different Anthropic models
 # Based on official Anthropic documentation (as of 2025)
@@ -70,6 +73,10 @@ DEFAULT_MODEL: AnthropicModel = 'claude-3-7-sonnet-latest'
 # Some models support higher limits with additional configuration (e.g., Claude 3.7 supports
 # 128K with 'anthropic-beta: output-128k-2025-02-19' header, but this is not currently implemented).
 ANTHROPIC_MODEL_MAX_TOKENS = {
+    # Claude 4.5 models - 64K tokens
+    'claude-sonnet-4-5-latest': 65536,
+    'claude-sonnet-4-5-20250929': 65536,
+    'claude-haiku-4-5-latest': 65536,
     # Claude 3.7 models - standard 64K tokens
     'claude-3-7-sonnet-latest': 65536,
     'claude-3-7-sonnet-20250219': 65536,

graphiti_core/llm_client/azure_openai_client.py

@@ -66,21 +66,21 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         """Create a structured completion using Azure OpenAI's responses.parse API."""
         supports_reasoning = self._supports_reasoning_features(model)
         request_kwargs = {
-            "model": model,
-            "input": messages,
-            "max_output_tokens": max_tokens,
-            "text_format": response_model,  # type: ignore
+            'model': model,
+            'input': messages,
+            'max_output_tokens': max_tokens,
+            'text_format': response_model,  # type: ignore
         }
 
         temperature_value = temperature if not supports_reasoning else None
         if temperature_value is not None:
-            request_kwargs["temperature"] = temperature_value
+            request_kwargs['temperature'] = temperature_value
 
         if supports_reasoning and reasoning:
-            request_kwargs["reasoning"] = {"effort": reasoning}  # type: ignore
+            request_kwargs['reasoning'] = {'effort': reasoning}  # type: ignore
 
         if supports_reasoning and verbosity:
-            request_kwargs["text"] = {"verbosity": verbosity}  # type: ignore
+            request_kwargs['text'] = {'verbosity': verbosity}  # type: ignore
 
         return await self.client.responses.parse(**request_kwargs)
 
@@ -96,20 +96,20 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         supports_reasoning = self._supports_reasoning_features(model)
 
         request_kwargs = {
-            "model": model,
-            "messages": messages,
-            "max_tokens": max_tokens,
-            "response_format": {"type": "json_object"},
+            'model': model,
+            'messages': messages,
+            'max_tokens': max_tokens,
+            'response_format': {'type': 'json_object'},
         }
 
         temperature_value = temperature if not supports_reasoning else None
         if temperature_value is not None:
-            request_kwargs["temperature"] = temperature_value
+            request_kwargs['temperature'] = temperature_value
 
         return await self.client.chat.completions.create(**request_kwargs)
 
     @staticmethod
     def _supports_reasoning_features(model: str) -> bool:
         """Return True when the Azure model supports reasoning/verbosity options."""
-        reasoning_prefixes = ("o1", "o3", "gpt-5")
+        reasoning_prefixes = ('o1', 'o3', 'gpt-5')
         return model.startswith(reasoning_prefixes)

graphiti_core/llm_client/client.py

@@ -48,7 +48,11 @@ def get_extraction_language_instruction(group_id: str | None = None) -> str:
     Returns:
         str: Language instruction to append to system messages
     """
-    return '\n\nAny extracted information should be returned in the same language as it was written in.'
+    return (
+        '\n\nAny extracted information should be returned in the same language as it was written in. '
+        'Only output non-English text when the user has written full sentences or phrases in that non-English language. '
+        'Otherwise, output English.'
+    )
 
 
 logger = logging.getLogger(__name__)

graphiti_core/llm_client/gemini_client.py

@@ -45,7 +45,7 @@ else:
 logger = logging.getLogger(__name__)
 
 DEFAULT_MODEL = 'gemini-2.5-flash'
-DEFAULT_SMALL_MODEL = 'gemini-2.5-flash-lite-preview-06-17'
+DEFAULT_SMALL_MODEL = 'gemini-2.5-flash-lite'
 
 # Maximum output tokens for different Gemini models
 GEMINI_MODEL_MAX_TOKENS = {
@@ -53,7 +53,6 @@ GEMINI_MODEL_MAX_TOKENS = {
     'gemini-2.5-pro': 65536,
     'gemini-2.5-flash': 65536,
     'gemini-2.5-flash-lite': 64000,
-    'models/gemini-2.5-flash-lite-preview-06-17': 64000,
     # Gemini 2.0 models
     'gemini-2.0-flash': 8192,
     'gemini-2.0-flash-lite': 8192,

graphiti_core/llm_client/openai_base_client.py

@@ -31,8 +31,8 @@ from .errors import RateLimitError, RefusalError
 
 logger = logging.getLogger(__name__)
 
-DEFAULT_MODEL = 'gpt-5-mini'
-DEFAULT_SMALL_MODEL = 'gpt-5-nano'
+DEFAULT_MODEL = 'gpt-4o-mini'
+DEFAULT_SMALL_MODEL = 'gpt-4o-mini'
 DEFAULT_REASONING = 'minimal'
 DEFAULT_VERBOSITY = 'low'
 
@@ -166,13 +166,17 @@ class BaseOpenAIClient(LLMClient):
         except openai.RateLimitError as e:
             raise RateLimitError from e
         except openai.AuthenticationError as e:
-            logger.error(f'OpenAI Authentication Error: {e}. Please verify your API key is correct.')
+            logger.error(
+                f'OpenAI Authentication Error: {e}. Please verify your API key is correct.'
+            )
             raise
         except Exception as e:
             # Provide more context for connection errors
             error_msg = str(e)
             if 'Connection error' in error_msg or 'connection' in error_msg.lower():
-                logger.error(f'Connection error communicating with OpenAI API. Please check your network connection and API key. Error: {e}')
+                logger.error(
+                    f'Connection error communicating with OpenAI API. Please check your network connection and API key. Error: {e}'
+                )
             else:
                 logger.error(f'Error in generating LLM response: {e}')
             raise

graphiti_core/llm_client/openai_client.py

@@ -74,7 +74,9 @@ class OpenAIClient(BaseOpenAIClient):
     ):
         """Create a structured completion using OpenAI's beta parse API."""
         # Reasoning models (gpt-5 family) don't support temperature
-        is_reasoning_model = model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
+        is_reasoning_model = (
+            model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
+        )
 
         response = await self.client.responses.parse(
             model=model,
@@ -100,7 +102,9 @@ class OpenAIClient(BaseOpenAIClient):
     ):
         """Create a regular completion with JSON format."""
         # Reasoning models (gpt-5 family) don't support temperature
-        is_reasoning_model = model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
+        is_reasoning_model = (
+            model.startswith('gpt-5') or model.startswith('o1') or model.startswith('o3')
+        )
 
         return await self.client.chat.completions.create(
             model=model,

graphiti_core/search/search_filters.py

@@ -41,6 +41,16 @@ class DateFilter(BaseModel):
     )
 
 
+class PropertyFilter(BaseModel):
+    property_name: str = Field(description='Property name')
+    property_value: str | int | float | None = Field(
+        description='Value you want to match on for the property'
+    )
+    comparison_operator: ComparisonOperator = Field(
+        description='Comparison operator for the property'
+    )
+
+
 class SearchFilters(BaseModel):
     node_labels: list[str] | None = Field(
         default=None, description='List of node labels to filter on'
@@ -53,6 +63,7 @@ class SearchFilters(BaseModel):
     created_at: list[list[DateFilter]] | None = Field(default=None)
     expired_at: list[list[DateFilter]] | None = Field(default=None)
     edge_uuids: list[str] | None = Field(default=None)
+    property_filters: list[PropertyFilter] | None = Field(default=None)
 
 
 def cypher_to_opensearch_operator(op: ComparisonOperator) -> str:

graphiti_core/utils/text_utils.py

@@ -17,7 +17,7 @@ limitations under the License.
 import re
 
 # Maximum length for entity/node summaries
-MAX_SUMMARY_CHARS = 250
+MAX_SUMMARY_CHARS = 500
 
 
 def truncate_at_sentence(text: str, max_chars: int) -> str:

mcp_server/.env

@@ -0,0 +1,49 @@
+# Graphiti MCP Server Environment Configuration
+MCP_SERVER_HOST=gmakai.online
+# Neo4j Database Configuration
+# These settings are used to connect to your Neo4j database
+NEO4J_URI=bolt://neo4j:7687
+NEO4J_USER=neo4j
+NEO4J_PASSWORD=kg3Jsdb2
+
+# OpenAI API Configuration
+# Required for LLM operations
+OPENAI_API_KEY=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
+MODEL_NAME=gpt5.1-nano
+
+# Optional: Only needed for non-standard OpenAI endpoints
+OPENAI_BASE_URL=https://openrouter.ai/api/v1
+
+# Optional: Group ID for namespacing graph data
+# GROUP_ID=my_project
+
+# Concurrency Control
+# Controls how many episodes can be processed simultaneously
+# Default: 10 (suitable for OpenAI Tier 3, mid-tier Anthropic)
+# Adjust based on your LLM provider's rate limits:
+#   - OpenAI Tier 1 (free): 1-2
+#   - OpenAI Tier 2: 5-8
+#   - OpenAI Tier 3: 10-15
+#   - OpenAI Tier 4: 20-50
+#   - Anthropic default: 5-8
+#   - Anthropic high tier: 15-30
+#   - Ollama (local): 1-5
+# See README.md "Concurrency and LLM Provider 429 Rate Limit Errors" for details
+SEMAPHORE_LIMIT=10
+
+# Optional: Path configuration for Docker
+# PATH=/root/.local/bin:${PATH}
+
+# Optional: Memory settings for Neo4j (used in Docker Compose)
+# NEO4J_server_memory_heap_initial__size=512m
+# NEO4J_server_memory_heap_max__size=1G
+# NEO4J_server_memory_pagecache_size=512m
+
+# Azure OpenAI configuration
+# Optional: Only needed for Azure OpenAI endpoints
+# AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint_here
+# AZURE_OPENAI_API_VERSION=2025-01-01-preview
+# AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-gpt-4o-mini-deployment
+# AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
+# AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=text-embedding-3-large-deployment
+# AZURE_OPENAI_USE_MANAGED_IDENTITY=false

mcp_server/config/config-docker-falkordb-combined.yaml

@@ -8,7 +8,7 @@ server:
 
 llm:
   provider: "openai"  # Options: openai, azure_openai, anthropic, gemini, groq
-  model: "gpt-5-mini"
+  model: "gpt-4o-mini"
   max_tokens: 4096
 
   providers:

mcp_server/config/config-docker-falkordb.yaml

@@ -8,7 +8,7 @@ server:
   
 llm:
   provider: "openai"  # Options: openai, azure_openai, anthropic, gemini, groq
-  model: "gpt-5-mini"
+  model: "gpt-4o-mini"
   max_tokens: 4096
   
   providers:

mcp_server/config/config-docker-neo4j.yaml

@@ -8,7 +8,7 @@ server:
   
 llm:
   provider: "openai"  # Options: openai, azure_openai, anthropic, gemini, groq
-  model: "gpt-5-mini"
+  model: "gpt-4o-mini"
   max_tokens: 4096
   
   providers:

mcp_server/config/config.yaml

@@ -12,7 +12,7 @@ server:
   
 llm:
   provider: "openai"  # Options: openai, azure_openai, anthropic, gemini, groq
-  model: "gpt-5-mini"
+  model: "gpt-4o-mini"
   max_tokens: 4096
   
   providers:

mcp_server/docker/docker-compose-neo4j.yml

@@ -1,23 +1,23 @@
 services:
   neo4j:
-    image: neo4j:5.26.0
+    image: neo4j:latest
     ports:
       - "7474:7474" # HTTP
       - "7687:7687" # Bolt
     environment:
-      - NEO4J_AUTH=${NEO4J_USER:-neo4j}/${NEO4J_PASSWORD:-demodemo}
-      - NEO4J_server_memory_heap_initial__size=512m
-      - NEO4J_server_memory_heap_max__size=1G
-      - NEO4J_server_memory_pagecache_size=512m
+      - NEO4J_AUTH=${NEO4J_USER:-neo4j}/${NEO4J_PASSWORD:-kg3Jsdb2}
     volumes:
-      - neo4j_data:/data
-      - neo4j_logs:/logs
+      - /data/neo4j/data:/data
+      - /data/neo4j/logs:/logs
+      - /data/neo4j/plugins:/plugins
+      - /data/neo4j/config:/config
     healthcheck:
       test: ["CMD", "wget", "-O", "/dev/null", "http://localhost:7474"]
       interval: 10s
       timeout: 5s
       retries: 5
       start_period: 30s
+    restart: always
 
   graphiti-mcp:
     image: zepai/knowledge-graph-mcp:standalone
@@ -27,9 +27,9 @@ services:
     build:
       context: ..
       dockerfile: docker/Dockerfile.standalone
-    env_file:
-      - path: ../.env
-        required: false
+    #env_file:
+    #  - path: ../.env
+    #    required: true
     depends_on:
       neo4j:
         condition: service_healthy
@@ -37,13 +37,18 @@ services:
       # Database configuration
       - NEO4J_URI=${NEO4J_URI:-bolt://neo4j:7687}
       - NEO4J_USER=${NEO4J_USER:-neo4j}
-      - NEO4J_PASSWORD=${NEO4J_PASSWORD:-demodemo}
+      - NEO4J_PASSWORD=${NEO4J_PASSWORD:-kg3Jsdb2}
       - NEO4J_DATABASE=${NEO4J_DATABASE:-neo4j}
       # Application configuration
       - GRAPHITI_GROUP_ID=${GRAPHITI_GROUP_ID:-main}
       - SEMAPHORE_LIMIT=${SEMAPHORE_LIMIT:-10}
       - CONFIG_PATH=/app/mcp/config/config.yaml
       - PATH=/root/.local/bin:${PATH}
+      - MCP_SERVER_HOST=gmakai.online
+      - OPENAI_API_KEY=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
+      - MODEL_NAME=gpt5.1-nano
+      - OPENAI_BASE_URL=https://openrouter.ai/api/v1
+
     volumes:
       - ../config/config-docker-neo4j.yaml:/app/mcp/config/config.yaml:ro
     ports:

mcp_server/src/config/schema.py

@@ -147,7 +147,7 @@ class LLMConfig(BaseModel):
     """LLM configuration."""
 
     provider: str = Field(default='openai', description='LLM provider')
-    model: str = Field(default='gpt-4.1', description='Model name')
+    model: str = Field(default='gpt-4o-mini', description='Model name')
     temperature: float | None = Field(
         default=None, description='Temperature (optional, defaults to None for reasoning models)'
     )

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "graphiti-core"
 description = "A temporal graph building library"
-version = "0.24.0"
+version = "0.24.3"
 authors = [
     { name = "Paul Paliychuk", email = "paul@getzep.com" },
     { name = "Preston Rasmussen", email = "preston@getzep.com" },

signatures/version1/cla.json

@@ -455,6 +455,62 @@
       "created_at": "2025-11-06T08:39:46Z",
       "repoId": 840056306,
       "pullRequestNo": 1053
+    },
+    {
+      "name": "supmo668",
+      "id": 28805779,
+      "comment_id": 3550309664,
+      "created_at": "2025-11-19T01:56:25Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1072
+    },
+    {
+      "name": "donbr",
+      "id": 7340008,
+      "comment_id": 3568970102,
+      "created_at": "2025-11-24T05:19:42Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1081
+    },
+    {
+      "name": "apetti1920",
+      "id": 4706645,
+      "comment_id": 3572726648,
+      "created_at": "2025-11-24T21:07:34Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1084
+    },
+    {
+      "name": "ZLBillShaw",
+      "id": 55940186,
+      "comment_id": 3583997833,
+      "created_at": "2025-11-27T02:45:53Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1085
+    },
+    {
+      "name": "ronaldmego",
+      "id": 17481958,
+      "comment_id": 3617267429,
+      "created_at": "2025-12-05T14:59:42Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1094
+    },
+    {
+      "name": "NShumway",
+      "id": 29358113,
+      "comment_id": 3634967978,
+      "created_at": "2025-12-10T01:26:49Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1102
+    },
+    {
+      "name": "husniadil",
+      "id": 10581130,
+      "comment_id": 3650156180,
+      "created_at": "2025-12-14T03:37:59Z",
+      "repoId": 840056306,
+      "pullRequestNo": 1105
     }
   ]
 }

tests/llm_client/test_anthropic_client.py

@@ -81,7 +81,7 @@ class TestAnthropicClientInitialization:
         config = LLMConfig(api_key='test_api_key')
         client = AnthropicClient(config=config, cache=False)
 
-        assert client.model == 'claude-3-7-sonnet-latest'
+        assert client.model == 'claude-haiku-4-5-latest'
 
     @patch.dict(os.environ, {'ANTHROPIC_API_KEY': 'env_api_key'})
     def test_init_without_config(self):
@@ -89,7 +89,7 @@ class TestAnthropicClientInitialization:
         client = AnthropicClient(cache=False)
 
         assert client.config.api_key == 'env_api_key'
-        assert client.model == 'claude-3-7-sonnet-latest'
+        assert client.model == 'claude-haiku-4-5-latest'
 
     def test_init_with_custom_client(self):
         """Test initialization with a custom AsyncAnthropic client."""

tests/llm_client/test_gemini_client.py

@@ -455,7 +455,6 @@ class TestGeminiClientGenerateResponse:
             ('gemini-2.5-flash', 65536),
             ('gemini-2.5-pro', 65536),
             ('gemini-2.5-flash-lite', 64000),
-            ('models/gemini-2.5-flash-lite-preview-06-17', 64000),
             ('gemini-2.0-flash', 8192),
             ('gemini-1.5-pro', 8192),
             ('gemini-1.5-flash', 8192),

tests/test_text_utils.py

@@ -87,7 +87,7 @@ def test_truncate_at_sentence_strips_trailing_whitespace():
 
 def test_max_summary_chars_constant():
     """Test that MAX_SUMMARY_CHARS is set to expected value."""
-    assert MAX_SUMMARY_CHARS == 250
+    assert MAX_SUMMARY_CHARS == 500
 
 
 def test_truncate_at_sentence_realistic_summary():

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10, <4"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -262,35 +262,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
 ]
 
-[[package]]
-name = "azure-core"
-version = "1.36.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "requests" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/0a/c4/d4ff3bc3ddf155156460bff340bbe9533f99fac54ddea165f35a8619f162/azure_core-1.36.0.tar.gz", hash = "sha256:22e5605e6d0bf1d229726af56d9e92bc37b6e726b141a18be0b4d424131741b7", size = 351139, upload-time = "2025-10-15T00:33:49.083Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b1/3c/b90d5afc2e47c4a45f4bba00f9c3193b0417fad5ad3bb07869f9d12832aa/azure_core-1.36.0-py3-none-any.whl", hash = "sha256:fee9923a3a753e94a259563429f3644aaf05c486d45b1215d098115102d91d3b", size = 213302, upload-time = "2025-10-15T00:33:51.058Z" },
-]
-
-[[package]]
-name = "azure-identity"
-version = "1.25.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "azure-core" },
-    { name = "cryptography" },
-    { name = "msal" },
-    { name = "msal-extensions" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/06/8d/1a6c41c28a37eab26dc85ab6c86992c700cd3f4a597d9ed174b0e9c69489/azure_identity-1.25.1.tar.gz", hash = "sha256:87ca8328883de6036443e1c37b40e8dc8fb74898240f61071e09d2e369361456", size = 279826, upload-time = "2025-10-06T20:30:02.194Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/83/7b/5652771e24fff12da9dde4c20ecf4682e606b104f26419d139758cc935a6/azure_identity-1.25.1-py3-none-any.whl", hash = "sha256:e9edd720af03dff020223cd269fa3a61e8f345ea75443858273bcb44844ab651", size = 191317, upload-time = "2025-10-06T20:30:04.251Z" },
-]
-
 [[package]]
 name = "babel"
 version = "2.17.0"
@@ -549,71 +520,6 @@ wheels = [
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