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Agents Towards Production

Agents Towards Production is a public, open-source repository of code-first tutorials that teach developers to build generative-AI agents, from stateful workflows and memory to deployment, security and evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agents Towards Production — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-first learning path or implementation reference for moving an agent project beyond a prototype, especially around workflows, memory, deployment and operational practices.

Who it is for

  • Developers learning to build generative-AI agents through code and implementation examples.
  • Practitioners exploring how agent applications can add memory, web search, APIs, or multi-agent coordination.
  • Teams seeking tutorial-based references for deployment and production practices such as Docker, security guardrails, observability and evaluation.

Documented uses

  • Study agent architecture, implementation patterns and integration approaches in code.
  • Explore stateful workflows and vector memory for agent applications.
  • Learn about exposing agent functionality through FastAPI endpoints and deploying with Docker.
  • Review examples and material covering browser automation, multi-agent coordination, security and evaluation.
Before you choose

Requirements & limits

The repository is publicly accessible and describes itself as open source. Its README says tutorials can be browsed on GitHub without local setup; cloning is presented as the route for local experimentation.

  • Tutorial topics include stateful workflows, vector memory, web search APIs, browser automation and multi-agent coordination.
  • Deployment-related coverage includes Docker and FastAPI, while other material addresses security, GPU scaling, observability and evaluation.
  • Local setup is tutorial-specific: the README directs readers to the target tutorial’s requirements and preferred interface.
  • The repository’s primary language is Jupyter Notebook; its README also describes Python scripts as a way to launch some tutorials.

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

Clone the repository and prepare a tutorial

Set up the repository locally and install dependencies for a selected tutorial.

Before you start

  • Git and a local command-line environment.
  • The repository README documents Jupyter notebooks and Python scripts as tutorial interfaces; dependencies are installed from the selected tutorial’s requirements file.

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/NirDiamant/agents-towards-production.git

  2. Change into the repository directory with cd agents-towards-production.

  3. Navigate to the target tutorial and follow its dependency instructions; the README example enters tutorials/agentic-applications-by-xpander.ai and installs meeting-recorder-agent/requirements.txt with pip.

Expected outcome

The repository is available locally and the selected tutorial’s documented dependencies are installed.

Guide limitations

  • The example dependency path applies to the named tutorial, not necessarily to other tutorials.
  • The README describes notebook and script launch methods but does not establish that every tutorial uses the same entry point or setup.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
15 October 2026
Original website title
NirDiamant/agents-towards-production · Official homepage title · retained in its source language
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