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AgentStack

Name: AgentStack
Brand: AgentStack
Availability: InStock

Open-source CLI tool for scaffolding AI agent projects across multiple frameworks including CrewAI, LangGraph, OpenAI Swarms, and LlamaStack — the create-react-app for AI agent development.

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In Plain English

AgentStack is like 'create-react-app' for AI agents — a command-line tool that instantly creates complete agent projects with all the setup done for you. Instead of spending hours configuring frameworks, dependencies, and boilerplate, you run one command and get a ready-to-go project for building AI agents with your choice of framework.

Overview

AgentStack is a free, open-source CLI that scaffolds production-ready AI agent projects in seconds, supporting CrewAI, LangGraph, OpenAI Swarms, and LlamaStack from a single tool with built-in observability via AgentOps. It eliminates the hours of boilerplate setup that developers typically face when starting new agent projects. By running a single agentstack init command, developers get a complete project with framework-specific code, YAML-based agent and task configuration, dependency management, environment variable handling, and pre-wired AgentOps monitoring. The CLI also provides ongoing development commands: agentstack generate creates new agents and tasks, agentstack tools add installs framework-agnostic integrations, and agentstack run launches the project with built-in logging. AgentStack supports all major LLM providers through LiteLLM integration, so developers can switch between OpenAI, Anthropic, Google, and open-source models without code changes. The project structure and configuration conventions remain consistent across all four supported frameworks, which makes it straightforward for teams to evaluate and compare frameworks using identical scaffolding. Installation is available via curl, Homebrew, pipx, or UV. As a developer-focused tool, it requires Python 3.10+ and command-line proficiency. There is no graphical interface, managed hosting, or commercial support tier — AgentStack is purely a scaffolding and development workflow tool backed by community support through Discord and GitHub.

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Editorial Review

AgentStack saves hours of boilerplate per agent project by scaffolding complete setups for CrewAI, LangGraph, OpenAI Swarms, and LlamaStack. Free, open source, and framework-agnostic, it is best suited for developers who build multiple agent projects and want consistent structure. The main trade-offs are CLI-only interaction, four-framework limitation, and no managed deployment.

Key Features

Complete project scaffolding system that generates production-ready agent projects with preconfigured frameworks, YAML-based configuration, dependency management, and development workflows in seconds via a single CLI command.+

Multi-framework support including CrewAI for multi-agent collaboration, LangGraph for stateful graph-based workflows, OpenAI Swarms for lightweight agent coordination, and LlamaStack for Meta's Llama ecosystem — all sharing consistent project structure.+

Intelligent code generation via agentstack generate that creates new agents and tasks through simple CLI commands, automatically updating YAML configuration files and generating framework-specific boilerplate code.+

Curated tooling ecosystem featuring core tools (Directory Search, File Read, FTP, Code Interpreter, Vision, SQL) plus community-contributed integrations, all addable with a single agentstack tools add command across supported frameworks.+

Built-in AgentOps observability integration providing day-one access to agent execution monitoring, LLM cost tracking across providers, multi-step workflow debugging, and performance analytics without additional setup.+

Provider-agnostic architecture supporting all major LLMs and providers through LiteLLM and LangChain integration, enabling seamless switching between OpenAI, Anthropic, Google, and open-source models without code changes.+

Zero-configuration development environment with sensible defaults — run agentstack init followed by agentstack run to have a working agent immediately with logging, environment management, and observability pre-wired.+

Standardized project structure with consistent directory layouts and YAML configuration across all supported frameworks, enabling teams to benchmark different frameworks without relearning project conventions.+

Multiple installation paths including a recommended curl installer, Homebrew tap, pipx, and UV-based virtual environment setup, accommodating different development environment preferences and OS configurations.+

Active open-source community with MIT licensing, Discord support channel, GitHub repository (github.com/AgentOps-AI/AgentStack), contribution guidelines for tool developers, and regular updates adding new framework support.+

Pricing Plans

Plan 1

Free

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with AgentStack

1Install AgentStack CLI using the recommended curl installer: run 'curl --proto =https --tlsv1.2 -LsSf https://install.agentstack.sh | sh' in your terminal (alternatives: brew tap agentops-ai/tap && brew install agentstack, or pipx install agentstack).
2Verify the installation by running 'agentstack --version', then create your first project with 'agentstack init my-agent-project' — the interactive wizard will guide you through selecting a framework and configuring your agent.
3Navigate into your new project directory with 'cd my-agent-project', install dependencies with 'uv pip install', then launch your agent with 'agentstack run' to see it execute with built-in logging and observability.
4Extend your agent by generating new agents and tasks with 'agentstack generate agent <name>' and 'agentstack generate task <name>', and add tools with 'agentstack tools add <tool_name>' to expand capabilities.

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Best Use Cases

🎯

Quickly prototyping multi-agent systems when evaluating which framework (CrewAI vs LangGraph vs Swarms) fits a given use case

⚡

Bootstrapping production-oriented agent projects with consistent directory structure, logging, and environment management out of the box

🔧

Teaching and workshops where participants need a working agent project in minutes rather than spending the first hour on setup

🚀

Hackathons and rapid prototyping where time-to-first-running-agent is critical and boilerplate is a meaningful tax

💡

Teams standardizing internal agent tooling who want every new project to follow the same scaffolding conventions

🔄

Research projects exploring agent architectures across multiple frameworks using a single, consistent developer workflow

Limitations & What It Can't Do

We believe in transparent reviews. Here's what AgentStack doesn't handle well:

⚠Requires Python 3.10 or higher — no support for older Python versions or other programming languages
⚠No graphical user interface available; all project management is CLI-only through the terminal
⚠Production deployment tooling is still under development with no built-in hosting or CI/CD pipeline support as of 2026
⚠Framework support limited to CrewAI, LangGraph, OpenAI Swarms, and LlamaStack — Pydantic AI, AG2, and Autogen remain on the roadmap
⚠No managed cloud service or SaaS offering — developers must provision and manage their own infrastructure and LLM API keys
⚠AgentOps is the only built-in observability provider with no option to swap in LangSmith, Helicone, or other monitoring tools natively
⚠Community-only support via Discord and GitHub issues with no commercial support tier or SLA available

Pros & Cons

✓ Pros

✓Completely free and open source under MIT license with no usage limits or paywalls
✓Framework-agnostic design supports CrewAI, LangGraph, OpenAI Swarms, and LlamaStack from a single CLI
✓Built-in AgentOps observability provides monitoring, cost tracking, and debugging from day one without extra setup
✓Dramatically reduces agent project setup time from days to minutes with intelligent scaffolding
✓No vendor lock-in — generated code is standard framework code that can be modified or migrated freely
✓Growing ecosystem of framework-agnostic tools addable with a single CLI command
✓Multiple installation methods accommodate different development environment preferences
✓Active community with Discord support and regular updates

✗ Cons

✗Requires Python 3.10+ and command-line proficiency — not suitable for non-technical users
✗Limited to four agent frameworks currently; support for Pydantic AI, AG2, and Autogen still on roadmap
✗No managed cloud hosting or deployment services — developers must handle their own infrastructure
✗Production deployment tooling is still in development as of 2026
✗No graphical user interface — all interaction is through the terminal
✗Community support only with no commercial SLA or guaranteed response times
✗Tool ecosystem, while growing, may lack specific niche integrations compared to framework-native tool libraries
✗AgentOps is the only built-in observability provider with no option to swap in alternative monitoring tools natively

Frequently Asked Questions

Is AgentStack free to use?+

Yes, AgentStack is completely free and open source under the MIT license. There are no paid tiers, usage limits, or premium features. The only costs you'll incur are from the LLM API providers (OpenAI, Anthropic, etc.) that your agents call.

Do I need to be a developer to use AgentStack?+

Yes. AgentStack is a developer tool that requires Python 3.10+ proficiency and command-line experience. It is not a low-code or no-code platform. You'll need to understand your chosen agent framework to customize the generated code.

Which agent framework should I choose when initializing a project?+

CrewAI is best for multi-agent collaboration with role-based agents working together. LangGraph excels at complex stateful workflows with conditional branching. OpenAI Swarms is suited for lightweight agent coordination. LlamaStack targets Meta's Llama ecosystem. AgentStack's consistent scaffolding makes it easy to try multiple frameworks.

Can I switch frameworks after starting a project?+

While generated code is framework-specific, AgentStack's consistent project structure and YAML-based configuration make framework migration easier than starting from scratch. You would initialize a new project with the target framework and port your agent logic and tool configurations.

How does AgentStack compare to using LangChain directly?+

LangChain is a framework; AgentStack is a scaffolding tool that can generate projects using LangGraph (LangChain's agent framework). AgentStack adds framework-agnostic tooling, project structure conventions, and built-in observability on top of the underlying framework.

Does AgentStack support production deployment?+

As of early 2026, production deployment tooling is still under active development. AgentStack currently focuses on development scaffolding and workflow. You'll need to handle production deployment, hosting, and CI/CD configuration independently.

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What's New in 2026

As of 2026, AgentStack is in its early public rollout phase, with the landing page positioning it as 'coming soon' and directing users to the quickstart and documentation. The CLI supports four frameworks with production deployment tooling actively under development.

Alternatives to AgentStack

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CrewAI

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Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.

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Quick Info

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More about AgentStack

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

In Plain English

Overview

Editorial Review

Key Features

Getting Started with AgentStack

1Install AgentStack CLI using the recommended curl installer: run 'curl --proto =https --tlsv1.2 -LsSf https://install.agentstack.sh | sh' in your terminal (alternatives: brew tap agentops-ai/tap && brew install agentstack, or pipx install agentstack).

2Verify the installation by running 'agentstack --version', then create your first project with 'agentstack init my-agent-project' — the interactive wizard will guide you through selecting a framework and configuring your agent.

3Navigate into your new project directory with 'cd my-agent-project', install dependencies with 'uv pip install', then launch your agent with 'agentstack run' to see it execute with built-in logging and observability.

4Extend your agent by generating new agents and tasks with 'agentstack generate agent <name>' and 'agentstack generate task <name>', and add tools with 'agentstack tools add <tool_name>' to expand capabilities.

Best Use Cases

🎯

Quickly prototyping multi-agent systems when evaluating which framework (CrewAI vs LangGraph vs Swarms) fits a given use case

⚡

Bootstrapping production-oriented agent projects with consistent directory structure, logging, and environment management out of the box

🔧

Teaching and workshops where participants need a working agent project in minutes rather than spending the first hour on setup

🚀

Hackathons and rapid prototyping where time-to-first-running-agent is critical and boilerplate is a meaningful tax

💡

Teams standardizing internal agent tooling who want every new project to follow the same scaffolding conventions

🔄

Research projects exploring agent architectures across multiple frameworks using a single, consistent developer workflow

Limitations & What It Can't Do

We believe in transparent reviews. Here's what AgentStack doesn't handle well:

⚠Requires Python 3.10 or higher — no support for older Python versions or other programming languages

⚠No graphical user interface available; all project management is CLI-only through the terminal

⚠Production deployment tooling is still under development with no built-in hosting or CI/CD pipeline support as of 2026

⚠Framework support limited to CrewAI, LangGraph, OpenAI Swarms, and LlamaStack — Pydantic AI, AG2, and Autogen remain on the roadmap

⚠No managed cloud service or SaaS offering — developers must provision and manage their own infrastructure and LLM API keys

⚠AgentOps is the only built-in observability provider with no option to swap in LangSmith, Helicone, or other monitoring tools natively

⚠Community-only support via Discord and GitHub issues with no commercial support tier or SLA available

Pros & Cons

✓ Pros

✓Completely free and open source under MIT license with no usage limits or paywalls
✓Framework-agnostic design supports CrewAI, LangGraph, OpenAI Swarms, and LlamaStack from a single CLI
✓Built-in AgentOps observability provides monitoring, cost tracking, and debugging from day one without extra setup
✓Dramatically reduces agent project setup time from days to minutes with intelligent scaffolding
✓No vendor lock-in — generated code is standard framework code that can be modified or migrated freely
✓Growing ecosystem of framework-agnostic tools addable with a single CLI command
✓Multiple installation methods accommodate different development environment preferences
✓Active community with Discord support and regular updates

✗ Cons

✗Requires Python 3.10+ and command-line proficiency — not suitable for non-technical users
✗Limited to four agent frameworks currently; support for Pydantic AI, AG2, and Autogen still on roadmap
✗No managed cloud hosting or deployment services — developers must handle their own infrastructure
✗Production deployment tooling is still in development as of 2026
✗No graphical user interface — all interaction is through the terminal
✗Community support only with no commercial SLA or guaranteed response times
✗Tool ecosystem, while growing, may lack specific niche integrations compared to framework-native tool libraries
✗AgentOps is the only built-in observability provider with no option to swap in alternative monitoring tools natively