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Production AI Customer Support

Production AI Customer Support is an open-source Python project for a multi-agent customer-service system. A master agent routes queries to policy and ticket specialists, with retrieval-augmented policy knowledge and database-backed support operations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Production AI Customer Support — OpenViST project profile in Multi-Agent Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you want a documented Python example of multi-agent customer support, especially one combining policy retrieval with ticket-related database operations.

Who it is for

  • Developers learning how to build multi-agent customer support systems.
  • Teams exploring a starting point for retrieval-backed policy responses and ticket workflows.
  • Practitioners studying agent evaluation, guardrails and support-system tutorials.

Documented uses

  • Route incoming support questions to specialized agents while retaining conversation context.
  • Answer policy questions using retrieval-augmented knowledge.
  • Explore ticket searches, bookings and modifications in the train-support example.
  • Run supplied scenarios or an interactive demo to examine support conversations.
  • Study evaluation of responses across accuracy, helpfulness, task completion, tone, routing and policy adherence.
Before you choose

Requirements & limits

The repository is public and specifies an MIT license. Running the interactive demo requires Python 3.8 or later and a Google AI API key.

  • The documented framework is Google ADK, with Google AI or Vertex AI and Gemini mentioned in the stack.
  • The README lists SQLite, PostgreSQL and MySQL database support.
  • The project’s example domain is train-ticket customer support, including searches, bookings and changes.
  • The README describes safety callbacks and evaluation features; these descriptions are project documentation, not an independent audit.
  • Tutorials cover setup through testing and deployment, while implementation details should be checked in the repository’s linked documentation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run the interactive demo

Install the project, configure its Google AI key, initialize its database and start the interactive demo.

Before you start

  • Python 3.8 or later
  • A Google AI API key
  • Git and pip

Steps

  1. Clone the repository and change into its directory.

  2. Install dependencies with pip install -r requirements.txt.

  3. Copy .env.example to .env and add GOOGLE_API_KEY.

  4. Initialize the database schema and sample data using the README commands.

  5. Run python interactive_test.py.

Expected outcome

The README identifies interactive_test.py as the interactive demo for trying support conversations.

Guide limitations

  • The guide describes the repository's demo workflow, not a production deployment.
  • A Google AI API key is required; the README does not establish whether use will incur costs.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
16 October 2026
Original website title
ntg2208/production-ai-customer-support · Official homepage title · retained in its source language
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