Repository History
94 repositories tagged with llm
aimock: Comprehensive Mocking for AI Application Testing
aimock is a powerful tool designed for comprehensive testing of AI applications by mocking various AI APIs and services. It offers a unified solution to simulate interactions with LLM APIs, vector databases, and other AI infrastructure, ensuring deterministic and efficient testing workflows. With features like record and replay, chaos testing, and seamless framework integrations, aimock significantly simplifies the development and validation of robust AI systems.
Awesome AI Agents 2026: The Ultimate List of AI Tools and Frameworks
This repository, `awesome-ai-agents-2026`, is a comprehensive and frequently updated collection of over 340 AI agents, frameworks, and tools across more than 20 categories. It serves as an essential resource for developers and researchers looking to explore the rapidly evolving landscape of artificial intelligence in 2026, covering everything from coding agents to creative AI and governance.
awesome-ai: A Curated List of 400+ AI APIs, Tools, and Frameworks
The awesome-ai repository by edwardtay offers a comprehensive, curated list of over 400 AI APIs, tools, frameworks, and platforms. Spanning more than 40 categories, it serves as an invaluable resource for developers and researchers navigating the vast landscape of artificial intelligence. This list helps users discover solutions for LLMs, agents, image/video generation, MLOps, and more.
code-review-graph: AI-Powered Code Intelligence for Smarter Reviews
code-review-graph is a local-first code intelligence graph that optimizes AI coding tools by building a persistent map of your codebase. It significantly reduces context for AI reviews and large-repo workflows, ensuring AI assistants read only the most relevant code. This leads to more efficient and cost-effective code analysis.
NVIDIA NeMo Speech: Scalable Generative AI for Speech Models
NVIDIA NeMo Speech is a powerful, scalable generative AI framework designed for researchers and developers focused on Large Language Models, Multimodal, and Speech AI. It provides tools for Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), enabling efficient creation, customization, and deployment of new AI models using existing code and pre-trained checkpoints. This framework supports a wide range of applications, from real-time streaming ASR to high-quality multilingual TTS.
Axolotl: Streamlining LLM Fine-tuning with a Powerful Open-Source Framework
Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs.
Mergoo: Efficiently Merge and Train Multiple LLM Experts
Mergoo is an open-source Python library designed to simplify the merging of multiple Large Language Model (LLM) experts. It enables efficient training of these merged LLMs, allowing users to integrate knowledge from various generic or domain-specific models. The library supports several merging methods, including Mixture-of-Experts and Mixture-of-Adapters, across popular base models.
Zero: The AI Coding Agent for Your Local Terminal
Zero is an innovative AI coding agent designed for your local terminal, offering powerful capabilities to inspect repositories, edit files, run commands, and utilize browser/terminal helpers. It provides durable local sessions while giving users full control over the AI model and permission levels. This tool empowers developers with a customizable and secure AI assistant directly within their development environment.
Griptape: Modular Python Framework for AI Agents and Workflows
Griptape is a modular Python framework designed to simplify the development of generative AI applications. It provides a flexible set of abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and various other AI components. With its structured approach, Griptape enables developers to build sophisticated AI agents and workflows efficiently.
Memoripy: An AI Memory Layer for Context-Aware Applications
Memoripy is a Python library designed to provide an AI memory layer for context-aware applications. It offers both short-term and long-term storage, semantic clustering, and optional memory decay. This robust tool helps AI systems manage and retrieve relevant information efficiently, supporting various LLM APIs like OpenAI and Ollama.
torchchat: Run PyTorch LLMs Locally on Servers, Desktop, and Mobile
torchchat is a PyTorch-native codebase designed to showcase the ability to run large language models (LLMs) seamlessly across various platforms. It enables local execution of LLMs using Python, within C/C++ applications on desktop or servers, and directly on iOS and Android devices. Although no longer under active development, it remains a valuable resource for understanding and implementing local LLM deployment strategies.
Evidently: Open-Source ML and LLM Observability Framework
Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning and large language model systems. It provides over 100 built-in metrics for various tasks, from data drift detection to LLM judges, supporting both tabular and text data. This framework helps ensure the quality and performance of AI-powered systems throughout their lifecycle.