diff --git a/ragas/pyproject.toml b/ragas/pyproject.toml
index d93f76ab3..375a60289 100644
--- a/ragas/pyproject.toml
+++ b/ragas/pyproject.toml
@@ -9,6 +9,7 @@ dependencies = [
     "langchain-core",
     "langchain-community",
     "langchain_openai",
+    "langchain_ollama",
     "nest-asyncio",
     "appdirs",
     "pydantic>=2",
diff --git a/ragas/src/ragas/embeddings/__init__.py b/ragas/src/ragas/embeddings/__init__.py
index acdda8f02..b4f839e86 100644
--- a/ragas/src/ragas/embeddings/__init__.py
+++ b/ragas/src/ragas/embeddings/__init__.py
@@ -6,6 +6,7 @@
     embedding_factory,
 )
 from ragas.embeddings.haystack_wrapper import HaystackEmbeddingsWrapper
+from ragas.embeddings.ollama_wrapper import OllamaEmbeddingsWrapper
 
 __all__ = [
     "BaseRagasEmbeddings",
@@ -14,4 +15,5 @@
     "LangchainEmbeddingsWrapper",
     "LlamaIndexEmbeddingsWrapper",
     "embedding_factory",
+    "OllamaEmbeddingsWrapper",
 ]
diff --git a/ragas/src/ragas/embeddings/ollama_wrapper.py b/ragas/src/ragas/embeddings/ollama_wrapper.py
new file mode 100644
index 000000000..8a1ea4e27
--- /dev/null
+++ b/ragas/src/ragas/embeddings/ollama_wrapper.py
@@ -0,0 +1,56 @@
+import typing as t
+from langchain_ollama import OllamaEmbeddings
+
+from ragas.cache import CacheInterface
+from ragas.embeddings.base import BaseRagasEmbeddings
+from ragas.run_config import RunConfig
+
+
+class OllamaEmbeddingsWrapper(BaseRagasEmbeddings):
+    """
+    A wrapper class for using Ollama Embeddings within the Ragas framework.
+
+    This class integrates Ollama's Embeddings into Ragas, enabling both synchronous and
+    asynchronous embedding generation.
+
+    Parameters
+    ----------
+    ollama_embeddings : OllamaEmbeddings
+        An instance of Ollama embeddings model.
+    run_config : RunConfig, optional
+        Configuration object to manage embedding execution settings, by default None.
+    cache : CacheInterface, optional
+        A cache instance for storing results, by default None.
+    """
+
+    def __init__(
+        self,
+        ollama_embeddings: OllamaEmbeddings,
+        run_config: t.Optional[RunConfig] = None,
+        cache: t.Optional[CacheInterface] = None,
+    ):
+        super().__init__(cache=cache)
+        self.embeddings = ollama_embeddings
+
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def embed_query(self, text: str) -> t.List[float]:
+        """Generate embedding for a single text."""
+        return self.embeddings.embed_query(text)
+
+    def embed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        """Generate embeddings for multiple texts."""
+        return self.embeddings.embed_documents(texts)
+
+    async def aembed_query(self, text: str) -> t.List[float]:
+        """Generate embedding for a single text asynchronously."""
+        return await self.embeddings.aembed_query(text)
+
+    async def aembed_documents(self, texts: t.List[str]) -> t.List[t.List[float]]:
+        """Generate embeddings for multiple texts asynchronously."""
+        return await self.embeddings.aembed_documents(texts)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(embeddings={self.embeddings.model}(...))" 
\ No newline at end of file
diff --git a/ragas/src/ragas/llms/__init__.py b/ragas/src/ragas/llms/__init__.py
index 440013051..0d369ae52 100644
--- a/ragas/src/ragas/llms/__init__.py
+++ b/ragas/src/ragas/llms/__init__.py
@@ -5,11 +5,13 @@
     llm_factory,
 )
 from ragas.llms.haystack_wrapper import HaystackLLMWrapper
+from ragas.llms.ollama_wrapper import OllamaLLMWrapper
 
 __all__ = [
     "BaseRagasLLM",
     "HaystackLLMWrapper",
     "LangchainLLMWrapper",
     "LlamaIndexLLMWrapper",
+    "OllamaLLMWrapper",
     "llm_factory",
 ]
diff --git a/ragas/src/ragas/llms/ollama_wrapper.py b/ragas/src/ragas/llms/ollama_wrapper.py
new file mode 100644
index 000000000..bf2f9b57e
--- /dev/null
+++ b/ragas/src/ragas/llms/ollama_wrapper.py
@@ -0,0 +1,98 @@
+import typing as t
+from langchain_core.callbacks import Callbacks
+from langchain_core.outputs import Generation, LLMResult
+from langchain_core.prompt_values import PromptValue
+from langchain_ollama import ChatOllama
+from langchain_openai import ChatOpenAI
+from ragas.cache import CacheInterface
+from ragas.llms import BaseRagasLLM
+from ragas.run_config import RunConfig
+
+
+class OllamaLLMWrapper(BaseRagasLLM):
+    """
+    A wrapper class for using Ollama LLM within the Ragas framework.
+
+    This class integrates Ollama's LLM into Ragas, enabling both synchronous and
+    asynchronous text generation.
+
+    Parameters
+    ----------
+    ollama_llm : ChatOllama
+        An instance of Ollama chat model.
+    run_config : RunConfig, optional
+        Configuration object to manage LLM execution settings, by default None.
+    cache : CacheInterface, optional
+        A cache instance for storing results, by default None.
+    """
+
+    def __init__(
+        self,
+        ollama_llm: ChatOllama,
+        run_config: t.Optional[RunConfig] = None,
+        cache: t.Optional[CacheInterface] = None,
+    ):
+        super().__init__(cache=cache)
+        self.llm = ollama_llm  # 直接用 ChatOllama 实例
+        if run_config is None:
+            run_config = RunConfig()
+        self.set_run_config(run_config)
+
+    def is_finished(self, response: LLMResult) -> bool:
+        """Check if the generation is finished."""
+        return True
+
+    def generate_text(
+        self,
+        prompt: str,
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model."""
+        response = self.llm.invoke(prompt)
+        print("LLM raw output:", response.content)
+        return LLMResult(generations=[[Generation(text=response.content)]])
+
+    async def agenerate_text(
+        self,
+        prompt: str,
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model asynchronously."""
+        response = await self.llm.ainvoke(prompt)
+        print("LLM raw output:", response.content)
+        return LLMResult(generations=[[Generation(text=response.content)]])
+
+    def _generate(
+        self,
+        prompts: t.List[PromptValue],
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model."""
+        generations = []
+        for prompt in prompts:
+            response = self.llm.invoke(prompt.to_messages())
+            generations.append([Generation(text=response.content)])
+        return LLMResult(generations=generations)
+
+    async def _agenerate(
+        self,
+        prompts: t.List[PromptValue],
+        stop: t.Optional[t.List[str]] = None,
+        run_manager: t.Optional[Callbacks] = None,
+        **kwargs: t.Any,
+    ) -> LLMResult:
+        """Generate text from the model asynchronously."""
+        generations = []
+        for prompt in prompts:
+            response = await self.llm.ainvoke(prompt.to_messages())
+            generations.append([Generation(text=response.content)])
+        return LLMResult(generations=generations)
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(llm={self.llm.model}(...))" 
\ No newline at end of file