Search Results for "file-studio"
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Showing 41 open source projects for "file-studio"
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1
LangGraph Studio
Desktop app for prototyping and debugging LangGraph applications
...While in Beta, LangGraph Studio is available for free to all LangSmith users on any plan tier. LangGraph Studio requires docker-compose version 2.22.0+ or higher. Please make sure you have Docker installed and running before continuing. When you open LangGraph Studio desktop app for the first time, you need to login via LangSmith. Once you have successfully authenticated, you can choose the LangGraph application folder to use, you can either drag and drop or manually select it in the file picker. -
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. -
3
Local File Organizer
An AI-powered file management tool that ensures privacy
Local-File-Organizer is an AI-powered file management system designed to automatically analyze, categorize, and reorganize files stored on a user’s local machine. The project focuses on privacy-first file organization by performing all processing locally rather than sending data to external cloud services. It uses language and vision models to understand the contents of documents, images, and other file types so that files can be grouped intelligently according to their meaning or context. ... -
4
lms
LM Studio CLI
...LMS is built using the LM Studio JavaScript SDK and integrates tightly with the LM Studio runtime environment. The interface is designed to simplify automation workflows and scripting tasks related to local AI deployment. By exposing model management capabilities through command-line commands, the tool enables developers to integrate local LLM operations into development pipelines and backend services. -
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5
Nanocoder
A beautiful local-first coding agent running in your terminal
...Built with TypeScript and distributed as a CLI application, nanocoder enables developers to interact with AI agents that can read files, modify code, execute commands, and assist with debugging tasks. The platform supports multiple AI providers through OpenAI-compatible APIs and can also integrate with local model runtimes such as Ollama or LM Studio. Its architecture emphasizes extensibility through custom commands and integration with Model Context Protocol servers that allow the AI agent to access additional tools. -
6
swark.io
Create architecture diagrams from code automatically using LLMs
Swark is an open-source developer tool and Visual Studio Code extension that automatically generates software architecture diagrams directly from source code using large language models. The project aims to help developers quickly understand complex codebases by analyzing repositories and producing visual diagrams that represent system architecture, dependencies, and component relationships. -
7
llamafile
Distribute and run LLMs with a single file
llamafile lets you distribute and run LLMs with a single file. (announcement blog post). Our goal is to make open LLMs much more accessible to both developers and end users. We're doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file executable (called a "llamafile") that runs locally on most computers, with no installation. -
8
Flowise
Drag & drop UI to build your customized LLM flow
...Open source is the core of Flowise, and it will always be free for commercial and personal usage. Flowise support different environment variables to configure your instance. You can specify the following variables in the .env file inside the packages/server folder. -
9
llama.vscode
VS Code extension for LLM-assisted code/text completion
llama.vscode is a Visual Studio Code extension that provides AI-assisted coding features powered primarily by locally running language models. The extension is designed to be lightweight and efficient, enabling developers to use AI tools even on consumer-grade hardware. It integrates with the llama.cpp runtime to run language models locally, eliminating the need to rely entirely on external APIs or cloud providers. -
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10
ClaraVerse
Claraverse is a opesource privacy focused ecosystem to replace ChatGPT
...The platform combines chat interfaces, workflow automation, and long-running task management into a single application that can connect to both local and cloud-based AI models. Users can integrate models from multiple providers such as OpenAI, Anthropic, Google, or locally hosted systems like Ollama and LM Studio, enabling flexibility in how AI capabilities are deployed and managed. The system includes a visual workflow builder that allows users to create automation pipelines where AI tools interact with external services, APIs, or datasets. ClaraVerse also includes task-tracking capabilities that allow complex research, coding, or analysis jobs to run in the background while users monitor their progress through a dashboard. -
11
Flexpilot IDE
Open-Source AI Native IDE
Flexpilot IDE is an open-source integrated development environment designed specifically for AI-assisted software development. Built as a fork of Visual Studio Code, the project provides a familiar development interface while integrating advanced AI capabilities directly into the coding workflow. The platform is designed to be privacy-focused and flexible, allowing developers to use their own API keys for different large language models rather than relying on a single proprietary AI provider. ... -
12
repo2txt
Web-based tool converts GitHub repository contents
...The application can load repositories from platforms such as GitHub or from local directories and provides an interface for selecting which files should be included in the generated output. It also supports filtering by file types and automatically respecting ignore patterns defined in project configuration files. -
13
files-to-prompt
Concatenate a directory full of files into a single prompt
...The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
14
text-extract-api
Document (PDF, Word, PPTX ...) extraction and parse API
...Instead of requiring developers to integrate multiple document parsing libraries individually, the system centralizes text extraction capabilities into a unified API that standardizes the output. The platform supports automated processing pipelines that detect file types and apply the appropriate extraction method to obtain the most accurate text representation possible. It can be integrated into document analysis systems, knowledge retrieval tools, and AI pipelines that rely on clean textual data. The architecture is designed to be lightweight and easily deployable, making it suitable for both local installations and cloud environments. -
15
CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
...Its backend powers the CodeGeeX IDE plugins for VS Code, JetBrains, and other editors, offering developers interactive AI assistance with features like infilling and cross-file completion. -
16
MegaParse
File Parser optimised for LLM Ingestion with no loss
MegaParse is a file parser optimized for Large Language Model (LLM) ingestion, ensuring no loss of information. It efficiently parses various document formats, such as PDFs, DOCX, and PPTX, converting them into formats ideal for processing by LLMs. This tool is essential for applications that require accurate and comprehensive data extraction from diverse document types. -
17
Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. ... -
18
oh my PI
AI Coding agent for the terminal
Oh-My-Pi is an open-source AI agent toolkit focused on creating intelligent coding assistants that operate directly from the terminal environment. The project provides a command-line coding agent capable of analyzing repositories, generating commits, editing code, and interacting with development tools through an integrated tool system. Instead of functioning as a simple prompt-based assistant, the system includes an agent architecture that can inspect Git repositories, analyze changes, and... -
19
llama2.c
Inference Llama 2 in one file of pure C
llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). While it can technically load Meta’s official Llama 2... -
20
OneFileLLM
Specify a github or local repo, github pull request
OneFileLLM is an open-source project designed to simplify the distribution and execution of large language model applications by packaging them into a single portable file. The concept behind the project is to eliminate the complexity normally associated with deploying AI systems, which often require multiple dependencies, frameworks, and configuration steps. Instead, the entire runtime environment, model interface, and application logic are bundled together into a single executable artifact. This design allows developers to share AI tools in a format that can be easily distributed and executed across different machines without complicated installation procedures. ... -
21
Aix-DB
Based on the LangChain/LangGraph framework
...Its architecture includes multiple layers such as a web interface, API gateway, AI service layer, and data storage layer that support relational databases, vector stores, graph databases, and file systems. -
22
Fulling
Full-stack Engineer Agent. Built with Next.js, Claude, shadcn/ui
...It integrates an AI pair programmer that can generate code, implement features, and assist with debugging tasks through natural language instructions. The environment also includes web-based terminals, file management tools, and version control capabilities to support collaborative software development workflows. Developers can connect external services by simply providing API credentials, allowing the AI system to automatically integrate features such as authentication or payment processing. -
23
II Agent
A new open-source framework to build and deploy intelligent agents
...The platform allows users to interact with multiple AI models within a single environment while connecting those models to external services and knowledge sources. Through a unified interface, users can switch between models, access specialized tools, and execute tasks that require information retrieval, code execution, or file analysis. The architecture focuses on transforming traditional software tools into autonomous assistants capable of completing tasks independently based on user instructions. II-Agent supports integration with modern AI services and can coordinate interactions between different models and capabilities within the same workflow. -
24
multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
...The system then automatically distributes work among multiple worker agents, allowing tasks to run simultaneously rather than sequentially. The architecture uses tools such as tmux sessions and file-based message queues to coordinate communication between agents while maintaining parallel execution. Developers can monitor the progress of the agent swarm through dashboards that show the status of tasks and worker activity in real time. -
25
Paper2Slides
From Paper to Presentation in One Click
...It is designed to replace the repetitive work of turning dense technical documents into presentation-friendly structure by extracting key points, figures, and data into a coherent visual narrative. The system supports multiple input formats, so you can process PDFs and common office documents rather than being locked to a single file type. It uses an extraction approach intended to capture critical insights comprehensively, including important visuals and data points that often get missed in naive summarization. A major focus is traceability: generated slide content is designed to remain linked back to the source material so you can verify accuracy and reduce information drift. ...
