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Agentic Architectures

Agentic Architectures is an MIT-licensed Python library and runnable textbook that presents 35 agentic AI patterns as Architecture classes, with notebooks, multi-provider LLM support, and a comparative leaderboard covering 17 benchmark tasks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agentic Architectures — 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 selecting or learning an agent design and you want runnable Python examples plus task-oriented comparisons, rather than a general-purpose hosted agent application.

Who it is for

  • Python developers comparing agent architecture patterns for a task.
  • Learners who want to study agent designs alongside captured LLM runs.
  • Teams seeking a shared interface for trying different agent patterns.

Documented uses

  • Study how reasoning, search, retrieval, and memory architectures are structured.
  • Run an architecture on a task through a common .run(task) interface.
  • Compare architecture results across the project's benchmark tasks.
  • Use notebooks and examples as starting points for implementation.
Before you choose

Requirements & limits

The project is a public GitHub repository licensed under MIT. Its documented setup uses Python and provider configuration for model calls.

  • The library uses LangGraph state machines and exposes a shared .run(task) interface across its architectures.
  • The benchmark leaderboard covers 17 tasks; its comparisons are useful within that task set.
  • Nine LLM providers are listed, but setup can involve provider-specific configuration and optional dependencies.
  • The quickstart example installs Nebius, FAISS, and Tavily extras; the fresh-clone instructions list additional development and documentation extras.
  • The repository includes 35 architectures across reasoning and reflection, sampling and search, retrieval, and memory.

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 the repository in a Python virtual environment

Clone the project, install its documented development and provider extras, and prepare the environment file.

Before you start

  • Python
  • Git
  • A supported provider API key for the configured provider

Steps

  1. Clone the GitHub repository and change into its directory.

  2. Create and activate a virtual environment using the commands documented for your operating system.

  3. Install the editable package with the documented development, test, documentation, Nebius, FAISS, Tavily, and NetworkX extras.

  4. Copy the example environment file to .env and fill in the relevant provider API key.

Expected outcome

A local editable installation with the documented extras and an environment file prepared for provider configuration.

Guide limitations

  • The documented environment-file example names NEBIUS_API_KEY; provider-specific configuration may differ.
  • This guide covers setup only, not running or deploying an architecture.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
7 October 2026
Original website title
FareedKhan-dev/all-agentic-architectures · Official homepage title · retained in its source language
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