diff --git a/examples/extensions/langchain/langchain_agent.py b/examples/extensions/langchain/langchain_agent.py
new file mode 100644
index 00000000..a3ff2706
--- /dev/null
+++ b/examples/extensions/langchain/langchain_agent.py
@@ -0,0 +1,108 @@
+"""LangChain agent
+
+The agent chooses a sequence of actions to respond to a human's question. It has access to a set of tools.
+The agent memorizes the conversation history and can use it to make decisions.
+"""
+
+from typing import Optional
+
+from dotenv import load_dotenv
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_log_to_str
+from langchain.agents.output_parsers import JSONAgentOutputParser
+from langchain.memory import ConversationBufferMemory
+from langchain.tools.render import render_text_description_and_args
+from langchain_core.callbacks import CallbackManagerForToolRun
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.tools import BaseTool
+
+from genai import Client, Credentials
+from genai.extensions.langchain import LangChainChatInterface
+from genai.text.generation import TextGenerationParameters
+
+load_dotenv()
+
+
+class WordLengthTool(BaseTool):
+    name = "GetWordLength"
+    description = "Returns the length of a word."
+
+    def _run(self, word: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> int:
+        return len(word)
+
+
+tools: list[BaseTool] = [WordLengthTool()]
+
+system_prompt = """Respond to the human as helpfully and accurately as possible. You have access to the following tools:
+{tools}
+Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
+Valid "action" values: "Final Answer" or {tool_names}
+Provide only ONE action per $JSON_BLOB, as shown:
+```
+{{
+  "action": $TOOL_NAME,
+  "action_input": $INPUT
+}}
+```
+Follow this format:
+Question: input question to answer
+Thought: consider previous and subsequent steps
+Action:
+```
+$JSON_BLOB
+```
+Observation: action result
+... (repeat Thought/Action/Observation N times)
+Thought: I know what to respond
+Action:
+```
+{{
+  "action": "Final Answer",
+  "action_input": "Final response to human"
+}}
+Begin! Reminder to ALWAYS respond with a valid json blob of a single action.
+Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation"""
+human_prompt = """{input}
+{agent_scratchpad}
+(reminder to respond in a JSON blob no matter what)"""
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        MessagesPlaceholder("chat_history", optional=True),
+        ("human", human_prompt),
+    ]
+)
+
+client = Client(credentials=Credentials.from_env())
+llm = LangChainChatInterface(
+    client=client,
+    model_id="meta-llama/llama-2-70b-chat",
+    parameters=TextGenerationParameters(
+        max_new_tokens=250, min_new_tokens=20, temperature=0, stop_sequences=["\nObservation"]
+    ),
+)
+prompt = prompt.partial(
+    # get tools with their descriptions and args in plain text
+    tools=render_text_description_and_args(list(tools)),
+    tool_names=", ".join([t.name for t in tools]),
+)
+
+memory = ConversationBufferMemory()
+
+agent = (
+    RunnablePassthrough.assign(
+        # format the agent's scratchpad to a string
+        agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
+        # pass the memory as the chat history
+        chat_history=lambda x: memory.chat_memory.messages,
+    )
+    | prompt
+    | llm
+    | JSONAgentOutputParser()
+)
+agent_executor = AgentExecutor(agent=agent, tools=tools, handle_parsing_errors=True, verbose=True, memory=memory)
+
+agent_executor.invoke({"input": "How many letters are in the word educa?"})
+agent_executor.invoke({"input": "That's not a real word, can you tell me a valid word?"})