Open-source alternatives to OpenChatKit

AdalFlow is an autonomous AI agent framework and LLM application library designed for building modular workflows. It serves as a model-agnostic interface and RAG pipeline orchestrator, allowing users to develop ReAct agents that utilize iterative reasoning and external tool execution to solve complex tasks. The project distinguishes itself through a prompt optimization system that uses textual gradient descent to automatically refine prompt templates and few-shot examples. It treats model feedback as a differentiable signal, enabling a form of LLM backpropagation to iteratively improve output

MiniCPM is a collection of small language models designed for local, on-device deployment in resource-constrained environments. The project focuses on running dense Transformer models on consumer hardware, including GPUs, CPUs, and Apple Silicon, without requiring custom code forks. The project distinguishes itself through heavy optimization for edge hardware, utilizing quantized weight compression in GGUF and MLX formats to reduce memory overhead. It implements advanced inference techniques such as speculative sampling and radix-tree prefix caching to accelerate generation speed and throughp

Llama 3 is a collection of pretrained, autoregressive transformer-based models designed for natural language generation, reasoning, and complex instruction following. It functions as a generative AI framework that provides the infrastructure for managing model weights, executing neural network inference, and handling computational workloads across diverse knowledge domains. The project distinguishes itself through an integrated AI safety toolkit that employs secondary classification filtering to inspect inputs and outputs, ensuring adherence to usage compliance and safety standards. It suppor

FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications. The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn di

sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting model weights. It provides a collection of scripts for executing Stable Diffusion training through methods such as DreamBooth, textual inversion, and full fine-tuning, alongside a framework for creating and managing Low-Rank Adaptation weights. The project features specialized capabilities for model weight conversion between different architectures and precision formats. It includes tools for merging adaptation weights into base models, extracting weights from trained models,

LEANN is a framework for local retrieval augmented generation and vector indexing. It functions as a system for building local knowledge bases and source code search engines that combine large language models with retrieved private data to generate context-aware responses. The project distinguishes itself through a vision-model based document layout extractor for parsing complex PDF figures and diagrams, and a source code search engine that employs structure-aware chunking to preserve function and class boundaries. It also implements the Model Context Protocol to integrate real-time data sour

This project provides a search service designed to retrieve and rerank web content for use in large language model applications. It functions as a retrieval augmented search engine that processes natural language queries to fetch contextually relevant information from external web sources. The system distinguishes itself through a combination of semantic retrieval and precision-focused reranking. It converts user queries into high-dimensional embeddings to perform similarity searches across indexed collections, then refines these results by passing candidate pairs through a secondary model to

h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services. The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of

Long Llama is a transformer-based language model and fine-tuning framework designed to process and maintain logical coherence across input sequences that significantly exceed standard length limits. By utilizing a focused transformer architecture, the project enables models to handle massive documents or entire books by training attention layers to track distant tokens. The framework distinguishes itself through specialized attention mechanisms that allow for the processing of hundreds of thousands of tokens. It incorporates memory-efficient inference techniques, such as key-value caching and

This project is a comprehensive learning resource and set of demonstrations focused on large language model integration, deployment, and fine-tuning. It provides educational content and practical guides for working with artificial intelligence models. The resource includes specific tutorials and courses on adapting pre-trained models to specialized datasets using parameter-efficient fine-tuning techniques. It also provides instructional content for running quantized models on consumer hardware and building retrieval augmented generation pipelines using vector databases and document indexing.

This project provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems. It includes a set of core components for managing model assets, a fine-tuning framework, and structural definitions required to instantiate transformer-based architectures. The system is distinguished by its ability to process combined text and image inputs through multimodal transformer models for visual reasoning and document analysis. It also supports the deployment of quantized models, reducing memory footprints through low-precision

SmolLM is a project dedicated to the development of small language models. It focuses on training and fine-tuning compact models that maintain high performance while utilizing fewer parameters. The project emphasizes efficient AI inference and on-device text generation, aiming to enable the deployment of lightweight models on edge devices with limited memory and processing power. It utilizes synthetic data generation to produce artificial datasets that improve the reasoning and training of these AI systems. The system supports a variety of optimization and training capabilities, including we

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

llm-universe is a structured learning resource and technical guide focused on the development of large language model applications. It serves as a curriculum for mastering model orchestration, the creation of autonomous conversational agents, and the implementation of retrieval-augmented generation systems. The project provides detailed instructions on connecting model APIs with memory and tools to create execution chains. It specifically covers the construction of retrieval pipelines, including the process of cleaning raw documents, generating embeddings, and integrating vector databases to

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b

This repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the o

Superduper is an AI agent development kit and LLM application framework designed to build autonomous agents and data-driven applications. It functions as a RAG orchestration platform and vector search infrastructure, coordinating AI models with database storage to perform multi-step computations and actions using persisted data states. The project distinguishes itself by providing a database-integrated machine learning pipeline that executes training and inference tasks directly on data hosted within SQL and NoSQL databases. It allows for the deployment of self-hosted AI infrastructure on pri

This project provides a system for managing agent context and session memory, featuring an agent context compactor, an AI session memory manager, and a tool output sandbox. It functions as a middleware layer and server extension for the Model Context Protocol to optimize context windows and reduce token usage. The system optimizes agent performance by sandboxing tool outputs and externalizing large data sets, replacing raw I/O with pointers and concise summaries. It employs a persistent knowledge base that indexes session history and tool outputs for retrieval via full-text search, ensuring s

This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the construction of systems that utilize language models to plan, manage memory, and execute multi-step tasks through iterative reasoning loops and tool-based actions. The framework distinguishes itself by offering specialized capabilities for interacting with graphical user interfaces and legacy software, allowing agents to perceive visual elements and perform actions like a human user. It supports complex, cross-application workflows through graph-based orchestration and provid

Acontext is an LLM orchestration backend and agent memory framework designed to manage session state and knowledge for AI agents. It functions as a context manager and orchestration layer that integrates model providers with a secure code sandbox and a zero-knowledge data store. The project is distinguished by its approach to knowledge distillation, capturing agent learnings as reusable Markdown skills and structured memory files. It provides a secure execution environment where shell commands and scripts run in isolated containers with the ability to mount these persistent skill files direct

ChatGLM-Efficient-Tuning is a fine-tuning framework and toolkit designed to optimize large language models using parameter-efficient fine-tuning techniques. It provides a pipeline for adjusting model behavior and reducing the memory and compute requirements necessary for training. The project features a web-based trainer and orchestration interface for configuring and executing the fine-tuning process on a single GPU. It supports quantized training in lower precision formats to enable fine-tuning on hardware with limited memory, as well as reinforcement learning from human feedback for model

pdfGPT is a retrieval augmented generation application and chatbot designed to analyze PDF documents. It functions as a document analyzer and vector search interface, using large language models to answer questions grounded in the content of uploaded files. The system implements a pipeline that extracts text from PDFs, splits content into overlapping segments, and uses vector-based semantic search to retrieve relevant context. This process allows the application to provide responses with verifiable source citations, including page number references to the original document. The project also

This project is a web-based user interface and multi-model API gateway for interacting with various large language model providers and local inference services. It functions as a retrieval-augmented generation chatbot for private document questioning, a manager for model fine-tuning, and an autonomous agent framework. The system distinguishes itself by integrating an autonomous assistant mode that uses web search and external tools to solve complex, multi-step tasks without manual prompting. It also features an API gateway capable of rotating multiple authentication keys to balance usage and

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset

Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical reasoning tasks. These models are trained on high-quality programming datasets to interpret natural language prompts and generate functional source code, explain complex logic, repair code defects, and produce technical documentation. The project distinguishes itself through specialized training methodologies that align model behavior with complex programming instructions and mathematical problem-solving. By utilizing chain-of-thought reasoning and instruction-tuned parameter

This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover