gpt-engineer
NOTE: This is different from https://gptengineer.app Experimental playground for a terminal based AI Software Engineer
Stars: 51907
GPT-Engineer is a tool that allows you to specify a software in natural language, sit back and watch as an AI writes and executes the code, and ask the AI to implement improvements.
README:
gpt-engineer lets you:
- Specify software in natural language
- Sit back and watch as an AI writes and executes the code
- Ask the AI to implement improvements
For stable release:
python -m pip install gpt-engineer
For development:
git clone https://github.com/gpt-engineer-org/gpt-engineer.git
cd gpt-engineer
poetry install
-
poetry shell
to activate the virtual environment
We actively support Python 3.10 - 3.12. The last version to support Python 3.8 - 3.9 was 0.2.6.
Choose one of:
- Export env variable (you can add this to .bashrc so that you don't have to do it each time you start the terminal)
export OPENAI_API_KEY=[your api key]
- .env file:
- Create a copy of
.env.template
named.env
- Add your OPENAI_API_KEY in .env
- Create a copy of
- Custom model:
- See docs, supports local model, azure, etc.
Check the Windows README for Windows usage.
Other ways to run:
- Use Docker (instructions)
- Do everything in your browser:
- Create an empty folder for your project anywhere on your computer
- Create a file called
prompt
(no extension) inside your new folder and fill it with instructions - Run
gpte <project_dir>
with a relative path to your folder- For example:
gpte projects/my-new-project
from the gpt-engineer directory root with your new folder inprojects/
- For example:
- Locate a folder with code which you want to improve anywhere on your computer
- Create a file called
prompt
(no extension) inside your new folder and fill it with instructions for how you want to improve the code - Run
gpte <project_dir> -i
with a relative path to your folder- For example:
gpte projects/my-old-project -i
from the gpt-engineer directory root with your folder inprojects/
- For example:
- gpt-engineer installs the binary 'bench', which gives you a simple interface for benchmarking your own agent implementations against popular public datasets.
- The easiest way to get started with benchmarking is by checking out the template repo, which contains detailed instructions and an agent template.
- Currently supported benchmark:
By running gpt-engineer, you agree to our terms.
gptengineer.app is a commercial project for the automatic generation of web apps. It features a UI for non-technical users connected to a git-controlled codebase. The gptengineer.app team is actively supporting the open source community.
You can specify the "identity" of the AI agent by overriding the preprompts
folder with your own version of the preprompts
. You can do so via the --use-custom-preprompts
argument.
Editing the preprompts
is how you make the agent remember things between projects.
By default, gpt-engineer expects text input via a prompt
file. It can also accept image inputs for vision-capable models. This can be useful for adding UX or architecture diagrams as additional context for GPT Engineer. You can do this by specifying an image directory with the —-image_directory
flag and setting a vision-capable model in the second CLI argument.
E.g. gpte projects/example-vision gpt-4-vision-preview --prompt_file prompt/text --image_directory prompt/images -i
By default, gpt-engineer supports OpenAI Models via the OpenAI API or Azure OpenAI API, as well as Anthropic models.
With a little extra setup, you can also run with open source models like WizardCoder. See the documentation for example instructions.
The gpt-engineer community mission is to maintain tools that coding agent builders can use and facilitate collaboration in the open source community.
If you are interested in contributing to this, we are interested in having you.
If you want to see our broader ambitions, check out the roadmap, and join discord to learn how you can contribute to it.
gpt-engineer is governed by a board of long-term contributors. If you contribute routinely and have an interest in shaping the future of gpt-engineer, you will be considered for the board.
https://github.com/gpt-engineer-org/gpt-engineer/assets/4467025/40d0a9a8-82d0-4432-9376-136df0d57c99
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for gpt-engineer
Similar Open Source Tools
gpt-engineer
GPT-Engineer is a tool that allows you to specify a software in natural language, sit back and watch as an AI writes and executes the code, and ask the AI to implement improvements.
bia-bob
BIA `bob` is a Jupyter-based assistant for interacting with data using large language models to generate Python code. It can utilize OpenAI's chatGPT, Google's Gemini, Helmholtz' blablador, and Ollama. Users need respective accounts to access these services. Bob can assist in code generation, bug fixing, code documentation, GPU-acceleration, and offers a no-code custom Jupyter Kernel. It provides example notebooks for various tasks like bio-image analysis, model selection, and bug fixing. Installation is recommended via conda/mamba environment. Custom endpoints like blablador and ollama can be used. Google Cloud AI API integration is also supported. The tool is extensible for Python libraries to enhance Bob's functionality.
depthai
This repository contains a demo application for DepthAI, a tool that can load different networks, create pipelines, record video, and more. It provides documentation for installation and usage, including running programs through Docker. Users can explore DepthAI features via command line arguments or a clickable QT interface. Supported models include various AI models for tasks like face detection, human pose estimation, and object detection. The tool collects anonymous usage statistics by default, which can be disabled. Users can report issues to the development team for support and troubleshooting.
robocorp
Robocorp is a platform that allows users to create, deploy, and operate Python automations and AI actions. It provides an easy way to extend the capabilities of AI agents, assistants, and copilots with custom actions written in Python. Users can create and deploy tools, skills, loaders, and plugins that securely connect any AI Assistant platform to their data and applications. The Robocorp Action Server makes Python scripts compatible with ChatGPT and LangChain by automatically creating and exposing an API based on function declaration, type hints, and docstrings. It simplifies the process of developing and deploying AI actions, enabling users to interact with AI frameworks effortlessly.
NekoImageGallery
NekoImageGallery is an online AI image search engine that utilizes the Clip model and Qdrant vector database. It supports keyword search and similar image search. The tool generates 768-dimensional vectors for each image using the Clip model, supports OCR text search using PaddleOCR, and efficiently searches vectors using the Qdrant vector database. Users can deploy the tool locally or via Docker, with options for metadata storage using Qdrant database or local file storage. The tool provides API documentation through FastAPI's built-in Swagger UI and can be used for tasks like image search, text extraction, and vector search.
linkedin-api
The Linkedin API for Python allows users to programmatically search profiles, send messages, and find jobs using a regular Linkedin user account. It does not require 'official' API access, just a valid Linkedin account. However, it is important to note that this library is not officially supported by LinkedIn and using it may violate LinkedIn's Terms of Service. Users can authenticate using any Linkedin account credentials and access features like getting profiles, profile contact info, and connections. The library also provides commercial alternatives for extracting data, scraping public profiles, and accessing a full LinkedIn API. It is not endorsed or supported by LinkedIn and is intended for educational purposes and personal use only.
crewAI
CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.
obs-cleanstream
CleanStream is an OBS plugin that utilizes real-time local AI to clean live audio streams by removing unwanted words and utterances, such as 'uh' and 'um', and configurable words like profanity. It employs a neural network (OpenAI Whisper) to predict speech in real-time and eliminate undesired words. The plugin runs efficiently using the Whisper.cpp project from ggerganov. CleanStream offers users the ability to adjust settings and add the plugin to any audio-generating source in OBS, providing a seamless experience for content creators looking to enhance the quality of their live audio streams.
open-source-slack-ai
This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.
superflows
Superflows is an open-source alternative to OpenAI's Assistant API. It allows developers to easily add an AI assistant to their software products, enabling users to ask questions in natural language and receive answers or have tasks completed by making API calls. Superflows can analyze data, create plots, answer questions based on static knowledge, and even write code. It features a developer dashboard for configuration and testing, stateful streaming API, UI components, and support for multiple LLMs. Superflows can be set up in the cloud or self-hosted, and it provides comprehensive documentation and support.
obs-cleanstream
CleanStream is an OBS plugin that utilizes AI to clean live audio streams by removing unwanted words and utterances, such as 'uh's and 'um's, and configurable words like profanity. It uses a neural network (OpenAI Whisper) in real-time to predict speech and eliminate unwanted words. The plugin is still experimental and not recommended for live production use, but it is functional for testing purposes. Users can adjust settings and configure the plugin to enhance audio quality during live streams.
slack-machine
Slack Machine is a simple, yet powerful and extendable Slack bot framework. More than just a bot, Slack Machine is a framework that helps you develop your Slack workspace into a ChatOps powerhouse. Slack Machine is built with an intuitive plugin system that lets you build bots quickly, but also allows for easy code organization.
labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.
neural
Neural is a Vim and Neovim plugin that integrates various machine learning tools to assist users in writing code, generating text, and explaining code or paragraphs. It supports multiple machine learning models, focuses on privacy, and is compatible with Vim 8.0+ and Neovim 0.8+. Users can easily configure Neural to interact with third-party machine learning tools, such as OpenAI, to enhance code generation and completion. The plugin also provides commands like `:NeuralExplain` to explain code or text and `:NeuralStop` to stop Neural from working. Neural is maintained by the Dense Analysis team and comes with a disclaimer about sending input data to third-party servers for machine learning queries.
actions
Sema4.ai Action Server is a tool that allows users to build semantic actions in Python to connect AI agents with real-world applications. It enables users to create custom actions, skills, loaders, and plugins that securely connect any AI Assistant platform to data and applications. The tool automatically creates and exposes an API based on function declaration, type hints, and docstrings by adding '@action' to Python scripts. It provides an end-to-end stack supporting various connections between AI and user's apps and data, offering ease of use, security, and scalability.
telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)
For similar tasks
lollms-webui
LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.
continue
Continue is an open-source autopilot for VS Code and JetBrains that allows you to code with any LLM. With Continue, you can ask coding questions, edit code in natural language, generate files from scratch, and more. Continue is easy to use and can help you save time and improve your coding skills.
anterion
Anterion is an open-source AI software engineer that extends the capabilities of `SWE-agent` to plan and execute open-ended engineering tasks, with a frontend inspired by `OpenDevin`. It is designed to help users fix bugs and prototype ideas with ease. Anterion is equipped with easy deployment and a user-friendly interface, making it accessible to users of all skill levels.
sglang
SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system. The core features of SGLang include: - **A Flexible Front-End Language**: This allows for easy programming of LLM applications with multiple chained generation calls, advanced prompting techniques, control flow, multiple modalities, parallelism, and external interaction. - **A High-Performance Runtime with RadixAttention**: This feature significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. It also supports other common techniques like continuous batching and tensor parallelism.
ChatDBG
ChatDBG is an AI-based debugging assistant for C/C++/Python/Rust code that integrates large language models into a standard debugger (`pdb`, `lldb`, `gdb`, and `windbg`) to help debug your code. With ChatDBG, you can engage in a dialog with your debugger, asking open-ended questions about your program, like `why is x null?`. ChatDBG will _take the wheel_ and steer the debugger to answer your queries. ChatDBG can provide error diagnoses and suggest fixes. As far as we are aware, ChatDBG is the _first_ debugger to automatically perform root cause analysis and to provide suggested fixes.
aider
Aider is a command-line tool that lets you pair program with GPT-3.5/GPT-4 to edit code stored in your local git repository. Aider will directly edit the code in your local source files and git commit the changes with sensible commit messages. You can start a new project or work with an existing git repo. Aider is unique in that it lets you ask for changes to pre-existing, larger codebases.
chatgpt-web
ChatGPT Web is a web application that provides access to the ChatGPT API. It offers two non-official methods to interact with ChatGPT: through the ChatGPTAPI (using the `gpt-3.5-turbo-0301` model) or through the ChatGPTUnofficialProxyAPI (using a web access token). The ChatGPTAPI method is more reliable but requires an OpenAI API key, while the ChatGPTUnofficialProxyAPI method is free but less reliable. The application includes features such as user registration and login, synchronization of conversation history, customization of API keys and sensitive words, and management of users and keys. It also provides a user interface for interacting with ChatGPT and supports multiple languages and themes.
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.
agentcloud
AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.
oss-fuzz-gen
This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.
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.
VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
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.
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.
Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.