AI agent frameworks: Definition, comparison, and guide

Over the last year, I've seen a shift in how teams talk about AI. Chatbots, once the center of attention, are no longer the primary focus. Instead, more businesses are moving toward autonomous AI systems. AI agents are what you reach for when you want a system that can break down a task, make decisions, interact with tools, and learn from its mistakes (unlike me).

Designing and integrating these complex systems with external tools isn't straightforward. AI agent frameworks, which offer pre-built components to help you develop, deploy, and manage agents, simplify this process. But to get meaningful results, you need to choose the right stack for your needs.

In this guide, I'll explain what an AI agent framework is, compare the most widely used options (with names that sound like they were generated by AI, ironically), and walk you through how to pick one based on your use case and constraints.

Table of contents:

• What are AI agent frameworks?

• Core components of agentic AI frameworks

• Top AI agent frameworks

• How to choose the right AI agent framework

• Build your own AI agents with Zapier

What are AI agent frameworks?

AI agent frameworks are dedicated platforms that help teams develop, deploy, and manage autonomous systems. More specifically, a framework gives you reusable building blocks and patterns for things like planning, tool use (API calls), state/memory, handoffs between agents, and tracing what happened.

Remember Raspberry Pi toolkits? Those DIY boards let you snap sensors and modules onto a base to build custom hardware projects. AI agent frameworks work the same way, but on the software side. (And with significantly less risk of electrocuting yourself.)

Before picking a framework, it's worth being clear on what kind of system you're building—because the answer changes everything about which option is right. Chatbots and agent-based systems are pretty different. They can use the same underlying models, but the interaction patterns differ.

AI agent frameworks are what enable this proactive behavior. They're the structure that lets your agents think, act, and not completely fall apart when something unexpected happens (again, unlike me).

Core components of agentic AI frameworks

If you're wondering why frameworks are important for building AI agents, the one-word answer is: components. 

Think of these as the building blocks that determine your agent's capabilities—what it can remember, what tools it can call, and how well you can monitor it once it's running.

• LLM integration: In AI agent frameworks, you can use built-in connectors that enable agents to connect with various large language models (like GPT, Claude, or open-source models you're running on your janky local setup).

• Tools and function calling: This component enables agents to call tools like web search, code interpreters, and third-party APIs. They can fetch data, update systems, or activate specific workflows (which makes it more capable than many people I've worked with).

• Memory management: Agents need context to make decisions. Short-term memory tracks the current session; long-term memory lets them recall past interactions across sessions.

• Orchestration: AI agent orchestration helps manage multi-agent workflows. This component helps assign roles to different agents and boosts cross-agent collaboration.  

• Observability: This component allows you to observe the decisions, tool use, actions, and failures of an agent. The main features include logging, tracing, and monitoring.

• Personalized decision-making: Agents are smart enough to analyze user behavior and give product recommendations. Like "hey, based on your past purchases, you might like this $47 candle that smells like a bookstore" (and honestly, yes, I would).

Top AI agent frameworks

These are the most widely used framework options. Most are open source, each with unique strengths and suitability.

LangGraph

LangGraph is a framework with a graph-based architecture and stateful workflows. It's an extension of LangChain, a popular AI orchestration tool, and focuses on human-in-the-loop function.

It models agent actions as nodes and defines transitions as edges. The agent behavior is displayed as a graph, making it ideal for non-linear workflows. Such AI agent frameworks make it easy to design systems that need constant iterations and recover from failures.

LangGraph is best suited for use in the following conditions:

• You have multi-step agent systems.

• There's a need for automated decision-making.

• You're familiar with the LangChain library.

• You want to make optimum use of human intelligence and machine learning.

Customer support, lead qualification, research agents, and approval-based workflows are a strong fit for LangGraph.

CrewAI

CrewAI is built around the idea of role-based agent teams—each agent gets a clearly defined job, like a planner, a writer, or a reviewer—and they collaborate to get things done.

The autonomous systems communicate based on their assigned functions, like planning, reviewing, or writing. It's similar to how human teams operate, without the need for step-by-step orchestration.

Use CrewAI if:

• You want collaborative AI agent teams.

• Flexibility and delegation are more important than tight control.

• You want autonomous decision-making.

CrewAI is a natural fit for content marketing teams, sales outreach workflows, and structured production pipelines.

AutoGen

This open-source framework from Microsoft prioritizes conversational chats between AI agents. AutoGen offers a low-code approach to designing multi-agent AI applications.

Think of multiple agents talking with each other like humans. This framework puts messages at the forefront to drive task delegation, reasoning, and execution. The AI agents can exchange messages, ask questions, and call tools, maintaining a conversational flow.

You can use this framework if:

• You need human-in-the-loop flexibility.

• Low-code feels appealing to you.

• You can use pluggable components to customize the systems.

Such AI agent frameworks are helpful for building interactive problem-solving agents, teaching assistants, conversational planning systems, and similar use cases.

LlamaIndex

LlamaIndex is a retrieval augmented generation (RAG) toolkit with a rich ecosystem of connectors and indexes. You can use it as part of an LLM agent framework, but its core functionality is data retrieval.

This framework uses structured and unstructured data to enable agents to perform actions as intended. Using LlamaIndex, you can connect data sources like documents, APIs, and knowledge bases to LLMs.

Here's when using this framework makes sense:

• The AI agents sit on top of the framework.

• Data retrieval is your primary goal.

• You want to build knowledge-aware AI agents.

With LlamaIndex, you can build customer support systems, enterprise knowledge assistants, document analysis tools, and much more.

Semantic Kernel

Microsoft has four main AI agent frameworks. Semantic Kernel is one of them, focusing on integrating large language models with existing applications.

Semantic Kernel is an enterprise-focused AI agent software development kit (SDK) that supports C#, Python, and Java. You can use this framework for orchestration, planning, and integration. Notably, the deep integration with Microsoft and the Azure ecosystem makes it suitable for enterprise setups.

Semantic Kernel can be a good option if:

• Your organization uses Microsoft systems extensively.

• The AI agents need to interact with cloud services and internal APIs.

• You're ok with adopting AI systems step-by-step in your workflow.

Such AI agent frameworks are well-suited to build internal knowledge assistants, developer tools, business assistants, and MS-based automation systems. 

PydanticAI

PydanticAI is one of the popular Python-based AI agent frameworks. It uses Pydantic models (Python classes that define the structure of your data with type hints) to define the agent response schema.

This framework focuses on data integrity and correctness. It's quite popular among developers who want predictability in the agent's behavior. (Because nothing says "living on the edge" like wanting your software to behave exactly the same way every single time.)

You can explore this framework if:

• The agent system is production-oriented.

• AI agents need to interact with APIs or databases reliably.

• You want to use agents as dependable components.

API-driven automation, AI-assisted internal tools, and system integration agents are some popular use cases of this framework.

Haystack

Haystack is built for document-oriented and search-first agents.

It has components for document ingestion, querying, and indexing, so AI agents can use relevant content to produce apt output.

You can explore Haystack if:

• You need a framework for RAG systems.

• Free-form reasoning isn't as vital as accuracy.

• You need structured and predictable workflows.

Due to its heavy reliance on document search, this framework is well-suited for developing natural language processing (NLP) systems. You can also build agents for enterprise search, question-answer apps, and knowledge base assistance using Haystack.

OpenAI Agents SDK

This software development kit from OpenAI enables you to build AI agents on the native platform. The OpenAI Agents SDK supports self-hosted AI agents and doesn't require you to create a separate orchestration layer.

Notably, you can handle execution, memory, and safety controls on OpenAI's managed environment. For teams that prefer operational simplicity, this framework can prove useful and work like an AI agent builder.

Here's when you can prefer OpenAI Agents SDK:

• You want to build multi-agent workflows.

• You're ok working in the OpenAI ecosystem.

• Vendor neutrality isn't a huge concern for your team.

This framework is suitable for building agents that handle workflow automation, research, and customer support.

How to choose the right AI agent framework

Picking a framework comes down to four questions: what are you building, who's building it, what does it need to connect to, and how much overhead can you realistically manage? Here's how to work through them.

1. Start with your use case

Before you hover through these fancy tech names, answer the following questions:

• Are you automating business processes or building a product?

• Do you need multi-step workflows or single-task execution?

Some AI agent frameworks are excellent at building multi-step systems, whereas others are useful for developing single-task agents.

You can start by distinguishing between an internal tool and a customer app: tools within your org may require more flexibility, while customer applications need to be highly reliable.

2. Consider your team's technical skills

It's vital to gauge your assets and capabilities.

For example, if you have skilled in-house developers, choose frameworks like LangGraph. If you have a business or ops team, a low-code option like AutoGen can be the best AI agent framework.

If you have a hybrid team, select frameworks that balance flexibility with practical components.

3. Evaluate integration requirements

You need first-hand info about the tools, APIs, and data sources your agent will touch.

For instance, the AI agent may need access to:

• Internal systems such as ticketing tools, CRMs, etc.

• Data sources like document stores and knowledge bases.

• Tools such as emails, spreadsheets, calendars, or payment systems.

Once you have an idea about these prerequisites, assess the pointers below:

• Native integrations: Does the agent already support the desired tools?

• API extensibility: How easy is it to connect new services with APIs?

• Authorizations and permissions: Can the framework handle OAuth, service accounts, and special access?

• Events and triggers: Can agents react to changes (e.g., tickets/new form submissions), or do they need custom prompts most of the time?

That's exactly where you need to think of choosing the right ecosystem. A tool like Zapier can reduce setup time by handling triggers and integrations across thousands of apps so you can focus purely on agent logic.

4. Think about scale and cost

When you consider the scalability and cost of the frameworks, you have two options: open source or managed platforms.

Open-source frameworks are less expensive upfront, but can require a learning curve and more development effort.

As your business grows, you'll need to consider the following:

• Hosting and compute costs: These expenses are directly proportional to the number of users and workflows.

• Maintenance costs: You need to cover updates, security patches, and breakdowns.

• Monitoring and engineering: You'll need to fix issues when integrations fail or models hallucinate, and keep constant logs. (You'll also need coffee. Budget accordingly.)

On the other hand, managed platforms can reduce these operational hurdles, but costs can climb due to:

• Scaled usage: The more your agents run, the greater the recurring platform costs.

• Complex workflows: Many paid platforms charge "per action." So when you move to multi-agent setups, the increased calls can lead to high bills.

• Vendor lock-in: The more you build on a single platform, the harder and more expensive it becomes to switch later.  

Bottom line, your requirements will grow, so pick something that can grow with them. The cheapest option today isn't always the cheapest option at scale. 

Build your own AI agents with Zapier

You don't have to build from scratch. The AI agent frameworks in this guide exist precisely so you can skip the boilerplate and focus on what logic, tools, and decisions your agent needs.

Once you've assessed your use case, team skills, and integration requirements, the right framework usually becomes clearer. Start with one that matches your current reality—you can always migrate as your needs evolve. (And you probably will. Put it in the roadmap now, while you're still feeling optimistic.)

If you're ready to start building agents with no-code, Zapier Agents lets you build AI agents in minutes and connect them across 9,000+ apps. In just a few steps, you can design tailored agents, give them access to your company's knowledge base, set up triggers, and start automating.

Related reading:

• Zapier Agents: Combine AI agents with automation

• 5 examples of AI agents in the workplace

• AI agents for marketing: A complete guide

• State of agentic AI adoption survey