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Build A Product Agent

Build A Product Agent is a public MIT-licensed Python repository containing a full-chain technical handbook and base framework for designing, implementing, testing and operating production-oriented AI agent systems.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Build A Product Agent — 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 at the planning and development stages of an agent project when you need one path covering architecture, implementation patterns, evaluation and operations.

Who it is for

  • Developers with programming experience building AI agent backends.
  • Teams planning agent orchestration, memory, RAG, testing or operations.
  • Readers who want a structured route from technology selection to infrastructure planning.

Documented uses

  • Plan the architecture and technology choices for an AI agent backend.
  • Design agent workflows, tools, memory and RAG modules.
  • Structure single-agent and multi-agent collaboration paths.
  • Plan functional, quality, security, performance and release-readiness testing.
  • Review model infrastructure and operations considerations.
Before you choose

Requirements & limits

The repository is public and identified as MIT-licensed. Its README provides Chinese documentation and links to an English landing page.

  • It is a documentation-led repository and base framework rather than evidence of a hosted agent application.
  • The documented development style uses Python 3.13+ and asynchronous examples.
  • The stack includes FastAPI, LangGraph, LangChain, Pydantic, PostgreSQL, Redis, Milvus and RabbitMQ.
  • Memory coverage combines Redis and Milvus, while workflow coverage includes LangGraph checkpointing and resumable execution.
  • The repository discusses MCP for tools and context and A2A for agent-to-agent collaboration.

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

Follow the repository’s documented learning route

Use the handbook as a structured study path from agent architecture through testing and infrastructure planning.

Before you start

  • A local copy of the public GitHub repository.
  • Python 3.13 or later for the documented development style.
  • Programming experience, as the repository targets developers with a programming foundation.

Steps

  1. Clone the repository and change into its directory.

  2. Read the technology-selection chapter to review language, framework, middleware, protocol and operations choices.

  3. Continue through development requirements, module development and agent paths.

  4. Read the system-testing chapter, then review the AI infrastructure chapter for model deployment and selection concepts.

Expected outcome

A staged understanding of the decisions and development areas involved in a production-oriented AI agent system.

Guide limitations

  • This recipe documents the recommended reading sequence; it does not establish that a complete agent service is deployed or executed by following it.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
3 October 2026
Original website title
ADW-19/build_a_product_agent · Official homepage title · retained in its source language
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