Top 8 Agentic AI Frameworks You Should Know in 2026

Quick Overview

• Agentic AI frameworks in 2026 include LangGraph, CrewAI, Microsoft Agent Framework, AutoGen, Google ADK, OpenAI Agents SDK, LlamaIndex, and AWS Strands Agents.
• LangGraph is suited to complex, stateful workflows, while CrewAI and AutoGen support multi-agent collaboration and role-based task delegation.
• Microsoft Agent Framework fits enterprise workflows, Google ADK supports Google and Gemini applications, and OpenAI Agents SDK focuses on agents, tools, handoffs, guardrails, and tracing.
• LlamaIndex is useful for data and RAG-based agents, while AWS Strands Agents supports model-driven agents and AWS-based applications.
• This guide covers the key capabilities, use cases, comparison factors, and selection criteria for the leading agentic AI frameworks in 2026.

Want to build practical AI agents using modern frameworks and workflows? Explore the Applied Agentic AI Certification Course [Best in 2026] to develop hands-on skills in agentic AI. 

What are the top agentic AI frameworks in 2026?

The following is a list of the best agentic AI frameworks in 2026, and each framework serves different purposes.

Framework	Primary focus	Suitable for
LangGraph	Stateful agent orchestration	Complex, long-running workflows
CrewAI	Multi-agent collaboration	Role-based agent teams
Microsoft Agent Framework	Enterprise agent workflows	Microsoft/Azure applications
AutoGen	Multi-agent conversations	Agent-to-agent applications
Google ADK	Agent development	Google/Gemini applications
OpenAI Agents SDK	Lightweight agent orchestration	Tool-using and delegated agents
LlamaIndex	Data and retrieval workflows	Data/RAG-centric agents
AWS Strands Agents	Model-driven agents	AWS-based agent applications

Also Read: What is Agentic AI?

Now, let’s discuss each framework in detail.

1. LangGraph

LangGraph is specifically used for the development of agents that require structured and stateful workflows. Rather than viewing the agent’s entire process as a single interaction, developers can model its workflow as a graph of steps and decisions.

Key capabilities include:

• Stateful and long-running agent workflows
• Graph-based orchestration
• Persistence and durable execution
• Human-in-the-loop workflows
• Fine-grained control over agent behavior

Example: A customer service agent can validate an order, recognize an issue, request a human employee to approve the refund, and continue with the process.

2. CrewAI 

CrewAI follows the approach of team-based agent development. Rather than relying on a single large agent to perform all tasks, CrewAI relies on a combination of several smaller agents, where every agent is assigned a role, a target, and a set of responsibilities, and agents collaborate like regular colleagues..

Key capabilities include:

• Role-based multi-agent systems
• Agent collaboration
• Crews and Flows
• Task delegation
• Workflow automation

Example: A content research process can use one agent for gathering information, second one for organizing information and third one for reviewing the output.

Also Read: LangChain vs CrewAI vs AutoGen

3. Microsoft Agent Framework

Microsoft Agent Framework is the platform developed by Microsoft to enable creation of applications that operate based on agents, covering enterprise usage and Python and .NET languages.

The framework provides support for graph-based workflow design, state management, multi agent orchestration, and Microsoft technology integration. Companies that have previously used Azure products will find it easy to integrate.

Key areas include:

• Enterprise agent applications
• Multi-agent orchestration
• Graph-based workflows
• State and sessions
• Python and .NET support
• Microsoft and Azure ecosystem integration

Example: An organization may establish an IT support workflow internally in which an agent looks into the knowledge base, another takes care of the tickets, and a human approves actions that need additional authorization.

Also Read: How to Build Agentic AI Using Python

4. AutoGen 

AutoGen is famous for creating applications that involve collaboration between various agents. A developer can create a number of agents each having its own role and make them communicate or use some tools within the workflow.

The popularity of AutoGen increased due to the fact that agent-to-agent collaboration was simple to implement with it.

Key capabilities include:

• Multi-agent conversations
• Agent-to-agent collaboration
• Tool and code execution
• Human participation in workflows
• Support for multi-agent application patterns

Example: One agent can write a piece of code, another can review it, and a third can test or improve it based on the results.

5. Google Agent Development Kit (ADK)

Google Agent Development Kit is Google's platform for designing AI agents. It is suitable for developing agent workflows, tools, and multi-agent systems and naturally integrates within Google's AI system.

This may prove beneficial when the developer is designing applications using Google's models and services.

The main features include:

• Agent development
• Multi-agent applications
• Integration with tools
• Google and Gemini ecosystem support
• Agent workflows

Example: An assistant in travel could make use of one agent for searching information, another for organizing the itinerary, and tools to retrieve relevant data.

6. OpenAI Agents SDK

The OpenAI Agents SDK offers a relatively lightweight framework for building agent-based applications around agents, tools, handoffs, sessions, and guardrails.

This is helpful when an application requires an agent to do some work while delegating certain pieces of work to other agents.

The capabilities offered by the framework include:

• Agents and tools
• Handoffs
• Guardrails
• Sessions
• Human-in-the-loop workflows
• Built-in tracing

Example: A customer service application could begin with a general support agent and then pass a billing issue to a specialized billing agent.

7. LlamaIndex

LlamaIndex can be very helpful for applications that require the connection of the agents with data that is either private or external in nature.

It provides tools for working with documents, data sources, retrieval systems, and RAG workflows. This makes it a practical option for knowledge-intensive applications.

Some features provided by this framework include:

• Data-centric agents
• Document-heavy applications
• Retrieval and RAG workflows
• Data connectors
• Event-driven workflows

Example: An organization may develop a knowledge assistant internally that retrieves information from policies, product documentation, and technical manuals before answering a query.

8. AWS Strands Agents

AWS Strands Agents follows a model-driven strategy for building agents. Developers can provide agents with tools and allow the model to determine how such tools are used as part of the task. This is especially applicable to applications developed within the AWS environment.

Key capabilities include:

• Model-driven agent development
• Tool use
• Multi-agent applications
• AWS service integration
• Agent workflows

Example: An AWS-powered operations agent could analyze information about an application, employ cloud tools to analyze the problem, and conduct multiple tasks before delivering the results.

Also Read: What Frameworks Are Taught In Agentic AI Training?

Which agentic AI framework is best for different use cases?

The right choice depends on the workflow, data, tools, infrastructure, and level of control required.

Best for production AI agents

Recommended Frameworks: LangGraph, OpenAI Agents SDK, Microsoft Agent Framework

Production AI agents not only need access to models but also reliable execution, state management, observability, guardrails, and human approval process when required.

• LangGraph will be suitable for long-running processes with checkpoints and persistent states.
• OpenAI Agents SDK allows easy tool calling, handoffs, traceability, and guardrails in production deployments.
• Microsoft Agent Framework offers enterprise-level workflow orchestration, governance, and state management.

Example: An AI agent built by a financial services firm for loan processing can make use of these frameworks due to their capability of managing approvals, state of workflow, and executing multi-step tasks.

Best for multi-agent systems

Recommended Frameworks: CrewAI, AutoGen, Microsoft Agent Framework

For some applications, several dedicated agents are needed to interact with each other. In these situations, collaboration and delegation of tasks play an essential role. These frameworks allow the agents to collaborate and delegate tasks between each other.

• With CrewAI, agents are organized into teams with different roles.
• Agents can exchange information among themselves and work on problems using conversation in AutoGen.
• Microsoft Agent Framework helps orchestrate multi-agents in more structured ways.

Example: A content creation system can make use of a Research Agent, a Writer Agent, an Editor Agent, and an SEO Agent together to create high quality content.

Also Read: Building Multi-Agent Systems in Agentic AI

Best for complex and stateful workflows

Recommended Framework: LangGraph

For tasks that require a high number of tasks, loops, and pauses, state persistence is necessary. LangGraph provides persistence, checkpoints, and workflow orchestration for lengthy tasks, such as a research task that takes place over several days.

LangGraph is best suited to:

• Persistent state
• Checkpointing
• Durable execution
• Human-in-the-loop reviews
• Controlled workflow orchestration

Example: In an insurance claim workflow, the process might take place by collecting documents, verifying details, approving requests, and performing other activities over several days. LangGraph can track progress throughout the entire process.

Best for enterprise AI applications

Recommended Framework: Microsoft Agent Framework

Big companies care about governance, middleware, and observability. Microsoft Agent Framework can be considered if the corporation already works with Azure and requires state management and monitoring.

The following areas need to be analyzed:

• Enterprise workflows
• Microsoft and Azure ecosystem
• State management
• Middleware
• Observability
• Governance requirements

Example: A big company can utilize Microsoft Agent Framework to develop internal agents for HR, IT Support, and Knowledge Management while adhering to organizational governance standards.

Best for data and RAG-based agents

Recommended Framework: LlamaIndex

Many AI applications depend much on company-related data, databases, documents, and knowledge bases rather than general knowledge models.

LlamaIndex is designed for:

• Retrieval-Augmented Generation (RAG)
• Document search
• Knowledge-base applications
• Data connectors
• Information retrieval workflows

Example: A legal assistant that searches contracts, policies, and compliance documents can use LlamaIndex to retrieve the information before generating a response.

Best for role-based agent teams

Recommended Framework: CrewAI

A CrewAI approach works best where an AI workflow consists of an actual team of specialists working towards a common objective.

Its strengths include:

• Specialized agent roles
• Task delegation
• Cooperative workflows
• Crews and Flows architecture
• Easy multi-agent collaboration

Example: In a marketing workflow, market research can be delegated to one agent, while content writing is given to another and campaign analysis to a third, all coordinated through CrewAI.

Building AI agents starts with understanding how LLMs respond to instructions and use tools. Strengthen these skills with the Generative AI and Prompt Engineering Course for Professionals.

How to compare the leading agentic AI frameworks?

With so many agentic AI frameworks available, it can be tough to know which one to use. While features can be helpful in deciding, the best method would be to evaluate the frameworks on their performance in areas such as workflow, collaboration, memory, integrations, and deployment in production.

1. Framework Architecture and Orchestration

The first consideration would be how the framework architecture is implemented and orchestrates the agent workflows.

There are several orchestration models for different frameworks:

• Graph-based orchestration (LangGraph, Microsoft Agent Framework): The workflow is defined in the form of nodes and links between them.
• Role-based orchestration (CrewAI): Agents are assigned with roles and responsibilities.
• Conversation-based orchestration (AutoGen): The agents' workflow is organized based on conversation exchange.
• Model-driven orchestration (AWS Strands Agents): The orchestration model is focused on agent activity in the context of models and tools usage.

In addition, consider how framework supports:

• Sequence of tasks
• Parallel execution
• Agent handoffs
• Conditional workflows
• Delegation of tasks

Example: if you need to create a customer onboarding workflow with approval and reviews process, then graph-based orchestration will provide you with additional options compared to a conversation-based framework.

Also Read: Agentic AI Orchestration Explained: How Intelligent Systems Coordinate Actions

2. Multi-Agent Capabilities

Today’s AI systems are often based on multiple agents performing the work instead of one agent alone doing all the work.

While considering different frameworks, it is important to check for:

• Agent-to-agent communication
• Specialized agent roles
• Task delegation
• Team coordination
• Workflow collaboration

For example, a content creation system might have:

• A Research Agent
• A Writer Agent
• An Editor Agent
• A SEO Agent

Frameworks like CrewAI and AutoGen excel in this field because they are designed around multi-agent collaboration.

3. Memory and State Management

The aspect of memory becomes important if the agents are expected to remember details through several interactions or workflows.

Consider frameworks that provide:

• Short-term memory
• Long-term memory
• Persistent state
• Sessions
• Checkpointing
• Context management

Example: An insurance claims agent might need to interrupt a process while awaiting customer documentation and then resume it after several days. Frameworks with persistent state management such as LangGraph are better suited to such cases.

4. Tool and Model Integration

The majority of AI agents require the ability to use external tools and sources of information. A framework should allow for easy integrations of such type.

See how well frameworks integrate:

• Multiple LLM providers
• Function calling
• Tool usage
• API integrations
• MCP support
• Retrieval-Augmented Generation (RAG)

Example: A travel planning agent will require the ability to interact with flight APIs, hotel booking services, calendars, maps, and payments services. Strong integration capabilities can greatly reduce development effort.

5. Production and Enterprise Readiness

Building a prototype is simple. Deploying the same with reliability in production is hard.

Frameworks that support production should support:

• Observability and tracing
• Guardrails
• Human-in-the-loop workflows
• Error handling
• Monitoring
• Deployment flexibility
• Security controls

Example: A healthcare or financial app might need to have human-in-the-loop before executing some actions. An enterprise-ready framework with governance and approvals capabilities is more suitable for such cases.

6. Programming Languages and Ecosystem

The framework should fit your team's existing skills and technology stack.

Consider support for:

• Python
• TypeScript/JavaScript
• .NET
• Java
• Cloud platforms
• Community resources

A strong ecosystem often means:

• Better documentation
• More tutorials
• Faster updates
• Larger developer communities
• More integrations

Looking to apply AI agents and AI-powered workflows to product development? Build practical product skills with the AI-Powered Product Management Certification with Microsoft by upGrad KnowledgeHut. 

What features to evaluate before choosing an agentic AI framework?

Below are the factors that should be considered before the selection of an AI framework.

1. Workflow Complexity

Not all agents operate the same way. Some just give an answer to a question and launch a tool, while some others have to plan several operations, make decisions, repeat actions, try again when something fails, or wait for some other process to be completed.

The more complex the workflow is, the more control you will want to have from the framework.

What to look for: Does the framework support branching, looping, retrying, and performing tasks in parallel without any complex tricks?

2. State and Memory Requirements

State refers to the information that an agent remembers during its operation. Memory refers to the information it can remember during the entire conversation.

Some agents can reset their state after every message, while some require to continue from where they left off last time. So, state management and persistence become important for complex workflows.

What to look for: Does the framework support sessions, checkpoints, and state persistence?

3. Tool Calling and MCP Support

The agents may require access to various tools or datasets that will help them perform their tasks effectively. This includes web searches, database access, API requests, email, and business applications.

MCP (Model Context Protocol) is a universal framework that allows agents to interact with tools and data sources easily, thus making future integrations possible even when the number of tools grows.

What to look for: How convenient it is to call an external tool or function? Is it supported by the framework to use MCP, common APIs, and RAG together with external data sources? What about multi-model support?

Also Read: Which Microsoft Tools Are Used for Agentic AI Development?

4. Human-in-the-Loop Capabilities

Not all processes can be automated. Sometimes certain tasks require some human intervention in order to get approval or make changes to the ongoing workflow.

The proper framework must provide means to interrupt the workflow, get feedback from human users, and then continue the process with previously set context.

What to look for: Ability to interrupt the workflow, take user input and continue it later.

5. Observability and Monitoring

In case of agent malfunctioning, the team needs to get an insight into what went wrong. Observability allows for tracking all the agent actions, tools used, errors occurred, and decisions made.

In lack of proper monitoring, debugging can turn out to be difficult and time-consuming.

What to look for: Is there any tracing and logging in place? Does it integrate well with the available monitoring tools? Are there some decent guardrails and error handling features?

6. Ecosystem and Community Support

The framework itself is not the whole story. There is also the ecosystem and the community to take into consideration.

A good ecosystem typically allows getting all the necessary tutorials, samples, solutions, and integrations from third parties. Language support is also crucial since different teams can have experience with such languages as Python, TypeScript, Java, or .NET.

What to look for: Which programming languages are supported? How active are the documentation, community, and the release cycle?

7. Deployment and Scalability

A good framework for the development phase is not always one that fits the production phase. As usage increases, the agent will have to deal with increased load while still maintaining reliability, security, and manageability.

The framework must also integrate well with the existing infrastructure and cloud environments of the company.

What to look for: Does the framework integrate well with your infrastructure and cloud environment? Does it scale as usage increases? Does it have security, deployment, and governance capabilities?

Also Read: How to Build Your First AI Agent: A Practical Developer's Guide for 2026

Conclusion

Choosing among the leading agentic AI frameworks depends on the type of agent, workflow complexity, data requirements, and deployment environment. LangGraph, CrewAI, Microsoft Agent Framework, AutoGen, Google ADK, OpenAI Agents SDK, LlamaIndex, and AWS Strands Agents each support different development needs.

Consider factors such as orchestration, memory, tool integration, observability, and scalability before deciding. A framework that matches the application's requirements can make agent development easier to build, manage, and scale. 

Have A Query? Get in Touch With Our Customer Support | upGrad KnowledgeHut