Description

• A no-code platform
• Process:
  • Assigning a goal to the agent
  • Witnessing its thinking process
  • Formulation of an execution plan
  • Taking actions accordingly
• Uses OpenAI functions
• Supports gpt-3.5-16k, pinecone and pg_vector databases
• Stack
  • Frontend: NextJS + Typescript
  • Backend: FastAPI + Python
  • DB: MySQL through docker with the option of running SQLite locally

Links

• Documentation
• Website
• GitHub

Description

• An LLM-powered autonomous agent platform
• A framework for autonomous agents who can work together to accomplish tasks
• Interaction with agents done via console direct messages

Links

• Author: eumemic
• Website
• GitHub
• Twitter

Description

• Chains together LLM "thoughts", to autonomously achieve whatever goal you set
• Internet access for searches and information gathering
• Long-term and short-term memory management
• Can execute many commands such as Google Search, browse websites, write to files, and execute Python files and much more
• GPT-4 instances for text generation
• Access to popular websites and platforms
• File storage and summarization with GPT-3.5
• Extensibility with Plugins
• "A lot like BabyAGI combined with LangChain tools"
• Features added in release 0.4.0
  • File reading
  • Commands customization
  • Enhanced testing

Links

• Twitter
• GitHub
• Facebook
• Linkedin
• Discord
• Author: Matt Pogla

Description

• A pared-down version of the original Task-Driven Autonomous Agent
• Creates tasks based on the result of previous tasks and a predefined objective.
• The script then uses OpenAI's NLP capabilities to create new tasks based on the objective
• Leverages OpenAI's GPT-4, pinecone vector search, and LangChainAI framework
• Default model is OpenAI GPT3-turbo
• The system maintains a task list for managing and prioritizing tasks
• It autonomously creates new tasks based on completed results and reprioritizes the task list accordingly, showcasing the adaptability of AI-powered language models

Links

• Paper: Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications
• Discord
• Founder's Twitter
• Twitter thread describing the system

Description

• A more advanced version of the original BabyAGI code
• - Improves upon the original framework, by introducing a more complex task management prompt, allowing for more comprehensive analysis and synthesis of information
• Designed to handle multiple functions within one task management prompt
• Built on top of the GPT-4 architecture, resulting in slower processing speeds and occasional errors
• Provides a framework that can be further built upon and improved, paving the way for more sophisticated AI applications
• One of the significant differences between BabyAGI and BabyBeeAGI is the complexity of the task management prompt

Links

• Replit

Description

• Just 300 lines of code
• This was built as a continued iteration on the original BabyAGI code in a lightweight way. Differences to BabyAGI include the following:
  • Task Creation Agent runs once
  • Execution Agent loops through tasks
  • Task dependencies for pulling relevant results
  • Two tools: search tool and text completion
  • “Mini-agent” as tool
  • Search tool combines search, scrape, chunking, and extraction.
  • Results combined to create summary report

Links

• Twitter
• GitHub
• Author: @yoheinakajima (Twitter)

Description

• Features
  • Parallel tasks (making it faster)
  • 3.5-turbo only (GPT-4 not required)
  • User input tool
  • Query rewrite in web search tool
  • Saves results

Links

• Tweet announcing the agent

Description

• An AI agent based on @yoheinakajima's BabyAGI which executes shell commands
• Successfully installed a Flutter environment on Linux in a container, created the default Flutter app, and launched it. The procedure can be found here.
• Aside from setting up the environment, it seems to be able to handle a bit of general tasks such as Generating text, like poems, code, scripts, musical pieces, email, and letters, translating languages
• There is a risk of breaking the environment. Please run in a virtual environment such as Docker.
• GPT-4 or higher is recommended

Links

• Founder's Twitter
• Twitter thread describing the system

Description

• Powered by GPT-4 and semantic code search, precise code navigation
• Built on stack graphs and scope queries
• Fast code search and regex matching engine written in Rust
• Allows to find Code on Rust and Typescript
• Allows to stage changes
• The agent searches both your local and remote repositories with natural language, regex and filtered queries
• Bloop can be run via app (easy to download via GitHub)

Links

• GitHub
• "Getting started" guide
• Bloop apps

Description

1) AI user agent: give instructions to the AI assistant with the goal of completing the task. 2) AI assistant agent: follow AI user’s instructions and respond with solutions to the task

Links

• Paper - CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society
• Colab demo
• GitHub
• Hugging face datasets
• Slack
• Authors: Guohao Li∗ Hasan Abed Al Kader Hammoud* Hani Itani* Dmitrii Khizbullin, Bernard Ghanem

Description

• One of the easiest and fastest no-code platform I have encountered
• After creating new agent, picking a model, data and other settings, they are ready to be deployed to website, Slack, Crisp, or Zapier
• Limit of agent in the free version
• Stack
  • Next.js
  • Joy UI
  • LangchainJS
  • PostgreSQL
  • Prisma
  • Qdrant
• Features
  • Streamline customer support, onboard new team members, and more
  • Load data from anywhere
  • No-code: User-friendly interface to manage your datastores and chat with your data
  • Secured API endpoint for querying your data
  • Auto sync data sources (coming soon)
  • Auto generates a ChatGPT Plugin for each datastore

Links

• Documentation
• Discord
• GitHub

Description

• Deepnote's AI Copilot, with its efficient and contextual code suggestions, is paving the way for a future of AI-powered data exploration in notebooks
• AI Copilot
• Cut back on repetition and increase efficiency for data scientists
• Code suggestions
• Understanding the full scope of your notebook
• The more text and code you write as context, the more relevant code suggestions you will see
• "We are soon introducing conversational AI features that will aid in generating, editing, debugging, and understanding both code and SQL. Beyond this, we are working on more ambitious projects designed to harness the unique attributes of notebooks as a computational medium."

Links

• Twitter

Description

• Provides a core prompt for building the foundation of your application
• Allows you to add unlimited sections, each of which is a prompt representing a specific part of your app
• Features
  • Friday utilizes GPT-4 for AI assistance, but it has been tested and optimized with GPT-4-32k for improved speed and better results.
  • It requires 2 small requests for your app's base and 1 request per section you provide.
  • Friday employs esbuild behind the scenes for every app created by it.

Links

• Author: Amirreza Salimi

Description

• GitWit uses a GPT-based agent to generate code and git to track changes made to files
• GitWit ties together large language models and modern developer tools
• It can spawn and modify codebases using just a single prompt
• GitWit is primarily aimed at full-stack developers, and is particularly loved by those with a learning mindset—such as those learning a new stack or technology
• It is in early beta and may require some experimentation with the prompts you enter
• You are offered to choose from code bases:
  • React + NextJS
  • Python using pip
  • A Chrome extension written in JavaScript
  • An AngularJS using npm.
  • Custom stack

Description

• A web platform inspired by AutoGPT and BabyAGI
• What it can do:
  • Order your coffee at Starbucks
  • Perform market analysis
  • Find and negotiate a lease
• Supports GPT-3.5 & GPT-4

Links

• GitHub
• Authors: Emil Ahlbäck, Lonis
• Discord
• Tweet

Description

• Model: GPT 4
• Specify your project, and the AI agent asks for clarification, and then constructs the entire code base
• Features
  • Made to be easy to adapt, extend, and make your agent learn how you want your code to look. It generates an entire codebase based on a prompt
  • You can specify the "identity" of the AI agent by editing the files in the identity folder
  • Editing the identity and evolving the main prompt is currently how you make the agent remember things between projects
  • Each step in steps.py will have its communication history with GPT4 stored in the logs folder, and can be rerun with scripts/reruneditedmessage_logs.py

Links

• Discord
• Author: Anton Osika
• Twitter review by @Attack

Description

• Languages: Python
• Default model: GPT-3.5-turbo (also possible with GPT-4)
• Modular Auto-GPT Framework
• Plug N Play" API - Extensible and modular "Pythonic" framework, not just a command line tool
• Features
  • "Easy to add new features, integrations and custom agent capabilities, all from python code, no nasty config files!"
  • "Minimal prompt overhead - Every token counts. We are continuously working on getting the best results with the least possible number of tokens."
  • "Human in the Loop - Ability to "course correct" agents who go astray via human feedback."
  • "Full state serialization - can save the complete state of an agent, including memory and the states of its tools to a file or python object. No external databases or vector stores required (but they are still supported)!"

Description

• Most of the description on readme is inspired by the original privateGPT
• Model: Vicuna-7B
• Using InstructorEmbeddings
• Both Embeddings as well as LLM will run on GPU. It also has CPU support if you do not have a GPU
• Built with Langchain

Links

• YouTube - LocalGPT: OFFLINE CHAT FOR YOUR FILES [Installation & Code Walkthrough]

Description

• MiniAGI is a minimal general-purpose autonomous agent based on GPT-3.5 / GPT-4
• Can analyze stock prices, perform network security tests, create art, and order pizza
• MiniAGI is a simple autonomous agent compatible with GPT-3.5-Turbo and GPT-4
• It combines a robust prompt with a minimal set of tools, chain-of-thoughts, and short-term memory with summarization
• Capable of inner monologue and self-criticism

Description

• An experimental multi-agent system
• Multiple "expertGPTs" collaborate to perform a task
• Each with their own short and long-term memory and the ability to communicate with each other
• Features
  • Set a task and watch the experts get to work.
  • Internet access for searches and information gathering
  • Long-Term and Short-Term memory management
  • GPT-4 instances for text generation
  • Access to popular websites and platforms
  • File storage and summarization with GPT-3.5

Links

• Demo
• Authors: Max Rumpf and Significant Gravitas

Description

• Powered by various language models such as GPT-4, Vicuna, LLaMA, and Flan-T5
• Supports multi-modality tool learning and task solving such as text, image, video and audio
• Supports task decomposition into both linear task-solving plans and non-linear task-solving plans
• Allows both benchmark task solving and open-ended task solving
• Provides easy-to-use evaluation protocols to evaluate task-solving ability
• Provide Reinforcement Learning from Task Feedback (RLTF) to allow continuously self-improving agent

Links

• GitHub
• Paper
• Demo

Description

• "Whether you are a technical person or a stakeholder, you can use Pezzo effectively. We don't believe that AI prompts should be designed in a developer's code editor. Aside from the technical issues with this approach, it blocks productivity."
• Features
  • Centralized Prompt Management: Manage all AI prompts in one place for maximum visibility and efficiency.
  • Streamlined Prompt Design, Publishing & Versioning: Create, edit, test and publish prompts with ease.
  • Observability: Access detailed prompt execution history, stats and metrics (duration, prompt cost, completion cost, etc.) for better insights.
  • Troubleshooting: Effortlessly resolve issues with your prompts. Time travel to retroactively fine-tune failed prompts and commit the fix instantly.
  • Cost Transparency: Gain comprehensive cost transparency across all prompts and AI models.
  • Simplified Integration: Reduce code overhead by 90% by consuming your AI prompts using the Pezzo Client, regardless of the model provider.

Links

• Documentation
• GitHub

Description

• Built with LangChain, GPT4All, LlamaCpp, Chroma and SentenceTransformers
• A test project to validate the feasibility of a fully private solution for question answering using LLMs and Vector embeddings, not production ready

Links

• GitHub

Description

• An experimental autonomous agent
• Model: GPT-4
• Purpose: Gnerate and compose React components from user stories
• Stack
  • React
  • TailwindCSS
  • Typescript
  • Radix UI
  • Shandcn UI
  • OpenAI API
• The agent is taking a user story text and generating and composing multiple react components to generate the relevant screens, based on atomic design principles
• Features
  • Generate React Components from user stories
  • Compose React Components from existing components
  • Use a local design system to generate React Components
  • Use React, TailwindCSS, Typescript, Radix UI, Shandcn UI
  • Built with Atomic Design Principles
• It is still experimental but very interesting results, It is completely open-sourced, looking for contributors!

Links

• GitHub
• Documentation
• Authors: Eylon Miz and and Lee Twito

Description

• Human-centric, coherent whole program synthesis
• Your own junior developer
• Allows to develop, debug, and decompile
• 200 LOC, half english
• 100k context can summarize both content and codebases
• Markdown is the best prompting DSL
• Copy and paste your errors as prompts
• Copy and paste curl output as prompts
• Write CSS animation by describing what u want
• GPT4 >>> GPT3.5/Anthropic Claude for codegen

Links

• Author: Swyx
• Demo
• Twitter
• Meme

Description

• Simplifies the configuration and deployment of LLM Agents to production
• "One of the core principals of SuperAgent is to build with any third-party dependencies to proprietary tech"
• It provides a range of features and functionalities to make it easier for developers to build, manage and deploy AI agents to production including features such as built in memory and document retrieval via vector dbs, powerful tools, webhooks, cron jobs etc.
• There are two main types of agents: action agents and plan-and-execute agents

Links

• GitHub
• Documentation
• Discord
• Author: Ismail Pelaseyed

Description

• An AI agent framework
• Open source, but infrastructure is closed-source
• Features
  • Provision, Spawn & Deploy Autonomous AI Agents
  • Extend Agent Capabilities with Tools
  • Run Concurrent Agents Seamlessly
  • Graphical User Interface
  • Action Console
  • Multiple Vector DBs
  • Multi-Modal Agents
  • Agent Trajectory Fine-Tuning
  • Performance Telemetry
  • Optimized Token Usage
  • Agent Memory Storage
  • Looping Detection Heuristics
  • Concurrent Agents
  • Resource Manager

Links

• YouTube
• Discord
• Subreddit
• Twitter
• Author: Ishaan Bhola

Description

• Model: GPT-4
• Language: Python
• Uses OpenAI and Pinecone to give memory to an AI agent and also allows it to "think" before making an action (outputting text)
• Also, just by shutting down the AI, it doesn't forget its memories since it lives on Pinecone and its memory counter saves the index that it's on
• A process that happens every time the AI is queried by the user:
  • AI vectorizes the query and stores it in a Pinecone Vector Database
  • AI looks inside its memory and finds memories and past queries that are relevant to the current query
  • AI thinks about what action to take
  • AI stores the thought from Step 3
  • Based on the thought from Step 3 and relevant memories from Step 2, AI generates an output
  • AI stores the current query and its answer in its Pinecone vector database memory

Links

• Created by @sean_pixel
• Inspired by paper "Generative Agents: Interactive Simulacra of Human Behavior"

Description

• Researchers from Stanford and Google created an interactive sandbox env with 25 Gen AI agents can simulate human behavior
• They walk in the park, join for coffee at a cafe, and share news with colleagues. They demonstrated surprisingly good social
• Westworld's inspiration is drawn from Unity software and Unity ML Agents, adapted in Python
• Languages
  • The library is available on PyPi via pip install westworld
  • Javascript version (being developed)
• Features
  • Easy creation of Grid and non-grid environments
  • Objects (Agents, Obstacles, Collectibles, Triggers)
  • Subclassing of different objects to create custom objects
  • Spawner to generate objects randomly in the environment
  • Basic rigid body system for all objects
  • Simple agent behaviors (pathfinding, wandering, random walk, fleeing, vision range)
  • Automatic maze generation
  • Layer integration to convert image to obstacle and snap it to a grid
  • Sample simulations and sample agents for classic simulations
  • Simulation visualization, replay and export (gif or video)

Links

• GitHub
• Documentation
• Underlying paper - Generative Agents
• A paper simulating interactions between tens of agents
• Presenting an architecture that extends a language model to store and synthesize the agent's experiences, enabling dynamic behavior planning in an interactive sandbox environment with generative agents

Description

• A LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention
• Voyager consists of three key components:
  • 1) an automatic curriculum that maximizes exploration
  • 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors
  • 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement
• Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning

Links

• GitHub
• Paper - Voyager: An Open-Ended Embodied Agent with Large Language Models
• YouTube video
• Tweet

Description

• WorkGPT is an agent framework in a similar fashion to AutoGPT or LangChain. You give it a directive and an array of APIs and it will converse back and forth with the AI until its directive is complete.
• For example, a directive could be to research the web for something, to crawl a website, or to order you an Uber. We support any and all APIs that can be represented with an OpenAPI file.
• WorkGPT now has OpenAI's new function invocation feature baked into it
  • While chaining together APIs was possible before (see AutoGPT), it was slow, expensive, and error prone
  • The tweet announcing this feature

Links

• Author: Alex MacCaw

Description

• Designed to help build, prototype, and deploy AI-powered tools and agents easily and efficiently.
• Built on the LangChain framework, allowing users of any technical background to create, improve, and deploy AI agents.
• Equipped with an interactive command line interface for real-time feedback and ease of navigation.
• Features session persistent memory to ensure data preservation across multiple sessions
• Quick and easy installation via pip.
• Contributions to expand and improve yAgents are highly encouraged.
• Warnings
  • Requires GPT-4 API access.
  • Not tested for Windows systems

Links

• GitHub Repository
• Discord
• License: MIT

Description

• "This is a Swift port of BabyAGI, an example of an AI-powered task management system that uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective."

Links

• Author: PJ Gray

Description

• A developer-centric approach to building internal UIs and workflows
• Turning APIs, SQL queries, and scripts into apps for the entire team
• Features
  • Airplane lets you turn SQL queries, JavaScript/Python code, HTTP requests, etc into tasks
  • Allows to run tasks through a no-code dashboard
  • Tasks for customer support, on-call runbooks, and scheduled tasks

Links

• Profile of the company
• Documentation
• Twitter
• They're building an AI assistant here

Description

• Breaks down a high level research question into a step-by-step plan, and executes it
• Diverse tools, including a full web browser
• Can access internet information without the need for an API
• "We don't generate content using AI, as it can be unreliable. Instead, we extract relevant information from trusted sources, cluster and process it into a user-friendly format."
• AI-powered query planner intelligently routes and executes requests, ensuring correctness and diverse source selection

Links

• Discord

Description

• Create an Issue
• Approve the Implementation Plan
• Review the Pull Request
• Features
  • Writing code
  • Reviewing changes
  • Addressing comments
  • Answering questions
• BitBuilder Junior
  • BitBuilder Junior is an intern-level code generation tool that lives in your GitHub repository. It follows instructions to implement unambiguous code changes by changing multiple files, writing tests for generated code, and matching the style, framework, and libraries you're already using.

Links

• GitHub Marketplace
• Slack
• Installation

Description

• No coding required thanks to user-friendly interface
• Full SEO optimization
• Picture Upload: Users can conveniently upload and regenerate their own pictures for unlimited customization of their profiles
• Profile Customization: Users have the flexibility to customize their profiles by hiding sections, adding social media links, and sharing contact details, allowing them to showcase their unique personality and brand
• Instant Preview: Users can instantly visualize their profile changes through a conveniently placed preview button, ensuring a quick assessment of the desired appearance
• 30% Faster Speed: The app achieves an impressive 30% increase in website generation speed, providing users with a fast and efficient website building experience.

Description

• Friendly UI for building AI agents

Links

• Author: Sully Omarr

Description

• Magic Copy writes, edits, and rewrites Figma text layers so you can design with real copy
• Generating images in Figma while designing
• Magic Rename intelligently names your layers so you can spend more time designing
• Magician works right inside your favorite design tool (e.g., Figma)
• Possible to get all the latest AI design advancements + future spells in one convenient plugin

Description

• In aplha stage
• It’s not supposed to be just another coding copilots like GitHub Copilot or Codeium, but autonomous agents capable of autonomously building software from end to end

Links

• CEO

• With Fine-Tuner, you can build sophisticated, tailored AI agents at scale without any need for technical skills or coding. Just bring your data and ideas, and we'll provide the toolset you need to transform them into powerful AI solutions, capable of handling vast amounts of data and users. Take advantage of our scalable platform to meet your growing needs with ease and efficiency

• Connecting Your Chatbot to Your App

• FineTuner.ai is a no-code AI platform that enables users to create and deploy custom AI agents and components without any coding. With an intuitive UI/UX and rapid API deployment, FineTuner.ai simplifies AI development, allowing users to focus on their unique use cases and ideas.

4.1. Access the API tab for an overview of the required tokens and parameters to connect your chatbot to your app using REST API endpoints.

• The Fine-Tuner REST API provides API endpoints for Fine-Tuner data types that allow to interact with your AI models remotely by sending and receiving JSON
• Authentication to the Fine-Tuner API is performed via HTTP Bearer Authentication
• Front end: Bubble

Links

• Twitter
• Step-by-step guide
• Author

Description

• Building and managing LLM powered applications
• A cloud-based platform-as-a-service that allows developers to build smart agents that couple LLMs with back-end logic to interface to data, systems, and tools

Links

• GitHub
• Fixie Developer Portal
• AI.JSX
• Twitter
• Discord

Description

• An AI executive assistant for augmented communication
• Automation of communication - from generating answers to extracting tasks or scheduling meetings
• Keeping all conversations in the same place
• Automatically sorted, labelled and summed up
• AI assisted writing
• Tasks & meetings scheduled in autopilot, ready at any time

Links

• Author: Sarah Allali

Description

• Grit uses machine learning and static analysis to auto-generate pull requests for cleaning up technical debt
• Users can declare how they want their code to be structured and let Grit rewrite it for them

Links

• Linkedin
• Twitter

Description

• "A suite of powerful AI features meant to augment data people"
• Hex can explain and document your code
• Hex Magic features know about database schemas, past operations, and the project’s execution graph, so they can make deeper, more insightful recommendations

You can see more – and sign up for the waitlist – over here.

Links

• Launch post

Description

• Personal assistant for life: to keep you on top of your calendar, tasks and information
• Currently in a beta version

Description

• Lindy is still in a beta version
• Features
  • Lindy triages your email
  • She learns from your inbox and automatically surfaces the highest-priority emails for you
  • Automatic conflict handling
  • Daily briefing
  • Contract management
  • Meeting note taking
  • Summarization

Links

• Author: Flo Crivello

Links

• Twitter
• Author: Alex Graveley

Description

• The agent runs and controls the local Google Chrome, which allows it to interact with the world/services/web apps, just like people interact with the world/services/web apps using Google Chrome
• The agent itself probably also runs locally and currently, it needs the local Google Chrome to function
• Our understanding from the demo video is that they use local code and a custom plugin in ChatGPT to control a web browser (e.g., Google Chrome). This setup enables MultiOn to perform tasks like ordering plane tickets as if a human were interacting with the browser directly
• Use cases
  • A lot of cool real use cases, e.g., -Sending an email fully autonomously -Posting a tweet -Sending a tweet reply to a specific person with a specific message -Sending a Facebook message to a friend -Searching for vacation rentals and check pricing for an upcoming trip -Searching for a wedding venue and starting the wedding planning process -Scheduling a car wash
• After introducing the GPT function calling, MultiOn can call itself recursively to spawn more sub-agents
• Instead of calling multiple functions or APIs you just need one Universal Function that can interact with all services and have it call itself to accomplish more complex tasks in parallel

Links

• Twitter
• Author: Div Garg

Description

"Build your team of AI agents that work for you. Early access now live. Join waitlist."

Links

• Twitter

Description

• An end-to-end solution, with which it takes 3 minutes not weeks to get a user-facing agent up and running in your app (currently 3 SDKs including React)
• Powerful tools built into the admin dashboard and Admin API including analytics, monitoring, rate-limiting, content moderation, etc.
• minimizes or eliminates the need for custom backend infrastructure so you can focus on implementing the business logic
• Technology-agnostic solution that supports multiple LLM providers (currently 7 models from OpenAI and Anthropic) allowing you to easily switch between models with 1 click
• Ready-to-use, highly customizable and beautiful UI components rendering complex interaction trees with support for advanced features like streaming

Links

• Documentation
• GitHub
• Discord

Description

• Generating content
• Brainstorming ideas
• Translation
• Grammer check
• Roadmap

Links

• Twitter
• Slack
• Discord

Description

• Migrate frameworks such as Angular to React, libraries such as Redux to React Context, or languages such as JavaScript to TypeScript
• Perform major version upgrades on any number of applications, of any size
• Upgrade frameworks such as Next.js 12 to 13, libraries such as MUI 4 to 5, or languages such as Python 2 to 3
• Target users: enterprise codebases
• Roadmap

Links

• YCombinator
• Twitter
• Linkedin
• Discord
• Founder: Eric Rowell

Description

• "Delegate your tasks to autonomous AI agents. Transform your daily work with revolutionary and intuitive AI tools powered by GPT4"
• Access APIs like Zapier, Wolfram, etc.
• Open links
• Manipulate files
• Search web

Links

• Author's Twitter

Description

• To install, click the install button
• Then add the repository you want, make a quick ticket (e.g. writing tests)
• Prepend the ticket with "Sweep:" and let Sweep handle the rest

Links

• GitHub
• Discord
• Tricks for prompting Sweep