repopack

📦 Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, and Gemini.

Stars: 1656

Visit

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

README:

📦 Repopack

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file.
It is perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, and Gemini.

🌟 Features

AI-Optimized: Formats your codebase in a way that's easy for AI to understand and process.
Token Counting: Provides token counts for each file and the entire repository, useful for LLM context limits.
Simple to Use: You need just one command to pack your entire repository.
Customizable: Easily configure what to include or exclude.
Git-Aware: Automatically respects your .gitignore files.
Security-Focused: Incorporates Secretlint for robust security checks to detect and prevent inclusion of sensitive information.

🚀 Quick Start

You can try Repopack instantly in your project directory without installation:

npx repopack

Or install globally for repeated use:

# Install using npm
npm install -g repopack

# Alternatively using yarn
yarn global add repopack

# Alternatively using Homebrew (macOS)
brew install repopack

# Then run in any project directory
repopack

That's it! Repopack will generate a repopack-output.txt file in your current directory, containing your entire repository in an AI-friendly format.

📊 Usage

To pack your entire repository:

repopack

To pack a specific directory:

repopack path/to/directory

To pack specific files or directories using glob patterns:

repopack --include "src/**/*.ts,**/*.md"

To exclude specific files or directories:

repopack --ignore "**/*.log,tmp/"

To pack a remote repository:

repopack --remote https://github.com/yamadashy/repopack

# You can also use GitHub shorthand:
repopack --remote yamadashy/repopack

To initialize a new configuration file (repopack.config.json):

repopack --init

Once you have generated the packed file, you can use it with Generative AI tools like Claude, ChatGPT, and Gemini.

Prompt Examples

Once you have generated the packed file with Repopack, you can use it with AI tools like Claude, ChatGPT, and Gemini. Here are some example prompts to get you started:

Code Review and Refactoring

For a comprehensive code review and refactoring suggestions:

This file contains my entire codebase. Please review the overall structure and suggest any improvements or refactoring opportunities, focusing on maintainability and scalability.

Documentation Generation

To generate project documentation:

Based on the codebase in this file, please generate a detailed README.md that includes an overview of the project, its main features, setup instructions, and usage examples.

Test Case Generation

For generating test cases:

Analyze the code in this file and suggest a comprehensive set of unit tests for the main functions and classes. Include edge cases and potential error scenarios.

Code Quality Assessment

Evaluate code quality and adherence to best practices:

Review the codebase for adherence to coding best practices and industry standards. Identify areas where the code could be improved in terms of readability, maintainability, and efficiency. Suggest specific changes to align the code with best practices.

Library Overview

Get a high-level understanding of the library

This file contains the entire codebase of library. Please provide a comprehensive overview of the library, including its main purpose, key features, and overall architecture.

Feel free to modify these prompts based on your specific needs and the capabilities of the AI tool you're using.

Output File Format

Repopack generates a single file with clear separators between different parts of your codebase.
To enhance AI comprehension, the output file begins with an AI-oriented explanation, making it easier for AI models to understand the context and structure of the packed repository.

Plain Text Format (default)

This file is a merged representation of the entire codebase, combining all repository files into a single document.

================================================================
File Summary
================================================================
(Metadata and usage AI instructions)

================================================================
Repository Structure
================================================================
src/
  cli/
    cliOutput.ts
    index.ts
  config/
    configLoader.ts

(...remaining directories)

================================================================
Repository Files
================================================================

================
File: src/index.js
================
// File contents here

================
File: src/utils.js
================
// File contents here

(...remaining files)

================================================================
Instruction
================================================================
(Custom instructions from `output.instructionFilePath`)

XML Format

To generate output in XML format, use the --style xml option:

repopack --style xml

The XML format structures the content in a hierarchical manner:

This file is a merged representation of the entire codebase, combining all repository files into a single document.

<file_summary>
(Metadata and usage AI instructions)
</file_summary>

<repository_structure>
src/
  cli/
    cliOutput.ts
    index.ts

(...remaining directories)
</repository_structure>

<repository_files>
<file path="src/index.js">
// File contents here
</file>

(...remaining files)
</repository_files>

<instruction>
(Custom instructions from `output.instructionFilePath`)
</instruction>

For those interested in the potential of XML tags in AI contexts:
https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags

When your prompts involve multiple components like context, instructions, and examples, XML tags can be a game-changer. They help Claude parse your prompts more accurately, leading to higher-quality outputs.

This means that the XML output from Repopack is not just a different format, but potentially a more effective way to feed your codebase into AI systems for analysis, code review, or other tasks.

Markdown Format

To generate output in Markdown format, use the --style markdown option:

repopack --style markdown

The Markdown format structures the content in a hierarchical manner:

This file is a merged representation of the entire codebase, combining all repository files into a single document.

# File Summary
(Metadata and usage AI instructions)

# Repository Structure
```
src/
  cli/
    cliOutput.ts
    index.ts
```
(...remaining directories)

# Repository Files

## File: src/index.js
```
// File contents here
```

(...remaining files)

# Instruction
(Custom instructions from `output.instructionFilePath`)

This format provides a clean, readable structure that is both human-friendly and easily parseable by AI systems.

Command Line Options

-v, --version: Show tool version
-o, --output <file>: Specify the output file name
--include <patterns>: List of include patterns (comma-separated)
-i, --ignore <patterns>: Additional ignore patterns (comma-separated)
-c, --config <path>: Path to a custom config file
--style <style>: Specify the output style (plain, xml, markdown)
--top-files-len <number>: Number of top files to display in the summary
--output-show-line-numbers: Show line numbers in the output
--remote <url>: Process a remote Git repository
--verbose: Enable verbose logging

Examples:

repopack -o custom-output.txt
repopack -i "*.log,tmp" -v
repopack -c ./custom-config.json
repopack --style xml
repopack --remote https://github.com/user/repo.git
npx repopack src

Updating Repopack

To update a globally installed Repopack:

# Using npm
npm update -g repopack

# Using yarn
yarn global upgrade repopack

Using npx repopack is generally more convenient as it always uses the latest version.

Remote Repository Processing

Repopack supports processing remote Git repositories without the need for manual cloning. This feature allows you to quickly analyze any public Git repository with a single command.

To process a remote repository, use the --remote option followed by the repository URL:

repopack --remote https://github.com/user/repo.git

You can also use GitHub's shorthand format:

repopack --remote user/repo

⚙️ Configuration

Create a repopack.config.json file in your project root for custom configurations.

repopack --init

Here's an explanation of the configuration options:

Option	Description	Default
`output.filePath`	The name of the output file	`"repopack-output.txt"`
`output.style`	The style of the output (`plain`, `xml`, `markdown`)	`"plain"`
`output.headerText`	Custom text to include in the file header	`null`
`output.instructionFilePath`	Path to a file containing detailed custom instructions	`null`
`output.removeComments`	Whether to remove comments from supported file types	`false`
`output.removeEmptyLines`	Whether to remove empty lines from the output	`false`
`output.showLineNumbers`	Whether to add line numbers to each line in the output	`false`
`output.topFilesLength`	Number of top files to display in the summary. If set to 0, no summary will be displayed	`5`
`include`	Patterns of files to include (using glob patterns)	`[]`
`ignore.useGitignore`	Whether to use patterns from the project's `.gitignore` file	`true`
`ignore.useDefaultPatterns`	Whether to use default ignore patterns	`true`
`ignore.customPatterns`	Additional patterns to ignore (using glob patterns)	`[]`
`security.enableSecurityCheck`	Whether to perform security checks on files	`true`

Example configuration:

{
  "output": {
    "filePath": "repopack-output.xml",
    "style": "xml",
    "headerText": "Custom header information for the packed file.",
    "removeComments": false,
    "removeEmptyLines": false,
    "showLineNumbers": false,
    "topFilesLength": 5
  },
  "include": ["**/*"],
  "ignore": {
    "useGitignore": true,
    "useDefaultPatterns": true,
    "customPatterns": ["additional-folder", "**/*.log"]
  },
  "security": {
    "enableSecurityCheck": true
  }
}

Global Configuration

To create a global configuration file:

repopack --init --global

The global configuration file will be created in:

Windows: %LOCALAPPDATA%\Repopack\repopack.config.json
macOS/Linux: $XDG_CONFIG_HOME/repopack/repopack.config.json or ~/.config/repopack/repopack.config.json

Note: Local configuration (if present) takes precedence over global configuration.

Include and Ignore

Include Patterns

Repopack now supports specifying files to include using glob patterns. This allows for more flexible and powerful file selection:

Use **/*.js to include all JavaScript files in any directory
Use src/**/* to include all files within the src directory and its subdirectories
Combine multiple patterns like ["src/**/*.js", "**/*.md"] to include JavaScript files in src and all Markdown files

Ignore Patterns

Repopack offers multiple methods to set ignore patterns for excluding specific files or directories during the packing process:

.gitignore: By default, patterns listed in your project's .gitignore file are used. This behavior can be controlled with the ignore.useGitignore setting.
Default patterns: Repopack includes a default list of commonly excluded files and directories (e.g., node_modules, .git, binary files). This feature can be controlled with the ignore.useDefaultPatterns setting. Please see defaultIgnore.ts for more details.
.repopackignore: You can create a .repopackignore file in your project root to define Repopack-specific ignore patterns. This file follows the same format as .gitignore.
Custom patterns: Additional ignore patterns can be specified using the ignore.customPatterns option in the configuration file. You can overwrite this setting with the -i, --ignore command line option.

Priority Order (from highest to lowest):

Custom patterns ignore.customPatterns
.repopackignore
.gitignore (if ignore.useGitignore is true)
Default patterns (if ignore.useDefaultPatterns is true)

This approach allows for flexible file exclusion configuration based on your project's needs. It helps optimize the size of the generated pack file by ensuring the exclusion of security-sensitive files and large binary files, while preventing the leakage of confidential information.

Note: Binary files are not included in the packed output by default, but their paths are listed in the "Repository Structure" section of the output file. This provides a complete overview of the repository structure while keeping the packed file efficient and text-based.

Custom Instruction

The output.instructionFilePath option allows you to specify a separate file containing detailed instructions or context about your project. This allows AI systems to understand the specific context and requirements of your project, potentially leading to more relevant and tailored analysis or suggestions.

Here's an example of how you might use this feature:

Create a file named repopack-instruction.md in your project root:

# Coding Guidelines
- Follow the Airbnb JavaScript Style Guide
- Suggest splitting files into smaller, focused units when appropriate
- Add comments for non-obvious logic. Keep all text in English
- All new features should have corresponding unit tests

# Generate Comprehensive Output
- Include all content without abbreviation, unless specified otherwise
- Optimize for handling large codebases while maintaining output quality

In your repopack.config.json, add the instructionFilePath option:

{
  "output": {
    "instructionFilePath": "repopack-instruction.md",
    // other options...
  }
}

When Repopack generates the output, it will include the contents of repopack-instruction.md in a dedicated section.

Note: The instruction content is appended at the end of the output file. This placement can be particularly effective for AI systems. For those interested in understanding why this might be beneficial, Anthropic provides some insights in their documentation:
https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/long-context-tips

Put long-form data at the top: Place your long documents and inputs (~20K+ tokens) near the top of your prompt, above your query, instructions, and examples. This can significantly improve Claude's performance across all models. Queries at the end can improve response quality by up to 30% in tests, especially with complex, multi-document inputs.

Comment Removal

When output.removeComments is set to true, Repopack will attempt to remove comments from supported file types. This feature can help reduce the size of the output file and focus on the essential code content.

Supported languages include:
HTML, CSS, JavaScript, TypeScript, Vue, Svelte, Python, PHP, Ruby, C, C#, Java, Go, Rust, Swift, Kotlin, Dart, Shell, and YAML.

Note: The comment removal process is conservative to avoid accidentally removing code. In complex cases, some comments might be retained.

🔍 Security Check

Repopack includes a security check feature that uses Secretlint to detect potentially sensitive information in your files. This feature helps you identify possible security risks before sharing your packed repository.

The security check results will be displayed in the CLI output after the packing process is complete. If any suspicious files are detected, you'll see a list of these files along with a warning message.

Example output:

🔍 Security Check:
──────────────────
2 suspicious file(s) detected:
1. src/utils/test.txt
2. tests/utils/secretLintUtils.test.ts

Please review these files for potentially sensitive information.

By default, Repopack's security check feature is enabled. You can disable it by setting security.enableSecurityCheck to false in your configuration file:

{
  "security": {
    "enableSecurityCheck": false
  }
}

🤝 Contribution

We welcome contributions from the community! To get started, please refer to our Contributing Guide.

Contributors

📜 License

This project is licensed under the MIT License.

For Tasks:

Click tags to check more tools for each tasks

review codebase generate documentation generate test cases optimize code structure enhance ai model training

For Jobs:

software engineer data scientist ai researcher machine learning engineer developer advocate

Alternative AI tools for repopack

Similar Open Source Tools

repopack

github

: 1.7k

repomix

Repomix is a powerful tool that packs your entire repository into a single, AI-friendly file. It is designed to format your codebase for easy understanding by AI tools like Large Language Models (LLMs), Claude, ChatGPT, and Gemini. Repomix offers features such as AI optimization, token counting, simplicity in usage, customization options, Git awareness, and security-focused checks using Secretlint. It allows users to pack their entire repository or specific directories/files using glob patterns, and even supports processing remote Git repositories. The tool generates output in plain text, XML, or Markdown formats, with options for including/excluding files, removing comments, and performing security checks. Repomix also provides a global configuration option, custom instructions for AI context, and a security check feature to detect sensitive information in files.

github

: 6.3k

airflow-provider-great-expectations

The 'airflow-provider-great-expectations' repository contains a set of Airflow operators for Great Expectations, a Python library used for testing and validating data. The operators enable users to run Great Expectations validations and checks within Apache Airflow workflows. The package requires Airflow 2.1.0+ and Great Expectations >=v0.13.9. It provides functionalities to work with Great Expectations V3 Batch Request API, Checkpoints, and allows passing kwargs to Checkpoints at runtime. The repository includes modules for a base operator and examples of DAGs with sample tasks demonstrating the operator's functionality.

github

: 159

CodeGPT

CodeGPT is a CLI tool written in Go that helps you write git commit messages or do a code review brief using ChatGPT AI (gpt-3.5-turbo, gpt-4 model) and automatically installs a git prepare-commit-msg hook. It supports Azure OpenAI Service or OpenAI API, conventional commits specification, Git prepare-commit-msg Hook, customizing the number of lines of context in diffs, excluding files from the git diff command, translating commit messages into different languages, using socks or custom network HTTP proxies, specifying model lists, and doing brief code reviews.

github

: 1.2k

HuixiangDou

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

github

: 1.7k

suno-api

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

github

: 945

aiobotocore

aiobotocore is an async client for Amazon services using botocore and aiohttp/asyncio. It provides a mostly full-featured asynchronous version of botocore, allowing users to interact with various AWS services asynchronously. The library supports operations such as uploading objects to S3, getting object properties, listing objects, and deleting objects. It also offers context manager examples for managing resources efficiently. aiobotocore supports multiple AWS services like S3, DynamoDB, SNS, SQS, CloudFormation, and Kinesis, with basic methods tested for each service. Users can run tests using moto for mocked tests or against personal Amazon keys. Additionally, the tool enables type checking and code completion for better development experience.

github

: 1.2k

parrot.nvim

Parrot.nvim is a Neovim plugin that prioritizes a seamless out-of-the-box experience for text generation. It simplifies functionality and focuses solely on text generation, excluding integration of DALLE and Whisper. It supports persistent conversations as markdown files, custom hooks for inline text editing, multiple providers like Anthropic API, perplexity.ai API, OpenAI API, Mistral API, and local/offline serving via ollama. It allows custom agent definitions, flexible API credential support, and repository-specific instructions with a `.parrot.md` file. It does not have autocompletion or hidden requests in the background to analyze files.

github

: 243

evalscope

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

github

: 357

ppl.llm.kernel.cuda

Primitive cuda kernel library for ppl.nn.llm, part of PPL.LLM system, tested on Ampere and Hopper, requires Linux on x86_64 or arm64 CPUs, GCC >= 9.4.0, CMake >= 3.18, Git >= 2.7.0, CUDA Toolkit >= 11.4. 11.6 recommended. Provides cuda kernel functionalities for deep learning tasks.

github

: 140

aiodocker

Aiodocker is a simple Docker HTTP API wrapper written with asyncio and aiohttp. It provides asynchronous bindings for interacting with Docker containers and images. Users can easily manage Docker resources using async functions and methods. The library offers features such as listing images and containers, creating and running containers, and accessing container logs. Aiodocker is designed to work seamlessly with Python's asyncio framework, making it suitable for building asynchronous Docker management applications.

github

: 447

litdata

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

github

: 395

airllm

AirLLM is a tool that optimizes inference memory usage, enabling large language models to run on low-end GPUs without quantization, distillation, or pruning. It supports models like Llama3.1 on 8GB VRAM. The tool offers model compression for up to 3x inference speedup with minimal accuracy loss. Users can specify compression levels, profiling modes, and other configurations when initializing models. AirLLM also supports prefetching and disk space management. It provides examples and notebooks for easy implementation and usage.

github

: 4.1k

x

Ant Design X is a tool for crafting AI-driven interfaces effortlessly. It is built on the best practices of enterprise-level AI products, offering flexible and diverse atomic components for various AI dialogue scenarios. The tool provides out-of-the-box model integration with inference services compatible with OpenAI standards. It also enables efficient management of conversation data flows, supports rich template options, complete TypeScript support, and advanced theme customization. Ant Design X is designed to enhance development efficiency and deliver exceptional AI interaction experiences.

github

: 2.1k

client

Gemini PHP is a PHP API client for interacting with the Gemini AI API. It allows users to generate content, chat, count tokens, configure models, embed resources, list models, get model information, troubleshoot timeouts, and test API responses. The client supports various features such as text-only input, text-and-image input, multi-turn conversations, streaming content generation, token counting, model configuration, and embedding techniques. Users can interact with Gemini's API to perform tasks related to natural language generation and text analysis.

github

: 198

OpenAI-DotNet

OpenAI-DotNet is a simple C# .NET client library for OpenAI to use through their RESTful API. It is independently developed and not an official library affiliated with OpenAI. Users need an OpenAI API account to utilize this library. The library targets .NET 6.0 and above, working across various platforms like console apps, winforms, wpf, asp.net, etc., and on Windows, Linux, and Mac. It provides functionalities for authentication, interacting with models, assistants, threads, chat, audio, images, files, fine-tuning, embeddings, and moderations.

github

: 689

For similar tasks

repopack

github

: 1.7k

pr-agent

PR-Agent is a tool that helps to efficiently review and handle pull requests by providing AI feedbacks and suggestions. It supports various commands such as generating PR descriptions, providing code suggestions, answering questions about the PR, and updating the CHANGELOG.md file. PR-Agent can be used via CLI, GitHub Action, GitHub App, Docker, and supports multiple git providers and models. It emphasizes real-life practical usage, with each tool having a single GPT-4 call for quick and affordable responses. The PR Compression strategy enables effective handling of both short and long PRs, while the JSON prompting strategy allows for modular and customizable tools. PR-Agent Pro, the hosted version by CodiumAI, provides additional benefits such as full management, improved privacy, priority support, and extra features.

github

: 6.5k

shell_gpt

ShellGPT is a command-line productivity tool powered by AI large language models (LLMs). This command-line tool offers streamlined generation of shell commands, code snippets, documentation, eliminating the need for external resources (like Google search). Supports Linux, macOS, Windows and compatible with all major Shells like PowerShell, CMD, Bash, Zsh, etc.

github

: 9.0k

gpt-pilot

GPT Pilot is a core technology for the Pythagora VS Code extension, aiming to provide the first real AI developer companion. It goes beyond autocomplete, helping with writing full features, debugging, issue discussions, and reviews. The tool utilizes LLMs to generate production-ready apps, with developers overseeing the implementation. GPT Pilot works step by step like a developer, debugging issues as they arise. It can work at any scale, filtering out code to show only relevant parts to the AI during tasks. Contributions are welcome, with debugging and telemetry being key areas of focus for improvement.

github

: 30.3k

sirji

Sirji is an agentic AI framework for software development where various AI agents collaborate via a messaging protocol to solve software problems. It uses standard or user-generated recipes to list tasks and tips for problem-solving. Agents in Sirji are modular AI components that perform specific tasks based on custom pseudo code. The framework is currently implemented as a Visual Studio Code extension, providing an interactive chat interface for problem submission and feedback. Sirji sets up local or remote development environments by installing dependencies and executing generated code.

github

: 71

awesome-ai-devtools

Awesome AI-Powered Developer Tools is a curated list of AI-powered developer tools that leverage AI to assist developers in tasks such as code completion, refactoring, debugging, documentation, and more. The repository includes a wide range of tools, from IDEs and Git clients to assistants, agents, app generators, UI generators, snippet generators, documentation tools, code generation tools, agent platforms, OpenAI plugins, search tools, and testing tools. These tools are designed to enhance developer productivity and streamline various development tasks by integrating AI capabilities.

github

: 2.0k

doc-comments-ai

doc-comments-ai is a tool designed to automatically generate code documentation using language models. It allows users to easily create documentation comment blocks for methods in various programming languages such as Python, Typescript, Javascript, Java, Rust, and more. The tool supports both OpenAI and local LLMs, ensuring data privacy and security. Users can generate documentation comments for methods in files, inline comments in method bodies, and choose from different models like GPT-3.5-Turbo, GPT-4, and Azure OpenAI. Additionally, the tool provides support for Treesitter integration and offers guidance on selecting the appropriate model for comprehensive documentation needs.

github

: 130

EvoMaster

EvoMaster is an open-source AI-driven tool that automatically generates system-level test cases for web/enterprise applications. It uses Evolutionary Algorithm and Dynamic Program Analysis to evolve test cases, maximizing code coverage and fault detection. It supports REST, GraphQL, and RPC APIs, with whitebox testing for JVM-compiled APIs. The tool generates JUnit tests in Java or Kotlin, focusing on fault detection, self-contained tests, SQL handling, and authentication. Known limitations include manual driver creation for whitebox testing and longer execution times for better results. EvoMaster has been funded by ERC and RCN grants.

github

: 443

For similar jobs

weave

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

github

: 770

LLMStack

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

github

: 1.5k

VisionCraft

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

github

: 94

kaito

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

github

: 405

PyRIT

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

github

: 1.8k

tabby

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

github

: 26.4k

spear

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

github

: 224

Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

github

: 675