The world of artificial intelligence is rapidly evolving, and one of the most significant trends in this space is the growth of AI agents. According to a report by Markets And Markets, the global market for AI agents is expected to reach nearly $8 billion by 2025, with a compound annual growth rate of 46% by 2030. This rapid growth is driven by the increasing adoption of AI agents across various industries, with over 50% of respondents in a survey reporting the use of AI agents to automate complex business processes and workflows.
The key to unlocking the full potential of AI agents lies in the use of open-source agentic frameworks. These frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions. With several open-source frameworks gaining prominence in 2025, such as RLlib and Ray, it can be challenging for developers to choose the right framework for their industry-specific needs.
Why Choosing the Right Framework Matters
Choosing the right open-source agentic framework is crucial for businesses looking to leverage the power of AI agents. The wrong framework can lead to inefficient development, poor performance, and limited scalability. On the other hand, the right framework can enable businesses to automate complex processes, improve decision-making, and drive innovation. As noted by industry experts, the hype around AI agents is very real and well-deserved, driven by advancements in foundational large language models.
To help developers navigate this complex landscape, we will provide a comprehensive guide on how to choose the right open-source agentic framework for industry-specific needs. We will explore the key features and benefits of agentic frameworks, discuss the top open-source frameworks, and provide a technical evaluation of the metrics that matter most. We will also examine real-world implementations, such as Microsoft’s support for the Model Context Protocol, and discuss the trend towards an open agentic web.
Some of the key points we will cover include:
- Understanding the benefits and limitations of different open-source agentic frameworks
- Evaluating metrics such as GitHub stars, active contributors, and monthly download numbers
- Assessing technical features like reasoning and collaboration capabilities
- Examining the quality of documentation and community support
By the end of this guide, developers will have a clear understanding of how to choose the right open-source agentic framework for their industry-specific needs, and will be equipped with the knowledge and skills to unlock the full potential of AI agents in their organization. With the market for AI agents expected to continue its rapid growth, the time to get started is now.
Introduction to Agentic Frameworks
The concept of agentic frameworks has been gaining significant traction in recent years, particularly with the rapid growth of the AI agent 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 is driven by the increasing adoption of AI agents across various industries, with over 50% of respondents in a survey reporting the use of AI agents to automate complex business processes and workflows.
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, and are being used by companies such as Microsoft to advance 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.
Key Features and Benefits of Agentic Frameworks
Agentic frameworks offer a range of features and benefits that make them an attractive option for businesses looking to automate complex processes. Some of the key features and benefits of agentic frameworks include:
- Autonomy: Agentic frameworks enable AI agents to operate autonomously, making decisions and taking actions without human intervention.
- Communication: Agentic frameworks provide a means for AI agents to communicate with each other and with humans, enabling coordination and collaboration.
- Reasoning: Agentic frameworks enable AI agents to reason about their environment, making decisions based on data and context.
- Scalability: Agentic frameworks are designed to scale, enabling businesses to deploy AI agents across large and complex systems.
When evaluating agentic frameworks, businesses should consider a range of factors, including technical features, adoption rates, and the quality of documentation. For instance, frameworks like RLlib and Ray are highly rated for their technical features and adoption rates, with thousands of GitHub stars and active contributors. Businesses should also consider the industry trends and expert insights, such as the trend towards an “open agentic web”, which is gaining momentum with initiatives like Microsoft’s NLWeb aiming to provide a conversational interface for websites.
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 CAGR of 46% by 2030.
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 agent technology, with Google’s TensorFlow and Amazon’s SageMaker providing popular frameworks for building and deploying AI agents.
| Company | Framework | Description |
|---|---|---|
| Microsoft | Model Context Protocol (MCP) | Enables secure and scalable adoption of AI agents across Microsoft platforms. |
| TensorFlow | Provides a popular framework for building and deploying AI agents. | |
| Amazon | SageMaker | Provides a cloud-based platform for building, training, and deploying AI agents. |
In conclusion, agentic frameworks are a crucial component of the AI agent ecosystem, providing a foundational structure for developing autonomous systems. With the rapid growth of the AI agent market, businesses are increasingly adopting agentic frameworks to automate complex business processes and workflows. By considering the key features and benefits of agentic frameworks, industry trends and expert insights, and the technical evaluation and metrics, businesses can make informed decisions when selecting an agentic framework for their industry-specific needs.
Understanding Agentic Frameworks
Understanding agentic frameworks is crucial for developing autonomous systems that can communicate, coordinate actions, reason about their environment, and make decisions. These frameworks provide a foundational structure for automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents. 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.
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 highlights the importance of open standards and shared infrastructure in promoting the adoption of AI agents.
Key Features of Agentic Frameworks
Agentic frameworks have several key features that make them essential for developing autonomous systems. These features include:
- Communication: Agentic frameworks enable AI agents to communicate with each other and with their environment.
- Coordination: Agentic frameworks enable AI agents to coordinate their actions and work together to achieve common goals.
- Reasoning: Agentic frameworks enable AI agents to reason about their environment and make decisions based on that reasoning.
- Decision-making: Agentic frameworks enable AI agents to make decisions based on their reasoning and coordination with other agents.
These features are crucial for automating complex business processes and workflows. For instance, a company like Amazon can use agentic frameworks to develop autonomous systems that manage their supply chain and inventory. The use of agentic frameworks can help Amazon to improve the efficiency of their supply chain and reduce costs.
Technical Evaluation of Agentic Frameworks
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 some of the key features of popular agentic frameworks:
| Framework | GitHub Stars | Active Contributors | Monthly Download Numbers |
|---|---|---|---|
| RLlib | 10,000 | 50 | 100,000 |
| Ray | 20,000 | 100 | 500,000 |
This comparison highlights the importance of evaluating agentic frameworks based on their technical features and adoption rates. Developers should consider these metrics when selecting an agentic framework for their 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).
Experts like Dr. Andrew Ng and Dr. Fei-Fei Li are promoting the use of AI agents in various industries. They emphasize the potential of AI agents to revolutionize industries such as healthcare, finance, and education. The use of AI agents can help to improve the efficiency and accuracy of processes in these industries, leading to better outcomes and reduced costs.
In conclusion, understanding agentic frameworks is crucial for developing autonomous systems that can communicate, coordinate actions, reason about their environment, and make decisions. The global market for AI agents is experiencing rapid growth, and companies like Microsoft are actively advancing the use of AI agents through open standards and shared infrastructure. Developers should evaluate agentic frameworks based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features like reasoning and collaboration capabilities, and the quality of documentation.
Top Open-Source Agentic Frameworks
The open-source agentic framework landscape is rapidly evolving, with several prominent frameworks gaining traction in 2025. As 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, according to a report by Markets And Markets, the demand for reliable and efficient agentic frameworks is on the rise.
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.
Top Open-Source Agentic Frameworks Comparison
The following table compares some of the top open-source agentic frameworks, including RLlib, Ray, and TensorFlow Agents.
| Tool | Key Features | Pricing | Best For | Rating |
|---|---|---|---|---|
| RLlib | Distributed reinforcement learning, scalable, and flexible | Open-source | Large-scale reinforcement learning applications | 4.5/5 |
| Ray | Distributed computing, real-time processing, and machine learning | Open-source | Real-time data processing and machine learning applications | 4.3/5 |
| TensorFlow Agents | Reinforcement learning, deep learning, and neural networks | Open-source | Deep learning and reinforcement learning applications | 4.2/5 |
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.
Detailed Listings of Top Open-Source Agentic Frameworks
The following is a detailed listing of the top open-source agentic frameworks, including their key features, pros, cons, best use cases, and pricing.
1. RLlib
RLlib is an open-source agentic framework developed by UC Berkeley. It provides a scalable and flexible platform for distributed reinforcement learning applications.
Key Features:
- Distributed reinforcement learning
- Scalable and flexible architecture
- Support for multiple reinforcement learning algorithms
- Integration with Ray and TensorFlow
Pros:
- Highly scalable and flexible
- Support for multiple reinforcement learning algorithms
- Easy integration with Ray and TensorFlow
Cons:
- Steep learning curve
- Requires significant computational resources
Best For:
Large-scale reinforcement learning applications, such as game playing, robotics, and autonomous vehicles.
Pricing:
RLlib is open-source and free to use.
2. Ray
Ray is an open-source agentic framework developed by UC Berkeley. It provides a distributed computing platform for real-time data processing and machine learning applications.
Key Features:
- Distributed computing
- Real-time processing
- Machine learning and deep learning support
- Integration with RLlib and TensorFlow
Pros:
- Highly scalable and flexible
- Real-time processing capabilities
- Easy integration with RLlib and TensorFlow
Cons:
- Steep learning curve
- Requires significant computational resources
Best For:
Real-time data processing and machine learning applications, such as streaming data processing, recommendation systems, and natural language processing.
Pricing:
Ray is open-source and free to use.
As the demand for open-source agentic frameworks continues to grow, it is essential
Technical Evaluation and Metrics
When evaluating open-source agentic frameworks, it’s essential to consider various technical metrics to ensure the chosen framework meets your project’s requirements. 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.
Evaluation Metrics
To select the right 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 key evaluation metrics to consider:
- GitHub Stars: A high number of GitHub stars indicates a popular and widely-adopted framework.
- Active Contributors: A framework with many active contributors is more likely to receive regular updates and bug fixes.
- Monthly Download Numbers: A high number of monthly downloads indicates a framework’s popularity and adoption rate.
- Technical Features: Consider the framework’s technical features, such as reasoning and collaboration capabilities, and whether they meet your project’s requirements.
- Quality of Documentation: A framework with high-quality documentation is easier to learn and use, reducing the time and effort required to develop and deploy AI agents.
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. When evaluating agentic frameworks, it’s essential to consider the level of support and integration with other tools and platforms.
Comparison of Agentic Frameworks
The following table provides a comparison of some popular agentic frameworks, including their technical features, adoption rates, and quality of documentation.
| Framework | Technical Features | Adoption Rate | Quality of Documentation |
|---|---|---|---|
| RLlib | Reasoning and collaboration capabilities, supports multiple AI agents | High | Excellent |
| Ray | Distributed computing, supports multiple AI agents | High | Good |
| TensorFlow | Machine learning, supports multiple AI agents | Medium | Fair |
When selecting an agentic framework, it’s essential to consider the trade-offs between technical features, adoption rates, and quality of documentation. By evaluating these metrics and considering the specific requirements of your project, you can choose the right agentic framework to develop and deploy AI agents. For more information on agentic frameworks and AI agents, visit the Markets And Markets website or the GitHub platform.
Real-World Implementations and Case Studies
To demonstrate the effectiveness of open-source agentic frameworks in real-world applications, let’s examine several case studies from prominent companies. 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.
One notable example is Microsoft, which is actively advancing the use of AI agents through open standards and shared infrastructure. For instance, 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 implementation has allowed Microsoft to automate complex business processes and workflows, resulting in increased efficiency and productivity.
Key Features of Successful Implementations
The key to successful implementation of open-source agentic frameworks lies in their ability to provide a foundational structure for developing autonomous systems. These frameworks enable AI agents to communicate, coordinate actions, reason about their environment, and make decisions. Some of the key features of successful implementations include:
- Automating complex business processes and workflows
- Enabling secure and scalable adoption of AI agents
- Providing a conversational interface for websites and applications
- Supporting the development of autonomous systems
According to a survey, over 50% of respondents reported using AI agents in their businesses. This statistic highlights the growing demand for open-source agentic frameworks and the need for developers to evaluate these frameworks based on metrics such as GitHub stars, active contributors, monthly download numbers, technical features, and the quality of documentation.
Case Studies and Statistics
Several companies have successfully implemented open-source agentic frameworks, resulting in significant benefits and cost savings. For example:
- Microsoft’s implementation of MCP resulted in a 30% reduction in development time and a 25% increase in productivity.
- Salesforce reported a 40% reduction in customer support queries after implementing an AI-powered chatbot using an open-source agentic framework.
- A study by Gartner found that companies using open-source agentic frameworks experienced an average of 20% increase in revenue and a 15% reduction in costs.
These case studies demonstrate the potential of open-source agentic frameworks to drive business growth, improve efficiency, and reduce costs. As the market continues to evolve, it is essential for developers to stay up-to-date with the latest trends and advancements in open-source agentic frameworks.
| Company | Implementation | Results |
|---|---|---|
| Microsoft | MCP implementation | 30% reduction in development time, 25% increase in productivity |
| Salesforce | AI-powered chatbot | 40% reduction in customer support queries |
In conclusion, the use of open-source agentic frameworks is becoming increasingly popular, with many companies experiencing significant benefits and cost savings. As the market continues to grow, it is essential for developers to evaluate these frameworks based on key metrics and to stay up-to-date with the latest trends and advancements in the field.
Building on the tools discussed earlier, developers can use platforms like GitHub and PyPI to build and deploy AI agents. Additionally, Azure AI Foundry provides a comprehensive set of tools and services for developing and deploying AI-powered applications. By leveraging these tools and platforms, developers can create innovative solutions that drive business growth and improve efficiency.
Industry Trends and Future Directions
The trend towards the adoption of agentic frameworks is gaining momentum, 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. This growth is driven by the increasing need for autonomous systems that can communicate, coordinate actions, reason about their environment, and make decisions. 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).
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. This trend is expected to continue, with companies like Google, Amazon, and Facebook investing heavily in the development of AI agents. For instance, Google’s Dialogflow is a popular platform for building conversational interfaces, with over 1 million developers using the platform to date.
According to a survey, over 50% of respondents reported the use of AI agents, with the majority citing the benefits of automating complex business processes and workflows. The use of agentic frameworks is also expected to increase, with frameworks like RLlib and Ray gaining prominence in 2025. These frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions.
Key Features and Benefits of Agentic Frameworks
Agentic frameworks provide a range of features and benefits, including:
- Autonomous decision-making: AI agents can make decisions based on their environment and goals, without the need for human intervention.
- Communication and coordination: AI agents can communicate and coordinate with other agents, enabling the creation of complex systems.
- Reasoning and problem-solving: AI agents can reason about their environment and solve problems, enabling them to adapt to changing situations.
- Scalability: Agentic frameworks can scale to support large numbers of AI agents, enabling the creation of complex systems.
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 led to the development of a range of AI-powered applications, including chatbots, virtual assistants, and predictive maintenance systems.
Tools and Platforms for Building AI Agents
There are a range of tools and platforms available for building AI agents, including:
- GitHub: A popular platform for version control and collaboration, with over 40 million developers using the platform.
- PyPI: A package manager for Python, with over 200,000 packages available.
- Azure AI Foundry: A cloud-based platform for building and deploying AI models, with a range of tools and services available.
For instance, Azure AI Foundry provides a range of features and benefits, including automated machine learning, data preparation, and model deployment. This has led to the development of a range of AI-powered applications, including predictive maintenance systems, chatbots, and virtual assistants.
| Tool | Description | Pricing |
|---|---|---|
| GitHub | Version control and collaboration platform | Free – $21 per user per month |
| PyPI | Package manager for Python | Free |
| Azure AI Foundry | Cloud-based platform for building and deploying AI models | Custom pricing |
As the use of AI agents continues to grow, it is likely that we will see significant advancements in the development of agentic frameworks and tools. For more information on the use of AI agents, visit the Markets And Markets website, or check out the GitHub platform for a range of open-source agentic frameworks and tools.
Future Directions
The future of AI agents is likely to be shaped by a range of factors, including advancements in machine learning, natural language processing, and computer vision. As noted by industry experts, the development of more sophisticated AI agents will require significant advancements in these areas. For instance, the development of more advanced natural language processing algorithms will enable AI agents to better understand and respond to human language.
Additionally, the use of edge AI is likely to become more prevalent, with AI agents being deployed on edge devices such as smartphones, smart home devices, and autonomous vehicles. This will require the development of more efficient and scalable AI algorithms, as well as the use of specialized hardware such as GPUs and TPUs.
For more information on the future of AI agents, check out the Gartner website, or visit the Forrester website for a range of research reports and analysis.
Tools and Platforms for Building AI Agents
When it comes to building AI agents, having the right tools and platforms is crucial. 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. In this section, we will explore some of the essential tools and platforms for building AI agents.
Key Tools for Building AI Agents
There are several tools and platforms that are essential for building and deploying AI agents. Some of the most popular ones include GitHub, PyPI, and Azure AI Foundry. These tools provide a range of features and capabilities that make it easier to build, deploy, and manage AI agents.
| Tool | Key Features | Pricing | Best For | Rating |
|---|---|---|---|---|
| GitHub | Version control, collaboration, and deployment | Free – $21/user/month | Small to large teams | 4.5/5 |
| PyPI | Package management and deployment | Free | Small to large teams | 4.2/5 |
| Azure AI Foundry | AI model development, deployment, and management | $0.000004 – $0.000016/hour | Large teams and enterprises | 4.5/5 |
Detailed Comparison of Tools
Let’s take a closer look at each of these tools and their features.
1. GitHub
GitHub is a popular version control and collaboration platform that is widely used by developers. It provides a range of features, including version control, collaboration, and deployment. GitHub is free for public repositories and offers a range of pricing plans for private repositories, starting at $7/month.
Key Features:
- Version control
- Collaboration
- Deployment
- Issue tracking
- Project management
Pros:
- Easy to use
- Large community
- Free for public repositories
Cons:
- Limited features in free plan
- Can be slow for large repositories
2. PyPI
PyPI is a package management platform that is widely used by Python developers. It provides a range of features, including package management and deployment. PyPI is free to use and offers a range of features, including package management, deployment, and issue tracking.
Key Features:
- Package management
- Deployment
- Issue tracking
- Project management
Pros:
- Easy to use
- Free to use
- Large repository of packages
Cons:
- Limited features
- Can be slow for large packages
3. Azure AI Foundry
Azure AI Foundry is a cloud-based AI model development and deployment platform that is widely used by large teams and enterprises. It provides a range of features, including AI model development, deployment, and management. Azure AI Foundry offers a range of pricing plans, starting at $0.000004/hour.
Key Features:
- AI model development
- Deployment
- Management
- Issue tracking
- Project management
Pros:
- Scalable and secure
- Easy to use
- Large range of features
Cons:
- Expensive
- Ste
Conclusion
In conclusion, choosing the right open-source agentic framework for your industry-specific needs is a crucial step in unlocking the full potential of AI agents. As we’ve discussed throughout this blog post, agentic frameworks provide a foundational structure for developing autonomous systems, enabling AI agents to communicate, coordinate actions, reason about their environment, and make decisions. 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 clear that this technology is here to stay.
Key Takeaways and Insights
Our research has shown that top open-source frameworks like RLlib and Ray are highly rated for their technical features and adoption rates. 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. Additionally, 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.
Some of the key benefits of agentic frameworks include automating complex business processes and workflows, with over 50% of respondents in a survey reporting the use of AI agents. The trend towards an “open agentic web” is also 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).
Actionable Next Steps
So, what’s next? We recommend that readers take the following steps to get started with agentic frameworks:
- Evaluate top open-source frameworks based on technical features and adoption rates
- Assess industry trends and expert insights to stay ahead of the curve
- Explore tools and platforms like GitHub, PyPI, and Azure AI Foundry for building and deploying AI agents
- Consider the benefits of automating complex business processes and workflows with AI agents
For more information on how to choose the right open-source agentic framework for your industry-specific needs, visit www.superagi.com. Our team is dedicated to providing the latest insights and research on AI agents and agentic frameworks. Don’t miss out on the opportunity to stay ahead of the curve and unlock the full potential of AI agents for your business. Take the first step today and start exploring the world of agentic frameworks.
