{ "source_file": "0930", "source_platform": "Medium", "author": "Soner Yıldırım", "title_listed": "Connecting My LangGraph AI Agent to Postgres", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/connecting-my-langgraph-ai-agent-to-postgres/", "publish_date": "2026-08-28", "corpus_stage": "", "notes": "", "id": "5d358451610b", "collected_at": "2026-10-02T09:15:41", "title": "Connecting My LangGraph AI Agent to Postgres | 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. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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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 this article, we will test the Postgres backend using both Docker and a hosted Postgres. \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## Agent Structure\nThe following diagram shows the structure (i.e. graph workflow) of our AI agent:\n![](../images/001.png)\nConversation progress lives in LangGraph `AgentState` and is saved as checkpoints. \nWhen the agent offers time slots, it reads existing bookings from the database so it does not propose a time that is already taken. \nWhen the customer confirms a booking, the agent writes one booking row (technician, time range, address, price). \n## Testing the database\nAs I explained in the previous article, the agent has two persistence modes, which are in-memory and Postgres.\nIn-memory is for quick testing and demo purposes. If `DATABASE_URL` is unset, the app uses `InMemoryBookingRepository` and `MemorySaver`. No tables are created.\nIn order to run a local test with Streamlit UI, we first install dependencies with `poetry install` and then create a `.env` file by copying the `.env.example` .\nbash\n\n```\npoetry install\ncp .env.example .env \n```\n\nWe need to set `OPENAI_API_KEY` in `.env` . We can leave `DATABASE_URL` empty for this test. Then, we can start the Stremlit UI at [`localhost:8501`](http://localhost:8501/) using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nThen we see the following Streamlit UI:\n![](../images/body-4cfe09f453794e900a0d.png)\nHere is a small chat I had with the agent:\n![](../images/body-4bed0701cc376aa841c1.png)\nIf you haven't read the previous articles, the AI agent can understand If all required information is already present and proceed directly to calculating and presenting a quote. If I don't give the size or address in the first message, agent would ask for these before calculating the price.\nWe can complete a booking in the UI but conversation state disappear when the app restarts. To actually have data stored, we need to use the database options.\n## Testing with Docker\n### Why testing with Docker\nWe also have the option to test the project with Docker. It allows for testing against Postgres without having to installing a real database in our computers. \nTesting with Docker mimics the same database we would have in production but locally. The app talks to an actual PostgreSQL server.\nDocker also makes it reproducible for everyone. The URL and credentials live in `docker-compose.yml` / `.env.example`. You get the same setup as I have used.\nThanks to Docker, the app will be isolated from the rest of the computer. The database runs in a container. We can stop it (`docker compose down`) or wipe it (`docker compose down -v`) without touching other apps.\nSo Docker lets us test the durable backend on a real Postgres instance in minutes, with the same connection string every time, and without installing Postgres on our laptop.\n### How to test with Docker\nThis project has a `docker-compose.yml` file that starts a container with PostgreSQL 16:\nyaml\n\n```\nservices:\n postgres:\n image: postgres:16-alpine\n environment:\n POSTGRES_USER: booking\n POSTGRES_PASSWORD: booking\n POSTGRES_DB: booking_agent\n ports:\n - \"5432:5432\"\n volumes:\n - booking_pgdata:/var/lib/postgresql/data\n healthcheck:\n test: [\"CMD-SHELL\", \"pg_isready -U booking -d booking_agent\"]\n interval: 5s\n timeout: 5s\n retries: 10\n\nvolumes:\n booking_pgdata:\n```\n\nDocker pulls the official Postgres image and runs a database server inside the container.\nStreamlit app still runs locally and connects to that container over `localhost:5432`.\nDatabase files are stored in a Docker volume (`booking_pgdata`) , which is a persistent disk storage managed by Docker. It's not in RAM so data stays in the container unless we delete the volume.\nIn order to test it Docker, you need to Docker Desktop installed in your computer. Open Docker Desktop and wait until you see \"Engine is running\" or \"Docker is running\".\nThen, we can start the container using the following command:\nbash\n\n```\ndocker compose up -d\n```\n\nAnd we need to add `DATABASE_URL` in our `.env` file:\nbash\n\n```\nDATABASE_URL=postgresql://booking:booking@localhost:5432/booking_agent\n```\n\nThen, we can run the app using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nAfter running this command, Streamlit UI starts. \nI completed a test booking:\n![](../images/body-2ee5f702718b3b917980.png)\nTo verify it works properly, I open a second browser (localhost:8501) and asked for the same service. This time, it did not offer the time slop I previously booked:\n![](../images/body-9ba7b203f2c570fa4949.png)\nWe can also see it's running on Docker Desktop:\n![](../images/body-8d27f2814932a9bf19b3.png)\nStreamlit and Docker Postgres are separate processes.\nRestarting Streamlit only restarts the Python app. The Postgres container keeps running unless we stop it. \nAlso, the data lives in the Docker volume, not in Streamlit memory. So when we schedule a new appointment, the app sees the previous bookings in the database.\nWe only lose the database only if we stop or remove the container and delete the volume (`docker compose down -v`).\nbash\n\n```\ndocker compose down -v\n[+] down 3/3\n ✔ Container customer-service-agent-postgres-1 Removed 0.2s\n ✔ Volume customer-service-agent_booking_pgdata Removed 0.1s\n ✔ Network customer-service-agent_default Removed \n```\n\n## Testing with a hosted Postgres\nWe don't need to use Docker if we have Postgres in the cloud (e.g. Supabase, RDS).\nWe can simply create a Postgres database in the provider's dashboard and copy the connection string.\nIt looks something like this:\n`postgresql://USER:PASSWORD@HOST:PORT/DATABASE`\nWe then put that string in `.env` as `DATABASE_URL`.\nThen, we start the app as we did before:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nThe app behavior is identical to Docker. The only difference is where Postgres runs. It now runs on a remote host.\nWe are gradually turning this customer service agent into a product that could provide real business value.\nThere is still more to do. We can add other channels, such as WhatsApp. We also need a safety layer to reduce prompt injection risk. The booking workflow and chat experience can be improved as well.\nI will cover these in upcoming articles. \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. 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/)[Langgraph](https://towardsdatascience.com/tag/langgraph/)[Postgres](https://towardsdatascience.com/tag/postgres/)[Programming](https://towardsdatascience.com/tag/programming/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&title=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&title=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&text=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n\n\nTowards Data Science is a community publication. 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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 this article, we will test the Postgres backend using both Docker and a hosted Postgres. \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## Agent Structure\nThe following diagram shows the structure (i.e. graph workflow) of our AI agent:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/Untitled-Diagram-22.drawio.png)\nConversation progress lives in LangGraph `AgentState` and is saved as checkpoints. \nWhen the agent offers time slots, it reads existing bookings from the database so it does not propose a time that is already taken. \nWhen the customer confirms a booking, the agent writes one booking row (technician, time range, address, price). \n## Testing the database\nAs I explained in the previous article, the agent has two persistence modes, which are in-memory and Postgres.\nIn-memory is for quick testing and demo purposes. If `DATABASE_URL` is unset, the app uses `InMemoryBookingRepository` and `MemorySaver`. No tables are created.\nIn order to run a local test with Streamlit UI, we first install dependencies with `poetry install` and then create a `.env` file by copying the `.env.example` .\nbash\n\n```\npoetry install\ncp .env.example .env \n```\n\nWe need to set `OPENAI_API_KEY` in `.env` . We can leave `DATABASE_URL` empty for this test. Then, we can start the Stremlit UI at [`localhost:8501`](http://localhost:8501/) using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nThen we see the following Streamlit UI:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-128-1024x695.png)\nHere is a small chat I had with the agent:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-129-940x1024.png)\nIf you haven't read the previous articles, the AI agent can understand If all required information is already present and proceed directly to calculating and presenting a quote. If I don't give the size or address in the first message, agent would ask for these before calculating the price.\nWe can complete a booking in the UI but conversation state disappear when the app restarts. To actually have data stored, we need to use the database options.\n## Testing with Docker\n### Why testing with Docker\nWe also have the option to test the project with Docker. It allows for testing against Postgres without having to installing a real database in our computers. \nTesting with Docker mimics the same database we would have in production but locally. The app talks to an actual PostgreSQL server.\nDocker also makes it reproducible for everyone. The URL and credentials live in `docker-compose.yml` / `.env.example`. You get the same setup as I have used.\nThanks to Docker, the app will be isolated from the rest of the computer. The database runs in a container. We can stop it (`docker compose down`) or wipe it (`docker compose down -v`) without touching other apps.\nSo Docker lets us test the durable backend on a real Postgres instance in minutes, with the same connection string every time, and without installing Postgres on our laptop.\n### How to test with Docker\nThis project has a `docker-compose.yml` file that starts a container with PostgreSQL 16:\nyaml\n\n```\nservices:\n postgres:\n image: postgres:16-alpine\n environment:\n POSTGRES_USER: booking\n POSTGRES_PASSWORD: booking\n POSTGRES_DB: booking_agent\n ports:\n - \"5432:5432\"\n volumes:\n - booking_pgdata:/var/lib/postgresql/data\n healthcheck:\n test: [\"CMD-SHELL\", \"pg_isready -U booking -d booking_agent\"]\n interval: 5s\n timeout: 5s\n retries: 10\n\nvolumes:\n booking_pgdata:\n```\n\nDocker pulls the official Postgres image and runs a database server inside the container.\nStreamlit app still runs locally and connects to that container over `localhost:5432`.\nDatabase files are stored in a Docker volume (`booking_pgdata`) , which is a persistent disk storage managed by Docker. It's not in RAM so data stays in the container unless we delete the volume.\nIn order to test it Docker, you need to Docker Desktop installed in your computer. Open Docker Desktop and wait until you see \"Engine is running\" or \"Docker is running\".\nThen, we can start the container using the following command:\nbash\n\n```\ndocker compose up -d\n```\n\nAnd we need to add `DATABASE_URL` in our `.env` file:\nbash\n\n```\nDATABASE_URL=postgresql://booking:booking@localhost:5432/booking_agent\n```\n\nThen, we can run the app using the following command:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nAfter running this command, Streamlit UI starts. \nI completed a test booking:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-153-1024x637.png)\nTo verify it works properly, I open a second browser (localhost:8501) and asked for the same service. This time, it did not offer the time slop I previously booked:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-154-1024x273.png)\nWe can also see it's running on Docker Desktop:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-155-1024x319.png)\nStreamlit and Docker Postgres are separate processes.\nRestarting Streamlit only restarts the Python app. The Postgres container keeps running unless we stop it. \nAlso, the data lives in the Docker volume, not in Streamlit memory. So when we schedule a new appointment, the app sees the previous bookings in the database.\nWe only lose the database only if we stop or remove the container and delete the volume (`docker compose down -v`).\nbash\n\n```\ndocker compose down -v\n[+] down 3/3\n ✔ Container customer-service-agent-postgres-1 Removed 0.2s\n ✔ Volume customer-service-agent_booking_pgdata Removed 0.1s\n ✔ Network customer-service-agent_default Removed \n```\n\n## Testing with a hosted Postgres\nWe don't need to use Docker if we have Postgres in the cloud (e.g. Supabase, RDS).\nWe can simply create a Postgres database in the provider's dashboard and copy the connection string.\nIt looks something like this:\n`postgresql://USER:PASSWORD@HOST:PORT/DATABASE`\nWe then put that string in `.env` as `DATABASE_URL`.\nThen, we start the app as we did before:\nbash\n\n```\npoetry run streamlit run customer_service_agent/streamlit_app.py\n```\n\nThe app behavior is identical to Docker. The only difference is where Postgres runs. It now runs on a remote host.\nWe are gradually turning this customer service agent into a product that could provide real business value.\nThere is still more to do. We can add other channels, such as WhatsApp. We also need a safety layer to reduce prompt injection risk. The booking workflow and chat experience can be improved as well.\nI will cover these in upcoming articles. \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. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657090323_hn276a.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/)[Langgraph](https://towardsdatascience.com/tag/langgraph/)[Postgres](https://towardsdatascience.com/tag/postgres/)[Programming](https://towardsdatascience.com/tag/programming/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&title=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&title=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fconnecting-my-langgraph-ai-agent-to-postgres%2F&text=Connecting%20My%20LangGraph%20AI%20Agent%20to%20Postgres)\n\n\nTowards Data Science is a community publication. 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