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AgentStack

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Basic Information

AgentStack is a developer-focused CLI and scaffolding project that accelerates building AI agent projects by generating a complete, opinionated project structure and preconfiguring common agent components. It provides installers and a Python package to bootstrap projects, templates to jumpstart common agent types, and utilities to generate agents and tasks from the command line. The repo emphasizes provider-agnostic integration with many LLM providers via LiteLLM or LangChain and supports multiple agent frameworks such as CrewAI, LangGraph, OpenAI Swarms, and LlamaStack. It bundles tooling and observability by default, hides low-level configuration so developers can focus on code, and works cross-platform on macOS, Windows, and Linux. The project is explicitly intended for developers rather than being a low-code product, requires Python 3.10 or newer, and includes a roadmap of additional stack layers and tooling enhancements.

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

App Details

Features
AgentStack provides a CLI-driven workflow with commands to initialize projects, generate agents and tasks, add tools, and run the agent in development mode. It installs a curated set of dependencies and preconfigures popular frameworks and LLM providers so developers do not need to manually configure LangChain or LlamaIndex. The repo ships templates for common agent types, maintains a large framework-agnostic tools catalog, and includes built-in observability via AgentOps. Cross-platform installation options are provided, including a shell installer and pip package. Additional features on the roadmap include a prompting layer, evaluation layer, UI layer, generated testing, integrated benchmarking, an interactive test runner, and a production build script. The design emphasizes no vendor lock-in and easy customization.
Use Cases
AgentStack reduces the friction of starting and iterating on AI agent projects by providing a ready-made, opinionated stack that wires together frameworks, LLM providers, tools, and observability. Developers can create a new project with a single command, generate boilerplate for agents and tasks, and run a development agent immediately, which saves time on setup and configuration. Preinstalled tool integrations and a large tools repository let teams add capabilities quickly while remaining framework agnostic. The scaffolded structure and CLI code-generation utilities help enforce reproducible patterns, ease onboarding, and speed prototyping. Roadmap items like testing, benchmarking, and a UI layer aim to further simplify development and production-readiness for agent projects.

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