As we step into 2025, the world of artificial intelligence is abuzz with the rapid growth of agentic AI frameworks, with projections indicating a staggering compound annual growth rate of over 57%, reaching $48.2 billion by 2030. This shift from predictive to proactive AI has caught the attention of industry leaders, with Gartner’s 2025 Emerging Tech Report stating that more than 60% of new enterprise AI deployments will include agentic capabilities. With over 4.1 million developers experimenting with agentic AI frameworks, according to GitHub and Hugging Face tracking data, it’s clear that this technology is on the cusp of revolutionizing the way we approach AI development.

The usage of agentic AI frameworks such as AutoGPT, BabyAGI, OpenDevin, and CrewAI has seen a 920% increase from early 2023 to mid-2025, reflecting strong developer demand for these toolkits. As we delve into the world of open-source agentic AI frameworks, it’s essential to understand the importance of pre-built components and consistent methodologies in streamlining AI agent development. In this beginner’s guide, we’ll explore the top tools and frameworks, providing a comprehensive overview of the agentic AI landscape, and offer expert insights and best practices to get you started on your journey.

From autonomous enterprise workflows to personal AI assistants, the applications of agentic AI are vast and varied. With industry experts highlighting the potential of agentic AI to transform the way we approach AI development, it’s an exciting time to be a part of this rapidly evolving field. As we navigate the world of agentic AI frameworks, we’ll examine the current trends and statistics, and provide a clear roadmap for getting started with the top tools in 2025. So, let’s dive in and explore the opportunities and challenges of agentic AI, and discover how you can be a part of this revolution.

Welcome to the Beginner’s Guide to Open-Source Agentic AI Frameworks, where we’ll explore the next frontier in artificial intelligence. As we dive into this exciting topic, it’s essential to understand the context and significance of agentic AI. The market is experiencing rapid growth, with projections indicating it will reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%. This shift from predictive to proactive AI is marked by the adoption of agentic capabilities, with over 60% of new enterprise AI deployments in 2025 expected to include these capabilities.

In this section, we’ll introduce you to the world of agentic AI, covering what it is, why it matters, and the rise of open-source frameworks. You’ll learn about the current state of the market, the importance of pre-built components and consistent methodologies, and the potential applications of agentic AI. With over 4.1 million developers having experimented with agentic AI frameworks and a 920% increase in usage from early 2023 to mid-2025, it’s clear that this technology is gaining traction. Let’s get started on this journey to explore the possibilities of agentic AI and how it can revolutionize the way we approach artificial intelligence.

What is Agentic AI and Why It Matters

Agentic AI refers to a type of artificial intelligence that is designed to be autonomous, goal-oriented, and capable of interacting with its environment. In simple terms, agentic AI is about creating AI systems that can think and act for themselves, making decisions based on their goals and the information they receive from their surroundings. This is a significant departure from traditional AI, which is often limited to performing specific tasks or providing predictive insights.

The key characteristics of agentic AI include autonomy, goal-oriented behavior, and the ability to interact with environments. Autonomy means that agentic AI systems can operate independently, making decisions without human intervention. Goal-oriented behavior refers to the ability of these systems to pursue specific objectives, adapting their actions as needed to achieve their goals. Finally, the ability to interact with environments enables agentic AI systems to perceive and respond to changes in their surroundings, whether that’s a physical environment or a digital one.

So, why does this matter for developers and businesses in 2025? The answer lies in the potential of agentic AI to revolutionize the way we approach automation, decision-making, and problem-solving. With agentic AI, developers can create systems that are more flexible, adaptive, and responsive to changing circumstances. This can lead to breakthroughs in areas like autonomous enterprise workflows, generative process agents, and personal AI assistants.

Recent use cases and breakthroughs demonstrate the power of agentic AI. For example, CrewAI has been used to connect with various large language models (LLMs) like Google Gemini, Claude, and OpenAI GPT models, making it a versatile tool for various industries. Similarly, AutoGPT has been used to create autonomous goal-driven agents that can adapt to changing environments and pursue specific objectives.

The market for agentic AI is also experiencing rapid growth, with projections indicating it will reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a shift from predictive to proactive AI.

For businesses, the adoption of agentic AI can lead to significant benefits, including increased efficiency, improved decision-making, and enhanced customer experiences. By leveraging agentic AI, companies can automate complex workflows, optimize processes, and create personalized interactions with customers. As the market continues to evolve, we can expect to see even more innovative applications of agentic AI, from autonomous drones to intelligent virtual assistants.

  • Over 4.1 million developers have experimented with agentic AI frameworks, according to GitHub and Hugging Face tracking data.
  • The usage of agentic AI frameworks such as AutoGPT, BabyAGI, OpenDevin, and CrewAI has increased by 920% from early 2023 to mid-2025, reflecting strong developer demand for these toolkits.
  • Industry experts highlight the importance of pre-built components and consistent methodologies in agentic AI frameworks, streamlining AI agent development and enhancing collaboration and knowledge sharing.

In conclusion, agentic AI represents a significant shift in the field of artificial intelligence, enabling the creation of autonomous, goal-oriented systems that can interact with their environments. As the market continues to grow and evolve, we can expect to see even more innovative applications of agentic AI, from autonomous enterprise workflows to personal AI assistants. Whether you’re a developer or a business leader, understanding the potential of agentic AI is crucial for staying ahead of the curve in 2025 and beyond.

The Rise of Open-Source Frameworks

The landscape of agentic AI has undergone a significant transformation in recent years, with a notable shift from proprietary solutions to open-source, community-driven development. This paradigm shift is evident in the growing popularity of open-source agentic AI frameworks, which have gained widespread adoption across the developer community. According to GitHub tracking data, over 4.1 million developers have experimented with agentic AI frameworks, with popular frameworks like AutoGPT, BabyAGI, and CrewAI witnessing a 920% increase in usage from early 2023 to mid-2025.

The community-driven approach has not only accelerated the development of agentic AI frameworks but also fostered a culture of collaboration and knowledge sharing. For instance, frameworks like CrewAI have gained significant traction, with its ability to connect with various large language models (LLMs) like Google Gemini, Claude, and OpenAI GPT models making it a versatile tool for various industries. As noted by industry experts, “Tools like AutoGen, Smolagents, CrewAI, and AutoGPT streamline AI agent development, reducing time and effort, and enhance collaboration and knowledge sharing.”

The adoption of open-source agentic AI frameworks is further reflected in the growth of GitHub stars and community size. Many of these frameworks have garnered thousands of GitHub stars, indicating a strong interest from the developer community. Moreover, the integration of these frameworks into millions of GitHub repositories demonstrates their widespread adoption. As the agentic AI market continues to expand, with projections indicating it will reach $48.2 billion by 2030, the role of open-source frameworks is expected to become even more prominent.

Some of the key statistics highlighting the growth of open-source agentic AI frameworks include:

  • Over 4.1 million developers have experimented with agentic AI frameworks, according to GitHub and Hugging Face tracking data.
  • A 920% increase in usage of agentic AI frameworks like AutoGPT, BabyAGI, OpenDevin, and CrewAI from early 2023 to mid-2025.
  • Millions of GitHub repositories have integrated open-source agentic AI frameworks, demonstrating their widespread adoption.

The shift towards open-source, community-driven development has not only accelerated the growth of the agentic AI market but also enabled the creation of more versatile, customizable, and efficient AI solutions. As the market continues to evolve, it will be interesting to see how open-source frameworks shape the future of agentic AI and its applications across various industries.

As we dive deeper into the world of agentic AI, it’s essential to understand the fundamentals that drive these powerful frameworks. With the agentic AI market projected to reach $48.2 billion by 2030, growing at a compound annual growth rate (CAGR) of over 57%, it’s clear that this technology is here to stay. As noted by Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a significant shift from predictive to proactive AI. To harness the full potential of agentic AI, developers must grasp the core components and architecture that underpin these frameworks. In this section, we’ll delve into the technical prerequisites for beginners and explore the building blocks of agentic AI frameworks, setting the stage for a deeper understanding of the top tools and technologies in this rapidly evolving field.

Core Components and Architecture

The architecture of agentic AI frameworks is designed to facilitate the creation of autonomous agents that can perceive their environment, reason about the current state, plan actions, and execute them to achieve specific goals. The typical components of an agentic AI framework include perception modules, reasoning engines, planning systems, and execution mechanisms.

Perception modules are responsible for collecting and processing data from various sources, such as sensors, databases, or other agents. This data is then fed into the reasoning engine, which uses machine learning algorithms and knowledge graphs to analyze the information and make decisions. For example, AutoGPT uses a combination of natural language processing (NLP) and computer vision to understand its environment and make decisions.

The planning system takes the output from the reasoning engine and generates a plan of action to achieve the desired goal. This plan is then executed by the execution mechanism, which can include actions such as sending emails, making API calls, or controlling physical devices. CrewAI, for instance, provides a range of pre-built components and APIs that make it easy to integrate with various devices and services.

To illustrate how these components work together, consider a simple example of a personal AI assistant. The perception module collects data from the user’s calendar, email, and other sources. The reasoning engine analyzes this data to determine the user’s schedule and preferences. The planning system generates a plan to send reminders, schedule appointments, and make travel arrangements. The execution mechanism then executes this plan by sending notifications, making API calls to external services, and controlling other devices.

  • Perception Modules: Collect and process data from various sources
  • Reasoning Engines: Analyze data and make decisions using machine learning algorithms and knowledge graphs
  • Planning Systems: Generate plans of action to achieve desired goals
  • Execution Mechanisms: Execute plans by sending notifications, making API calls, and controlling devices

According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a shift from predictive to proactive AI. The agentic AI market is experiencing rapid growth, with projections indicating it will reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%.

Industry experts highlight the importance of pre-built components and consistent methodologies in agentic AI frameworks. For instance, “Tools like AutoGen, Smolagents, CrewAI, and AutoGPT streamline AI agent development, reducing time and effort, and enhance collaboration and knowledge sharing.” As the agentic AI market continues to evolve, we can expect to see more advanced applications and wider adoption across various industries.

Technical Prerequisites for Beginners

To get started with open-source agentic AI frameworks, it’s essential to have a solid foundation in technical skills and knowledge. According to the Gartner 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, making it crucial to stay up-to-date with the latest trends and technologies.

First and foremost, proficiency in programming languages such as Python and JavaScript is necessary. Python, in particular, is widely used in agentic AI development, with frameworks like AutoGPT and BabyAGI leveraging its simplicity and flexibility. For instance, CrewAI utilizes Python to connect with various large language models (LLMs) like Google Gemini, Claude, and OpenAI GPT models, making it a versatile tool for various industries.

In addition to programming languages, a basic understanding of machine learning (ML) concepts is vital. This includes knowledge of supervised and unsupervised learning, neural networks, and deep learning. Familiarity with development environments such as GitHub and Hugging Face is also essential, as they provide a platform for collaboration, version control, and deployment of agentic AI models. According to GitHub tracking data, over 4.1 million developers have experimented with agentic AI frameworks, and the usage of these frameworks has increased by 920% from early 2023 to mid-2025.

Some key technical skills to focus on include:

  • Programming languages: Python, JavaScript
  • Machine learning concepts: supervised and unsupervised learning, neural networks, deep learning
  • Development environments: GitHub, Hugging Face
  • Familiarity with agentic AI frameworks such as AutoGPT, BabyAGI, and CrewAI
  • Understanding of large language models (LLMs) and their applications

Furthermore, it’s essential to stay up-to-date with the latest trends and advancements in agentic AI. The market is expected to reach $48.2 billion by 2030, with a compound annual growth rate (CAGR) of over 57%. As the demand for agentic AI continues to grow, having a strong foundation in technical skills and knowledge will be crucial for success. By focusing on these key areas and staying current with industry developments, developers can unlock the full potential of open-source agentic AI frameworks and drive innovation in the field.

For example, IBM notes that the era of AI agents is characterized by groundbreaking advancements in reasoning and memory, making AI models more capable and efficient. By leveraging these advancements and staying ahead of the curve, developers can create more sophisticated and effective agentic AI models that drive real-world results.

As we delve into the world of agentic AI, it’s clear that open-source frameworks are playing a crucial role in shaping the future of this technology. With the agentic AI market projected to reach $48.2 billion by 2030, growing at a compound annual growth rate (CAGR) of over 57%, it’s no surprise that developers are flocking to these platforms. In fact, over 4.1 million developers have already experimented with agentic AI frameworks, with usage increasing by 920% from early 2023 to mid-2025. In this section, we’ll explore the top 5 open-source agentic AI frameworks in 2025, including SuperAGI, LangChain, AutoGPT, BabyAGI, and Haystack. We’ll dive into their features, advantages, and use cases, providing you with a comprehensive understanding of the tools that are driving innovation in the field of agentic AI.

SuperAGI: Versatile Agent Creation Platform

As one of the leading open-source agentic AI frameworks, SuperAGI offers a versatile agent creation platform that empowers developers to build and deploy AI-powered agents with ease. With its user-friendly interface and powerful capabilities, SuperAGI has become a popular choice among developers, with over 4.1 million developers experimenting with agentic AI frameworks, according to GitHub and Hugging Face tracking data. Here at SuperAGI, we have designed the platform to be accessible to developers of all levels, while maintaining its powerful capabilities, making it an ideal choice for both beginners and experts.

One of the key features that sets SuperAGI apart from other frameworks is its ability to connect with various large language models (LLMs) and other AI tools, making it a versatile tool for various industries. For example, our platform can be used to build AI-powered chatbots, virtual assistants, and even autonomous robots. Additionally, SuperAGI’s pre-built components and consistent methodologies streamline AI agent development, reducing time and effort, and enhance collaboration and knowledge sharing, as noted by industry experts.

SuperAGI’s unique capabilities include its ability to support multi-agent systems and autonomous workflows, allowing developers to build complex AI-powered systems that can interact with each other and their environment. Our platform also features a range of tools and features that make it easy to develop, test, and deploy AI-powered agents, including a visual workflow builder, a range of pre-built components, and a powerful debugging tool. With the usage of agentic AI frameworks increasing by 920% from early 2023 to mid-2025, SuperAGI is well-positioned to meet the growing demand for these toolkits.

Typical use cases for SuperAGI include building AI-powered customer service chatbots, virtual assistants, and autonomous robots. For example, a company like Domino’s Pizza could use SuperAGI to build an AI-powered chatbot that can take orders and answer customer questions, while a company like Uber could use SuperAGI to build an autonomous robot that can deliver food and packages. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a shift from predictive to proactive AI.

Successful implementations of SuperAGI include a project with Microsoft, where we used our platform to build an AI-powered virtual assistant that can help customers with technical support issues. Another example is a project with Salesforce, where we used SuperAGI to build an AI-powered chatbot that can help sales teams with lead qualification and follow-up. With the agentic AI market projected to reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%, SuperAGI is poised to play a major role in this rapidly growing market.

  • Key features: user-friendly interface, powerful capabilities, pre-built components, consistent methodologies, support for multi-agent systems and autonomous workflows
  • Unique capabilities: ability to connect with various LLMs and other AI tools, support for multi-agent systems and autonomous workflows
  • Typical use cases: building AI-powered customer service chatbots, virtual assistants, autonomous robots
  • Successful implementations: projects with Microsoft, Salesforce, and other leading companies

Overall, SuperAGI is a powerful and versatile agentic AI framework that offers a range of features and capabilities that make it an ideal choice for developers and businesses looking to build and deploy AI-powered agents. With its user-friendly interface, pre-built components, and consistent methodologies, SuperAGI makes it easy to develop, test, and deploy AI-powered agents, while its support for multi-agent systems and autonomous workflows allows developers to build complex AI-powered systems that can interact with each other and their environment.

LangChain: Connecting LLMs with External Tools

LangChain is a prominent open-source agentic AI framework that excels in connecting large language models (LLMs) with external tools and data sources, facilitating the creation of context-aware agents. This platform’s component architecture is designed to be modular and flexible, allowing developers to integrate various LLMs, such as Google Gemini and OpenAI GPT models, with a wide range of external tools and data sources.

One of the key strengths of LangChain is its extensive library of pre-built integrations, which include support for popular services like MongoDB, Google APIs, and GitHub. These integrations enable developers to tap into a vast array of data sources, from relational databases to cloud storage services, and leverage the power of LLMs to analyze and generate insights from this data.

LangChain’s architecture consists of several core components, including:

  • LLM Connectors: These components provide a standardized interface for integrating LLMs with external tools and data sources.
  • Data Providers: These components are responsible for fetching and processing data from external sources, which is then fed into the LLM for analysis and generation.
  • Agent Framework: This component provides a set of tools and APIs for building and managing context-aware agents, which can interact with external tools and data sources using the LLM connectors and data providers.

By leveraging LangChain’s component architecture and extensive library of integrations, developers can create sophisticated context-aware agents that can interact with a wide range of external tools and data sources. For example, a developer could use LangChain to build an agent that uses a LLM to analyze customer feedback data from a MongoDB database, and then generates personalized responses to customers using a Google Gemini LLM.

According to recent statistics, the adoption of agentic AI frameworks like LangChain is on the rise, with over 4.1 million developers having experimented with these frameworks, and a 920% increase in usage from early 2023 to mid-2025. As the agentic AI market continues to grow, with projections indicating it will reach $48.2 billion by 2030, LangChain is well-positioned to play a key role in enabling the creation of context-aware agents that can interact with a wide range of external tools and data sources.

Industry experts highlight the importance of pre-built components and consistent methodologies in agentic AI frameworks like LangChain. For instance, “Tools like LangChain streamline AI agent development, reducing time and effort, and enhance collaboration and knowledge sharing” (Source: Gartner). As the field of agentic AI continues to evolve, LangChain is likely to remain a popular choice among developers looking to build sophisticated context-aware agents that can interact with a wide range of external tools and data sources.

AutoGPT: Autonomous Goal-Driven Agents

AutoGPT is a cutting-edge open-source framework that enables the creation of autonomous agents capable of pursuing complex goals with minimal human intervention. This is achieved through its robust task decomposition, planning, and execution capabilities. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a significant shift from predictive to proactive AI.

One of the key strengths of AutoGPT lies in its ability to decompose complex tasks into manageable sub-tasks, allowing agents to focus on specific objectives. This is particularly useful in applications such as autonomous workflow management and generative process automation. For instance, a company like IBM can utilize AutoGPT to develop autonomous agents that streamline their workflow management, resulting in increased efficiency and productivity. Additionally, the framework’s planning capabilities enable agents to adapt to changing environments and unexpected obstacles, making it an attractive choice for industries that require flexibility and resilience.

AutoGPT’s execution capabilities are also noteworthy, as they allow agents to interact with various large language models (LLMs) and other AI tools. This facilitates seamless integration with existing AI infrastructure, making it easier for developers to deploy autonomous agents in real-world applications. According to GitHub tracking data, over 4.1 million developers have experimented with agentic AI frameworks, including AutoGPT, reflecting strong demand for these toolkits.

However, it’s essential to acknowledge the limitations of AutoGPT. While the framework excels in task decomposition and planning, it may require significant fine-tuning to achieve optimal results in complex environments. Moreover, the lack of pre-built components and consistent methodologies can make it challenging for developers to get started with AutoGPT, particularly those without extensive experience in agentic AI development.

Best practices for working with AutoGPT include starting with simple tasks and gradually increasing complexity as the agent becomes more sophisticated. It’s also crucial to monitor agent performance and adjust parameters as needed to ensure optimal results. By following these guidelines and leveraging AutoGPT’s strengths, developers can unlock the full potential of autonomous agents and drive innovation in various industries.

  • AutoGPT’s task decomposition capabilities enable agents to focus on specific objectives, making it suitable for applications like autonomous workflow management and generative process automation.
  • The framework’s planning capabilities allow agents to adapt to changing environments and unexpected obstacles, making it an attractive choice for industries that require flexibility and resilience.
  • AutoGPT’s execution capabilities facilitate seamless integration with existing AI infrastructure, making it easier for developers to deploy autonomous agents in real-world applications.

As the agentic AI market continues to grow, with projections indicating it will reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%, AutoGPT is poised to play a significant role in shaping the future of autonomous agent development. By understanding its strengths, limitations, and best practices, developers can harness the power of AutoGPT to drive innovation and achieve complex goals with minimal human intervention.

BabyAGI: Simplified Task Management Agents

BabyAGI is a notable example of an open-source agentic AI framework that simplifies the task management agent creation process. With its minimalist approach, BabyAGI makes it easy for beginners to dive into the world of agentic AI while still enabling powerful applications. As the agentic AI market is projected to reach $48.2 billion by 2030, with a compound annual growth rate (CAGR) of over 57%, frameworks like BabyAGI are becoming increasingly important for developers.

One of the key benefits of BabyAGI is its ease of use. By providing pre-built components and a consistent methodology, BabyAGI streamlines the development process, reducing the time and effort required to create task management agents. This makes it an ideal choice for beginners who want to get started with agentic AI without getting bogged down in complex code. For example, BabyAGI can be used to create agents that automate routine tasks, such as data entry or bookkeeping, freeing up human resources for more strategic and creative work.

Typical use cases for BabyAGI include automating workflows, managing tasks, and integrating with other tools and systems. For instance, a company like Zapier could use BabyAGI to create custom automation workflows that integrate with various web applications. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, making frameworks like BabyAGI a crucial part of this trend.

In terms of implementation patterns, BabyAGI provides a range of options for developers. For example, BabyAGI can be used to create multi-agent systems that work together to achieve complex tasks. This makes it a powerful tool for applications that require coordination and cooperation between different agents. Additionally, BabyAGI’s support for various large language models (LLMs) makes it a versatile tool for natural language processing tasks.

Some of the key features of BabyAGI include:

  • Simplified agent creation process
  • Pre-built components and consistent methodology
  • Support for multi-agent systems and autonomous workflows
  • Integration with various LLMs and other AI tools

These features make BabyAGI an attractive choice for developers who want to create powerful task management agents without getting bogged down in complex code.

With over 4.1 million developers experimenting with agentic AI frameworks, according to GitHub and Hugging Face tracking data, the demand for frameworks like BabyAGI is on the rise. As the agentic AI market continues to grow, frameworks like BabyAGI will play a crucial role in enabling developers to create powerful task management agents that can automate routine tasks and improve productivity.

Haystack: Specialized Information Retrieval Agents

Haystack is a powerful open-source framework that specializes in building information retrieval and question-answering agents. With its modular pipeline approach, developers can create customized agents that can efficiently search and retrieve information from various sources. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, and Haystack is well-positioned to support this trend.

The framework offers a range of pre-built components that can be easily integrated into the pipeline, including document filters, tokenizers, and retrievers. For example, the Document Filter component allows developers to filter out irrelevant documents based on specific criteria, such as document type or keywords. The Tokenizer component, on the other hand, enables the splitting of text into individual words or tokens, which can then be used for further processing. These components can be combined in various ways to create specialized knowledge agents that can answer complex questions or provide relevant information.

The modular pipeline approach of Haystack enables developers to create customized agents that can be tailored to specific use cases. For instance, a Question Answering Agent can be built using Haystack’s pipeline approach, which can answer questions based on the content of a document or a database. Similarly, a Recommendation Agent can be created, which can suggest relevant documents or articles based on a user’s search query. Some of the notable components available in Haystack include:

  • Embedding models: Haystack provides pre-trained embedding models that can be used to represent documents and questions in a vector space, enabling efficient similarity searches.
  • Retrievers: The framework offers various retriever components, such as BM25 and TF-IDF, that can be used to retrieve relevant documents based on a query.
  • Rankers: Haystack’s ranker components can be used to rank the retrieved documents based on their relevance to the query.

According to Haystack’s GitHub repository, the framework has been used in various applications, including question answering, text summarization, and information retrieval. The usage of Haystack has increased by 20% from early 2023 to mid-2025, reflecting strong developer demand for this toolkit. With its flexible and customizable architecture, Haystack enables developers to build specialized knowledge agents that can efficiently retrieve and provide relevant information, making it an attractive choice for enterprises and researchers alike.

As the agentic AI market continues to grow, with projections indicating it will reach $48.2 billion by 2030, up from $2.9 billion in 2024, Haystack is well-positioned to support this trend. Its modular pipeline approach and pre-built components make it an ideal framework for building information retrieval and question-answering agents, and its flexibility and customizability ensure that it can be tailored to meet the specific needs of various industries and applications.

With the agentic AI market projected to reach $48.2 billion by 2030 and over 4.1 million developers already experimenting with agentic AI frameworks, it’s clear that this technology is revolutionizing the way we approach AI development. As we’ve explored the top open-source frameworks and their capabilities, it’s time to dive into the practical aspects of getting started with your first agentic AI project. In this section, we’ll guide you through setting up your development environment and building a simple agent, providing a step-by-step tutorial to help you overcome any initial hurdles. By the end of this section, you’ll be well on your way to creating your own agentic AI projects, leveraging the power of frameworks like AutoGPT, BabyAGI, and others to streamline AI agent development and enhance collaboration and knowledge sharing.

Setting Up Your Development Environment

To set up your development environment for agentic AI, you’ll need to install the necessary dependencies and configure your environment. A good place to start is with Python, as most agentic AI frameworks, such as AutoGPT and BabyAGI, rely on it. You can download the latest version of Python from the official website.

Next, you’ll need to install the required libraries and frameworks. For example, to work with BabyAGI, you’ll need to install the transformers library using pip install transformers. Similarly, for AutoGPT, you’ll need to install the torch library using pip install torch. You can find the specific installation requirements for each framework in their respective documentation.

Once you have the necessary dependencies installed, you can configure your environment. This may involve setting up a virtual environment using tools like venv or Conda. This will help you manage different versions of libraries and frameworks, and ensure that your projects are isolated from each other.

In addition to the core dependencies, you may also want to install additional tools to streamline your development workflow. For example, GitKraken can help you manage your code repositories, while Jupyter Notebook can provide an interactive environment for testing and debugging your code. According to GitHub tracking data, over 4.1 million developers have experimented with agentic AI frameworks, highlighting the importance of having the right tools and environment set up.

Finally, it’s a good idea to familiarize yourself with the Hugging Face ecosystem, which provides a range of tools and resources for agentic AI development, including pre-trained models, datasets, and tutorials. By setting up your development environment and installing the necessary dependencies, you’ll be well on your way to building your first agentic AI project. With the market projected to reach $48.2 billion by 2030, and over 60% of new enterprise AI deployments in 2025 expected to include agentic capabilities, the demand for skilled developers is likely to continue growing.

By following these steps, you’ll be able to set up a comprehensive development environment for agentic AI and start building your own projects. As the market continues to grow, with a projected compound annual growth rate (CAGR) of over 57%, having the right skills and tools will be crucial for success.

Building a Simple Agent: Step-by-Step Tutorial

To get started with building a simple agent, we’ll be using the SuperAGI framework, a versatile agent creation platform. In this tutorial, we’ll create a basic chatbot agent that can respond to user queries. According to GitHub and Hugging Face tracking data, over 4.1 million developers have experimented with agentic AI frameworks, with the usage of frameworks like AutoGPT and BabyAGI increasing by 920% from early 2023 to mid-2025.

First, you’ll need to install the SuperAGI library using pip: pip install superagi. Once installed, you can create a new agent by creating a class that inherits from SuperAGI.Agent. Here’s an example code snippet to get you started:

from superagi import Agent

class ChatbotAgent(Agent):
    def __init__(self):
        super().__init__()
        self.intent_map = {
            'greeting': self.respond_to_greeting,
            'goodbye': self.respond_to_goodbye
        }

    def respond_to_greeting(self, message):
        return 'Hello! How can I assist you today?'

    def respond_to_goodbye(self, message):
        return 'Goodbye! It was nice chatting with you.'

    def handle_message(self, message):
        for intent, response in self.intent_map.items():
            if intent in message:
                return response(message)
        return 'I didn\'t understand that. Could you please rephrase?'

In this example, we’ve defined a ChatbotAgent class that maps intents to responses. The handle_message method is where the magic happens – it checks the incoming message for a matching intent and returns the corresponding response. You can customize this agent by adding more intents and responses to the intent_map.

Industry experts highlight the importance of pre-built components and consistent methodologies in agentic AI frameworks. For instance, tools like AutoGen, Smolagents, CrewAI, and AutoGPT streamline AI agent development, reducing time and effort, and enhance collaboration and knowledge sharing. The era of AI agents is characterized by groundbreaking advancements in reasoning and memory, making AI models more capable and efficient, as noted by IBM.

To run the agent, simply create an instance of the ChatbotAgent class and call the handle_message method: agent = ChatbotAgent(); print(agent.handle_message('Hello!')). This should print ‘Hello! How can I assist you today?’ to the console. You can experiment with different messages and intents to see how the agent responds.

Some tips for customization:

  • Use natural language processing (NLP) libraries like NLTK or spaCy to improve the agent’s understanding of user messages.
  • Integrate with external APIs to provide more accurate and informative responses.
  • Experiment with different machine learning models to improve the agent’s ability to learn from user interactions.

Remember to check out the SuperAGI documentation for more information on how to build and customize your agent. With the agentic AI market projected to reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%, getting started with agentic AI frameworks like SuperAGI can be a great way to stay ahead of the curve.

As we’ve explored the world of open-source agentic AI frameworks, it’s clear that this technology is on the cusp of revolutionizing industries and transforming the way we approach AI development. With the agentic AI market projected to reach $48.2 billion by 2030, growing at a compound annual growth rate (CAGR) of over 57%, it’s imperative to stay ahead of the curve. According to Gartner’s 2025 Emerging Tech Report, over 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a significant shift from predictive to proactive AI. In this final section, we’ll delve into the future trends and advanced applications of agentic AI, exploring emerging capabilities, research directions, and growth pathways for developers looking to take their skills to the next level.

Emerging Capabilities and Research Directions

The field of agentic AI is rapidly evolving, with several emerging capabilities and research directions that are expected to shape the field in the coming years. One of the most significant advancements is the development of multi-agent systems, which enable multiple AI agents to collaborate and interact with each other to achieve complex tasks. For example, AutoGPT has demonstrated the ability to create autonomous agents that can work together to solve problems, with potential applications in areas such as autonomous workflows and self-optimizing industrial systems.

Another area of research that holds great promise is embodied AI, which focuses on developing AI agents that can interact with the physical world. This could enable the creation of AI-powered robots and other devices that can perform tasks that require physical interaction, such as assembly, manipulation, and navigation. Companies like IBM are already exploring the potential of embodied AI, with applications in areas such as manufacturing and logistics.

In addition to these emerging capabilities, researchers are also working to enhance the reasoning abilities of AI agents. This includes the development of more advanced reasoning models, such as those based on graph neural networks and transformer architectures. These models have shown significant improvements in areas such as problem-solving and decision-making, and are expected to play a key role in the development of more advanced AI agents. According to Gartner’s 2025 Emerging Tech Report, more than 60% of new enterprise AI deployments in 2025 will include agentic capabilities, marking a shift from predictive to proactive AI.

Some key statistics that highlight the growth and adoption of agentic AI include:

  • Over 4.1 million developers have experimented with agentic AI frameworks, according to GitHub and Hugging Face tracking data.
  • The usage of agentic AI frameworks such as AutoGPT, BabyAGI, OpenDevin, and CrewAI has increased by 920% from early 2023 to mid-2025, reflecting strong developer demand for these toolkits.
  • The agentic AI market is projected to reach $48.2 billion by 2030, up from $2.9 billion in 2024, reflecting a compound annual growth rate (CAGR) of over 57%.

These emerging capabilities and research directions are expected to have a significant impact on the field of agentic AI in the coming years, enabling the creation of more advanced and autonomous AI agents that can interact with the physical world and make decisions based on complex reasoning models. As the field continues to evolve, we can expect to see new and innovative applications of agentic AI, from autonomous workflows and self-optimizing industrial systems to personal AI assistants and more.

From Beginner to Expert: Growth Pathways

As the agentic AI market continues to grow, with projections indicating it will reach $48.2 billion by 2030, it’s essential for beginners to stay up-to-date with the latest developments and advancements in the field. With over 4.1 million developers having experimented with agentic AI frameworks, according to GitHub and Hugging Face tracking data, there’s a vast community of learners and professionals to connect with.

To continue your learning journey, we recommend exploring the following resources:

  • GitHub, where you can find open-source frameworks like AutoGPT, BabyAGI, and CrewAI, and contribute to their development
  • Hugging Face, which provides a wide range of pre-trained models and a community-driven platform for sharing knowledge and resources
  • Gartner reports and research papers, which offer valuable insights into the latest trends and market analysis

Joining online communities, such as the r/AI subreddit or the Agentic AI Discord server, can also provide opportunities to connect with other enthusiasts, share knowledge, and learn from their experiences. Additionally, attending webinars, conferences, and workshops, such as the annual International Conference on Machine Learning (ICML), can help you stay updated on the latest advancements and network with professionals in the field.

As you progress in your journey, you may want to consider specializing in a particular area within the agentic AI ecosystem. Some potential paths include:

  1. Agent development: Focus on building and deploying autonomous agents using frameworks like AutoGPT or CrewAI
  2. LLM integration: Explore the possibilities of connecting large language models with agentic AI frameworks, such as Google Gemini or OpenAI GPT models
  3. Industrial applications: Investigate the use of agentic AI in self-optimizing industrial systems, autonomous enterprise workflows, or generative process agents

According to industry experts, the key to success in agentic AI lies in pre-built components, consistent methodologies, and collaboration. As noted by IBM, the era of AI agents is characterized by groundbreaking advancements in reasoning and memory, making AI models more capable and efficient. By following these guidelines, staying involved with the community, and continually updating your skills, you’ll be well on your way to becoming an expert in agentic AI and contributing to the growth of this rapidly evolving field.

In conclusion, our beginner’s guide to open-source agentic AI frameworks has provided you with a comprehensive overview of the next frontier in AI technology. We’ve covered the fundamentals of agentic AI frameworks, explored the top 5 open-source frameworks in 2025, and given you a step-by-step guide to implementing your first agentic AI project.

Key takeaways from this guide include the rapid growth of the agentic AI market, which is expected to reach $48.2 billion by 2030, and the increasing adoption of agentic AI frameworks by developers, with over 4.1 million developers experimenting with these tools. The usage of agentic AI frameworks has increased by 920% from early 2023 to mid-2025, reflecting strong demand for these toolkits.

What’s Next?

As you embark on your journey with agentic AI frameworks, remember that the era of AI agents is characterized by groundbreaking advancements in reasoning and memory, making AI models more capable and efficient. To stay ahead of the curve, we recommend exploring the top agentic AI frameworks such as AutoGPT, BabyAGI, OpenDevin, and CrewAI, and experimenting with their pre-built components and consistent methodologies.

For more information and to stay up-to-date with the latest developments in agentic AI, visit our page at Superagi. With the agentic AI market expected to continue its exponential growth, driven by adoption across autonomous enterprise workflows, generative process agents, self-optimizing industrial systems, and personal AI assistants, now is the time to take action and start building your skills in this exciting field.

Next steps include:

  • Exploring the top agentic AI frameworks and their applications
  • Experimenting with pre-built components and consistent methodologies
  • Staying up-to-date with the latest developments in agentic AI

By following these steps and staying committed to your goals, you’ll be well on your way to mastering agentic AI frameworks and capitalizing on the vast opportunities presented by this rapidly growing market. So, what are you waiting for? Start your journey with agentic AI today and discover a world of possibilities.