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Learn Agent Architecture

Learn Agent Architecture is an open-source educational repository explaining how a harness surrounds a language model with control loops and mechanisms for tool use, state, permissions, context, tasks and interfaces.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Learn Agent Architecture — 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 as a structured reading resource when learning agent architecture, or when you want to compare how selected systems implement particular harness layers.

Who it is for

  • Readers learning how AI agent systems are assembled around a language model.
  • Developers comparing harness design choices across coding agents, assistants and autonomous runners.

Documented uses

  • Understand the model-and-harness distinction and how an agent loop proceeds.
  • Study how tools are registered, dispatched and governed by permissions.
  • Explore how agent systems handle memory, context, tasks and interfaces.
  • Compare documented harness mechanisms across selected real-world systems.
Before you choose

Requirements & limits

The repository is public on GitHub and licensed under MIT.

  • The material is organized as sequential layers, with each section building on the previous one.
  • Examples cover Claude Code, Hermes Agent, mini-swe-agent and deepseek-harness, with specific studied versions listed in the README.
  • Some sections include runnable demos; the README directs readers to their src/loop.py and demo.py files.
  • The main repository’s memory coverage continues in a separate companion project.

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 learning path and inspect a runnable section

Study agent architecture layer by layer and examine a section’s documented demo.

Before you start

  • Access to the repository on GitHub
  • A section identified by the README as runnable

Steps

  1. Read the sections in order, since each builds on the layer before it.

  2. For a runnable section, read its src/loop.py.

  3. Run that section’s demo.py.

  4. Compare the section’s src/ directory with the preceding section to identify the added mechanism.

Expected outcome

A structured way to study the agent layers and relate a runnable section’s implementation to the preceding one.

Guide limitations

  • The README’s runnable-section instructions do not specify environment setup or dependencies.
  • The guide’s sections and examples explain particular systems and versions; they are not a guarantee that every agent follows the same design.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
12 October 2026
Original website title
hardness1020/learn-agent-architecture · Official homepage title · retained in its source language
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