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AgentStack

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Basic Information

AgentStack is a developer-focused scaffolding and CLI toolkit to create, configure, and run AI agent projects. It provides a standardized "agent stack" boilerplate that generates project structure, preconfigures transit dependencies, and exposes utilities to generate agents and tasks from the command line. The project targets developers who want a head start building agents rather than a low-code solution, and it is provider-agnostic and framework-agnostic. It supports Python 3.10+ and works on macOS, Windows, and Linux. AgentStack bundles and hides common libraries like LangChain and LlamaIndex so developers can focus on code, and it integrates observability via AgentOps. The README emphasizes CLI workflows for init, generate, run, and tools management, and points to templates, documentation, and a community Discord for support.

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App URL
https://github.com/AgentOps-AI/AgentStack
Github Repository
https://github.com/AgentOps-AI/AgentStack/blob/main/README.md

App Details

Features
AgentStack offers an install script and pip installation, a CLI with commands such as init to create a new project, generate to scaffold agents and tasks, run to start the agent in development mode, and tools add to install framework-agnostic tools. It ships preconfigured support for many LLM providers via LiteLLM or LangChain and supports frameworks including CrewAI, LangGraph, OpenAI Swarms, and LlamaStack. The repo includes pre-built templates, a curated tools repository described as a large collection of framework-agnostic tools, built-in AgentOps observability, and opinionated defaults so minimal configuration is required. Roadmap items in the README include prompt and eval layers, UI integration, generated testing, benchmarking, an interactive test runner, and production build scripts.
Use Cases
AgentStack reduces initial development overhead by generating a ready-to-use project layout, installing transitive dependencies, and preconfiguring common integrations so developers can begin implementing agent logic quickly. It hides and configures complex libraries so teams do not need to manually wire LangChain or LlamaIndex, and it provides code-generation CLI utilities to add agents and tasks, speeding iteration. The toolset encourages provider and framework portability to avoid lock-in while offering observability and a tools ecosystem to extend agent capabilities. Templates and community resources help new contributors and teams bootstrap projects, and the roadmap promises additional testing, benchmarking, UI, and deployment aids for maturing agent stacks.

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