Just like every impressive building starts with a strong foundation, every remarkable capability in an AI agent can be traced back to its framework. AI agent frameworks or agentic AI frameworks make it possible to create smart, efficient AI agents that can serve as simple chatbots, facilitate agentic automation, or contribute to complex use cases in finance, supply chain, healthcare, manufacturing, and robotics as part of a multi-agent system. 

But what are AI agent frameworks? How many different kinds of frameworks are there? And what are the factors you need to know to pick the right one for your use case? 

Let’s find out.

What is an AI Agent Framework? 

An AI agent framework is a specialized software platform or library designed to build, deploy and manage intelligent autonomous agents. These agents perceive their environment, make decisions and act accordingly.

Agentic frameworks contain tools and modular components to design agent functionalities.  They simplify complex procedures in agent creation by offering pre-defined structures, reusable logic, and abstraction layers that break down intricate tasks into smaller, more manageable steps.

Build Scalable AI Agents—Faster, Smarter, and Without the Guesswork

Want AI agents without weeks of coding or complex setup? Skip the technical hurdles. Astera AI Agent Builder gives you a drag-and-drop interface, powerful integrations, and enterprise-ready performance. No data science degree required.

Learn More

AI Agent Framework vs. AI Agent Architecture 

There’s a degree of overlap between agentic AI agent frameworks and agentic architectures, as both share similar components. In fact, agentic AI frameworks often implement architectural elements directly, using pre-built tools to bring those structures to life. 

Let’s quickly differentiate between the two: 

• An AI agent framework provides the tools and infrastructure to develop AI agents.
• AI agent architecture refers to the fundamental components (such as perception, reasoning, and learning) that enable AI functionality.

Key Components of an Agentic AI Framework 

Agentic AI frameworks comprise various components that contribute to an agent’s decision-making, interactions, and adaptability.

Different Kinds of AI Agent Frameworks and Their Use Cases 

Agentic frameworks can be categorized based on how agents operate, learn, and interact. Some frameworks enable multi-agent collaboration, while others focus on decision-making, reinforcement learning, or hybrid intelligence. Here’s a complete breakdown of the different AI agent framework types, with examples for each.

1. Multi-Agent System (MAS) Frameworks 

These frameworks allow multiple AI agents to work together toward a common goal. They are commonly used in smart grids, IoT networks, and swarm robotics, where different agents need to coordinate their actions. MAS frameworks are especially useful for large-scale simulations and distributed AI applications. However, they require advanced AI expertise and can be challenging to set up and manage. 

Examples: 

• JADE: Used for distributed AI, IoT, and simulations.
• SPADE: Supports real-time AI communication for automation and chatbots.
• MASON: Designed for multi-agent simulations in economics and social sciences.

2. Reinforcement Learning (RL) Agent Frameworks

These frameworks help AI agents learn by trial and error, improving their decision-making over time. They are widely used in robotics, gaming, and automation, where AI needs to adapt to new situations.  

RL frameworks are great for training AI to handle complex tasks, such as self-driving cars or game-playing bots. On the other hand, they require large amounts of data and computing power to be effective. 

Examples: 

• OpenAI Gym: A popular platform for training reinforcement learning models.
• Ray RLlib: Scalable RL designed for enterprise-level AI training.
• Unity ML-Agents: Tailored for AI training in gaming and simulations.

3. Application-Specific Frameworks 

These frameworks come with built-in AI capabilities designed for specific tasks, such as customer service chatbots or predictive analytics. They are ideal for businesses that want to use AI without needing deep technical knowledge. They’re easy to implement but may not offer as much flexibility as more advanced AI frameworks. 

Examples: 

• Rasa: Open-source framework for conversational AI.
• Dialogflow: Google’s NLP-powered chatbot framework.
• Mesa: Agent-based simulation modeling tool

4. Cognitive Architecture-Based Frameworks 

These frameworks are designed to mimic human thinking, allowing AI to reason, learn, and make decisions. They are used in advanced applications such as cognitive robotics and AI assistants that can adapt to users’ needs. Despite being powerful, they are complex to develop and mainly used in specialized fields. 

Examples: 

• SOAR: AI decision-making modeled on human cognition.
• ACT-R: A cognitive model for human-like problem-solving.
• NARS: Adaptive AI for real-time decision-making.

5. Goal-oriented AI Agent Frameworks 

These frameworks help AI agents achieve specific objectives based on predefined rules and logic. They are widely used in supply chain optimization, logistics, and automated planning systems. While effective in structured environments, they can struggle with unpredictable real-world conditions. 

Examples: 

• GOAL: A rule-based AI decision-making framework.
• Jason: Uses the Belief-Desire-Intention (BDI) model for behavior modeling.

6. Hybrid AI Agent Frameworks

Hybrid frameworks combine multiple AI techniques, such as rule-based systems and reinforcement learning, to create more flexible and adaptable agents. They’re often used in advanced simulations, self-driving cars, and military applications. Although they’re highly capable, they require significant resources and expertise to develop. 

Examples: 

• PySC2: Combines reinforcement learning with strategic decision-making.
• CARLA: Self-driving car simulation using multiple AI paradigms.

7. Embodied AI Frameworks (AI for Robotics and Physical Agents)

These frameworks allow AI to interact with the physical world, making them essential for robotics, factory automation, and self-driving technology. They enable robots to process sensory information and respond to their surroundings. However, they require specialized hardware and high development costs. 

Examples: 

• ROS (Robot Operating System): The leading AI framework for robotics.
• Webots: A simulation platform for AI-driven robotics research.

8. Agentic Language and Data Frameworks

This emerging category includes frameworks designed to create language-enabled agents that operate over complex data, often using LLMs and structured reasoning. They support modular agent chains, graph-based logic, and retrieval-augmented generation. Use cases include knowledge agents, document analysis, and LLM orchestration.

Examples: 

• LangChain: Framework for building LLM-driven agent pipelines
• LangGraph: For creating stateful, multi-step agents with graph logic
• LlamaIndex: Enables agents to query and reason over structured and unstructured data

9. No-Code AI Agent Frameworks

No-code AI agent frameworks provide a visual, drag-and-drop interface that allows users to build AI agents without writing code. These frameworks simplify AI development by offering pre-built templates, automated workflows, and integrations with business applications. They are ideal for organizations that need to deploy AI quickly without requiring specialized AI expertise or extensive development cycles. 

No-code AI agent frameworks are best suited for business automation, customer service, and workflow optimization, where rapid deployment and ease of use take priority over deep customization. 

Example: 

• Astera AI Agent Builder: An AI-powered solution for building AI agents for a variety of use cases.

Your Enterprise-Grade AI Agents Are Just Hours Away

With Astera, building AI agents of your own has never been easier. Use our visual, drag-and-drop builder and array of pre-built functions to develop and deploy AI agents for a variety of use cases.

Speak to Our Team

How to Choose the Right Agentic AI Framework 

Agentic frameworks require careful selection based on multiple technical and operational factors. Intuitive AI-powered agent builders, on the other hand, eliminate much of this complexity, making AI adoption significantly easier. 

The ideal AI agent framework should align with your specific use case, technical expertise, and long-term scalability goals.  

With choices ranging from open-source libraries to enterprise-ready, low-code platforms, the key is to find a solution that balances flexibility, ease of use, and deployment efficiency. Consider the following important aspects to make an informed decision: 

1. Define Your Use Case and Requirements 

Start by defining your goals. Are you developing an AI-powered chatbot, an autonomous decision-making system, or a multi-agent simulation? Each framework is designed to address specific use cases, so understanding your requirements upfront will help you make the right choice. 

Key questions to ask: 

• Does the framework support rule-based, reinforcement learning, or LLM-driven agents?
• Do you need single-agent or multi-agent capabilities?
• How complex are the tasks your agent needs to perform?

2. Consider The Framework’s Architecture and Stability 

AI agents need to process large amounts of data quickly and efficiently. The right agentic framework depends on your project’s scale and complexity. Some frameworks are optimized for lightweight applications, while others are built to support large-scale, distributed systems. 

Look for: 

• Modularity: Can you extend the framework with plugins and APIs?
• Concurrency support: Does it handle multiple requests efficiently?
• Cloud compatibility: Can it integrate with cloud platforms like AWS, GCP, or Azure?

3. Evaluate Ease of Development and Integration 

The learning curve and integration complexity play a crucial role in development time. Low-code AI agent builders enable fast, efficient deployment by letting users create agents without deep programming knowledge which makes them ideal for non-technical teams. 

Consider: 

• Pre-built components: Does it have built-in NLP, reasoning, or memory modules?
• Integration with existing tools: Can it connect with APIs, databases, or messaging services?
• Developer support and documentation: Is the framework well-documented and actively maintained?

4. Assess Performance and Efficiency 

An AI agent’s ability to respond quickly and operate efficiently is essential for delivering real-time results and managing complex tasks with precision. 

To assess a framework’s performance, consider: 

• Latency and Throughput: How quickly does it process requests, and can it handle high workloads without slowing down?
• Resource Utilization: Does it efficiently manage CPU, GPU, and memory resources to prevent bottlenecks?
• Scalability: Can it maintain high performance as workloads increase, whether through distributed processing or parallel computing? 

5. Security, Compliance, and Maintenance 

Security is a top priority, especially for AI agents processing sensitive or confidential data. When choosing a framework, it’s important to assess whether it adheres to best practices in data protection, authentication, and regulatory compliance (such as GDPR or HIPAA). Robust security measures not only safeguard data but also help build trust in AI-powered solutions. 

Beyond security, consider the framework’s long-term sustainability. Is it actively maintained with regular updates to address vulnerabilities? Does it have strong community or enterprise support to ensure ongoing development and troubleshooting? 

Key factors to evaluate: 

• Data Privacy Measures: How does the framework manage encryption, access control, and data retention policies?
• Compliance Certifications: Is it designed for use in regulated industries, meeting standards like GDPR, HIPAA, or SOC 2?
• Long-Term Viability: Is the framework widely adopted, with active development and a strong support network?

6. Support for Advanced Features

Some agentic AI frameworks go beyond basic automation, offering advanced capabilities that enhance an AI system’s intelligence and adaptability. Features like long-term memory, seamless integration with external tools, and multi-agent collaboration can greatly improve how an AI agent learns, makes decisions, and tackles complex challenges. 

When evaluating a framework, keep these factors in mind: 

• Persistent Memory: Can the AI agent retain past interactions and use them to improve responses over time?
• External Tool Support: Does it integrate smoothly with APIs, databases, or external knowledge sources to expand its functionality?
• Multi-Agent Collaboration: Can multiple AI agents communicate and coordinate their efforts to solve complex tasks more efficiently?

7. Cost and Licensing Considerations 

Budget is a key consideration when selecting an agentic framework. While open-source options provide cost-effective solutions, they often require significant in-house development and ongoing maintenance.  

Proprietary frameworks—especially enterprise-grade AI agent builders—may come at a higher cost, but that expense is often offset by the ability for non-technical users to build and deploy agents without relying on costly technical personnel, thanks to built-in managed services and streamlined deployment. 

Focus on the following: 

• Open-Source vs. Proprietary: Is the framework free to use, or does it require a subscription or licensing fee?
• Infrastructure Costs: Will the solution require dedicated computing resources, such as cloud-based servers or on-premises hardware? 
• Support and Maintenance: Does the framework offer enterprise-grade support, updates, and security patches, or will your team need to handle these internally?

AI-Powered AI Agent Builders vs. Conventional AI Frameworks

Summing It Up 

Standard AI agent frameworks have their place. If you have a team of expert developers on hand, need deep customization and control over every aspect of AI agent development, and are comfortable with high development overheads and maintenance costs, then there’s a framework out there that’s right for you and the criteria discussed here will help you find it. 

However, if you’re looking for faster deployment times, more accessible AI agent development, and a solution that can do the technical heavy lifting for you, then a low-code AI agent builder is your ticket to fast, reliable AI agent implementation. 

Astera AI Agent Builder: Your Data, Your Agents, Your Way 

Astera AI Agent Builder is the fastest way to bring AI into your enterprise without specialized coding or large AI teams. With a visual, drag-and-drop designer, support for simple English-language commands, seamless data integration, and flexible deployment options, you can build intelligent AI agents that enhance automation, improve decision-making, and drive business efficiency.  

Whether you’re optimizing customer service, streamlining operations, or enabling data-driven insights, Astera puts AI development into the hands of your team. 

Don’t let complexity slow down your AI adoption. Empower your organization to build AI agents that work securely with your data. Contact us for more information!