{ "source_file": "0930", "source_platform": "Medium", "author": "Soner Yıldırım", "title_listed": "Building a Proper Backend 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-proper-backend-for-my-langgraph-ai-agent/", "publish_date": "2026-08-22", "corpus_stage": "", "notes": "", "id": "593482410774", "collected_at": "2026-10-02T09:17:46", "title": "Building a Proper Backend 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\nThe next phase is to build a proper backend and we start by implementing a Postgres database instead of keeping everything in memory.\nWe keep Streamlit as the user interface and replace the in-memory adapters with PostgreSQL.\nThis will also allow us to have multiple fronts (e.g. WhatsApp, Streamlit) that share the same backend. So we are turning this into a proper product that will handle a real business.\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.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## What the database looks like now\nIt's hard to even call it a database as it's just two Python objects that lived inside the process:\nThe first one is a LangGraph checkpointer, which is a state persistence layer that saves a snapshot of an agent’s graph state at every step of execution. \nWhen the graph is compiled, conversation state is stored in memory. \npython\n\n```\ngraph.compile(checkpointer=checkpointer or MemorySaver())\n```\n\nThe checkpointer allows the agent resume across turns. If we don't have it, every customer message would be a new conversation.\nThe second object is a Python list behind a lock. Confirmed appointments are stored in an in-memory repository that looks like this:\npython\n\n```\nclass InMemoryBookingRepository:\n def __init__(self) -> None:\n self._lock = threading.RLock()\n self.technicians = {...} # hardcoded cleaners\n self._bookings: list[Booking] = []\n\n def list_bookings(self) -> list[Booking]:\n with self._lock:\n return list(self._bookings)\n\n def create_booking(self, option, details, price) -> Booking:\n # check overlap in Python, then append to self._bookings\n ...\n```\n\nThe scheduling engine called `list_bookings()` to avoid double-booking. Confirmation called `create_booking()`, which re-checked overlap and appended to the list.\nThis is a very simple structure designed for initial testing and demo purposes. It allows us to test LangGraph routing and logic.\n## Why we need a proper database\nThe current \"database\" relies on in-memory persistence so it fails as soon as we leave a single demo process.\nWhen the process restarts, conversation checkpoints and bookings vanish.\nSince it's in memory, there is no shared availability. Session A cannot see bookings created by Session B, which means every process has its own calendar.\nEven worse for a booking product is that the agent can offer a slot based on a stale in-memory view, then “confirm” a booking that another session already took.\nWhen we use the Streamlit UI, it looked like a product but the storage still behaved like a notebook kernel.\nLong story short, we need a proper database for our agent to be considered as a product.\nWe will use Postgres, which is a free and open-source relational database system. We need a relational database with bookings and technician information stored in separate (and related) tables.\nBefore Postgres implementation, the agent structure looks like this:\n![](../images/001.png)\nAnd after we complete Postgres implementation, it will look like this:\n![](../images/body-01f7ed86f3e81ce9252a.png)\nAfter the Postgres backend, `AgentState` will still be the working memory of the graph but it will be persisted through a checkpointer and a booking engine.\nWe will learn how these are implemented and they function in the remaining part of the article.\n## Postgres implementation\nWe first create a protocol so that the graph and engines can depend on a stable interface, not on Postgres (or memory) specifically.\npython\n\n```\nfrom typing import Protocol\n\nclass BookingRepository(Protocol):\n \"\"\"Persistence interface used by scheduling and confirmation.\"\"\"\n\n @property\n def technicians(self) -> dict[str, Technician]:\n \"\"\"Return technicians keyed by id.\"\"\"\n\n def list_bookings(self) -> list[Booking]:\n \"\"\"Return all confirmed bookings.\"\"\"\n\n def create_booking(\n self, option: TimeOption, details: BookingDetails, price: float\n ) -> Booking:\n \"\"\"Persist a booking after re-checking overlap; raise ValueError if taken.\"\"\"\n```\n\nWith this protocol, we just plug `PostgresBookingRepository` or `InMemoryBookingRepository` at startup (depending on using Postgres or in-memory). Then, the nodes can call `list_bookings` and `create_booking` functions.\nWhen we use `InMemoryBookingRepository`, no database tables are created. Confirmed bookings are kept in a Python list inside the running process, and the same repository methods (`list_bookings`, `create_booking`) still work. They just never touch Postgres. \nThe in-memory mode ideal for unit tests and quick local demos. It's important to also mention that, with the in-memory mode, everything disappears when the app restarts.\nWhen we use `PostgresBookingRepository` , there is an actual database. Inside the `postgres.py` script, you can see the database schema that consists of two tables, which are `technicians` and `bookings` . \nYou can also see the definition of the `PostgresBookingRepository` class. I won't copy it here because it's close to 100 lines of code. We also define the functions `list_bookings` and `create_booking` inside this class.\nAt app startup, `create_persistence()` chooses Postgres vs in-memory. When `DATABASE_URL` is set, both the booking repository and LangGraph checkpointer use Postgres. Otherwise both stay in memory.\nSo the `repository` is either a `PostgresBookingRepository` or `InMemoryBookingRepository` (both satisfy the `BookingRepository` protocol), and that instance is passed into the `build_graph` function:\npython\n\n```\ndef build_graph(\n llm: BaseChatModel,\n *,\n repository: BookingRepository | None = None,\n checkpointer: Any | None = None,\n) -> Any:\n \"\"\"Build a compiled, multi-turn booking graph.\"\"\"\n repository = repository or InMemoryBookingRepository()\n graph = StateGraph(AgentState)\n\n # truncated\n```\n\nThe `repository` is then used by the graph nodes to interact with the database.\nFor example, we define the `confirm_booking_node` function as follows:\npython\n\n```\n def confirm_booking_node(state: AgentState) -> dict[str, Any]:\n option = state.get(\"selected_slot\")\n if option is None:\n raise ValueError(\"A slot must be selected before confirmation.\")\n booking = repository.create_booking(\n option, state[\"booking_details\"], float(state[\"calculated_price\"])\n )\n return {\n \"booking_id\": booking.id,\n \"status\": \"confirmed\",\n \"messages\": [\n AIMessage(\n content=(\n f\"Confirmed! Booking {booking.id} is scheduled for \"\n f\"{option.start_at}. Your total is ${booking.price:.2f}.\"\n )\n )\n ],\n }\n```\n\nWe can see that it's using the `repository` to create a booking in the database.\n### Database interactions\nThe current agentic workflow is as follows: \n![](../images/body-1b1c43826574a8019880.png)\nDuring a booking session, conversation lives in `AgentState`, which can be considered as the working memory of the graph. Each node returns a partial update, and LangGraph merges it into that state. The checkpointer persists it across turns with Postgres.\nOnly two nodes talk to the booking repository (either `PostgresBookingRepository` or `InMemoryBookingRepository`):\n * generate options (read): Load existing bookings (+ technicians), then compute free slots\n * confirm booking (write): Insert the confirmed appointment\n\n\nThe other nodes only read or update the `AgentState`. They do not query the bookings table.\nTo propose appointments, we add `generate_schedule_options_node` to the graph:\npython\n\n```\ndef generate_schedule_options_node(state: AgentState) -> dict[str, Any]:\n options = generate_schedule_options(state[\"booking_details\"], repository)\n lines = [\"Great—please choose one of these optimized appointments:\"]\n for index, option in enumerate(options, 1):\n lines.append(f\"{index}. {option.start_at} ({option.technician_id})\")\n return {\n \"time_options\": options,\n \"status\": \"awaiting_slot_selection\",\n \"messages\": [AIMessage(content=\"\\n\".join(lines))],\n }\n```\n\nThis node calls the `generate_schedule_options()` function from `engines.py` , which:\n 1. Calls `repository.list_bookings()` (a `SELECT` from `bookings` when using Postgres)\n 2. Uses `repository.technicians`\n 3. Applies deterministic rules (next 7 days, skip Sundays, fixed start times, duration, travel scoring)\n 4. Returns the best 3 available time options\n\n\nLangGraph handles merging this information into `AgentState`, updating `time_options`, `status`, and `messages` :\npython\n\n```\nclass AgentState(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n booking_details: BookingDetails\n calculated_price: NotRequired[float | None]\n time_options: NotRequired[list[TimeOption]]\n selected_slot: NotRequired[TimeOption | None]\n status: BookingStatus\n booking_id: NotRequired[str | None]\n```\n\nAfter the customer confirms a slot, `select_slot_node` only sets `selected_slot` in `AgentState`. The write happens in `confirm_booking_node`:\npython\n\n```\ndef confirm_booking_node(state: AgentState) -> dict[str, Any]:\n option = state.get(\"selected_slot\")\n if option is None:\n raise ValueError(\"A slot must be selected before confirmation.\")\n booking = repository.create_booking(\n option, state[\"booking_details\"], float(state[\"calculated_price\"])\n )\n return {\n \"booking_id\": booking.id,\n \"status\": \"confirmed\",\n \"messages\": [\n AIMessage(\n content=(\n f\"Confirmed! Booking {booking.id} is scheduled for \"\n f\"{option.start_at}. Your total is ${booking.price:.2f}.\"\n )\n )\n ],\n }\n```\n\nOn success, LangGraph merges `booking_id`, `status=\"confirmed\"`, and the confirmation message into `AgentState`, and the checkpointer saves that snapshot for the conversation `thread_id`.\nWe now have a proper Postgres backend for our customer service agent. 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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 Proper Backend for My LangGraph AI Agent\nTurning a demo agent into something that can keep real booking data\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 22, 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 the first two articles of this series, I built a [stateful LangGraph agent](https://towardsdatascience.com/i-replaced-a-15-minute-booking-process-with-a-langgraph-ai-agent/) that handles a 15-minute booking process and wrapped it up with a [Streamlit UI](https://towardsdatascience.com/building-a-streamlit-ui-for-my-langgraph-ai-agent/) to improve user experience.\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\nThe next phase is to build a proper backend and we start by implementing a Postgres database instead of keeping everything in memory.\nWe keep Streamlit as the user interface and replace the in-memory adapters with PostgreSQL.\nThis will also allow us to have multiple fronts (e.g. WhatsApp, Streamlit) that share the same backend. So we are turning this into a proper product that will handle a real business.\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.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## What the database looks like now\nIt's hard to even call it a database as it's just two Python objects that lived inside the process:\nThe first one is a LangGraph checkpointer, which is a state persistence layer that saves a snapshot of an agent’s graph state at every step of execution. \nWhen the graph is compiled, conversation state is stored in memory. \npython\n\n```\ngraph.compile(checkpointer=checkpointer or MemorySaver())\n```\n\nThe checkpointer allows the agent resume across turns. If we don't have it, every customer message would be a new conversation.\nThe second object is a Python list behind a lock. Confirmed appointments are stored in an in-memory repository that looks like this:\npython\n\n```\nclass InMemoryBookingRepository:\n def __init__(self) -> None:\n self._lock = threading.RLock()\n self.technicians = {...} # hardcoded cleaners\n self._bookings: list[Booking] = []\n\n def list_bookings(self) -> list[Booking]:\n with self._lock:\n return list(self._bookings)\n\n def create_booking(self, option, details, price) -> Booking:\n # check overlap in Python, then append to self._bookings\n ...\n```\n\nThe scheduling engine called `list_bookings()` to avoid double-booking. Confirmation called `create_booking()`, which re-checked overlap and appended to the list.\nThis is a very simple structure designed for initial testing and demo purposes. It allows us to test LangGraph routing and logic.\n## Why we need a proper database\nThe current \"database\" relies on in-memory persistence so it fails as soon as we leave a single demo process.\nWhen the process restarts, conversation checkpoints and bookings vanish.\nSince it's in memory, there is no shared availability. Session A cannot see bookings created by Session B, which means every process has its own calendar.\nEven worse for a booking product is that the agent can offer a slot based on a stale in-memory view, then “confirm” a booking that another session already took.\nWhen we use the Streamlit UI, it looked like a product but the storage still behaved like a notebook kernel.\nLong story short, we need a proper database for our agent to be considered as a product.\nWe will use Postgres, which is a free and open-source relational database system. We need a relational database with bookings and technician information stored in separate (and related) tables.\nBefore Postgres implementation, the agent structure looks like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-83.png)\nAnd after we complete Postgres implementation, it will look like this:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/Untitled-Diagram-22.drawio.png)\nAfter the Postgres backend, `AgentState` will still be the working memory of the graph but it will be persisted through a checkpointer and a booking engine.\nWe will learn how these are implemented and they function in the remaining part of the article.\n## Postgres implementation\nWe first create a protocol so that the graph and engines can depend on a stable interface, not on Postgres (or memory) specifically.\npython\n\n```\nfrom typing import Protocol\n\nclass BookingRepository(Protocol):\n \"\"\"Persistence interface used by scheduling and confirmation.\"\"\"\n\n @property\n def technicians(self) -> dict[str, Technician]:\n \"\"\"Return technicians keyed by id.\"\"\"\n\n def list_bookings(self) -> list[Booking]:\n \"\"\"Return all confirmed bookings.\"\"\"\n\n def create_booking(\n self, option: TimeOption, details: BookingDetails, price: float\n ) -> Booking:\n \"\"\"Persist a booking after re-checking overlap; raise ValueError if taken.\"\"\"\n```\n\nWith this protocol, we just plug `PostgresBookingRepository` or `InMemoryBookingRepository` at startup (depending on using Postgres or in-memory). Then, the nodes can call `list_bookings` and `create_booking` functions.\nWhen we use `InMemoryBookingRepository`, no database tables are created. Confirmed bookings are kept in a Python list inside the running process, and the same repository methods (`list_bookings`, `create_booking`) still work. They just never touch Postgres. \nThe in-memory mode ideal for unit tests and quick local demos. It's important to also mention that, with the in-memory mode, everything disappears when the app restarts.\nWhen we use `PostgresBookingRepository` , there is an actual database. Inside the `postgres.py` script, you can see the database schema that consists of two tables, which are `technicians` and `bookings` . \nYou can also see the definition of the `PostgresBookingRepository` class. I won't copy it here because it's close to 100 lines of code. We also define the functions `list_bookings` and `create_booking` inside this class.\nAt app startup, `create_persistence()` chooses Postgres vs in-memory. When `DATABASE_URL` is set, both the booking repository and LangGraph checkpointer use Postgres. Otherwise both stay in memory.\nSo the `repository` is either a `PostgresBookingRepository` or `InMemoryBookingRepository` (both satisfy the `BookingRepository` protocol), and that instance is passed into the `build_graph` function:\npython\n\n```\ndef build_graph(\n llm: BaseChatModel,\n *,\n repository: BookingRepository | None = None,\n checkpointer: Any | None = None,\n) -> Any:\n \"\"\"Build a compiled, multi-turn booking graph.\"\"\"\n repository = repository or InMemoryBookingRepository()\n graph = StateGraph(AgentState)\n\n # truncated\n```\n\nThe `repository` is then used by the graph nodes to interact with the database.\nFor example, we define the `confirm_booking_node` function as follows:\npython\n\n```\n def confirm_booking_node(state: AgentState) -> dict[str, Any]:\n option = state.get(\"selected_slot\")\n if option is None:\n raise ValueError(\"A slot must be selected before confirmation.\")\n booking = repository.create_booking(\n option, state[\"booking_details\"], float(state[\"calculated_price\"])\n )\n return {\n \"booking_id\": booking.id,\n \"status\": \"confirmed\",\n \"messages\": [\n AIMessage(\n content=(\n f\"Confirmed! Booking {booking.id} is scheduled for \"\n f\"{option.start_at}. Your total is ${booking.price:.2f}.\"\n )\n )\n ],\n }\n```\n\nWe can see that it's using the `repository` to create a booking in the database.\n### Database interactions\nThe current agentic workflow is as follows: \n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-82-1024x469.png)\nDuring a booking session, conversation lives in `AgentState`, which can be considered as the working memory of the graph. Each node returns a partial update, and LangGraph merges it into that state. The checkpointer persists it across turns with Postgres.\nOnly two nodes talk to the booking repository (either `PostgresBookingRepository` or `InMemoryBookingRepository`):\n * generate options (read): Load existing bookings (+ technicians), then compute free slots\n * confirm booking (write): Insert the confirmed appointment\n\n\nThe other nodes only read or update the `AgentState`. They do not query the bookings table.\nTo propose appointments, we add `generate_schedule_options_node` to the graph:\npython\n\n```\ndef generate_schedule_options_node(state: AgentState) -> dict[str, Any]:\n options = generate_schedule_options(state[\"booking_details\"], repository)\n lines = [\"Great—please choose one of these optimized appointments:\"]\n for index, option in enumerate(options, 1):\n lines.append(f\"{index}. {option.start_at} ({option.technician_id})\")\n return {\n \"time_options\": options,\n \"status\": \"awaiting_slot_selection\",\n \"messages\": [AIMessage(content=\"\\n\".join(lines))],\n }\n```\n\nThis node calls the `generate_schedule_options()` function from `engines.py` , which:\n 1. Calls `repository.list_bookings()` (a `SELECT` from `bookings` when using Postgres)\n 2. Uses `repository.technicians`\n 3. Applies deterministic rules (next 7 days, skip Sundays, fixed start times, duration, travel scoring)\n 4. Returns the best 3 available time options\n\n\nLangGraph handles merging this information into `AgentState`, updating `time_options`, `status`, and `messages` :\npython\n\n```\nclass AgentState(TypedDict):\n messages: Annotated[list[AnyMessage], add_messages]\n booking_details: BookingDetails\n calculated_price: NotRequired[float | None]\n time_options: NotRequired[list[TimeOption]]\n selected_slot: NotRequired[TimeOption | None]\n status: BookingStatus\n booking_id: NotRequired[str | None]\n```\n\nAfter the customer confirms a slot, `select_slot_node` only sets `selected_slot` in `AgentState`. The write happens in `confirm_booking_node`:\npython\n\n```\ndef confirm_booking_node(state: AgentState) -> dict[str, Any]:\n option = state.get(\"selected_slot\")\n if option is None:\n raise ValueError(\"A slot must be selected before confirmation.\")\n booking = repository.create_booking(\n option, state[\"booking_details\"], float(state[\"calculated_price\"])\n )\n return {\n \"booking_id\": booking.id,\n \"status\": \"confirmed\",\n \"messages\": [\n AIMessage(\n content=(\n f\"Confirmed! Booking {booking.id} is scheduled for \"\n f\"{option.start_at}. Your total is ${booking.price:.2f}.\"\n )\n )\n ],\n }\n```\n\nOn success, LangGraph merges `booking_id`, `status=\"confirmed\"`, and the confirmation message into `AgentState`, and the checkpointer saves that snapshot for the conversation `thread_id`.\nWe now have a proper Postgres backend for our customer service agent. In the next article, I’ll walk through how to run and verify this setup with Docker, and how to point the same app at a hosted Postgres instance.\nThank 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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