Building AI agents from scratch usually means wrestling with boilerplate and configuration instead of real logic. AgentStack fixes that by scaffolding projects in minutes with frameworks like CrewAI, LangGraph, or OpenAI Swarms already set up.

We will also cover how persistent memory can be layered in with tools like Mem0.

TLDR:

• AgentStack eliminates setup friction by scaffolding AI agent projects with frameworks like CrewAI, LangGraph, and OpenAI Swarms in minutes.

• You get framework-agnostic templates and tools, so you can experiment and switch between agent approaches without rebuilding.

• Production-ready features include testing frameworks, deployment scripts, and monitoring patterns built into every template.

What Is AgentStack?

AgentStack is an open-source framework that eliminates the painstaking setup work that usually comes with agent development.

Think of AgentStack as intelligent scaffolding. It doesn't lock you into a specific way of building agents. Instead, you get a clean starting point with popular frameworks like CrewAI, LangGraph, and OpenAI Swarms already configured and ready to go.

The beauty lies in its simplicity. Run a single command and choose your preferred framework, and you get a working agent project with proper structure, dependencies, and examples. No more spending hours figuring out import statements or wrestling with configuration files.

AgentStack's framework-agnostic approach means you can switch between different agent frameworks without starting from scratch. This makes it easier to experiment.

AgentStack is unique in its focus on developer experience. The AgentStack documentation shows that you still need to understand the underlying frameworks. AgentStack just handles the boring setup stuff so you can focus on building actual agent logic.

The GitHub repository shows active development with regular updates and community contributions.

AgentStack vs Other Agent Frameworks

AgentStack is not trying to compete with AutoGen, CrewAI, or LangGraph. Those are full frameworks with their own philosophies and approaches to agent orchestration.

AgentStack sits one layer above these frameworks. While AutoGen focuses on multi-agent conversations, and CrewAI instead focuses on role-based agent teams, AgentStack lets you choose any of these approaches without committing upfront.

The CLI-driven workflow is where AgentStack shines. Instead of reading through framework documentation and copying boilerplate code, you answer a few questions and get a working project. This approach works especially well when you're checking out different frameworks or need to spin up proof-of-concepts quickly.

Traditional frameworks often come with strong opinions about how agents should work. That's great when those opinions align with your needs, but it's limiting when they don't. AgentStack's framework-agnostic design means you can start with one approach and migrate to another as requirements evolve. Benchmarking different approaches becomes simpler when you can quickly scaffold projects with different underlying frameworks.

AgentStack Supported Frameworks

AgentStack supports the leading agent frameworks including CrewAI, LangGraph, OpenAI Swarms, and LlamaStack. Each framework has different strengths, from role-based teamwork to graph-driven execution and lightweight OpenAI integrations. AgentStack scaffolds projects for all of them, so you can choose the approach that fits your needs.

The comparison of different frameworks shows how varied the field is. AgentStack doesn't try to abstract away these differences. Instead, it helps you get started with whichever approach fits your needs.

This flexibility becomes important when you realize that stateless agents fail. Different frameworks handle state and memory differently, and AgentStack lets you try these approaches without starting from scratch each time.

AgentStack Tools and Integrations

AgentStack is built around one idea: agents need tools to be useful. The framework ships with a library of pre-built integrations for web scraping, file operations, API calls, and database queries. But what makes that even more powerful is that each tool uses the same interface. For example, a web scraping tool works identically whether you’re running CrewAI, LangGraph, or another framework. You don’t have to learn framework-specific APIs or rewrite code.

Of course, you aren't limited to just AgentStack's pre-integrated tools. You can also employ custom tools in the framework which operate much in the same way as the pre-integrated tools. Using templates, examples, and interface contracts, developers can build and plug in new tools without any friction. And, the AgentStack documentation covers common cases like API wrappers, data processors, and memory modules.

When considering AgentStack, keep three core benefits in mind:

• Dependencies. In AgentStack, these are handled automatically. When you add a tool, AgentStack updates your configuration and requirements files. No manual installs or version tracking required.

• Integrations. The ecosystem also includes direct integrations with third-party APIs and services such as search engines, storage providers, and communication platforms. Agents can interact with these systems securely and consistently.

• Persistence. Memory tools add persistence, letting agents maintain context across sessions, a key feature for a variety of applications like customer support, healthcare, education, sales, and e-commerce.

AgentStack’s standardized interface keeps all of this consistent, with tools, frameworks, and memory modules working together out of the box.

Templates and Project Structure

AgentStack includes templates for common use cases like customer support agents, research assistants, and data analysis. Each template comes with a working project structure and example implementations, giving developers a proven starting point they can customize quickly. This saves time compared to building from scratch and ensures best practices are baked in from the beginning.

To Use AgentStack (Or Not To Use AgentStack)

AgentStack is most valuable when speed, structure, and flexibility matter more than full control. It shines in the early and middle stages of development, when you need to go from idea to running agent quickly without getting buried in setup and helps teams that care about iteration velocity and developer experience. The CLI handles setup, dependency management, and baseline security so developers can focus on logic instead of boilerplate.

Consider using AgentStack when:

• You want to prototype fast across multiple frameworks like CrewAI, LangGraph, or OpenAI Swarms.

• You need consistent project structure for teams working across different agent frameworks.

• You’re building internal tools or proofs of concept that may evolve into production systems.

• You want production-ready scaffolding with built-in tests, deployment scripts, and monitoring hooks.

• You’re exploring persistent memory integrations such as Mem0 and want to test those patterns quickly.

However, it’s not a fit for every project. You should probably avoid AgentStack when:

• You’re building a custom framework or need fine-grained control over orchestration internals.

• Your agents rely on proprietary infrastructure or tightly coupled components that break standard scaffolding.

• You already have a mature production pipeline with CI/CD, observability, and deployment infrastructure in place.

• You need runtime-level performance optimizations such as custom schedulers or high-throughput serving layers.

AgentStack’s sweet spot is between exploration and production. It is fast enough to test ideas, structured enough to deploy real systems, and flexible enough for experimentation. For teams building their first or fifth agent framework, it provides a pragmatic balance that trades a bit of control for a lot of velocity.

Adding Memory to AgentStack Projects with Mem0

Most agents forget everything between conversations. If you need persistent context across sessions, you can add a memory layer such as Mem0. This reduces token usage and allows your agents to remember conversation history, user preferences, and key information over time.

The context-aware clients guide shows practical implementation patterns. The memory types deep dive explains the theoretical foundation.

Performance improvements are substantial. Agents with proper memory don't need to rebuild context in every conversation. Users don't have to repeat their preferences or background information. The experience becomes more natural and efficient.

FAQ

How do I get started with AgentStack if I'm new to AI agent development?

Start by installing AgentStack with the one-line bash script, and then run the CLI to create your first project. AgentStack will ask you to choose a framework (CrewAI, LangGraph, or OpenAI Swarms) and generate a working project with examples and proper structure, so you can focus on learning agent logic instead of setup.

What's the main difference between AgentStack and frameworks like CrewAI or LangGraph?

AgentStack is scaffolding that sits above these frameworks, not a replacement for them. While CrewAI focuses on role-based agent teams and LangGraph provides graph-based orchestration, AgentStack gives you the choice to use any of these approaches with pre-configured templates and project structure.

Can I switch between different agent frameworks after starting a project?

While AgentStack's framework-agnostic design makes this easier than starting from scratch, switching frameworks after project creation still requires manual work. The standardized project structure and tool interfaces help, but you'll need to adapt your agent logic to the new framework's patterns.

How do I add persistent memory to my AgentStack agents?

Install Mem0 with pip install mem0ai, configure memory settings in your agent initialization, and add memory calls to your workflow. This works consistently across all AgentStack-supported frameworks and can reduce token usage by up to 80% while allowing personalized experiences.

When should I consider using AgentStack for production applications?

AgentStack is production-ready when you need to quickly prototype or deploy agents with proven patterns. It's particularly valuable when you're experimenting with different frameworks, need standardized tool integrations, or want to use community templates for specific use cases like customer support or research assistants.

Final Thoughts on Building Scalable AI Agents with AgentStack

AgentStack removes the setup headaches that slow down agent development, giving you a clean starting point and the flexibility to try different frameworks without being locked in. It helps teams move faster from prototype to production by handling the scaffolding and best practices behind the scenes. If you also need persistent memory, tools like Mem0 can be layered in to make your agents more efficient and context-aware.