LLM App Frameworks

LLM App Frameworks

LLM app frameworks are libraries and tools that help developers integrate and manage AI language models in their software. They provide the necessary infrastructure to deploy, monitor, and scale LLM-based applications across various platforms.

These frameworks are essential for AI engineers, providing functionality for data and prompt management, API connectors, and model deployment. Frameworks like LangChain offer prompt engineering, conversation memory management, and prompt formulation, streamlining the development process for LLM-based applications, while LlamaIndex complements these capabilities with additional tools for indexing and retrieval.

Integration with various APIs, such as OpenAI, Anthropic, and Together AI, allows for seamless incorporation of natural language processing features into applications, enhancing their intelligence and user interaction.

Here are notable LLM app frameworks. Each offers different strengths, so the optimal choice depends on the specific needs of your application.

Best all-in-one for AI Teams

Klu.ai is an all-in-one LLM App Platform that enables AI teams to experiment, version, and fine-tune GPT-4 Apps. It provides a platform for collaborative prompt engineering, allowing teams to explore, save, and collaborate on their projects.

Klu also offers insights into usage and system performance across features and teams, helping users understand user preference, prompt performance, and label their data. It also provides a high-performance, private platform for building custom AI systems, simplifying model deployment and reducing overhead.

If you're not going to use Klu, here are our favorite alternatives.

Best performance for chained prompts

• Dust.ai is an AI platform that allows non-developers to build AI apps quickly and easily. It offers features like chained LLM apps, multiple inputs, model choice, and semantic search. Dust.ai provides an intuitive interface and pre-trained models, making it easy for users to create AI apps. It also offers easy deployment, enabling users to deploy their models quickly and easily to their favorite platforms.

Best Typescript framework

• Axflow is a TypeScript-first set of open-source modules that power the full AI lifecycle of AI applications, including LLM utilities, dataset management, continuous evaluation, fine-tuning, and model serving. It provides a family of modular libraries, which can be incrementally adopted, and together form an end-to-end opinionated framework for LLM development.

Best Python RAG framework

• LlamaIndex is a library designed for building search and retrieval applications with hierarchical indexing, increased control, and wider flexibility. It's particularly tailored for indexing and retrieving data, making it ideal for applications such as semantic search and context-aware query.

Best for ReACT agents

• LangChain is an open-source framework and developer toolkit that helps developers get started with LLMs. It provides extensive control and adaptability for various use cases, making it a more comprehensive framework compared to LlamaIndex.

• Flowise A drag & drop UI to build your customized LLM flow using LangchainJS.

• Dify An Open-Source Assistants API and GPTs alternative. Dify.AI is an LLM application development platform. It integrates the concepts of Backend as a Service and LLMOps, covering the core tech stack required for building generative AI-native applications, including a built-in RAG engine.

Best alternative to LangChain

• Griptape is an open-source Python framework and a managed cloud platform for building and deploying enterprise-class AI applications. It allows developers to create simple LLM-powered agents, compose sequential event-driven pipelines, or orchestrate complex DAG-based workflows.

Best RAG framework for data-intensive projects

• Haystack is an open-source LLM framework for building production-ready applications. It provides all tooling in one place, including preprocessing, pipelines, agents & tools, prompts, evaluation, and fine-tuning. It also allows developers to choose their favorite database and scale to millions of documents.

Best connector framework for platforms using multiple models

• LiteLLM is a relatively new framework that attempts to mitigate the pain when migrating between different AI APIs. It provides smart features for managing timeouts, cooldowns, and retries, ensuring your app is efficient, reliable, and user-friendly.

LLM Serving Frameworks

LLM Serving Frameworks are specialized tools that help developers deploy and manage AI language models. They make it easier to integrate these models into their private cloud, ensuring they run smoothly and efficiently.

• vLLM — A framework for LLM inference and serving. It's one of the open-source libraries for LLM inference and serving.

• Ray Serve — Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for accelerating ML workloads, including model serving.

• MLC LLM — A universal deployment solution that enables LLMs to run efficiently on consumer devices, leveraging native hardware acceleration.

• DeepSpeed-MII — A framework to use if you already have experience with the DeepSpeed library and wish to continue using it for deploying LLMs.

• OpenLLM — A framework that supports connecting multiple adapters to only one deployed LLM. It allows the use of different implementations: Pytorch, Tensorflow, or Flax.

• FlexGen — A tool for running large language models on a single GPU for throughput-oriented scenarios.

• lanarky — A FastAPI framework to build production-grade LLM applications.

• Xinference — A framework that gives you the freedom to use any LLM you need. It empowers you to run inference with any open-source language models, speech recognition models, and multimodal models.

• Giskard — A testing framework dedicated to ML models, from tabular to LLMs.

Remember, the best framework for a particular application will depend on the specific requirements of that application.

Top LLM Providers

The landscape of Large Language Model (LLM) providers is diverse, with several key players offering a range of services from API access to open-source models. Notable LLM vendors include:

• OpenAI — The creator of the GPT series, OpenAI popularized LLMs for a mainstream audience with ChatGPT.

• TogetherAI — Offers a range of generative AI models and tools, including hosted open-source models such as Mixtral.

• Anthropic — Known for its work on AI safety and interpretability, Anthropic develops the Claude family of models.

• Mistral — Known for its Mistral and Mixtral open-weight models, Mistral also operates a hosted API platform offering additional proprietary models.

• Cohere — Offers a platform for building AI applications with natural language understanding capabilities.

• Google — A major player whose model lineup has included LaMDA, PaLM, and Gemini.

• AI21Labs — Offers the Jurassic model family, alongside additional generative AI tools.

These companies are at the forefront of LLM development, each contributing to the advancement of natural language processing and generation technologies. They provide tools that enable a wide range of applications, from text generation to sentiment analysis, information retrieval, and code generation, through a mix of commercial and open-source options.

Popular AI Assistants

• ChatGPT — A conversational AI assistant developed by OpenAI, built on its GPT family of models. It's trained on a massive dataset of text and code, enabling it to learn the patterns of human conversation and generate natural and engaging responses.

• Gemini — Google's conversational AI assistant, built on its Gemini models. It succeeded the earlier Bard service, which Google rebranded to Gemini in 2024, and can draw on web search to inform its responses.

• Claude — Claude is a family of LLMs and the name of the assistant developed by Anthropic. Claude is differentiated by its large context window and emphasis on AI safety and alignment, making it a common choice for organizations with a lower risk appetite.

Open Source AI Assistants

The top open source AI assistants, include:

• Ollama — Ollama is an open-source project that allows you to run large language models locally. It provides a command-line interface and local API; several third-party projects offer web UIs on top of it.

• Flowise — Flowise is an open-source low-code LLM Apps Builder. It provides a visual interface for building customized LLM orchestration flows and AI agents.

• Chatbot UI — This is an open-source AI chat interface that can be used by anyone. It provides a simple and user-friendly interface for building chatbots.

• Mycroft — Mycroft is a privacy-focused open-source voice assistant. It is customizable and can run on many platforms, including desktop and smart speakers.

• Leon — Leon is an open-source personal assistant that can live on your server. It is built on top of Node.js, Python, and artificial intelligence concepts.

These open-source AI assistants cater to different interaction modes: Ollama and Chatbot UI are tailored for text-based communication, while Mycroft and Leon are voice-oriented. Flowise uniquely enables the construction of customized LLM orchestration flows.