In recent years, the growing popularity of Large Language Models (LLMs) has spurred the development of a large number of tools designed to manage, orchestrate and optimize the creation of conversational agents and natural language processing pipelines. This talk focuses on three prominent frameworks for building LLM-based software projects: Haystack, Llamaindex and LangChain. Its goal is to offer a comprehensive overview of their functionalities and evaluate their performance in terms of execution speed and complexity of use. In a technological environment where effective integration of LLMs into real-world applications hinges on prompt management, pipeline design and flexible agent construction, choosing a proven framework can significantly reduce development time and ground a project in open-source libraries supported by large user communities.

Haystack features a modular architecture in which well-defined components simplify the development of scalable and customizable applications. Llamaindex centers on rapid data access and extraction by managing optimized indexes that aim to deliver competitive performance. Finally, LangChain adopts a “chaining” approach, in which each module enriches the context for the next, allowing various components to be seamlessly integrated into a single pipeline.

During the talk, these three tools will be compared using multiple parameters. From a performance perspective, small-scale benchmarks will illustrate differences in execution time, responsiveness to simultaneous inputs and resilience under growing workloads. Attention will then turn to the number of lines of code required to create basic pipelines, highlighting how setup complexities can vary based on each tool’s architecture. Additional factors include the clarity of the APIs, availability of documentation and ease with which new users can begin building practical projects.

Another central point concerns the specific “role” each framework plays in the LLM-based application development chain. To this end, real-world examples will be presented to illustrate how a judicious choice of framework can substantially reduce initial effort by minimizing the need to write extraneous code. Haystack shows particular promise where especially complex retrieval pipelines or very large document repositories must be queried efficiently. Llamaindex, meanwhile, excels in scenarios requiring the rapid creation and updating of customized indexes for continuously evolving knowledge bases. Lastly, LangChain is often the preferred option for creating dynamic agents that combine multiple models into text-generation systems.

The main aim of the talk is to underscore how Haystack, Llamaindex and LangChain can be invaluable tools for quickly developing conversational agent prototypes, question-answering solutions and advanced text generation systems. By weighing the pros and cons of each framework, developers can better navigate the diverse LLM ecosystem, make more informed choices about which platform best suits their needs and maximize the benefits that intelligent agents and pipelines can bring to future projects.

At a time when competitiveness increasingly depends on rapidly moving from an idea to a functioning Minimum Viable Product (MVP), selecting the right framework can be decisive, influencing costs, speed of development and the robustness of final solutions.