5 LLM Frameworks to Boost Your Productivity

by Tobias Abdon

The world of LLM app development is a super exciting space. Like in other areas of software development, the right LLM tooling can make a huge difference in productivity, reliability, and security. While the overall LLM dev landscape is relatively new, there are some great tools available to help you build.

In this article I will introduce some of the tools available to developers building apps that use LLMs.



LangChain is perhaps the most robust framework developed to aid developers in building applications that leverage Language Models (LLMs). In fact, we wrote another article about it that you can check out here.

Whether you're looking to create a simple chatbot or a complex system that amalgamates multiple components, LangChain offers the tools and interfaces to make the job smoother.

LangChain comes with the following modules built-in:

1. Model I/O: Tools to interact, input, and process outputs from language models. 2. Retrieval: Fetches external data for LLM to enhance generation. 3. Chains: Interfaces to compose and streamline LLM components for easy application structure. 4. Memory: Supports recalling past interactions for fluent conversations. 5. Agents: Dynamic workflows using LLMs to determine task sequences. 6. Callbacks: Monitor and integrate feedback loops within LLM applications.

Site: https://docs.langchain.com/docs/


Formerly known as GPT Index, LlamaIndex is essential for managing the interaction between LLMs and domain-specific or private data. It excels in ingesting and structuring such data, allowing applications built on LLMs to augment pre-trained models with domain-specific information which may be ensnared in various data stores or formats, making it integral for developers dealing with siloed applications and private data.

Key Features

  • Data connectors for ingesting data from native sources and formats.
  • Data indexes for structuring data in a manner that’s easily consumable by LLMs.
  • Engines offer natural language access to structured data, with capabilities such as powerful retrieval interfaces and conversational interactions.
  • Data agents and Application integrations allow seamless interplay with other systems and tools in your ecosystem.

Site: https://www.llamaindex.ai/


Haystack stands out as a comprehensive NLP framework, enabling the development of applications powered by state-of-the-art NLP models and LLMs. It provides diverse capabilities such as question answering, answer generation, and semantic document search.

Core Concepts

  • Pipelines and Nodes structure and process data to perform various NLP tasks.
  • Agents, powered by LLMs, decide the course of action to resolve complex queries.
  • Tools are specialized components used by agents, exemplified by a calculator or a WebRetriever.
  • DocumentStores house the text data that Haystack accesses, with compatibility with various database technologies like Elasticsearch and FAISS.

Repo: https://github.com/deepset-ai/haystack


tinyllm serves as a robust alternative to other LLM libraries, designed to overcome challenges like lack of composability, intricate software designs, and readability issues prevalent in existing libraries. Its emphasis on simplicity and robust abstraction aids in seamless integration with living codebases.

Key Components

  • Function, Validator, Chain, Concurrent, and Decision are the foundational components that facilitate the creation, management, and efficient execution of LLM functions.
  • The library utilizes Tinyllm Vector Store, leveraging a Postgres DB with the pgvector extension, offering a flexible and cost-effective solution for managing embeddings.

Repo: https://github.com/zozoheir/tinyllm


Griptape is known for its modular framework that aids in building secure and efficient AI-powered applications. It enables developers to construct apps with modular structures and tools while maintaining extensive control over data access and LLM activities.

Notable Characteristics

  • AI Apps Construction: Offers modular structures and tools for composing apps in Python.
  • Data Access Control: Provides secure connectivity to data sources with granular access controls.
  • Scalability: Facilitates easy deployment and running of apps in the cloud, allowing processing of data as needed.

Site: https://www.griptape.ai/


The LLM Tooling Landscape is young but moving fast, with innovative tools like LangChain, LlamaIndex, tinyllm, Haystack, and Griptape, each bringing unique solutions and capabilities to the table. Whether it’s managing domain-specific data, simplifying interactions with LLMs, solving intricate NLP tasks, or building secure AI-powered applications, these tools are transforming the way developers leverage LLMs to build groundbreaking applications. Keep an eye on this evolving landscape to stay abreast of the latest advancements and optimize your development workflow.