Open-source CLI that scaffolds AI agent projects across frameworks like CrewAI, LangGraph, and LlamaStack with one command. Think create-react-app, but for agents.
A command-line tool that sets up new AI agent projects with best practices — like create-react-app but for AI agents.
AgentStack is a CLI tool that generates complete AI agent projects. You run agentstack init, pick a framework, and get a working project with directory structure, dependencies, config files, testing setup, and deployment scripts. It supports CrewAI, LangGraph, OpenAI Swarms, and LlamaStack.
The pitch is simple: starting an agent project from scratch means wiring up providers, installing tools, configuring observability, and setting up deployment. AgentStack does all of that in about 30 seconds.
AgentStack is not a framework. It does not replace CrewAI or LangGraph. It sits on top of them. You still write agents in your chosen framework's syntax, but AgentStack handles the scaffolding, tool integration, and project structure that every project needs.
This matters because the agent framework space moves fast. If you bet on a scaffolding tool instead of a framework, you can swap frameworks later without rebuilding your project infrastructure.
The tool repository is the other selling point. Running agentstack add tools web_search installs the tool, manages dependencies, and updates your agent config. No manual wiring.
Building an equivalent setup manually takes 2-4 hours: install framework, set up project structure, configure LLM providers, add tools, wire observability, create deployment configs. AgentStack compresses that to one CLI command.
If you value that time at $50/hour, AgentStack saves you $100-200 per project. For teams starting multiple agent projects per quarter, the math adds up fast.
Compared to framework-specific templates (like CrewAI's built-in CLI), AgentStack gives you framework choice. Compared to building from scratch, it gives you best-practice structure without the research.
AgentStack ships with AgentOps integration out of the box. Every scaffolded project includes monitoring hooks for tracking agent runs, costs, and performance. You do not need to bolt on observability after the fact.
| Plan | Price | What You Get |
|------|-------|--------------|
| Open Source | $0 | Full CLI, all templates, all tools, AgentOps integration, MIT license |
Source: GitHub
There is no paid tier. AgentStack is fully open source under the MIT license. The team behind it also builds AgentOps (observability platform), which has its own pricing, but AgentStack itself is free.
Developers on Reddit's r/AI_Agents and r/LangChain discuss AgentStack as part of broader "agent stack" conversations. The create-react-app analogy resonates with developers who remember the pain of setting up React projects before CRA existed.
Positive feedback centers on speed: "I went from zero to a working CrewAI project in under a minute." The tool repository gets praise for removing the friction of adding capabilities to existing projects.
Criticism focuses on opinions. AgentStack enforces a specific project structure, and developers who prefer custom layouts find it restrictive. Some developers also argue that scaffolding tools create a false sense of understanding, since you get a working project without learning why it's structured that way.
Sources: Reddit r/AI_Agents, r/LangChain, GitHub discussions
No. You choose your framework at init time (CrewAI, LangGraph, OpenAI Swarms, or LlamaStack). The generated project uses that framework's native code. AgentStack handles scaffolding, not runtime.
Yes. AgentStack supports most LLM providers through LiteLLM or LangChain integrations. You configure your provider in the generated project's settings.
CrewAI's CLI only scaffolds CrewAI projects. AgentStack works across multiple frameworks and maintains a larger tool repository. If you only use CrewAI, the built-in CLI might be enough. If you work with multiple frameworks or want framework-agnostic tooling, AgentStack covers more ground.
The scaffolded projects include Docker configs, deployment workflows, and testing setups. Production readiness depends more on your chosen framework and how you build your agents than on the scaffolding tool itself.
The roadmap includes Pydantic AI, Eliza, AG2, and AutoGen. Check the GitHub repo for the latest supported frameworks.
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AgentStack saves 2-4 hours of boilerplate per agent project by scaffolding complete setups for CrewAI, LangGraph, and other frameworks. Free, open source, and framework-agnostic. Best for teams that start multiple agent projects and want consistent structure without the setup tax.
Generate complete agent project structures with framework selection, provider configuration, and best-practice layouts.
Use Case:
Starting a new CrewAI agent project with proper directory structure, configs, and dependencies in under a minute.
Add pre-configured tool integrations with `agentstack tools add` — handles deps, config, and provides usage examples.
Use Case:
Adding web search, code execution, and database tools to an agent project without manual configuration.
Templates for common agent patterns (research, support, content, analysis) with framework-specific best practices.
Use Case:
Scaffolding a research agent from a template and customizing it for a specific domain.
Generated projects include test setups, evaluation harnesses, and CI/CD configurations for systematic quality management.
Use Case:
Running automated tests on agent outputs before deploying changes to production.
Supports CrewAI, LangGraph, and custom frameworks with framework-specific project generation.
Use Case:
Comparing agent implementations across different frameworks using consistent project structures.
Package and deploy agents to Docker, cloud functions, or hosting platforms with target-specific configurations.
Use Case:
Deploying a tested agent to Docker with a single command for production use.
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View Pricing Options →Rapid prototyping and development of AI agent projects across different frameworks
Team standardization ensuring consistent project structures and best practices
Educational environments teaching agent development with proper tooling and structure
Production agent deployment requiring integrated testing and monitoring capabilities
Multi-project organizations needing consistent development patterns across agent initiatives
We believe in transparent reviews. Here's what AgentStack doesn't handle well:
No, AgentStack is a CLI tool for scaffolding and managing agent projects. It generates projects that use existing frameworks like CrewAI and LangGraph.
Yes, the generated project is standard Python/TypeScript code that you fully own and customize. AgentStack just provides the starting point.
AgentStack includes integrations for web search, code execution, file operations, API calls, and many more. Run `agentstack tools list` to see all available tools.
AgentStack is primarily for new projects. Existing projects can adopt some patterns manually, but the CLI is optimized for scaffolding new projects.
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Added LlamaStack framework support with Pydantic AI, AG2, and AutoGen on the roadmap. Expanded the tool repository with more framework-agnostic integrations. Enhanced AgentOps observability with first-tier monitoring.
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