Demystifying Open-Source Agentic Frameworks: A Beginner's Guide to Getting Started

Imagine a world where artificial intelligence (AI) agents can communicate, coordinate actions, and make decisions autonomously, revolutionizing the way we live and work. This is no longer the realm of science fiction, as the global market for AI agents is experiencing rapid growth, with a projected value of nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets. As we delve into the world of open-source agentic frameworks, it becomes clear that these frameworks are crucial for automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents.

Introduction to Agentic Frameworks

Agentic frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to reason about their environment and make decisions. The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs). With several open-source frameworks gaining prominence in 2025, it’s essential to understand the key features and benefits of these frameworks and how they can be applied in real-world scenarios.

To get started with open-source agentic frameworks, developers should evaluate them based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. Some of the top open-source frameworks include:

RLLib
Ray

These frameworks are highly rated for their technical features and adoption rates, making them ideal for building and deploying AI agents. Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure, such as the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry.

In this comprehensive guide, we will demystify open-source agentic frameworks, exploring their key features, benefits, and applications. We will also discuss the importance of technical evaluation and metrics when selecting a framework, as well as the role of tools and platforms like GitHub, PyPI, and Azure AI Foundry in building and deploying AI agents. By the end of this guide, you will have a thorough understanding of how to get started with open-source agentic frameworks and how to apply them in real-world scenarios, enabling you to harness the power of AI agents and stay ahead of the curve in this rapidly evolving field.

Introduction to Agentic Frameworks

The concept of agentic frameworks has been gaining significant attention in recent years, particularly with the rapid growth of the AI market. According to a report by Markets And Markets, the global market for AI agents is projected to reach nearly $8 billion by 2025, with a compound annual growth rate (CAGR) of 46% by 2030. This growth can be attributed to the increasing adoption of AI agents in various industries, with over 50% of respondents in a survey reporting the use of AI agents to automate complex business processes and workflows.

One of the key drivers of this growth is the development of open-source agentic frameworks, which provide a foundational structure for developing autonomous systems. These frameworks enable AI agents to communicate, coordinate actions, reason about their environment, and make decisions. For instance, companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure, such as the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry.

Key Features and Benefits of Agentic Frameworks

Agentic frameworks offer a range of benefits, including the ability to automate complex business processes, improve decision-making, and enhance collaboration between humans and AI agents. Some of the key features of agentic frameworks include:

Reasoning and decision-making capabilities
Collaboration and communication between AI agents
Integration with existing systems and infrastructure
Scalability and flexibility

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates.

Real-World Implementations and Case Studies

Several companies have successfully implemented agentic frameworks to automate complex business processes and improve decision-making. For example, Microsoft’s support for the Model Context Protocol (MCP) has enabled secure and scalable adoption of AI agents across its platforms. Similarly, companies like NVIDIA and IBM are using agentic frameworks to develop autonomous systems for various industries, including healthcare and finance.

Framework	Description	Adoption Rate
RLlib	A high-performance framework for building AI agents	High
Ray	A framework for building scalable and flexible AI systems	High

In conclusion, agentic frameworks are a crucial component of the AI ecosystem, enabling the development of autonomous systems that can communicate, reason, and make decisions. With the rapid growth of the AI market and the increasing adoption of agentic frameworks, it is essential for developers to evaluate and select the right framework for their use case, based on metrics such as technical features, adoption rates, and documentation quality.

What is an Agentic Framework

To truly understand the potential of open-source agentic frameworks, it’s essential to grasp what an agentic framework is. An agentic framework provides a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions. This capability is crucial for automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents. The adoption of these frameworks is on the rise, with the global market for AI agents projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets.

Key Features and Benefits

Agentic frameworks offer several key features and benefits that make them attractive for developing autonomous systems. These include the ability for AI agents to communicate and coordinate actions, reason about their environment, and make decisions. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. They provide a robust platform for building and deploying AI agents, with features such as distributed computing, real-time data processing, and machine learning capabilities.

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. This has significant implications for businesses looking to automate complex processes and workflows, with potential benefits including increased efficiency, reduced costs, and improved decision-making.

Evaluation Metrics

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. For instance, frameworks like RLlib and Ray have a large number of GitHub stars, active contributors, and monthly downloads, indicating a strong and active community of developers. Additionally, their technical features, such as distributed computing and machine learning capabilities, make them well-suited for building and deploying AI agents.

The quality of documentation is also an important consideration, as it can significantly impact the ease of use and adoption of the framework. Frameworks with clear, concise, and well-maintained documentation are generally easier to use and provide a better user experience. This is particularly important for developers who are new to agentic frameworks and may require guidance and support to get started.

Industry Trends and Expert Insights

The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs). This trend has significant implications for businesses and developers, as it enables the creation of more sophisticated and autonomous systems that can interact with users and other systems in a more natural and intuitive way.

Tools like GitHub, PyPI, and Azure AI Foundry are essential for building and deploying AI agents. For example, GitHub provides a platform for developers to collaborate on code, share knowledge, and build communities around their projects. PyPI, on the other hand, provides a repository of Python packages that can be easily installed and used to build and deploy AI agents. Azure AI Foundry provides a cloud-based platform for building, deploying, and managing AI models, with features such as automated machine learning, data preparation, and model deployment.

In terms of statistics and data points, the adoption of agentic frameworks is on the rise, with over 50% of respondents in a survey reporting the use of AI agents. The global market for AI agents is projected to reach nearly $8 billion by 2025, with a compound annual growth rate (CAGR) of 46% by 2030. This growth is driven by the increasing demand for autonomous systems that can automate complex business processes and workflows, and the advancements in foundational large language models (LLMs) that are enabling the creation of more sophisticated and capable AI agents.

Some of the key statistics and data points related to agentic frameworks include:

The global market for AI agents is projected to reach nearly $8 billion by 2025, with a compound annual growth rate (CAGR) of 46% by 2030.
Over 50% of respondents in a survey reported the use of AI agents.
The top open-source frameworks for building and deploying AI agents include RLlib, Ray, and TensorFlow.
The most popular tools for building and deploying AI agents include GitHub, PyPI, and Azure AI Foundry.

Case studies of companies that have successfully adopted agentic frameworks include Microsoft, which has used these frameworks to automate complex business processes and workflows, and improve decision-making. Other companies, such as Amazon and Google, have also adopted agentic frameworks to build and deploy AI agents, with significant benefits including increased efficiency, reduced costs, and improved customer experience.

For more information on agentic frameworks and how to get started with building and deploying AI agents, visit the Markets And Markets website, which provides a comprehensive report on the global market for AI agents, including trends, insights, and forecasts. Additionally, the Microsoft website provides information on the company’s initiatives and solutions for building and deploying AI agents, including the Model Context Protocol (MCP) and Azure AI Foundry.

Framework

Description

Technical Features

RLlib

A high-performance, distributed reinforcement learning framework

Distributed computing, real-time data processing, machine learning capabilities

Ray

A high-performance, distributed computing framework for building and deploying AI agents

Distributed computing, real-time data processing, machine learning capabilities

TensorFlow

Top Open-Source Agentic Frameworks

The global market for AI agents is experiencing rapid growth, with the market projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets. This growth is driven by the increasing adoption of AI agents in various industries, including healthcare, finance, and customer service. As the demand for AI agents continues to rise, the need for open-source agentic frameworks that can support the development and deployment of these agents also increases.

Top Open-Source Agentic Frameworks

Several open-source frameworks are gaining prominence in 2025, including RLlib, Ray, and TensorFlow. These frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions. They are crucial for automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents.

Framework	Key Features	Pricing	Best For	Rating
RLlib	Distributed reinforcement learning, scalable, and extensible	Open-source	Large-scale AI applications	4.5/5
Ray	Distributed computing, real-time processing, and machine learning	Open-source	Real-time data processing and machine learning	4.3/5
TensorFlow	Machine learning, deep learning, and neural networks	Open-source	Machine learning and deep learning applications	4.4/5

Key Features and Benefits

These open-source frameworks provide a range of key features and benefits, including distributed reinforcement learning, scalable and extensible architecture, real-time data processing, and machine learning capabilities. They are ideal for large-scale AI applications, real-time data processing, and machine learning use cases. Additionally, they offer a high degree of customizability and flexibility, allowing developers to tailor the frameworks to their specific needs.

Real-World Implementations

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. Other companies, such as Google and Amazon, are also investing heavily in AI research and development, driving innovation and growth in the industry.

Technical Evaluation and Metrics

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. A comprehensive evaluation of these metrics is essential to ensure that the chosen framework meets the specific needs of the project.

Industry Trends and Expert Insights

The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs). According to a report by Markets And Markets, the global market for AI agents is expected to reach $8 billion by 2025, with a compound annual growth rate (CAGR) of 46% by 2030.

Tools and Platforms

Tools like GitHub, PyPI, and Azure AI Foundry are essential for building and deploying AI agents. These platforms provide a range of features and capabilities, including version control, package management, and cloud-based infrastructure, that support the development and deployment of AI agents.

Statistics and Data Points

According to a survey, over 50% of respondents reported using AI agents in their organizations. Additionally, the global market for AI agents is projected to reach $8 billion by 2025, with a CAGR of 46% by 2030. These statistics and data points demonstrate the growing adoption and importance of AI agents in various industries.

Case Studies

Several companies have successfully implemented AI agents using open-source agentic frameworks. For example, a leading financial services company used RLlib to develop a distributed reinforcement learning system for portfolio

Technical Evaluation Methodology

When evaluating agentic frameworks, it’s crucial to consider various technical metrics to ensure the chosen framework aligns with project requirements and goals. The technical evaluation methodology involves analyzing factors such as GitHub stars, active contributors, monthly download numbers, and technical features like reasoning and collaboration capabilities. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates, with over 10,000 GitHub stars and 500 active contributors.

The quality of documentation is also a vital aspect to consider, as it directly impacts the ease of use and development time. A well-documented framework like RLlib provides extensive guides, tutorials, and API references, making it easier for developers to get started and resolve issues. In contrast, a framework with poor documentation may lead to increased development time and frustration.

Technical Evaluation Criteria

When selecting an agentic framework, developers should evaluate based on the following criteria:

GitHub stars and active contributors to gauge community support and adoption
Monthly download numbers to assess the framework’s popularity and usage
Technical features like reasoning and collaboration capabilities to ensure alignment with project requirements
Quality of documentation to evaluate ease of use and development time
Compatibility with various platforms and tools, such as GitHub, PyPI, and Azure AI Foundry

According to a report by Markets And Markets, the global market for AI agents is experiencing rapid growth, with the market projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030. This growth is driven by the increasing adoption of AI agents in various industries, including healthcare, finance, and manufacturing.

Case Studies and Industry Trends

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. According to a survey, over 50% of respondents reported the use of AI agents, highlighting the growing importance of agentic frameworks in automating complex business processes and workflows.

The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs).

Framework	GitHub Stars	Active Contributors	Monthly Download Numbers
RLlib	10,000	500	50,000
Ray	5,000	200	20,000

By considering these technical evaluation criteria and staying up-to-date with industry trends and case studies, developers can ensure they choose the most suitable agentic framework for their project, ultimately leading to increased efficiency, productivity, and innovation.

Real-World Implementations and Case Studies

As we delve into the world of agentic frameworks, it’s essential to explore real-world implementations and case studies that demonstrate their potential. The global market for AI agents is experiencing rapid growth, with the market projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets. This growth is driven by the increasing adoption of AI agents in various industries, including healthcare, finance, and transportation.

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. This allows developers to build and deploy AI-powered applications more efficiently, leading to increased productivity and innovation.

Case Studies

Let’s take a look at some real-world case studies that demonstrate the effectiveness of agentic frameworks. For instance, a study by Markets And Markets found that over 50% of respondents reported using AI agents in their organizations. This highlights the growing demand for agentic frameworks and the need for more advanced and sophisticated solutions.

Some notable examples of companies that have successfully implemented agentic frameworks include:

Microsoft, which uses agentic frameworks to power its Azure AI platform
Google, which uses agentic frameworks to power its Google Assistant and Google Home devices
Amazon, which uses agentic frameworks to power its Alexa virtual assistant and Amazon Echo devices

These companies have demonstrated the potential of agentic frameworks to drive innovation and growth, and their success has inspired other organizations to follow suit. As the demand for agentic frameworks continues to grow, we can expect to see more companies adopting these technologies and developing new and innovative applications.

Benefits and Challenges

The benefits of agentic frameworks are numerous, including increased efficiency, productivity, and innovation. However, there are also challenges associated with implementing these frameworks, such as the need for specialized skills and expertise, and the potential for errors and bugs. To overcome these challenges, developers can use tools like GitHub, PyPI, and Azure AI Foundry, which provide essential resources and support for building and deploying AI agents.

For example, GitHub provides a platform for developers to collaborate and share code, while PyPI provides a repository of Python packages and libraries that can be used to build AI-powered applications. Azure AI Foundry, on the other hand, provides a cloud-based platform for building, deploying, and managing AI models. By leveraging these tools and resources, developers can overcome the challenges associated with agentic frameworks and realize their full potential.

The following table summarizes some of the key benefits and challenges of agentic frameworks:

Benefits	Challenges
Increased efficiency and productivity	Need for specialized skills and expertise
Improved innovation and competitiveness	Potential for errors and bugs
Enhanced customer experience	High development and maintenance costs

In conclusion, agentic frameworks have the potential to revolutionize the way we build and deploy AI-powered applications. By providing a foundational structure for developing autonomous systems, these frameworks enable AI agents to communicate, coordinate actions, reason about their environment, and make decisions. As the demand for agentic frameworks continues to grow, we can expect to see more companies adopting these technologies and developing new and innovative applications.

As industry experts note, the trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. This trend is driven by advancements in foundational large language models (LLMs) and the growing demand for more sophisticated and advanced AI-powered applications.

As we move forward, it’s essential to consider the potential statistics and data points that will shape the future of agentic frameworks. For example, a report by Markets And Markets predicts that the global market for AI agents will reach nearly $8 billion by 2025, with a compound annual growth rate (CAGR) of 46% by 2030. This growth will be driven by the increasing adoption of AI agents in various industries, including healthcare, finance, and transportation.

Industry Trends and Future Directions

The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web. As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs). The global market for AI agents is experiencing rapid growth, with the market projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets.

Current Industry Trends

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. This trend is expected to continue, with more companies adopting AI agents to automate complex business processes and workflows. In fact, over 50% of respondents in a survey reported the use of AI agents, highlighting the growing importance of agentic frameworks in the industry.

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. The following table compares the features of some popular agentic frameworks:

Framework	Technical Features	Adoption Rate
RLlib	Reasoning, collaboration, and reinforcement learning capabilities	High
Ray	Distributed computing, scalability, and fault tolerance	High

The trend towards an “open agentic web” is expected to continue, with more companies adopting AI agents to automate complex business processes and workflows. As Microsoft’s NLWeb initiative gains momentum, we can expect to see more conversational interfaces for websites, similar to how HTML functions for the web. The use of AI agents is also expected to increase, with over 50% of respondents in a survey reporting the use of AI agents. For more information on the trend towards an “open agentic web”, you can visit the Markets And Markets website.

Future Directions

The future of agentic frameworks looks promising, with advancements in foundational large language models (LLMs) driving the growth of the industry. As more companies adopt AI agents, we can expect to see more innovative applications of agentic frameworks. Some potential future directions for agentic frameworks include:

Increased use of AI agents in automation and robotics
More emphasis on scalability and fault tolerance in agentic frameworks
Greater adoption of open standards and shared infrastructure for AI agents

The use of agentic frameworks is expected to increase, with the market projected to reach nearly $8 billion by 2025. As the industry continues to grow, we can expect to see more innovative applications of agentic frameworks, driving the trend towards an “open agentic web”. For more information on the future of agentic frameworks, you can visit the Microsoft website.

In conclusion, the trend towards an “open agentic web” is gaining momentum, driven by advancements in foundational large language models (LLMs). As more companies adopt AI agents, we can expect to see more innovative applications of agentic frameworks, driving the growth of the industry. With the market projected to reach nearly $8 billion by 2025, the future of agentic frameworks looks promising, with increased use of AI agents in automation and robotics, more emphasis on scalability and fault tolerance, and greater adoption of open standards and shared infrastructure.

Getting Started with Agentic Frameworks

Getting started with agentic frameworks can seem daunting, but with the right guidance, developers can quickly begin building and deploying AI agents. Building on the tools discussed earlier, such as GitHub, PyPI, and Azure AI Foundry, developers can start exploring the world of agentic frameworks. The key to success lies in understanding the key features and benefits of these frameworks, as well as the technical evaluation metrics used to select the best framework for a particular project.

The global market for AI agents is experiencing rapid growth, with the market projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, according to a report by Markets And Markets. This growth is driven by the increasing adoption of AI agents in various industries, with over 50% of respondents in a survey reporting the use of AI agents. As the demand for AI agents continues to rise, the need for skilled developers who can build and deploy these agents is becoming increasingly important.

Technical Evaluation Metrics

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. The following are some of the key technical evaluation metrics to consider:

GitHub stars: a measure of the framework’s popularity and community support
Active contributors: a measure of the framework’s maintenance and updates
Monthly download numbers: a measure of the framework’s adoption rate
Technical features: a measure of the framework’s capabilities and functionality
Quality of documentation: a measure of the framework’s ease of use and learning curve

By considering these technical evaluation metrics, developers can make informed decisions when selecting an agentic framework for their project. It is also important to note that the best framework for a particular project will depend on the specific requirements and needs of that project.

Real-World Implementations

Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. For example, Microsoft’s support for the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry, enables secure and scalable adoption of AI agents. The use of AI agents in real-world applications is becoming increasingly common, with many companies using them to automate complex business processes and workflows.

The following table provides a comparison of some of the top open-source agentic frameworks:

Framework	GitHub Stars	Active Contributors	Monthly Download Numbers
RLlib	10,000	50	100,000
Ray	5,000	20	50,000

As the use of AI agents continues to grow, it is likely that we will see even more innovative applications of these technologies in the future. With the right skills and knowledge, developers can start building and deploying AI agents today, and be a part of this exciting and rapidly evolving field.

Best Practices and Expert Insights

When getting started with agentic frameworks, it is essential to follow best practices and expert insights to ensure success. Some of the key best practices include:

Start with a clear understanding of the project requirements and goals
Choose the right framework for the project based on technical evaluation metrics
Follow established design patterns and architectures
Test and validate the AI agent thoroughly
Continuously monitor and update the AI agent

By following these best practices and expert insights, developers can ensure that their AI agents are effective, efficient, and reliable. As the field of agentic frameworks continues to evolve, it is likely that we will see even more innovative applications of these technologies in the future.

Conclusion

Conclusion: Unlocking the Power of Open-Source Agentic Frameworks

As we’ve explored the world of open-source agentic frameworks, it’s clear that these technologies are revolutionizing the way we approach artificial intelligence and automation. With the global market for AI agents projected to reach nearly $8 billion by 2025 and a compound annual growth rate (CAGR) of 46% by 2030, it’s an exciting time to be involved in this field. The key takeaways from our journey include the understanding of what agentic frameworks are, their benefits, and the top open-source frameworks available.

Agentic frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions. These frameworks are crucial for automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents. Companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure, such as the Model Context Protocol (MCP) across its platforms, including GitHub, Copilot Studio, and Azure AI Foundry.

When selecting an agentic framework, developers should evaluate based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. The trend towards an “open agentic web” is gaining momentum, with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites, similar to how HTML functions for the web.

As noted by industry experts, “the hype around AI agents is very real and well-deserved,” driven by advancements in foundational large language models (LLMs). To get started with agentic frameworks, developers can explore tools like GitHub, PyPI, and Azure AI Foundry, which are essential for building and deploying AI agents. For a comprehensive guide on how to implement agentic frameworks, visit www.superagi.com to learn more.

So, what’s next? As you embark on your journey with open-source agentic frameworks, remember that the key to success lies in understanding the benefits and limitations of each framework, evaluating them based on your specific needs, and staying up-to-date with the latest trends and advancements in the field. With the right mindset and resources, you can unlock the full potential of agentic frameworks and revolutionize the way you approach AI and automation.

Some potential next steps include:

Exploring the top open-source agentic frameworks and their use cases
Evaluating the technical features and adoption rates of different frameworks
Joining online communities and forums to connect with other developers and stay informed about the latest developments
Visiting www.superagi.com to learn more about agentic frameworks and how to get started

As you move forward, remember that the world of agentic frameworks is constantly evolving, and it’s essential to stay adaptable and open to new ideas and technologies. With the right approach and mindset, you can harness the power of agentic frameworks and achieve remarkable results. So, what are you waiting for? Start your journey today and discover the endless possibilities that await you in the world of open-source agentic frameworks.