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Showing 104 open source projects for "stage"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
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DeepTutor
AI-Powered Personalized Learning Assistant
DeepTutor is an AI-powered tutoring and learning assistant framework designed to automatically teach, explain, and reinforce academic or technical concepts in depth according to a learner’s specific needs. It goes beyond simple Q&A by constructing multi-stage educational narratives, breaking down complex topics into sequenced “lesson steps,” and offering prompts, examples, and exercises that build on each other in a logical curriculum. The core architecture combines LLM-based reasoning with structured pedagogy modules so that explanations accommodate different learning styles and address misconceptions in follow-up responses. ... -
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LLM Council
LLM Council works together to answer your hardest questions
...Instead of relying on one provider, this application sends your query simultaneously to several LLMs supported via OpenRouter, collects each model’s independent response, and then orchestrates a multi-stage evaluation where the models critique and rank each other’s outputs anonymously. After this peer-review process, a designated “Chairman” model synthesizes a final consolidated answer drawing on the strengths and insights of all participants. The interface looks like a familiar chat app but under the hood it implements this ensemble and consensus workflow to reduce bias and leverage diverse reasoning styles. -
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PPTAgent
PPTAgent: Generating and Evaluating Presentations
PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. The repository highlights the EMNLP 2025 paper and provides links to resources for replication and study. ... -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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Vulnhuntr
AI tool for detecting complex vulnerabilities in Python codebases
...It focuses on Python projects and applies static code analysis combined with LLM reasoning to trace how user input flows through an application. Instead of scanning entire repositories at once, it builds call chains step by step, allowing deeper inspection of complex, multi-stage issues that traditional tools may miss. Vulnhuntr can generate detailed findings, including vulnerability explanations and potential exploit paths, helping developers and security teams understand risks faster. It supports multiple LLM providers such as OpenAI, Anthropic, and Ollama, and can be run via CLI, Docker, or pipx. Vulnhuntr is particularly useful for early-stage security reviews, bug bounty hunting, and auditing dependencies for hidden risks across open source projects. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. ... -
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SAG
SQL-Driven RAG Engine
...These vectors allow the system to identify relationships between concepts and construct a graph representation of knowledge at runtime. The architecture also includes a three-stage retrieval pipeline consisting of recall, expansion, and reranking steps to improve search accuracy. The engine integrates semantic vector similarity with traditional full-text search to improve both recall and precision. Because the knowledge graph is generated dynamically, the system can adapt to new information without requiring manual graph maintenance. -
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Switchyard
Switchyard lets LLM applications route traffic across models
...Requests can be distributed across vLLM, NVIDIA NIM, Ollama, OpenRouter, and other compatible endpoints. Routing strategies include random splits, LLM classification, signal-driven stage routing, escalation, and custom algorithms. Prometheus metrics track requests, errors, latency, tokens, and routing overhead. Developers can run it as a standalone server, launch coding agents through it, or embed its routing logic in Rust applications. The project is currently pre-alpha and explicitly marked experimental rather than production-ready. -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
WhisperJAV is an open-source speech transcription pipeline designed specifically for generating subtitles for Japanese adult video content. The project addresses challenges that standard speech recognition models face when transcribing this type of audio, which often includes low signal-to-noise ratios and large numbers of non-verbal vocalizations. Traditional automatic speech recognition systems can misinterpret these sounds as words, leading to inaccurate transcripts. WhisperJAV introduces... -
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HY-World 2.0
A Multi-Modal World Model for Reconstructing, Generating, Simulation
...It accepts text prompts, single-view images, multi-view images, and videos, and produces 3D world representations rather than limiting output to flat video generation. For text and single-image inputs, it generates high-fidelity 3D Gaussian Splatting scenes through a multi-stage pipeline that includes panorama generation, trajectory planning, world expansion, and world composition. The system also improves reconstruction from multi-view images and video by upgrading its feed-forward 3D prediction components and its memory-aware view generation process. Another major part of the project is WorldLens, a rendering platform designed for interactive exploration with an engine-agnostic architecture, automatic image-based lighting, collision detection, and support for character interaction. -
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GLM-TTS
Controllable & emotion-expressive zero-shot TTS
GLM-TTS is an advanced text-to-speech synthesis system built on large language model technologies that focuses on producing high-quality, expressive, and controllable spoken output, including features like emotion modulation and zero-shot voice cloning. It uses a two-stage architecture where a generative LLM first converts text into intermediate speech token sequences and then a Flow-based neural model converts those tokens into natural audio waveforms, enabling rich prosody and voice character even for unseen speakers. The system introduces a multi-reward reinforcement learning framework that jointly optimizes for voice similarity, emotional expressiveness, pronunciation, and intelligibility, yielding output that can rival commercial options in naturalness and expressiveness. ... -
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DocTR
Library for OCR-related tasks powered by Deep Learning
...Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document datasets, comparable with GoogleVision/AWS Textract. Easy integration (available templates for browser demo & API deployment). End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). ... -
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OpenMontage
World's first open-source, agentic video production system
OpenMontage is an open-source, agent-driven video production system that transforms AI coding assistants into fully automated multimedia creation pipelines. Instead of focusing on a single capability such as text-to-video generation, it treats video production as a structured, multi-stage workflow that mirrors how a real production team operates, including research, scripting, asset generation, editing, and final rendering. The system orchestrates a large collection of tools and models through coordinated pipelines, enabling an AI agent to autonomously gather information, write scripts, generate visuals, synthesize voiceovers, and assemble a complete video output. ... -
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omp (Oh My Pi)
AI Coding agent for the terminal
...Instead of functioning as a simple prompt-based assistant, the system includes an agent architecture that can inspect Git repositories, analyze changes, and perform development actions with fine-grained control. The platform also supports tool-based workflows where the agent can run shell commands, read files, modify code, and stage changes during development tasks. It includes infrastructure for integrating different AI providers and models through a unified API layer, allowing developers to switch between models while keeping the same agent interface. -
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...It uses large language models and multiple collaborating agents to simulate the typical cycle of research, experimentation, and improvement that human data scientists follow. It separates the process into two core phases: a research stage that proposes hypotheses and ideas, and a development stage that implements and evaluates them through code execution and experiments. By iterating through these stages, the framework continuously refines models and strategies using feedback from previous results. RD-Agent focuses heavily on automating complex tasks such as feature engineering, model design, and experimentation, which are traditionally time-consuming in machine learning and quantitative research workflows. ... -
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HuixiangDou
Overcoming Group Chat Scenarios with LLM-based Technical Assistance
...The project addresses a common problem in developer communities where discussion channels become overwhelmed by repeated or irrelevant questions. To solve this issue, HuixiangDou implements a multi-stage pipeline that analyzes incoming messages, filters irrelevant conversations, and selectively generates responses when the assistant determines it can provide useful information. This design allows the system to participate in group discussions without flooding the chat with unnecessary messages. The assistant uses retrieval and ranking methods along with language model reasoning to produce accurate answers for technical topics such as computer vision and machine learning projects. ... -
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
The Hunyuan3D-2 model, developed by Tencent, is designed for generating high-resolution 3D assets using large-scale diffusion models. This model offers advanced capabilities for creating detailed 3D models, including texture enhancements, multi-view shape generation, and rapid inference for real-time applications. It is particularly useful for industries requiring high-quality 3D content, such as gaming, film, and virtual reality. Hunyuan3D-2 supports various enhancements and is available... -
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Kiro
Kiro is an agentic IDE that works alongside you from prototype
Kiro is an agentic AI-powered integrated development environment and command-line tool designed to help developers move from early-stage prototypes to production-ready software through structured, spec-driven development. Unlike traditional AI coding assistants that rely heavily on ad hoc prompts, Kiro transforms natural language input into formalized requirements, architectural plans, and step-by-step implementation tasks, ensuring clarity and maintainability throughout the development lifecycle. ... -
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Feynman
The open source AI research agent
...It operates as a “Claude Code for research,” allowing users to input natural language queries and receive fully developed, source-grounded research briefs, literature reviews, or experimental analyses. The system is built around a multi-agent architecture that includes roles such as researcher, reviewer, writer, and verifier, each responsible for a specific stage of the research pipeline. It supports advanced workflows like deep research investigations, paper replication, peer review simulation, and autonomous experimentation, enabling users to go beyond simple question answering into full research automation. -
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OpenMythos
A theoretical reconstruction of the Claude Mythos architecture
...The project explores the idea that instead of stacking hundreds of unique transformer layers, a smaller set of layers can be reused iteratively during inference to achieve deeper reasoning without increasing parameter count. It divides computation into three main stages, including a pre-processing phase, a looped recurrent reasoning block, and a final output refinement stage, creating a structured pipeline for inference. The architecture incorporates advanced techniques such as mixture-of-experts routing, adaptive computation time, and multiple attention mechanisms to dynamically allocate compute where needed. It is highly configurable through a centralized configuration system, allowing experimentation with different architectural parameters such as loop depth, attention type. -
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Kilo Code
Gives you a whole dev team of AI agents in your code editor
Kilo Code is a leading open-source coding agent designed to help developers build, ship, and debug software faster with AI-driven workflows. It offers specialized modes such as Ask, Architect, Code, Debug, and Orchestrator to support every stage of development. With features like automatic failure recovery, hallucination-free code, and deep context awareness, Kilo ensures reliable and accurate outputs. Developers can install Kilo across terminals, VS Code, and JetBrains editors for seamless integration into existing workflows. The platform also provides parallel agents, smart autocomplete, one-click deployment, and automated code reviews. ... -
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
IQuest-Coder-V1 is a cutting-edge family of open-source large language models specifically engineered for code generation, deep code understanding, and autonomous software engineering tasks. These models range from tens of billions to smaller footprints and are trained on a novel code-flow multi-stage paradigm that captures how real software evolves over time — not just static code snapshots — giving them a deeper semantic understanding of programming logic. They support native long contexts up to 128K tokens, enabling them to reason across large codebases and multi-file interactions without context fragmentation, and include “Thinking” variants optimized for complex reasoning and “Loop” variants with recurrent mechanisms to improve inference efficiency. ... -
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T3 Code
Minimal web GUI for coding agents
...Users need to install and authenticate at least one supported provider before using it, which keeps the actual agent accounts under the user’s control. The repository includes guidance for exposing a T3 Code server to another device, such as a phone, tablet, or separate desktop app. Overall, it is an early-stage developer tool for managing AI coding agents through a lightweight, session-oriented web interface. -
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WhisperSpeech
An Open Source text-to-speech system built by inverting Whisper
...The project aims to be for speech what Stable Diffusion is for images: powerful, hackable, and safe for commercial use, with code under Apache-2.0/MIT and models trained only on properly licensed data. Its architecture follows a token-based, multi-stage pipeline inspired by AudioLM and SPEAR-TTS: Whisper is used to produce semantic tokens, EnCodec compresses the waveform into acoustic tokens, and Vocos reconstructs high-fidelity audio from those tokens. The repository includes notebooks and scripts for inference, long-form synthesis, and finetuning, as well as pre-trained models and converted datasets hosted on Hugging Face. ... -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture...
