diff --git a/README.md b/README.md
index daa22842..b83ad2d0 100644
--- a/README.md
+++ b/README.md
@@ -35,8 +35,8 @@ Use Graphiti to:
 
 <br />
 
-A knowledge graph is a network of interconnected facts, such as _“Kendra loves Adidas shoes.”_ Each fact is a “triplet” represented by two entities, or
-nodes (_”Kendra”_, _“Adidas shoes”_), and their relationship, or edge (_”loves”_). Knowledge Graphs have been explored
+A knowledge graph is a network of interconnected facts, such as _"Kendra loves Adidas shoes."_ Each fact is a "triplet" represented by two entities, or
+nodes ("Kendra", "Adidas shoes"), and their relationship, or edge ("loves"). Knowledge Graphs have been explored
 extensively for information retrieval. What makes Graphiti unique is its ability to autonomously build a knowledge graph
 while handling changing relationships and maintaining historical context.
 
@@ -96,7 +96,7 @@ Requirements:
 
 Optional:
 
-- Anthropic or Groq API key (for alternative LLM providers)
+- Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)
 
 > [!TIP]
 > The simplest way to install Neo4j is via [Neo4j Desktop](https://neo4j.com/download/). It provides a user-friendly
@@ -112,6 +112,22 @@ or
 poetry add graphiti-core
 ```
 
+You can also install optional LLM providers as extras:
+
+```bash
+# Install with Anthropic support
+pip install graphiti-core[anthropic]
+
+# Install with Groq support
+pip install graphiti-core[groq]
+
+# Install with Google Gemini support
+pip install graphiti-core[google-genai]
+
+# Install with multiple providers
+pip install graphiti-core[anthropic,groq,google-genai]
+```
+
 ## Quick Start
 
 > [!IMPORTANT]
@@ -211,6 +227,42 @@ graphiti = Graphiti(
 
 Make sure to replace the placeholder values with your actual Azure OpenAI credentials and specify the correct embedding model name that's deployed in your Azure OpenAI service.
 
+## Using Graphiti with Google Gemini
+
+Graphiti supports Google's Gemini models for both LLM inference and embeddings. To use Gemini, you'll need to configure both the LLM client and embedder with your Google API key.
+
+```python
+from graphiti_core import Graphiti
+from graphiti_core.llm_client.gemini_client import GeminiClient, LLMConfig
+from graphiti_core.embedder.gemini import GeminiEmbedder, GeminiEmbedderConfig
+
+# Google API key configuration
+api_key = "<your-google-api-key>"
+
+# Initialize Graphiti with Gemini clients
+graphiti = Graphiti(
+    "bolt://localhost:7687",
+    "neo4j",
+    "password",
+    llm_client=GeminiClient(
+        config=LLMConfig(
+            api_key=api_key,
+            model="gemini-2.0-flash"
+        )
+    ),
+    embedder=GeminiEmbedder(
+        config=GeminiEmbedderConfig(
+            api_key=api_key,
+            embedding_model="embedding-001"
+        )
+    )
+)
+
+# Now you can use Graphiti with Google Gemini
+```
+
+Make sure to replace the placeholder value with your actual Google API key. You can find more details in the example file at `examples/gemini_example.py`.
+
 ## Documentation
 
 - [Guides and API documentation](https://help.getzep.com/graphiti).
diff --git a/graphiti_core/embedder/__init__.py b/graphiti_core/embedder/__init__.py
index 9e952726..aea15619 100644
--- a/graphiti_core/embedder/__init__.py
+++ b/graphiti_core/embedder/__init__.py
@@ -1,4 +1,8 @@
 from .client import EmbedderClient
 from .openai import OpenAIEmbedder, OpenAIEmbedderConfig
 
-__all__ = ['EmbedderClient', 'OpenAIEmbedder', 'OpenAIEmbedderConfig']
+__all__ = [
+    'EmbedderClient',
+    'OpenAIEmbedder',
+    'OpenAIEmbedderConfig',
+]
diff --git a/graphiti_core/embedder/gemini.py b/graphiti_core/embedder/gemini.py
new file mode 100644
index 00000000..fc92f96e
--- /dev/null
+++ b/graphiti_core/embedder/gemini.py
@@ -0,0 +1,68 @@
+"""
+Copyright 2024, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from collections.abc import Iterable
+
+from google import genai  # type: ignore
+from google.genai import types  # type: ignore
+from pydantic import Field
+
+from .client import EmbedderClient, EmbedderConfig
+
+DEFAULT_EMBEDDING_MODEL = 'embedding-001'
+
+
+class GeminiEmbedderConfig(EmbedderConfig):
+    embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
+    api_key: str | None = None
+
+
+class GeminiEmbedder(EmbedderClient):
+    """
+    Google Gemini Embedder Client
+    """
+
+    def __init__(self, config: GeminiEmbedderConfig | None = None):
+        if config is None:
+            config = GeminiEmbedderConfig()
+        self.config = config
+
+        # Configure the Gemini API
+        self.client = genai.Client(
+            api_key=config.api_key,
+        )
+
+    async def create(
+        self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
+    ) -> list[float]:
+        """
+        Create embeddings for the given input data using Google's Gemini embedding model.
+
+        Args:
+            input_data: The input data to create embeddings for. Can be a string, list of strings,
+                       or an iterable of integers or iterables of integers.
+
+        Returns:
+            A list of floats representing the embedding vector.
+        """
+        # Generate embeddings
+        result = await self.client.aio.models.embed_content(
+            model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
+            contents=[input_data],
+            config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
+        )
+
+        return result.embeddings[0].values
diff --git a/graphiti_core/graphiti.py b/graphiti_core/graphiti.py
index daa63c06..c2e54b5a 100644
--- a/graphiti_core/graphiti.py
+++ b/graphiti_core/graphiti.py
@@ -71,10 +71,7 @@ from graphiti_core.utils.maintenance.graph_data_operations import (
     build_indices_and_constraints,
     retrieve_episodes,
 )
-from graphiti_core.utils.maintenance.node_operations import (
-    extract_nodes,
-    resolve_extracted_nodes,
-)
+from graphiti_core.utils.maintenance.node_operations import extract_nodes, resolve_extracted_nodes
 from graphiti_core.utils.maintenance.temporal_operations import get_edge_contradictions
 from graphiti_core.utils.ontology_utils.entity_types_utils import validate_entity_types
 
diff --git a/graphiti_core/llm_client/gemini_client.py b/graphiti_core/llm_client/gemini_client.py
new file mode 100644
index 00000000..02186fc4
--- /dev/null
+++ b/graphiti_core/llm_client/gemini_client.py
@@ -0,0 +1,186 @@
+"""
+Copyright 2024, Zep Software, Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import json
+import logging
+import typing
+
+from google import genai  # type: ignore
+from google.genai import types  # type: ignore
+from pydantic import BaseModel
+
+from ..prompts.models import Message
+from .client import LLMClient
+from .config import DEFAULT_MAX_TOKENS, LLMConfig
+from .errors import RateLimitError
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL = 'gemini-2.0-flash'
+
+
+class GeminiClient(LLMClient):
+    """
+    GeminiClient is a client class for interacting with Google's Gemini language models.
+
+    This class extends the LLMClient and provides methods to initialize the client
+    and generate responses from the Gemini language model.
+
+    Attributes:
+        model (str): The model name to use for generating responses.
+        temperature (float): The temperature to use for generating responses.
+        max_tokens (int): The maximum number of tokens to generate in a response.
+
+    Methods:
+        __init__(config: LLMConfig | None = None, cache: bool = False):
+            Initializes the GeminiClient with the provided configuration and cache setting.
+
+        _generate_response(messages: list[Message]) -> dict[str, typing.Any]:
+            Generates a response from the language model based on the provided messages.
+    """
+
+    def __init__(
+        self,
+        config: LLMConfig | None = None,
+        cache: bool = False,
+        max_tokens: int = DEFAULT_MAX_TOKENS,
+    ):
+        """
+        Initialize the GeminiClient with the provided configuration and cache setting.
+
+        Args:
+            config (LLMConfig | None): The configuration for the LLM client, including API key, model, temperature, and max tokens.
+            cache (bool): Whether to use caching for responses. Defaults to False.
+        """
+        if config is None:
+            config = LLMConfig()
+
+        super().__init__(config, cache)
+
+        self.model = config.model
+        # Configure the Gemini API
+        self.client = genai.Client(
+            api_key=config.api_key,
+        )
+        self.max_tokens = max_tokens
+
+    async def _generate_response(
+        self,
+        messages: list[Message],
+        response_model: type[BaseModel] | None = None,
+        max_tokens: int = DEFAULT_MAX_TOKENS,
+    ) -> dict[str, typing.Any]:
+        """
+        Generate a response from the Gemini language model.
+
+        Args:
+            messages (list[Message]): A list of messages to send to the language model.
+            response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
+            max_tokens (int): The maximum number of tokens to generate in the response.
+
+        Returns:
+            dict[str, typing.Any]: The response from the language model.
+
+        Raises:
+            RateLimitError: If the API rate limit is exceeded.
+            RefusalError: If the content is blocked by the model.
+            Exception: If there is an error generating the response.
+        """
+        try:
+            gemini_messages: list[types.Content] = []
+            # If a response model is provided, add schema for structured output
+            system_prompt = ''
+            if response_model is not None:
+                # Get the schema from the Pydantic model
+                pydantic_schema = response_model.model_json_schema()
+
+                # Create instruction to output in the desired JSON format
+                system_prompt += (
+                    f'Output ONLY valid JSON matching this schema: {json.dumps(pydantic_schema)}.\n'
+                    'Do not include any explanatory text before or after the JSON.\n\n'
+                )
+
+            # Add messages content
+            # First check for a system message
+            if messages and messages[0].role == 'system':
+                system_prompt = f'{messages[0].content}\n\n {system_prompt}'
+                messages = messages[1:]
+
+            # Add the rest of the messages
+            for m in messages:
+                m.content = self._clean_input(m.content)
+                gemini_messages.append(
+                    types.Content(role=m.role, parts=[types.Part.from_text(text=m.content)])
+                )
+
+            # Create generation config
+            generation_config = types.GenerateContentConfig(
+                temperature=self.temperature,
+                max_output_tokens=max_tokens or self.max_tokens,
+                response_mime_type='application/json' if response_model else None,
+                response_schema=response_model if response_model else None,
+                system_instruction=system_prompt,
+            )
+
+            # Generate content using the simple string approach
+            response = await self.client.aio.models.generate_content(
+                model=self.model or DEFAULT_MODEL,
+                contents=gemini_messages,
+                config=generation_config,
+            )
+
+            # If this was a structured output request, parse the response into the Pydantic model
+            if response_model is not None:
+                try:
+                    validated_model = response_model.model_validate(json.loads(response.text))
+
+                    # Return as a dictionary for API consistency
+                    return validated_model.model_dump()
+                except Exception as e:
+                    raise Exception(f'Failed to parse structured response: {e}') from e
+
+            # Otherwise, return the response text as a dictionary
+            return {'content': response.text}
+
+        except Exception as e:
+            # Check if it's a rate limit error
+            if 'rate limit' in str(e).lower() or 'quota' in str(e).lower():
+                raise RateLimitError from e
+            logger.error(f'Error in generating LLM response: {e}')
+            raise
+
+    async def generate_response(
+        self,
+        messages: list[Message],
+        response_model: type[BaseModel] | None = None,
+        max_tokens: int = DEFAULT_MAX_TOKENS,
+    ) -> dict[str, typing.Any]:
+        """
+        Generate a response from the Gemini language model.
+        This method overrides the parent class method to provide a direct implementation.
+
+        Args:
+            messages (list[Message]): A list of messages to send to the language model.
+            response_model (type[BaseModel] | None): An optional Pydantic model to parse the response into.
+            max_tokens (int): The maximum number of tokens to generate in the response.
+
+        Returns:
+            dict[str, typing.Any]: The response from the language model.
+        """
+        # Call the internal _generate_response method
+        return await self._generate_response(
+            messages=messages, response_model=response_model, max_tokens=max_tokens
+        )
diff --git a/graphiti_core/prompts/lib.py b/graphiti_core/prompts/lib.py
index 6757a1bd..dc569f3f 100644
--- a/graphiti_core/prompts/lib.py
+++ b/graphiti_core/prompts/lib.py
@@ -16,63 +16,27 @@ limitations under the License.
 
 from typing import Any, Protocol, TypedDict
 
-from .dedupe_edges import (
-    Prompt as DedupeEdgesPrompt,
-)
-from .dedupe_edges import (
-    Versions as DedupeEdgesVersions,
-)
-from .dedupe_edges import (
-    versions as dedupe_edges_versions,
-)
-from .dedupe_nodes import (
-    Prompt as DedupeNodesPrompt,
-)
-from .dedupe_nodes import (
-    Versions as DedupeNodesVersions,
-)
-from .dedupe_nodes import (
-    versions as dedupe_nodes_versions,
-)
+from .dedupe_edges import Prompt as DedupeEdgesPrompt
+from .dedupe_edges import Versions as DedupeEdgesVersions
+from .dedupe_edges import versions as dedupe_edges_versions
+from .dedupe_nodes import Prompt as DedupeNodesPrompt
+from .dedupe_nodes import Versions as DedupeNodesVersions
+from .dedupe_nodes import versions as dedupe_nodes_versions
 from .eval import Prompt as EvalPrompt
 from .eval import Versions as EvalVersions
 from .eval import versions as eval_versions
-from .extract_edge_dates import (
-    Prompt as ExtractEdgeDatesPrompt,
-)
-from .extract_edge_dates import (
-    Versions as ExtractEdgeDatesVersions,
-)
-from .extract_edge_dates import (
-    versions as extract_edge_dates_versions,
-)
-from .extract_edges import (
-    Prompt as ExtractEdgesPrompt,
-)
-from .extract_edges import (
-    Versions as ExtractEdgesVersions,
-)
-from .extract_edges import (
-    versions as extract_edges_versions,
-)
-from .extract_nodes import (
-    Prompt as ExtractNodesPrompt,
-)
-from .extract_nodes import (
-    Versions as ExtractNodesVersions,
-)
-from .extract_nodes import (
-    versions as extract_nodes_versions,
-)
-from .invalidate_edges import (
-    Prompt as InvalidateEdgesPrompt,
-)
-from .invalidate_edges import (
-    Versions as InvalidateEdgesVersions,
-)
-from .invalidate_edges import (
-    versions as invalidate_edges_versions,
-)
+from .extract_edge_dates import Prompt as ExtractEdgeDatesPrompt
+from .extract_edge_dates import Versions as ExtractEdgeDatesVersions
+from .extract_edge_dates import versions as extract_edge_dates_versions
+from .extract_edges import Prompt as ExtractEdgesPrompt
+from .extract_edges import Versions as ExtractEdgesVersions
+from .extract_edges import versions as extract_edges_versions
+from .extract_nodes import Prompt as ExtractNodesPrompt
+from .extract_nodes import Versions as ExtractNodesVersions
+from .extract_nodes import versions as extract_nodes_versions
+from .invalidate_edges import Prompt as InvalidateEdgesPrompt
+from .invalidate_edges import Versions as InvalidateEdgesVersions
+from .invalidate_edges import versions as invalidate_edges_versions
 from .models import Message, PromptFunction
 from .prompt_helpers import DO_NOT_ESCAPE_UNICODE
 from .summarize_nodes import Prompt as SummarizeNodesPrompt
diff --git a/graphiti_core/utils/maintenance/__init__.py b/graphiti_core/utils/maintenance/__init__.py
index 65fca2c2..b6d9da08 100644
--- a/graphiti_core/utils/maintenance/__init__.py
+++ b/graphiti_core/utils/maintenance/__init__.py
@@ -1,8 +1,5 @@
 from .edge_operations import build_episodic_edges, extract_edges
-from .graph_data_operations import (
-    clear_data,
-    retrieve_episodes,
-)
+from .graph_data_operations import clear_data, retrieve_episodes
 from .node_operations import extract_nodes
 
 __all__ = [
diff --git a/graphiti_core/utils/maintenance/community_operations.py b/graphiti_core/utils/maintenance/community_operations.py
index 0db68aed..b4927c9e 100644
--- a/graphiti_core/utils/maintenance/community_operations.py
+++ b/graphiti_core/utils/maintenance/community_operations.py
@@ -9,11 +9,7 @@ from graphiti_core.edges import CommunityEdge
 from graphiti_core.embedder import EmbedderClient
 from graphiti_core.helpers import DEFAULT_DATABASE, semaphore_gather
 from graphiti_core.llm_client import LLMClient
-from graphiti_core.nodes import (
-    CommunityNode,
-    EntityNode,
-    get_community_node_from_record,
-)
+from graphiti_core.nodes import CommunityNode, EntityNode, get_community_node_from_record
 from graphiti_core.prompts import prompt_library
 from graphiti_core.prompts.summarize_nodes import Summary, SummaryDescription
 from graphiti_core.utils.datetime_utils import utc_now
diff --git a/graphiti_core/utils/maintenance/node_operations.py b/graphiti_core/utils/maintenance/node_operations.py
index 730761cb..f5cc046f 100644
--- a/graphiti_core/utils/maintenance/node_operations.py
+++ b/graphiti_core/utils/maintenance/node_operations.py
@@ -26,11 +26,7 @@ from graphiti_core.llm_client import LLMClient
 from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode
 from graphiti_core.prompts import prompt_library
 from graphiti_core.prompts.dedupe_nodes import NodeDuplicate
-from graphiti_core.prompts.extract_nodes import (
-    EntityClassification,
-    ExtractedNodes,
-    MissedEntities,
-)
+from graphiti_core.prompts.extract_nodes import EntityClassification, ExtractedNodes, MissedEntities
 from graphiti_core.prompts.summarize_nodes import Summary
 from graphiti_core.utils.datetime_utils import utc_now
 
diff --git a/poetry.lock b/poetry.lock
index 74ec9d81..4625e193 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,92 +13,92 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.14"
+version = "3.11.16"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "aiohttp-3.11.14-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e2bc827c01f75803de77b134afdbf74fa74b62970eafdf190f3244931d7a5c0d"},
-    {file = "aiohttp-3.11.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e365034c5cf6cf74f57420b57682ea79e19eb29033399dd3f40de4d0171998fa"},
-    {file = "aiohttp-3.11.14-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c32593ead1a8c6aabd58f9d7ee706e48beac796bb0cb71d6b60f2c1056f0a65f"},
-    {file = "aiohttp-3.11.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4e7c7ec4146a94a307ca4f112802a8e26d969018fabed526efc340d21d3e7d0"},
-    {file = "aiohttp-3.11.14-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c8b2df9feac55043759aa89f722a967d977d80f8b5865a4153fc41c93b957efc"},
-    {file = "aiohttp-3.11.14-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c7571f99525c76a6280f5fe8e194eeb8cb4da55586c3c61c59c33a33f10cfce7"},
-    {file = "aiohttp-3.11.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b59d096b5537ec7c85954cb97d821aae35cfccce3357a2cafe85660cc6295628"},
-    {file = "aiohttp-3.11.14-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b42dbd097abb44b3f1156b4bf978ec5853840802d6eee2784857be11ee82c6a0"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b05774864c87210c531b48dfeb2f7659407c2dda8643104fb4ae5e2c311d12d9"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:4e2e8ef37d4bc110917d038807ee3af82700a93ab2ba5687afae5271b8bc50ff"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e9faafa74dbb906b2b6f3eb9942352e9e9db8d583ffed4be618a89bd71a4e914"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:7e7abe865504f41b10777ac162c727af14e9f4db9262e3ed8254179053f63e6d"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:4848ae31ad44330b30f16c71e4f586cd5402a846b11264c412de99fa768f00f3"},
-    {file = "aiohttp-3.11.14-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:2d0b46abee5b5737cb479cc9139b29f010a37b1875ee56d142aefc10686a390b"},
-    {file = "aiohttp-3.11.14-cp310-cp310-win32.whl", hash = "sha256:a0d2c04a623ab83963576548ce098baf711a18e2c32c542b62322a0b4584b990"},
-    {file = "aiohttp-3.11.14-cp310-cp310-win_amd64.whl", hash = "sha256:5409a59d5057f2386bb8b8f8bbcfb6e15505cedd8b2445db510563b5d7ea1186"},
-    {file = "aiohttp-3.11.14-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f296d637a50bb15fb6a229fbb0eb053080e703b53dbfe55b1e4bb1c5ed25d325"},
-    {file = "aiohttp-3.11.14-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:ec6cd1954ca2bbf0970f531a628da1b1338f594bf5da7e361e19ba163ecc4f3b"},
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 [package.dependencies]
@@ -894,13 +894,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2025.3.0"
+version = "2025.3.2"
 description = "File-system specification"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -1008,13 +1008,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.29.3"
+version = "0.30.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1032,6 +1032,7 @@ cli = ["InquirerPy (==0.3.4)"]
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 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 hf-transfer = ["hf-transfer (>=0.1.4)"]
+hf-xet = ["hf-xet (>=0.1.4)"]
 inference = ["aiohttp"]
 quality = ["libcst (==1.4.0)", "mypy (==1.5.1)", "ruff (>=0.9.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
@@ -1284,17 +1285,17 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.10.0"
+version = "0.12.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.extras]
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 [[package]]
 name = "jsonpatch"
@@ -1503,13 +1504,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.6"
+version = "4.4.0"
 description = "JupyterLab computational environment"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -1523,15 +1524,15 @@ jupyter-server = ">=2.4.0,<3"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
-setuptools = ">=40.8.0"
+setuptools = ">=41.1.0"
 tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
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-docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.1.0)", "sphinx-copybutton"]
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+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.9.9)"]
+docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.2.0)", "sphinx-copybutton"]
+docs-screenshots = ["altair (==5.5.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
 
@@ -1590,13 +1591,13 @@ pydantic = ">=2.7.4,<3.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.47"
+version = "0.3.51"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
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+    {file = "langchain_core-0.3.51-py3-none-any.whl", hash = "sha256:4bd71e8acd45362aa428953f2a91d8162318014544a2216e4b769463caf68e13"},
+    {file = "langchain_core-0.3.51.tar.gz", hash = "sha256:db76b9cc331411602cb40ba0469a161febe7a0663fbcaddbc9056046ac2d22f4"},
 ]
 
 [package.dependencies]
@@ -1645,28 +1646,28 @@ langgraph-sdk = ">=0.1.42,<0.2.0"
 
 [[package]]
 name = "langgraph-checkpoint"
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+version = "2.0.24"
 description = "Library with base interfaces for LangGraph checkpoint savers."
 optional = false
 python-versions = "<4.0.0,>=3.9.0"
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 name = "langgraph-sdk"
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+version = "0.1.61"
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 python-versions = "<4.0.0,>=3.9.0"
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 [package.dependencies]
@@ -1812,178 +1813,105 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
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+    {file = "rpds_py-0.24.0-pp39-pypy39_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:369d9c6d4c714e36d4a03957b4783217a3ccd1e222cdd67d464a3a479fc17796"},
+    {file = "rpds_py-0.24.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:24795c099453e3721fda5d8ddd45f5dfcc8e5a547ce7b8e9da06fecc3832e26f"},
+    {file = "rpds_py-0.24.0.tar.gz", hash = "sha256:772cc1b2cd963e7e17e6cc55fe0371fb9c704d63e44cacec7b9b7f523b78919e"},
 ]
 
 [[package]]
@@ -3902,13 +3880,13 @@ train = ["accelerate (>=0.20.3)", "datasets"]
 
 [[package]]
 name = "setuptools"
-version = "77.0.3"
+version = "78.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "setuptools-77.0.3-py3-none-any.whl", hash = "sha256:67122e78221da5cf550ddd04cf8742c8fe12094483749a792d56cd669d6cf58c"},
-    {file = "setuptools-77.0.3.tar.gz", hash = "sha256:583b361c8da8de57403743e756609670de6fb2345920e36dc5c2d914c319c945"},
+    {file = "setuptools-78.1.0-py3-none-any.whl", hash = "sha256:3e386e96793c8702ae83d17b853fb93d3e09ef82ec62722e61da5cd22376dcd8"},
+    {file = "setuptools-78.1.0.tar.gz", hash = "sha256:18fd474d4a82a5f83dac888df697af65afa82dec7323d09c3e37d1f14288da54"},
 ]
 
 [package.extras]
@@ -4288,18 +4266,18 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "transformers"
-version = "4.50.0"
+version = "4.51.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "transformers-4.50.0-py3-none-any.whl", hash = "sha256:d75465d523a28bcfef0028c671f682edee29418ab9a5a15cf8a05171e7c54cb7"},
-    {file = "transformers-4.50.0.tar.gz", hash = "sha256:d4b0f587ec88825981103fee0a1e80230d956ecc8a7f3feeaafbe49a233c88b8"},
+    {file = "transformers-4.51.0-py3-none-any.whl", hash = "sha256:2e6baa476735ab8adccbaee6961525a0d1ce8c21d49293af30ef5ee4b082f64d"},
+    {file = "transformers-4.51.0.tar.gz", hash = "sha256:2d302563ff6c2cc2d0e88ef352cf059f9a21ce18102fd43662bb1246f70b8a84"},
 ]
 
 [package.dependencies]
 filelock = "*"
-huggingface-hub = ">=0.26.0,<1.0"
+huggingface-hub = ">=0.30.0,<1.0"
 numpy = ">=1.17"
 packaging = ">=20.0"
 pyyaml = ">=5.1"
@@ -4317,13 +4295,14 @@ audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 benchmark = ["optimum-benchmark (>=0.3.0)"]
 codecarbon = ["codecarbon (>=2.8.1)"]
 deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.3.2,<0.4)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "kernels (>=0.3.2,<0.4)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.26.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "kernels (>=0.3.2,<0.4)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "kernels (>=0.3.2,<0.4)", "libcst", "librosa", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 ftfy = ["ftfy"]
+hf-xet = ["hf-xet"]
 hub-kernels = ["kernels (>=0.3.2,<0.4)"]
 integrations = ["kernels (>=0.3.2,<0.4)", "optuna", "ray[tune] (>=2.7.0)", "sigopt"]
 ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
@@ -4333,17 +4312,17 @@ num2words = ["num2words"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.5.1)", "urllib3 (<2.0.0)"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "libcst", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
 ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-ruff = ["ruff (==0.5.1)"]
+ruff = ["ruff (==0.11.2)"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
 serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk (<=3.8.1)", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -4353,7 +4332,7 @@ tokenizers = ["tokenizers (>=0.21,<0.22)"]
 torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.26.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.30.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
 video = ["av"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
@@ -4389,15 +4368,29 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.2"
+version = "4.13.1"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
-    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+    {file = "typing_extensions-4.13.1-py3-none-any.whl", hash = "sha256:4b6cf02909eb5495cfbc3f6e8fd49217e6cc7944e145cdda8caa3734777f9e69"},
+    {file = "typing_extensions-4.13.1.tar.gz", hash = "sha256:98795af00fb9640edec5b8e31fc647597b4691f099ad75f469a2616be1a76dff"},
 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.0"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f"},
+    {file = "typing_inspection-0.4.0.tar.gz", hash = "sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
 [[package]]
 name = "uri-template"
 version = "1.3.0"
@@ -4498,101 +4491,111 @@ test = ["websockets"]
 
 [[package]]
 name = "yarl"
-version = "1.18.3"
+version = "1.19.0"
 description = "Yet another URL library"
 optional = false
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 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
-propcache = ">=0.2.0"
+propcache = ">=0.2.1"
+
+[extras]
+anthropic = []
+google-genai = []
+groq = []
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "03d855eab191cb89a4f91966382e876ada40bd913ee142501cdfc592670215fa"
+content-hash = "8755ece09156e4b88e05953531d75a0bf59bddc4c86fd458ccd60cdfce3f8632"
diff --git a/pyproject.toml b/pyproject.toml
index 55c4b70a..922651ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,11 @@ openai = "^1.53.0"
 tenacity = "9.0.0"
 numpy = ">=1.0.0"
 python-dotenv = "^1.0.1"
-anthropic = "~0.49.0"
+
+[tool.poetry.extras]
+anthropic = ["anthropic"]
+groq = ["groq"]
+google-genai = ["google-genai"]
 
 [tool.poetry.group.dev.dependencies]
 mypy = "^1.11.1"
diff --git a/server/graph_service/dto/__init__.py b/server/graph_service/dto/__init__.py
index 284b26b1..375c9c43 100644
--- a/server/graph_service/dto/__init__.py
+++ b/server/graph_service/dto/__init__.py
@@ -1,12 +1,6 @@
 from .common import Message, Result
 from .ingest import AddEntityNodeRequest, AddMessagesRequest
-from .retrieve import (
-    FactResult,
-    GetMemoryRequest,
-    GetMemoryResponse,
-    SearchQuery,
-    SearchResults,
-)
+from .retrieve import FactResult, GetMemoryRequest, GetMemoryResponse, SearchQuery, SearchResults
 
 __all__ = [
     'SearchQuery',
diff --git a/tests/test_graphiti_int.py b/tests/test_graphiti_int.py
index 23def856..cde8c6ed 100644
--- a/tests/test_graphiti_int.py
+++ b/tests/test_graphiti_int.py
@@ -26,9 +26,7 @@ from graphiti_core.edges import EntityEdge, EpisodicEdge
 from graphiti_core.graphiti import Graphiti
 from graphiti_core.helpers import semaphore_gather
 from graphiti_core.nodes import EntityNode, EpisodicNode
-from graphiti_core.search.search_config_recipes import (
-    COMBINED_HYBRID_SEARCH_CROSS_ENCODER,
-)
+from graphiti_core.search.search_config_recipes import COMBINED_HYBRID_SEARCH_CROSS_ENCODER
 from graphiti_core.search.search_filters import SearchFilters
 
 pytestmark = pytest.mark.integration