diff --git a/docs/docs/integrations/providers/cratedb.mdx b/docs/docs/integrations/providers/cratedb.mdx
index 24e47930407..d367eb3e945 100644
--- a/docs/docs/integrations/providers/cratedb.mdx
+++ b/docs/docs/integrations/providers/cratedb.mdx
@@ -27,7 +27,7 @@ docker run --name=cratedb --rm \
 [free trial][CrateDB Cloud Console].
 
 ### Install Client
-Install the most recent version of the `langchain-cratedb` package
+Install the most recent version of the [langchain-cratedb] package
 and a few others that are needed for this tutorial.
 ```bash
 pip install --upgrade langchain-cratedb langchain-openai unstructured
@@ -120,13 +120,78 @@ message_history = CrateDBChatMessageHistory(
 message_history.add_user_message("hi!")
 ```
 
+### Full Cache
+The standard / full cache avoids invoking the LLM when the supplied
+prompt is exactly the same as one encountered already.
+See also [CrateDBCache Example].
+
+To use the full cache in your applications:
+```python
+import sqlalchemy as sa
+from langchain.globals import set_llm_cache
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_cratedb import CrateDBCache
+
+# Configure cache.
+engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
+set_llm_cache(CrateDBCache(engine))
+
+# Invoke LLM conversation.
+llm = ChatOpenAI(
+    model_name="chatgpt-4o-latest",
+    temperature=0.7,
+)
+print()
+print("Asking with full cache:")
+answer = llm.invoke("What is the answer to everything?")
+print(answer.content)
+```
+
+### Semantic Cache
+
+The semantic cache allows users to retrieve cached prompts based on semantic
+similarity between the user input and previously cached inputs. It also avoids
+invoking the LLM when not needed.
+See also [CrateDBSemanticCache Example].
+
+To use the semantic cache in your applications:
+```python
+import sqlalchemy as sa
+from langchain.globals import set_llm_cache
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_cratedb import CrateDBSemanticCache
+
+# Configure embeddings.
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Configure cache.
+engine = sa.create_engine("crate://crate@localhost:4200/?schema=testdrive")
+set_llm_cache(
+    CrateDBSemanticCache(
+        embedding=embeddings,
+        connection=engine,
+        search_threshold=1.0,
+    )
+)
+
+# Invoke LLM conversation.
+llm = ChatOpenAI(model_name="chatgpt-4o-latest")
+print()
+print("Asking with semantic cache:")
+answer = llm.invoke("What is the answer to everything?")
+print(answer.content)
+```
+
 
 [all features of CrateDB]: https://cratedb.com/docs/guide/feature/
 [CrateDB]: https://cratedb.com/database
 [CrateDB Cloud]: https://cratedb.com/database/cloud
 [CrateDB Cloud Console]: https://console.cratedb.cloud/?utm_source=langchain&utm_content=documentation
 [CrateDB installation options]: https://cratedb.com/docs/guide/install/
+[CrateDBCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
+[CrateDBSemanticCache Example]: https://github.com/crate/langchain-cratedb/blob/main/examples/basic/cache.py
 [CrateDBChatMessageHistory Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/conversational_memory.ipynb
 [CrateDBLoader Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/document_loader.ipynb
 [CrateDBVectorStore Tutorial]: https://github.com/crate/cratedb-examples/blob/main/topic/machine-learning/llm-langchain/vector_search.ipynb
+[langchain-cratedb]: https://pypi.org/project/langchain-cratedb/
 [using LangChain with CrateDB]: https://cratedb.com/docs/guide/integrate/langchain/