{ "source_file": "0930", "source_platform": "Medium", "author": "Soner Yıldırım", "title_listed": "Building a Streamlit UI for My LangGraph AI Agent", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/building-a-streamlit-ui-for-my-langgraph-ai-agent/", "publish_date": "2026-08-08", "corpus_stage": "", "notes": "", "id": "59317c1fa743", "collected_at": "2026-10-02T09:21:56", "title": "Building a Streamlit UI for My LangGraph AI Agent | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It's a LangGraph-based agent that orchestrates the following operations:\n * Responds to customer queries and understands their needs.\n * Calculates the price for the service and informs the customer.\n * Handles the customer’s acceptance or rejection.\n * Proposes optimized time slots.\n * Confirms and records the appointment.\n\n\nIn the first version of the agent, I did not focus much on the UI/UX part. I just built a Python CLI to test the functionality of the agent.\nThe customer service agent ran entirely in the terminal. The CLI worked well for testing but it was not the best way to demonstrate a customer-facing booking experience.\nThe full source code of this project is available on GitHub at [customer-service-agent](https://github.com/SonerYldrm/customer-service-agent). Feel free to clone the repo and test it yourself.\nIn this article, we will build a clean, interactive Streamlit UI on top of the existing LangGraph agent.\nA quick note on the terminology: Throughout this article, I use _agent_ and _graph_ interchangeably. In LangGraph, the agent architecture is defined and executed as a compiled state graph object so they essentially mean the same thing in this article.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## User interface for the agent\nIn terms of implementation, Streamlit is not very different from a Python CLI. Both serve as a wrapper for the LangGraph agent. Streamlit is, of course, much more user friendly and looks more appealing.\nThe CLI interface collected input, invoked the graph, and printed the response. The Streamlit page will do the same but it will also render structured information extracted from the graph state such as current booking details, price quote, and acceptance buttons.\nThe architecture still lies in the same application. Streamlit only presents the state to the user and sends user actions back to the agent.\n## Streamlit page\nSince we're using poetry for dependency management, we can install streamlit using:\npython\n\n```\npoetry add streamlit\n```\n\nThis updates both `pyproject.toml` and `poetry.lock` files. Then we create a new file `streamlit_app.py` .\nWe start by importing the graph builder, models, and observibility utilities.\npython\n\n```\nfrom __future__ import annotations\n\nimport os\nfrom datetime import datetime\nfrom typing import Any\nfrom uuid import uuid4\n\nimport streamlit as st\nfrom dotenv import load_dotenv\nfrom langchain_core.messages import AIMessage, HumanMessage\nfrom langchain_openai import ChatOpenAI\n\nfrom customer_service_agent.graph import build_graph\nfrom customer_service_agent.models import (\n AgentState,\n BookingDetails,\n TimeOption,\n)\nfrom customer_service_agent.observability import (\n create_langfuse_handler,\n flush_langfuse,\n graph_config,\n)\n```\n\nThe agent graph does not include any Streamlit-specific logic. This separation is important because it allows us to run the graph from a CLI, an API, WhatsApp, or another frontend later.\nThe graph expects an Agent State to be initialized ( `graph = StateGraph(AgentState)` ) so we add the following in `streamlit_app.py` :\npython\n\n```\nINITIAL_STATE: AgentState = {\n \"messages\": [],\n \"booking_details\": BookingDetails(),\n \"calculated_price\": None,\n \"time_options\": [],\n \"selected_slot\": None,\n \"status\": \"gathering_info\",\n}\n```\n\nAfter the first turn (i.e. first customer message), LangGraph’s checkpointer retains the state. \nStreamlit reruns the entire Python script whenever user interacts with a widget (e.g. sends a chat message, clicks a button, selects an appointment) so we cannot use local variables. To preserve the conversation across Streamlit runs, we need to use `session_state` .\nWe can initialize the session as follows:\npython\n\n```\ndef initialize_session() -> None:\n if \"graph\" in st.session_state:\n return\n\n llm = ChatOpenAI(\n model=os.getenv(\"OPENAI_MODEL\", \"gpt-4o-mini\"),\n temperature=0,\n )\n\n handler = create_langfuse_handler()\n\n st.session_state.graph = build_graph(llm)\n st.session_state.handler = handler\n st.session_state.config = graph_config(\n str(uuid4()),\n handler,\n )\n st.session_state.agent_state = INITIAL_STATE.copy()\n st.session_state.started = False\n```\n\nThe function first checks if the graph has already been created for this browser session (`if \"graph\" in st.session_state` ). Without this check, every Streamlit rerun would replace the graph.\nThe `graph_config` function generates a UUID to be used as `thread_id` , which is required by LangGraph to identify a conversation. If a new UUID were generated on every Streamlit rerun, LangGraph would see every message as belonging to a new conversation.\nWe also have the Langfuse tracing integration inside this function. The handler is saved so that the subsequent graph calls can use the same tracing configuration.\nThen, we have the `_invoke` function for handling user input.\npython\n\n```\ndef _invoke(customer_text: str) -> None:\n \"\"\"Submit one customer turn to the graph and retain its latest state.\"\"\"\n graph_input: dict[str, Any] = {\"messages\": [HumanMessage(content=customer_text)]}\n if not st.session_state.started:\n graph_input.update(INITIAL_STATE)\n graph_input[\"messages\"] = [HumanMessage(content=customer_text)]\n st.session_state.started = True\n\n try:\n result = st.session_state.graph.invoke(graph_input, config=st.session_state.config)\n st.session_state.agent_state = result\n flush_langfuse(st.session_state.handler)\n except Exception:\n st.session_state.started = bool(st.session_state.agent_state.get(\"messages\"))\n st.error(\"The assistant could not process that request. Please try again.\")\n```\n\nThis function sends the customer action to the LangGraph agent and saves the resulting state for the Streamlit interface. The customer action can be a chat input or a button click (e.g. \"Accept quote\").\nThe customer’s message is converted into a LangChain `HumanMessage`. The `messages` uses LangGraph’s `add_messages` reducer so new messages are added to the existing conversation instead of replacing it.\nFor the first message, we initialize the graph using the `INITIAL_STATE` defined earlier with empty booking details, scheduling options, price, and the initial status.\nThen, whenever we receive a new custom action, we invoke the graph and update its state with the result. This is where the LLM calls happen:\npython\n\n```\nresult = st.session_state.graph.invoke(graph_input, config=st.session_state.config)\nst.session_state.agent_state = result\n```\n\nThis runs the customer message through the graph. The returned graph state (i.e. `results` ) is stored in Streamlit’s session so that the page can render the latest details of messages, booking summary, price, appointment options, and status.\nFinally, we have some render functions (`_render...()` ) defined in `streamlit_app.py` to convert the current LangGraph state into visible Streamlit components.\npython\n\n```\ndef _render_messages(state: AgentState) -> None:\n if not state.get(\"messages\"):\n with st.chat_message(\"assistant\"):\n st.write(\n \"Hi! I can help you book house or couch cleaning. \"\n \"Tell me what you need, including the size and service address.\"\n )\n return\n\n for message in state[\"messages\"]:\n if isinstance(message, HumanMessage):\n role = \"user\"\n elif isinstance(message, AIMessage):\n role = \"assistant\"\n else:\n continue\n with st.chat_message(role):\n st.write(str(message.content))\n```\n\nFor example, the `_render_messages` function displays the conversation history as Streamlit chat bubbles. It receives the conversation through the latest LangGraph state using `state[\"messages\"]` . If the conversation has no messages yet, the function shows an initial greeting.\n## Let's see how it works\nWe've gone over the `streamlit_app.py` to learn how the page is structured. It's time to see it in action:\nWe can test it locally using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nIt will open up a page at [`http://localhost:8501/`](http://localhost:8501/) . The page looks like this:\n![](../images/001.png)\nIn order to test the agent, we need an OPENAI_API_KEY. It'll cost you a few cents to test.\nHere is a chat example:\n![](../images/body-3e99816ce91c5e93a770.png)\nI did not give the address and the agent asked for it as we'd expect. Let's try the same but providing the address in the first message:\n![](../images/body-fb23d34f0c41c29b27d3.png)\nSince I have the address in the first message, the agent did not ask for it. I accepted the quote and the agent gave me three options to choose and then completed the booking:\n![](../images/body-f2319a7145eabdd5d697.png)\nThere is a lot more we can do on the interface to make it more user friendly. But we now have a version that runs smoothly and with a nice and clean user interface.\nI'm planning to improve the agent and add more functionality such as WhatsApp integration. It may even turned out to be a product that I can sell to some local businesses.\nStay tuned for what's coming and thank you for reading!\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-11c1a04e02d8a14729da.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Agentic Ai](https://towardsdatascience.com/tag/agentic-ai/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Langgraph](https://towardsdatascience.com/tag/langgraph/)[Python](https://towardsdatascience.com/tag/python/)[Streamlit](https://towardsdatascience.com/tag/streamlit/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-streamlit-ui-for-my-langgraph-ai-agent%2F&title=Building%20a%20Streamlit%20UI%20for%20My%20LangGraph%20AI%20Agent)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-streamlit-ui-for-my-langgraph-ai-agent%2F&title=Building%20a%20Streamlit%20UI%20for%20My%20LangGraph%20AI%20Agent)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-streamlit-ui-for-my-langgraph-ai-agent%2F&text=Building%20a%20Streamlit%20UI%20for%20My%20LangGraph%20AI%20Agent)\n\n\nTowards Data Science is a community publication. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# Building a Streamlit UI for My LangGraph AI Agent\nBuilding a production-ready web interface for a stateful LangGraph agent\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 8, 20266 min read\n![Photo by Towfiqu barbhuiya on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/towfiqu-barbhuiya-9gPKrsbGmc-unsplash-scaled-1.jpg)\n[Photo](https://unsplash.com/photos/person-in-blue-long-sleeve-shirt-sitting-beside-black-laptop-computer--9gPKrsbGmc) by Towfiqu barbhuiya on Unsplash\nIn my [previous article](https://towardsdatascience.com/i-replaced-a-15-minute-booking-process-with-a-langgraph-ai-agent/), I walked through how I built a LangGraph-based AI agent to automate a 15-minute customer service booking session.\nThe agent handles the entire booking process like a real customer service representative. It's a LangGraph-based agent that orchestrates the following operations:\n * Responds to customer queries and understands their needs.\n * Calculates the price for the service and informs the customer.\n * Handles the customer’s acceptance or rejection.\n * Proposes optimized time slots.\n * Confirms and records the appointment.\n\n\nIn the first version of the agent, I did not focus much on the UI/UX part. I just built a Python CLI to test the functionality of the agent.\nThe customer service agent ran entirely in the terminal. The CLI worked well for testing but it was not the best way to demonstrate a customer-facing booking experience.\nThe full source code of this project is available on GitHub at [customer-service-agent](https://github.com/SonerYldrm/customer-service-agent). Feel free to clone the repo and test it yourself.\nIn this article, we will build a clean, interactive Streamlit UI on top of the existing LangGraph agent.\nA quick note on the terminology: Throughout this article, I use _agent_ and _graph_ interchangeably. In LangGraph, the agent architecture is defined and executed as a compiled state graph object so they essentially mean the same thing in this article.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## User interface for the agent\nIn terms of implementation, Streamlit is not very different from a Python CLI. Both serve as a wrapper for the LangGraph agent. Streamlit is, of course, much more user friendly and looks more appealing.\nThe CLI interface collected input, invoked the graph, and printed the response. The Streamlit page will do the same but it will also render structured information extracted from the graph state such as current booking details, price quote, and acceptance buttons.\nThe architecture still lies in the same application. Streamlit only presents the state to the user and sends user actions back to the agent.\n## Streamlit page\nSince we're using poetry for dependency management, we can install streamlit using:\npython\n\n```\npoetry add streamlit\n```\n\nThis updates both `pyproject.toml` and `poetry.lock` files. Then we create a new file `streamlit_app.py` .\nWe start by importing the graph builder, models, and observibility utilities.\npython\n\n```\nfrom __future__ import annotations\n\nimport os\nfrom datetime import datetime\nfrom typing import Any\nfrom uuid import uuid4\n\nimport streamlit as st\nfrom dotenv import load_dotenv\nfrom langchain_core.messages import AIMessage, HumanMessage\nfrom langchain_openai import ChatOpenAI\n\nfrom customer_service_agent.graph import build_graph\nfrom customer_service_agent.models import (\n AgentState,\n BookingDetails,\n TimeOption,\n)\nfrom customer_service_agent.observability import (\n create_langfuse_handler,\n flush_langfuse,\n graph_config,\n)\n```\n\nThe agent graph does not include any Streamlit-specific logic. This separation is important because it allows us to run the graph from a CLI, an API, WhatsApp, or another frontend later.\nThe graph expects an Agent State to be initialized ( `graph = StateGraph(AgentState)` ) so we add the following in `streamlit_app.py` :\npython\n\n```\nINITIAL_STATE: AgentState = {\n \"messages\": [],\n \"booking_details\": BookingDetails(),\n \"calculated_price\": None,\n \"time_options\": [],\n \"selected_slot\": None,\n \"status\": \"gathering_info\",\n}\n```\n\nAfter the first turn (i.e. first customer message), LangGraph’s checkpointer retains the state. \nStreamlit reruns the entire Python script whenever user interacts with a widget (e.g. sends a chat message, clicks a button, selects an appointment) so we cannot use local variables. To preserve the conversation across Streamlit runs, we need to use `session_state` .\nWe can initialize the session as follows:\npython\n\n```\ndef initialize_session() -> None:\n if \"graph\" in st.session_state:\n return\n\n llm = ChatOpenAI(\n model=os.getenv(\"OPENAI_MODEL\", \"gpt-4o-mini\"),\n temperature=0,\n )\n\n handler = create_langfuse_handler()\n\n st.session_state.graph = build_graph(llm)\n st.session_state.handler = handler\n st.session_state.config = graph_config(\n str(uuid4()),\n handler,\n )\n st.session_state.agent_state = INITIAL_STATE.copy()\n st.session_state.started = False\n```\n\nThe function first checks if the graph has already been created for this browser session (`if \"graph\" in st.session_state` ). Without this check, every Streamlit rerun would replace the graph.\nThe `graph_config` function generates a UUID to be used as `thread_id` , which is required by LangGraph to identify a conversation. If a new UUID were generated on every Streamlit rerun, LangGraph would see every message as belonging to a new conversation.\nWe also have the Langfuse tracing integration inside this function. The handler is saved so that the subsequent graph calls can use the same tracing configuration.\nThen, we have the `_invoke` function for handling user input.\npython\n\n```\ndef _invoke(customer_text: str) -> None:\n \"\"\"Submit one customer turn to the graph and retain its latest state.\"\"\"\n graph_input: dict[str, Any] = {\"messages\": [HumanMessage(content=customer_text)]}\n if not st.session_state.started:\n graph_input.update(INITIAL_STATE)\n graph_input[\"messages\"] = [HumanMessage(content=customer_text)]\n st.session_state.started = True\n\n try:\n result = st.session_state.graph.invoke(graph_input, config=st.session_state.config)\n st.session_state.agent_state = result\n flush_langfuse(st.session_state.handler)\n except Exception:\n st.session_state.started = bool(st.session_state.agent_state.get(\"messages\"))\n st.error(\"The assistant could not process that request. Please try again.\")\n```\n\nThis function sends the customer action to the LangGraph agent and saves the resulting state for the Streamlit interface. The customer action can be a chat input or a button click (e.g. \"Accept quote\").\nThe customer’s message is converted into a LangChain `HumanMessage`. The `messages` uses LangGraph’s `add_messages` reducer so new messages are added to the existing conversation instead of replacing it.\nFor the first message, we initialize the graph using the `INITIAL_STATE` defined earlier with empty booking details, scheduling options, price, and the initial status.\nThen, whenever we receive a new custom action, we invoke the graph and update its state with the result. This is where the LLM calls happen:\npython\n\n```\nresult = st.session_state.graph.invoke(graph_input, config=st.session_state.config)\nst.session_state.agent_state = result\n```\n\nThis runs the customer message through the graph. The returned graph state (i.e. `results` ) is stored in Streamlit’s session so that the page can render the latest details of messages, booking summary, price, appointment options, and status.\nFinally, we have some render functions (`_render...()` ) defined in `streamlit_app.py` to convert the current LangGraph state into visible Streamlit components.\npython\n\n```\ndef _render_messages(state: AgentState) -> None:\n if not state.get(\"messages\"):\n with st.chat_message(\"assistant\"):\n st.write(\n \"Hi! I can help you book house or couch cleaning. \"\n \"Tell me what you need, including the size and service address.\"\n )\n return\n\n for message in state[\"messages\"]:\n if isinstance(message, HumanMessage):\n role = \"user\"\n elif isinstance(message, AIMessage):\n role = \"assistant\"\n else:\n continue\n with st.chat_message(role):\n st.write(str(message.content))\n```\n\nFor example, the `_render_messages` function displays the conversation history as Streamlit chat bubbles. It receives the conversation through the latest LangGraph state using `state[\"messages\"]` . If the conversation has no messages yet, the function shows an initial greeting.\n## Let's see how it works\nWe've gone over the `streamlit_app.py` to learn how the page is structured. It's time to see it in action:\nWe can test it locally using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nIt will open up a page at [`http://localhost:8501/`](http://localhost:8501/) . The page looks like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-5-1024x539.png)\nIn order to test the agent, we need an OPENAI_API_KEY. It'll cost you a few cents to test.\nHere is a chat example:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-6-1024x687.png)\nI did not give the address and the agent asked for it as we'd expect. Let's try the same but providing the address in the first message:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-7-1024x509.png)\nSince I have the address in the first message, the agent did not ask for it. I accepted the quote and the agent gave me three options to choose and then completed the booking:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-8-1024x630.png)\nThere is a lot more we can do on the interface to make it more user friendly. But we now have a version that runs smoothly and with a nice and clean user interface.\nI'm planning to improve the agent and add more functionality such as WhatsApp integration. It may even turned out to be a product that I can sell to some local businesses.\nStay tuned for what's coming and thank you for reading!\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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