Picking an agent framework is getting harder. New options continue to emerge, existing ones keep evolving, and the boundaries between SDKs, orchestration frameworks, and full application platforms have become less distinct.
Rather than trying to catalog every framework on the market, this guide focuses on six of the leading approaches to building AI applications. So, while the list is by no means exhaustive, it does cover many of the architectural patterns developers are likely to encounter today.
Some emphasize production-ready tooling with workflows and observability built in. Others prioritize graph-based orchestration, retrieval, multi-agent collaboration, or staying close to the underlying model APIs.
By the end of this roundup, you'll understand where each framework excels, the tradeoffs behind its design, and the types of applications it's best suited for.
What Is an AI Agent Framework?
Building an AI application involves much more than calling an LLM.
As applications become more sophisticated, they need to execute tools, manage memory, coordinate multi-step workflows, handle failures, evaluate responses, and monitor behavior in production. Assembling that architecture from scratch quickly becomes expensive and difficult to maintain.
That's where agent frameworks come in.
They provide the structure and reusable components needed to develop AI applications. Instead of rebuilding common infrastructure for every project, developers can focus on product logic while the framework handles much of the underlying orchestration.
How to Evaluate an AI Agent Framework
Most platforms make it easy to create an initial prototype. The key differences between frameworks start to emerge as your application becomes more sophisticated and needs workflows, observability, evaluations, human approval steps, or support for increasingly complex business logic.
Every team has different priorities, but when it comes to selecting a framework, we've found that these five considerations are the best place to start.
Level of Abstraction
Before comparing features, decide how much of the application lifecycle you want the framework to manage.
Some frameworks focus on core agent capabilities, while others include workflows, evaluations, observability, and production tooling as part of the platform.
A broader platform can reduce the amount of infrastructure your team needs to build and maintain, while a narrower one gives you more flexibility over how your application is designed.
Programming Language
Your existing technology stack should play a major role in the decision.
AI frameworks are typically built around the conventions, tooling, and developer experience of a particular language. If your team already builds production applications in TypeScript or Python, choosing a framework that fits naturally into that ecosystem usually makes adoption much smoother.
Production Scale
Building an AI application is one thing. Running it reliably in production is another.
As applications mature, teams often need workflow orchestration, retries, evaluations, monitoring, debugging, and better visibility into how their systems behave. Whether those capabilities are built into the framework or assembled separately can have a significant impact on long-term development and maintenance.
Agent Architecture
Before comparing frameworks, think about the kind of agent you're actually building.
Many applications work well with a single agent that can reason, call tools, and complete tasks from start to finish. More complex workflows may benefit from multiple specialized agents that each handle a different responsibility, such as planning, research, execution, or review.
Extensibility
Few applications stay the same for long.
Models improve, product requirements evolve, and workflows become more feature-rich over time. A good framework should make those changes easier to accommodate, allowing you to integrate new tools and capabilities without forcing major architectural changes.
The 6 Best AI Agent Frameworks
1. Mastra
Best for: Teams developing production AI applications with TypeScript.
Mastra is an open-source framework for building, deploying, and operating AI agents in production. Rather than focusing on a single capability, it brings together many of the components developers typically need into a unified framework.
One of Mastra's defining characteristics is its TypeScript-first approach. For teams already building JavaScript or TypeScript applications, that means AI capabilities can fit neatly into existing development workflows instead of introducing a separate Python-based stack.
Mastra also places a strong emphasis on solving the challenges that emerge after an application leaves the prototype stage. Workflows, evaluations, observability, and structured agent development are built into the framework rather than treated as optional add-ons.
Beyond its TypeScript-native experience, Mastra includes production capabilities such as workflows, evaluations, observability, and suspend-and-resume execution out of the box. It also supports routing across hundreds of language models and is released under the Apache 2.0 license, giving teams flexibility in how they build and deploy AI applications.
Why You Might Choose Mastra
- TypeScript-native developer experience.
- Designed for production AI applications rather than simple demos.
- Combines agents, workflows, evaluations, and observability in one framework.
- Built-in model routing across hundreds of language models.
- Apache 2.0 open-source license.
- Strong fit for teams that prefer a batteries-included approach.
Potential Tradeoffs
Mastra is newer than some of the more established frameworks in this space, so its ecosystem is still growing. Also, teams already standardized on Python may find that a Python-native framework fits more naturally into their existing stack.
2. LangGraph
Best for: Developers building complex, stateful AI workflows.
LangGraph is an open-source framework that gives teams precise control over how AI agents execute and coordinate work.
Its core abstraction is a graph, where each step in a workflow is represented as a node and execution flows between them. That architecture makes LangGraph particularly well suited for applications with branching logic, iterative reasoning, human approval steps, or long-running processes.
As a result, LangGraph excels in large-scale agent systems where execution needs to be explicit, deterministic, and highly customizable.
Why You Might Choose LangGraph
- Excellent for complex workflow orchestration.
- Graph-based architecture provides fine-grained control over execution.
- Well suited for long-running, stateful agent systems.
- Strong fit for highly customized applications.
Potential Tradeoffs
That level of flexibility comes with additional complexity. If your application has relatively straightforward workflows, you may not need the level of control LangGraph provides, and a more opinionated framework may help your team move faster.
3. CrewAI
Best for: Teams designing collaborative, role-based multi-agent systems.
CrewAI is an open-source framework that’s built to take complex tasks, and divide them across multiple AI agents. Instead of relying on a single agent to handle every step, developers define agents with distinct roles, goals, and tools that work together to complete a workflow.
For example, one agent might research a topic, another analyzes the findings, and a third reviews the final output before it's delivered to a user. That approach makes CrewAI particularly well suited for workflows that naturally break into multiple stages or require different types of expertise.
It's an excellent fit for research assistants, workflow automation, and other applications where work can be distributed across multiple specialized agents.
Why You Might Choose CrewAI
- Built around multi-agent collaboration.
- Intuitive model for assigning specialized roles.
- Good fit for workflows involving multiple sequential tasks.
- Flexible architecture for coordinating teams of agents.
Potential Tradeoffs
CrewAI works best when different agents have clearly defined responsibilities. If your application doesn't benefit from that division of labor, a single-agent architecture may be simpler to build, operate, and maintain.
4. OpenAI Agents SDK
Best for: Developers building applications centered around OpenAI models.
For teams already investing in the OpenAI ecosystem, the OpenAI Agents SDK provides a straightforward way to build AI agents using OpenAI's models and APIs.
Instead of trying to solve every aspect of AI application development, the SDK focuses on the core building blocks for creating agents, defining tools, coordinating handoffs, and interacting with OpenAI models. That narrower scope makes it a strong fit for developers who want to stay close to the underlying APIs while avoiding much of the boilerplate involved in building agents from scratch.
Also, because it's developed by OpenAI, the SDK stays closely aligned with the platform's latest capabilities and APIs. It also supports both Python and TypeScript, making it accessible to teams working in either ecosystem.
Why You Might Choose the OpenAI Agents SDK
- Direct integration with OpenAI models.
- Lightweight developer experience.
- Good fit for teams already invested in the OpenAI ecosystem.
- Familiar API design for existing OpenAI users.
Potential Tradeoffs
The SDK is intentionally focused on the OpenAI ecosystem rather than providing a complete framework for building production AI applications. If your application requires workflow orchestration, evaluations, observability, or broader production infrastructure, you'll likely need to pair it with additional tooling or choose a more comprehensive framework.
5. LlamaIndex
Best for: AI applications that rely heavily on retrieval, search, and enterprise data.
For many AI applications, retrieving the right information is more difficult than generating the answer itself. LlamaIndex was built to solve that problem.
Originally developed to connect large language models with external data, LlamaIndex has evolved into a broader framework for building retrieval-augmented generation (RAG) applications and other knowledge-intensive AI systems. It's particularly well suited for teams working with internal documentation, knowledge bases, PDFs, structured databases, and other proprietary data. Today, it supports both Python and TypeScript, allowing teams to build retrieval pipelines in either language.
If retrieval is your application's biggest challenge, LlamaIndex provides a mature ecosystem for ingesting, indexing, and connecting data to language models.
Why You Might Choose LlamaIndex
- Strong focus on retrieval-augmented generation (RAG).
- Extensive support for connecting external data sources.
- Well suited for knowledge assistants and enterprise search.
- Mature ecosystem for data ingestion and indexing.
Potential Tradeoffs
LlamaIndex's primary strength is retrieval and data integration. Teams whose biggest challenge is workflow orchestration, multi-agent coordination, or broader production infrastructure may find other frameworks better aligned with those priorities.
6. Semantic Kernel
Best for: Organizations building AI applications within the Microsoft ecosystem.
Semantic Kernel is an open-source framework developed by Microsoft for integrating AI capabilities into existing software. Rather than focusing exclusively on AI agents, it provides a broader set of building blocks that help developers incorporate AI into existing enterprise systems.
It supports multiple programming languages and familiar enterprise development patterns, making it a natural fit for organizations already invested in Microsoft technologies, Azure, or .NET.
For those teams, that alignment can make adopting AI feel more like extending an existing application than building an entirely new one.
Why You Might Choose Semantic Kernel
- Strong enterprise orientation.
- Good fit for Microsoft and Azure environments.
- Supports multiple programming languages.
- Flexible architecture for integrating AI into existing applications.
Potential Tradeoffs
Semantic Kernel is designed with Microsoft’s ecosystem in mind. Teams building primarily in TypeScript or working outside Microsoft's developer platform may find that other frameworks fit more easily into their existing workflows.
Which AI Agent Framework Should You Choose?
Choose Mastra if...
You want a production-ready TypeScript framework with agents, workflows, evaluations, and observability built in.
Choose LangGraph if...
Your application depends on complex, stateful workflows and you need precise control over execution.
Choose CrewAI if...
Your application naturally divides work across multiple specialized agents.
Choose the OpenAI Agents SDK if...
You're already building with OpenAI's models and want to stay close to the underlying APIs.
Choose LlamaIndex if...
Retrieval is your biggest challenge, and your application depends on external knowledge or enterprise data.
Choose Semantic Kernel if...
Your team is already invested in Microsoft's ecosystem, including Azure or .NET.
Frequently Asked Questions
Do I Need an AI Agent Framework?
Not always. If you're building a simple prototype, an LLM API may be all you need. As applications become more sophisticated, however, most teams eventually need capabilities such as tool calling, memory, workflow orchestration, evaluations, and observability. An agent framework provides that infrastructure, making applications easier to develop and maintain.
Which AI Agent Framework Is Best for Beginners?
Start with a framework that matches your existing programming language and the type of application you want to build. Familiar tools usually lead to a shorter learning curve and faster development.
What's the Difference Between an AI Agent Framework and an AI SDK?
An SDK provides the APIs and building blocks for interacting with language models or creating agents. An agent framework goes further by helping developers structure, coordinate, and operate AI applications. The distinction isn't absolute, though, and many tools combine elements of both.
Should I Optimize for Flexibility or Speed?
If speed is your priority, choose a more opinionated framework. If flexibility matters more, choose one that exposes lower-level building blocks, even if it requires more engineering effort as your application grows.
Should I Choose a TypeScript or Python Framework?
In most cases, choose the language your team already uses. If your team builds production software in TypeScript or Python, choosing a framework that fits naturally into that ecosystem will usually simplify development and long-term maintenance.
Should I Build a Single-Agent or Multi-Agent System?
Start with a single agent unless you have a clear reason not to. Most applications don't need multiple agents. Add specialized agents only when your workflow naturally divides into distinct responsibilities that benefit from separate planning, reasoning, or execution.
Which Framework Is Best for Retrieval-Augmented Generation (RAG)?
If retrieval is your primary challenge, LlamaIndex is one of the strongest choices. It provides a mature ecosystem for connecting language models to external data. That said, retrieval isn't exclusive to LlamaIndex. Many frameworks support RAG when paired with the appropriate retrieval pipeline and vector database.
Can I Switch Frameworks Later?
Yes, but the effort needed depends on how tightly your application is coupled to a framework's APIs and abstractions. Keeping business logic separate from framework-specific code makes migration significantly easier if your requirements change.
