From ed935d43eb940e9cd4da579d0be6aaab53771baf Mon Sep 17 00:00:00 2001
From: RheagalFire <arishalam121@gmail.com>
Date: Wed, 17 Jun 2026 00:19:06 +0530
Subject: [PATCH] Add LiteLLM AI gateway provider for LLM and embeddings

Signed-off-by: RheagalFire <arishalam121@gmail.com>
---
 pyproject.toml                                |   1 +
 src/neo4j_graphrag/embeddings/__init__.py     |   2 +
 src/neo4j_graphrag/embeddings/litellm.py      | 116 +++++
 src/neo4j_graphrag/llm/__init__.py            |   2 +
 src/neo4j_graphrag/llm/litellm_llm.py         | 404 ++++++++++++++++++
 .../unit/embeddings/test_litellm_embedder.py  |  97 +++++
 tests/unit/llm/test_litellm_llm.py            | 284 ++++++++++++
 uv.lock                                       | 376 +++++++++++++++-
 8 files changed, 1280 insertions(+), 2 deletions(-)
 create mode 100644 src/neo4j_graphrag/embeddings/litellm.py
 create mode 100644 src/neo4j_graphrag/llm/litellm_llm.py
 create mode 100644 tests/unit/embeddings/test_litellm_embedder.py
 create mode 100644 tests/unit/llm/test_litellm_llm.py

diff --git a/pyproject.toml b/pyproject.toml
index ab3f47369..e18eec0cd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,6 +52,7 @@ anthropic = ["anthropic>=0.49.0,<0.50.0"]
 bedrock = ["boto3>=1.35.0,<2.0.0"]
 ollama = ["ollama>=0.4.4,<0.5.0"]
 openai = ["openai>=1.51.1,<2.0.0"]
+litellm = ["litellm>=1.80.0,<1.87.0"]
 mistralai = ["mistralai>=1.0.3,<2.0.0"]
 qdrant = ["qdrant-client>=1.11.3,<2.0.0"]
 kg_creation_tools = ["neo4j-viz>=0.4.2,<0.5.0"]
diff --git a/src/neo4j_graphrag/embeddings/__init__.py b/src/neo4j_graphrag/embeddings/__init__.py
index b44132d87..957deed10 100644
--- a/src/neo4j_graphrag/embeddings/__init__.py
+++ b/src/neo4j_graphrag/embeddings/__init__.py
@@ -16,6 +16,7 @@
 from .bedrock import BedrockEmbeddings
 from .cohere import CohereEmbeddings
 from .google_genai import GeminiEmbedder
+from .litellm import LiteLLMEmbeddings
 from .mistral import MistralAIEmbeddings
 from .ollama import OllamaEmbeddings
 from .openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
@@ -33,4 +34,5 @@
     "MistralAIEmbeddings",
     "CohereEmbeddings",
     "GeminiEmbedder",
+    "LiteLLMEmbeddings",
 ]
diff --git a/src/neo4j_graphrag/embeddings/litellm.py b/src/neo4j_graphrag/embeddings/litellm.py
new file mode 100644
index 000000000..86b643052
--- /dev/null
+++ b/src/neo4j_graphrag/embeddings/litellm.py
@@ -0,0 +1,116 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  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
+#  #
+#      https://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 __future__ import annotations
+
+from typing import Any, Optional
+
+from neo4j_graphrag.embeddings.base import Embedder
+from neo4j_graphrag.exceptions import EmbeddingsGenerationError
+from neo4j_graphrag.utils.rate_limit import (
+    RateLimitHandler,
+    async_rate_limit_handler,
+    rate_limit_handler,
+)
+
+
+class LiteLLMEmbeddings(Embedder):
+    """Embeddings via LiteLLM AI gateway.
+
+    LiteLLM provides a unified interface to 100+ embedding providers using the
+    OpenAI embedding format.
+
+    Args:
+        model (str): The embedding model identifier in LiteLLM format
+            (e.g. "text-embedding-ada-002", "cohere/embed-english-v3.0").
+        rate_limit_handler (Optional[RateLimitHandler]): A rate limit handler
+            to manage API rate limits. Defaults to None.
+        **kwargs (Any): Arguments passed to ``litellm.embedding``
+            (e.g. ``api_key``, ``api_base``).
+
+    Raises:
+        EmbeddingsGenerationError: If there's an error generating embeddings.
+
+    Example:
+
+    .. code-block:: python
+
+        from neo4j_graphrag.embeddings import LiteLLMEmbeddings
+
+        embedder = LiteLLMEmbeddings(model="text-embedding-ada-002", api_key="...")
+        vector = embedder.embed_query("Some text")
+    """
+
+    def __init__(
+        self,
+        model: str = "text-embedding-ada-002",
+        rate_limit_handler: Optional[RateLimitHandler] = None,
+        **kwargs: Any,
+    ) -> None:
+        try:
+            import litellm
+        except ImportError:
+            raise ImportError(
+                """Could not import litellm python client.
+                Please install it with `pip install "neo4j-graphrag[litellm]"`."""
+            )
+        super().__init__(rate_limit_handler)
+        self.litellm = litellm
+        self.model = model
+        self.kwargs = kwargs
+
+    @rate_limit_handler
+    def embed_query(self, text: str, **kwargs: Any) -> list[float]:
+        """Generate embeddings for a given query using LiteLLM.
+
+        Args:
+            text (str): The text to generate an embedding for.
+            **kwargs (Any): Additional arguments passed to ``litellm.embedding``.
+        """
+        try:
+            response = self.litellm.embedding(
+                model=self.model,
+                input=[text],
+                **self.kwargs,
+                **kwargs,
+            )
+            embedding: list[float] = response.data[0]["embedding"]
+            return embedding
+        except Exception as e:
+            raise EmbeddingsGenerationError(
+                f"Failed to generate embedding with LiteLLM: {e}"
+            ) from e
+
+    @async_rate_limit_handler
+    async def async_embed_query(self, text: str, **kwargs: Any) -> list[float]:
+        """Asynchronously generate embeddings for a given query using LiteLLM.
+
+        Args:
+            text (str): The text to generate an embedding for.
+            **kwargs (Any): Additional arguments passed to ``litellm.aembedding``.
+        """
+        try:
+            response = await self.litellm.aembedding(
+                model=self.model,
+                input=[text],
+                **self.kwargs,
+                **kwargs,
+            )
+            embedding: list[float] = response.data[0]["embedding"]
+            return embedding
+        except Exception as e:
+            raise EmbeddingsGenerationError(
+                f"Failed to generate embedding with LiteLLM: {e}"
+            ) from e
diff --git a/src/neo4j_graphrag/llm/__init__.py b/src/neo4j_graphrag/llm/__init__.py
index ec0277fad..fe9794b60 100644
--- a/src/neo4j_graphrag/llm/__init__.py
+++ b/src/neo4j_graphrag/llm/__init__.py
@@ -20,6 +20,7 @@
 from .bedrock_llm import BedrockLLM
 from .cohere_llm import CohereLLM
 from .google_genai_llm import GeminiLLM
+from .litellm_llm import LiteLLMChat
 from .mistralai_llm import MistralAILLM
 from .ollama_llm import OllamaLLM
 from .openai_llm import AzureOpenAILLM, OpenAILLM
@@ -31,6 +32,7 @@
     "BedrockLLM",
     "CohereLLM",
     "GeminiLLM",
+    "LiteLLMChat",
     "LLMResponse",
     "LLMUsage",
     "LLMBase",
diff --git a/src/neo4j_graphrag/llm/litellm_llm.py b/src/neo4j_graphrag/llm/litellm_llm.py
new file mode 100644
index 000000000..be7f462e6
--- /dev/null
+++ b/src/neo4j_graphrag/llm/litellm_llm.py
@@ -0,0 +1,404 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  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
+#  #
+#      https://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 __future__ import annotations
+
+from typing import (
+    Any,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Type,
+    Union,
+    cast,
+)
+
+from pydantic import BaseModel, ValidationError
+
+from neo4j_graphrag.exceptions import LLMGenerationError
+from neo4j_graphrag.llm.base import LLMBase
+from neo4j_graphrag.llm.types import (
+    BaseMessage,
+    LLMResponse,
+    LLMUsage,
+    MessageList,
+    SystemMessage,
+    ToolCall,
+    ToolCallResponse,
+    UserMessage,
+)
+from neo4j_graphrag.message_history import MessageHistory
+from neo4j_graphrag.tool import Tool
+from neo4j_graphrag.types import LLMMessage
+from neo4j_graphrag.utils.rate_limit import (
+    RateLimitHandler,
+)
+from neo4j_graphrag.utils.rate_limit import (
+    async_rate_limit_handler as async_rate_limit_handler_decorator,
+)
+from neo4j_graphrag.utils.rate_limit import (
+    rate_limit_handler as rate_limit_handler_decorator,
+)
+
+
+# pylint: disable=redefined-builtin, arguments-differ, raise-missing-from, no-else-return, import-outside-toplevel
+class LiteLLMChat(LLMBase):
+    """Interface for large language models via LiteLLM AI gateway.
+
+    LiteLLM provides a unified interface to 100+ LLM providers (OpenAI, Anthropic,
+    Google, Azure, AWS Bedrock, Ollama, and more) using the OpenAI completion format.
+
+    Args:
+        model_name (str): The model identifier in LiteLLM format
+            (e.g. "gpt-4o", "anthropic/claude-sonnet-4-20250514", "ollama/llama3").
+        model_params (Optional[dict], optional): Additional parameters passed to the
+            model when text is sent to it. Defaults to None.
+        rate_limit_handler (Optional[RateLimitHandler], optional): A rate limit handler
+            to manage API rate limits. Defaults to None.
+        **kwargs (Any): Arguments passed to ``litellm.completion`` as defaults
+            (e.g. ``api_key``, ``api_base``, ``drop_params``).
+
+    Raises:
+        LLMGenerationError: If there's an error generating the response from the model.
+
+    Example:
+
+    .. code-block:: python
+
+        from neo4j_graphrag.llm import LiteLLMChat
+
+        llm = LiteLLMChat(model_name="gpt-4o", api_key="...")
+        llm.invoke("Say something")
+
+        # Use any LiteLLM-supported provider:
+        llm = LiteLLMChat(model_name="anthropic/claude-sonnet-4-20250514", api_key="...")
+        llm = LiteLLMChat(model_name="ollama/llama3")
+    """
+
+    supports_structured_output = True
+
+    def __init__(
+        self,
+        model_name: str = "",
+        model_params: Optional[dict[str, Any]] = None,
+        rate_limit_handler: Optional[RateLimitHandler] = None,
+        **kwargs: Any,
+    ) -> None:
+        try:
+            import litellm
+        except ImportError:
+            raise ImportError(
+                """Could not import litellm python client.
+                Please install it with `pip install "neo4j-graphrag[litellm]"`."""
+            )
+        LLMBase.__init__(
+            self,
+            model_name=model_name,
+            model_params=model_params or {},
+            rate_limit_handler=rate_limit_handler,
+        )
+        self.litellm = litellm
+        self.kwargs = kwargs
+
+    def invoke(
+        self,
+        input: Union[str, List[LLMMessage]],
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+        response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        if isinstance(input, str):
+            return self._invoke_v1(input, message_history, system_instruction)
+        elif isinstance(input, list):
+            return self._invoke_v2(input, response_format=response_format, **kwargs)
+        else:
+            raise ValueError(f"Invalid input type for invoke method - {type(input)}")
+
+    async def ainvoke(
+        self,
+        input: Union[str, List[LLMMessage]],
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+        response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        if isinstance(input, str):
+            return await self._ainvoke_v1(input, message_history, system_instruction)
+        elif isinstance(input, list):
+            return await self._ainvoke_v2(
+                input, response_format=response_format, **kwargs
+            )
+        else:
+            raise ValueError(f"Invalid input type for ainvoke method - {type(input)}")
+
+    def invoke_with_tools(
+        self,
+        input: str,
+        tools: Sequence[Tool],
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+    ) -> ToolCallResponse:
+        try:
+            if isinstance(message_history, MessageHistory):
+                message_history = message_history.messages
+            messages = self.get_messages(input, message_history, system_instruction)
+            tool_defs = [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": t.get_name(),
+                        "description": t.get_description(),
+                        "parameters": t.get_parameters(),
+                    },
+                }
+                for t in tools
+            ]
+            res = self.litellm.completion(
+                model=self.model_name,
+                messages=messages,
+                tools=tool_defs,
+                **self.kwargs,
+                **self.model_params,
+            )
+        except Exception as e:
+            raise LLMGenerationError(e)
+
+        tool_calls = []
+        if res.choices[0].message.tool_calls:
+            import json
+
+            for tc in res.choices[0].message.tool_calls:
+                tool_calls.append(
+                    ToolCall(
+                        name=tc.function.name,
+                        arguments=json.loads(tc.function.arguments),
+                    )
+                )
+        return ToolCallResponse(
+            tool_calls=tool_calls,
+            content=res.choices[0].message.content,
+        )
+
+    async def ainvoke_with_tools(
+        self,
+        input: str,
+        tools: Sequence[Tool],
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+    ) -> ToolCallResponse:
+        try:
+            if isinstance(message_history, MessageHistory):
+                message_history = message_history.messages
+            messages = self.get_messages(input, message_history, system_instruction)
+            tool_defs = [
+                {
+                    "type": "function",
+                    "function": {
+                        "name": t.get_name(),
+                        "description": t.get_description(),
+                        "parameters": t.get_parameters(),
+                    },
+                }
+                for t in tools
+            ]
+            res = await self.litellm.acompletion(
+                model=self.model_name,
+                messages=messages,
+                tools=tool_defs,
+                **self.kwargs,
+                **self.model_params,
+            )
+        except Exception as e:
+            raise LLMGenerationError(e)
+
+        tool_calls = []
+        if res.choices[0].message.tool_calls:
+            import json
+
+            for tc in res.choices[0].message.tool_calls:
+                tool_calls.append(
+                    ToolCall(
+                        name=tc.function.name,
+                        arguments=json.loads(tc.function.arguments),
+                    )
+                )
+        return ToolCallResponse(
+            tool_calls=tool_calls,
+            content=res.choices[0].message.content,
+        )
+
+    # implementations
+    @rate_limit_handler_decorator
+    def _invoke_v1(
+        self,
+        input: str,
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+    ) -> LLMResponse:
+        try:
+            if isinstance(message_history, MessageHistory):
+                message_history = message_history.messages
+            messages = self.get_messages(input, message_history, system_instruction)
+            res = self.litellm.completion(
+                model=self.model_name,
+                messages=messages,
+                **self.kwargs,
+                **self.model_params,
+            )
+        except Exception as e:
+            raise LLMGenerationError(e)
+        return self._to_llm_response(res)
+
+    @rate_limit_handler_decorator
+    def _invoke_v2(
+        self,
+        input: List[LLMMessage],
+        response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        try:
+            messages = self._to_litellm_messages(input)
+            call_kwargs: dict[str, Any] = {
+                **self.kwargs,
+                **self.model_params,
+            }
+            if response_format is not None:
+                if isinstance(response_format, type) and issubclass(
+                    response_format, BaseModel
+                ):
+                    call_kwargs["response_format"] = {
+                        "type": "json_schema",
+                        "json_schema": {
+                            "name": response_format.__name__,
+                            "schema": response_format.model_json_schema(),
+                        },
+                    }
+                else:
+                    call_kwargs["response_format"] = response_format
+            res = self.litellm.completion(
+                model=self.model_name,
+                messages=messages,
+                **call_kwargs,
+            )
+        except Exception as e:
+            raise LLMGenerationError("Error calling LiteLLM") from e
+        return self._to_llm_response(res)
+
+    @async_rate_limit_handler_decorator
+    async def _ainvoke_v1(
+        self,
+        input: str,
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+    ) -> LLMResponse:
+        try:
+            if isinstance(message_history, MessageHistory):
+                message_history = message_history.messages
+            messages = self.get_messages(input, message_history, system_instruction)
+            res = await self.litellm.acompletion(
+                model=self.model_name,
+                messages=messages,
+                **self.kwargs,
+                **self.model_params,
+            )
+        except Exception as e:
+            raise LLMGenerationError(e)
+        return self._to_llm_response(res)
+
+    @async_rate_limit_handler_decorator
+    async def _ainvoke_v2(
+        self,
+        input: List[LLMMessage],
+        response_format: Optional[Union[Type[BaseModel], dict[str, Any]]] = None,
+        **kwargs: Any,
+    ) -> LLMResponse:
+        try:
+            messages = self._to_litellm_messages(input)
+            call_kwargs: dict[str, Any] = {
+                **self.kwargs,
+                **self.model_params,
+            }
+            if response_format is not None:
+                if isinstance(response_format, type) and issubclass(
+                    response_format, BaseModel
+                ):
+                    call_kwargs["response_format"] = {
+                        "type": "json_schema",
+                        "json_schema": {
+                            "name": response_format.__name__,
+                            "schema": response_format.model_json_schema(),
+                        },
+                    }
+                else:
+                    call_kwargs["response_format"] = response_format
+            res = await self.litellm.acompletion(
+                model=self.model_name,
+                messages=messages,
+                **call_kwargs,
+            )
+        except Exception as e:
+            raise LLMGenerationError("Error calling LiteLLM") from e
+        return self._to_llm_response(res)
+
+    # helper methods
+    def get_messages(
+        self,
+        input: str,
+        message_history: Optional[Union[List[LLMMessage], MessageHistory]] = None,
+        system_instruction: Optional[str] = None,
+    ) -> list[dict[str, str]]:
+        messages: list[dict[str, str]] = []
+        if system_instruction:
+            messages.append(SystemMessage(content=system_instruction).model_dump())
+        if message_history:
+            if isinstance(message_history, MessageHistory):
+                message_history = message_history.messages
+            try:
+                MessageList(messages=cast(list[BaseMessage], message_history))
+            except ValidationError as e:
+                raise LLMGenerationError(e.errors()) from e
+            messages.extend(cast(Iterable[dict[str, str]], message_history))
+        messages.append(UserMessage(content=input).model_dump())
+        return messages
+
+    def _to_litellm_messages(
+        self,
+        input: list[LLMMessage],
+    ) -> list[dict[str, str]]:
+        return [{"role": m["role"], "content": m["content"]} for m in input]
+
+    def _to_llm_response(self, res: Any) -> LLMResponse:
+        usage = None
+        if hasattr(res, "usage") and res.usage is not None:
+            input_tokens = getattr(res.usage, "prompt_tokens", None)
+            output_tokens = getattr(res.usage, "completion_tokens", None)
+            total = getattr(res.usage, "total_tokens", None)
+            if input_tokens is not None:
+                input_tokens = int(input_tokens)
+            if output_tokens is not None:
+                output_tokens = int(output_tokens)
+            if total is not None:
+                total = int(total)
+            usage = LLMUsage(
+                request_tokens=input_tokens,
+                response_tokens=output_tokens,
+                total_tokens=total,
+            )
+        content = ""
+        if res.choices and res.choices[0].message.content:
+            content = res.choices[0].message.content
+        return LLMResponse(content=content, usage=usage)
diff --git a/tests/unit/embeddings/test_litellm_embedder.py b/tests/unit/embeddings/test_litellm_embedder.py
new file mode 100644
index 000000000..9ddcf4d84
--- /dev/null
+++ b/tests/unit/embeddings/test_litellm_embedder.py
@@ -0,0 +1,97 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  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
+#  #
+#      https://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.
+
+# Generated by Claude Code
+
+import sys
+from typing import Generator
+from unittest.mock import AsyncMock, MagicMock, Mock, patch
+
+import pytest
+from neo4j_graphrag.embeddings.litellm import LiteLLMEmbeddings
+from neo4j_graphrag.exceptions import EmbeddingsGenerationError
+
+
+@pytest.fixture
+def mock_litellm() -> Generator[MagicMock, None, None]:
+    mock_litellm = MagicMock()
+    with patch.dict(sys.modules, {"litellm": mock_litellm}):
+        yield mock_litellm
+
+
+@patch("builtins.__import__", side_effect=ImportError)
+def test_litellm_embedder_missing_dependency(mock_import: Mock) -> None:
+    with pytest.raises(ImportError):
+        LiteLLMEmbeddings()
+
+
+def test_litellm_embedder_happy_path(mock_litellm: MagicMock) -> None:
+    mock_litellm.embedding.return_value = MagicMock(
+        data=[{"embedding": [1.0, 2.0, 3.0]}]
+    )
+    embedder = LiteLLMEmbeddings(model="text-embedding-ada-002", api_key="test-key")
+    result = embedder.embed_query("my text")
+    assert isinstance(result, list)
+    assert result == [1.0, 2.0, 3.0]
+    mock_litellm.embedding.assert_called_once_with(
+        model="text-embedding-ada-002",
+        input=["my text"],
+        api_key="test-key",
+    )
+
+
+@pytest.mark.asyncio
+async def test_litellm_embedder_async_happy_path(mock_litellm: MagicMock) -> None:
+    mock_litellm.aembedding = AsyncMock(
+        return_value=MagicMock(data=[{"embedding": [4.0, 5.0, 6.0]}])
+    )
+    embedder = LiteLLMEmbeddings(model="text-embedding-ada-002", api_key="test-key")
+    result = await embedder.async_embed_query("my text")
+    assert isinstance(result, list)
+    assert result == [4.0, 5.0, 6.0]
+
+
+def test_litellm_embedder_error_handling(mock_litellm: MagicMock) -> None:
+    mock_litellm.embedding.side_effect = Exception("API Error")
+    embedder = LiteLLMEmbeddings(model="text-embedding-ada-002")
+    with pytest.raises(
+        EmbeddingsGenerationError, match="Failed to generate embedding with LiteLLM"
+    ):
+        embedder.embed_query("my text")
+
+
+@pytest.mark.asyncio
+async def test_litellm_embedder_async_error_handling(
+    mock_litellm: MagicMock,
+) -> None:
+    mock_litellm.aembedding = AsyncMock(side_effect=Exception("API Error"))
+    embedder = LiteLLMEmbeddings(model="text-embedding-ada-002")
+    with pytest.raises(
+        EmbeddingsGenerationError, match="Failed to generate embedding with LiteLLM"
+    ):
+        await embedder.async_embed_query("my text")
+
+
+def test_litellm_embedder_kwargs_passed_through(mock_litellm: MagicMock) -> None:
+    mock_litellm.embedding.return_value = MagicMock(data=[{"embedding": [1.0, 2.0]}])
+    embedder = LiteLLMEmbeddings(
+        model="cohere/embed-english-v3.0",
+        api_key="test-key",
+        api_base="https://custom.endpoint.com",
+    )
+    embedder.embed_query("my text")
+    call_args = mock_litellm.embedding.call_args
+    assert call_args[1]["api_key"] == "test-key"
+    assert call_args[1]["api_base"] == "https://custom.endpoint.com"
diff --git a/tests/unit/llm/test_litellm_llm.py b/tests/unit/llm/test_litellm_llm.py
new file mode 100644
index 000000000..f37ee9330
--- /dev/null
+++ b/tests/unit/llm/test_litellm_llm.py
@@ -0,0 +1,284 @@
+#  Copyright (c) "Neo4j"
+#  Neo4j Sweden AB [https://neo4j.com]
+#  #
+#  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
+#  #
+#      https://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.
+
+# Generated by Claude Code
+
+import sys
+import warnings
+from typing import Generator, List
+from unittest.mock import AsyncMock, MagicMock, Mock, patch
+
+import pytest
+from neo4j_graphrag.exceptions import LLMGenerationError
+from neo4j_graphrag.llm import LLMResponse
+from neo4j_graphrag.llm.litellm_llm import LiteLLMChat
+from neo4j_graphrag.types import LLMMessage
+from pydantic import BaseModel, ConfigDict
+
+
+@pytest.fixture
+def mock_litellm() -> Generator[MagicMock, None, None]:
+    mock_litellm = MagicMock()
+    with patch.dict(sys.modules, {"litellm": mock_litellm}):
+        yield mock_litellm
+
+
+def _make_completion_response(
+    text: str = "litellm response text",
+    prompt_tokens: int = 10,
+    completion_tokens: int = 20,
+    total_tokens: int = 30,
+) -> MagicMock:
+    response = MagicMock()
+    response.choices = [MagicMock()]
+    response.choices[0].message.content = text
+    response.choices[0].message.tool_calls = None
+    response.usage.prompt_tokens = prompt_tokens
+    response.usage.completion_tokens = completion_tokens
+    response.usage.total_tokens = total_tokens
+    return response
+
+
+@patch("builtins.__import__", side_effect=ImportError)
+def test_litellm_missing_dependency(mock_import: Mock) -> None:
+    with pytest.raises(ImportError):
+        LiteLLMChat(model_name="something")
+
+
+def test_litellm_happy_path(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response()
+    llm = LiteLLMChat(model_name="gpt-4o")
+    res = llm.invoke("my text")
+    assert isinstance(res, LLMResponse)
+    assert res.content == "litellm response text"
+    assert res.usage is not None
+    assert res.usage.request_tokens == 10
+    assert res.usage.response_tokens == 20
+    assert res.usage.total_tokens == 30
+
+
+def test_litellm_invoke_with_message_history(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response()
+    llm = LiteLLMChat(model_name="gpt-4o")
+    message_history: List[LLMMessage] = [
+        {"role": "user", "content": "When does the sun come up in the summer?"},
+        {"role": "assistant", "content": "Usually around 6am."},
+    ]
+    system_instruction = "You are a helpful assistant."
+    res = llm.invoke("What about next season?", message_history, system_instruction)
+    assert isinstance(res, LLMResponse)
+    assert res.content == "litellm response text"
+    call_args = mock_litellm.completion.call_args
+    messages = call_args[1]["messages"]
+    assert messages[0]["role"] == "system"
+    assert messages[0]["content"] == system_instruction
+    assert messages[-1]["role"] == "user"
+    assert messages[-1]["content"] == "What about next season?"
+
+
+def test_litellm_invoke_with_message_history_validation_error(
+    mock_litellm: MagicMock,
+) -> None:
+    mock_litellm.completion.return_value = _make_completion_response()
+    llm = LiteLLMChat(model_name="gpt-4o")
+    message_history = [
+        {"role": "robot", "content": "Invalid role"},
+        {"role": "assistant", "content": "Usually around 6am."},
+    ]
+    with pytest.raises(LLMGenerationError) as exc_info:
+        llm.invoke("test", message_history)  # type: ignore
+    assert "Input should be 'user', 'assistant' or 'system" in str(exc_info.value)
+
+
+@pytest.mark.asyncio
+async def test_litellm_happy_path_async(mock_litellm: MagicMock) -> None:
+    mock_litellm.acompletion = AsyncMock(return_value=_make_completion_response())
+    llm = LiteLLMChat(model_name="gpt-4o")
+    res = await llm.ainvoke("my text")
+    assert isinstance(res, LLMResponse)
+    assert res.content == "litellm response text"
+
+
+def test_litellm_invoke_failed(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.side_effect = Exception("API Error")
+    llm = LiteLLMChat(model_name="gpt-4o")
+    with pytest.raises(LLMGenerationError):
+        llm.invoke("my text")
+
+
+@pytest.mark.asyncio
+async def test_litellm_ainvoke_failed(mock_litellm: MagicMock) -> None:
+    mock_litellm.acompletion = AsyncMock(side_effect=Exception("API Error"))
+    llm = LiteLLMChat(model_name="gpt-4o")
+    with pytest.raises(LLMGenerationError):
+        await llm.ainvoke("my text")
+
+
+# V2 Interface Tests
+
+
+def test_litellm_invoke_v2_happy_path(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response(
+        "litellm v2 response"
+    )
+    messages: List[LLMMessage] = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "What is the capital of France?"},
+    ]
+    llm = LiteLLMChat(model_name="gpt-4o")
+    response = llm.invoke(messages)
+    assert isinstance(response, LLMResponse)
+    assert response.content == "litellm v2 response"
+    call_args = mock_litellm.completion.call_args
+    assert call_args[1]["model"] == "gpt-4o"
+
+
+@pytest.mark.asyncio
+async def test_litellm_ainvoke_v2_happy_path(mock_litellm: MagicMock) -> None:
+    mock_litellm.acompletion = AsyncMock(
+        return_value=_make_completion_response("litellm v2 async response")
+    )
+    messages: List[LLMMessage] = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "What is the capital of France?"},
+    ]
+    llm = LiteLLMChat(model_name="gpt-4o")
+    response = await llm.ainvoke(messages)
+    assert isinstance(response, LLMResponse)
+    assert response.content == "litellm v2 async response"
+
+
+def test_litellm_invoke_v2_with_response_format(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response(
+        '{"value": "test"}'
+    )
+
+    class TestModel(BaseModel):
+        model_config = ConfigDict(extra="forbid")
+        value: str
+
+    messages: List[LLMMessage] = [{"role": "user", "content": "Test"}]
+    llm = LiteLLMChat(model_name="gpt-4o")
+    response = llm.invoke(messages, response_format=TestModel)
+    assert isinstance(response, LLMResponse)
+    call_args = mock_litellm.completion.call_args
+    rf = call_args[1]["response_format"]
+    assert rf["type"] == "json_schema"
+    assert rf["json_schema"]["name"] == "TestModel"
+
+
+def test_litellm_invoke_v2_with_dict_response_format(
+    mock_litellm: MagicMock,
+) -> None:
+    mock_litellm.completion.return_value = _make_completion_response("{}")
+    messages: List[LLMMessage] = [{"role": "user", "content": "Test"}]
+    llm = LiteLLMChat(model_name="gpt-4o")
+    response = llm.invoke(messages, response_format={"type": "json_object"})
+    assert isinstance(response, LLMResponse)
+    call_args = mock_litellm.completion.call_args
+    assert call_args[1]["response_format"] == {"type": "json_object"}
+
+
+def test_litellm_kwargs_passed_through(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response()
+    llm = LiteLLMChat(
+        model_name="gpt-4o",
+        api_key="test-key",
+        drop_params=True,
+    )
+    llm.invoke("test")
+    call_args = mock_litellm.completion.call_args
+    assert call_args[1]["api_key"] == "test-key"
+    assert call_args[1]["drop_params"] is True
+
+
+def test_litellm_model_params_passed_through(mock_litellm: MagicMock) -> None:
+    mock_litellm.completion.return_value = _make_completion_response()
+    llm = LiteLLMChat(
+        model_name="gpt-4o",
+        model_params={"temperature": 0.5, "max_tokens": 100},
+    )
+    llm.invoke("test")
+    call_args = mock_litellm.completion.call_args
+    assert call_args[1]["temperature"] == 0.5
+    assert call_args[1]["max_tokens"] == 100
+
+
+def test_litellm_invoke_with_tools(mock_litellm: MagicMock) -> None:
+    tool_call_mock = MagicMock()
+    tool_call_mock.function.name = "get_weather"
+    tool_call_mock.function.arguments = '{"city": "Paris"}'
+
+    response = MagicMock()
+    response.choices = [MagicMock()]
+    response.choices[0].message.content = None
+    response.choices[0].message.tool_calls = [tool_call_mock]
+    mock_litellm.completion.return_value = response
+
+    tool = MagicMock()
+    tool.get_name.return_value = "get_weather"
+    tool.get_description.return_value = "Get weather for a city"
+    tool.get_parameters.return_value = {
+        "type": "object",
+        "properties": {"city": {"type": "string"}},
+    }
+
+    llm = LiteLLMChat(model_name="gpt-4o")
+    res = llm.invoke_with_tools("What is the weather in Paris?", tools=[tool])
+    assert len(res.tool_calls) == 1
+    assert res.tool_calls[0].name == "get_weather"
+    assert res.tool_calls[0].arguments == {"city": "Paris"}
+
+
+@pytest.mark.asyncio
+async def test_litellm_ainvoke_with_tools(mock_litellm: MagicMock) -> None:
+    tool_call_mock = MagicMock()
+    tool_call_mock.function.name = "get_weather"
+    tool_call_mock.function.arguments = '{"city": "Paris"}'
+
+    response = MagicMock()
+    response.choices = [MagicMock()]
+    response.choices[0].message.content = None
+    response.choices[0].message.tool_calls = [tool_call_mock]
+    mock_litellm.acompletion = AsyncMock(return_value=response)
+
+    tool = MagicMock()
+    tool.get_name.return_value = "get_weather"
+    tool.get_description.return_value = "Get weather for a city"
+    tool.get_parameters.return_value = {
+        "type": "object",
+        "properties": {"city": {"type": "string"}},
+    }
+
+    llm = LiteLLMChat(model_name="gpt-4o")
+    res = await llm.ainvoke_with_tools("What is the weather in Paris?", tools=[tool])
+    assert len(res.tool_calls) == 1
+    assert res.tool_calls[0].name == "get_weather"
+    assert res.tool_calls[0].arguments == {"city": "Paris"}
+
+
+def test_litellm_close(mock_litellm: MagicMock) -> None:
+    llm = LiteLLMChat(model_name="gpt-4o")
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        llm.close()
+
+
+@pytest.mark.asyncio
+async def test_litellm_aclose(mock_litellm: MagicMock) -> None:
+    llm = LiteLLMChat(model_name="gpt-4o")
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        await llm.aclose()
diff --git a/uv.lock b/uv.lock
index 6865845f0..ecb916ae5 100644
--- a/uv.lock
+++ b/uv.lock
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 ]
 
+[[package]]
+name = "fastuuid"
+version = "0.14.0"
+source = { registry = "https://pypi.org/simple" }
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 ]
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