🔮 Awesome AI Agents


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Add Code Interpreter to your AI App

Chart of AI Agents Landscape

Welcome to our list of AI agents. We structured the list into two parts:

Open source projects
Closed-source projects and companies

To filter the products by categories and use-cases, see the 🌟 web version of this list. 🌟

The list is done according to our best knowledge, although definitely not comprehensive. Check out also the Awesome List of SDKs for AI Agents. Discussion and feedback appreciated! :heart:

Have anything to add?

Create a pull request or fill in this form. Please keep the alphabetical order and in the correct category.

For adding AI agents'-related SDKs, frameworks and tools, please visit Awesome SDKs for AI Agents. This list is only for AI assistants and agents.

Check out E2B - Code Interpreting for AI apps

Check out Code Interpreter SDK
Explore examples in E2B Cookbook
Read our docs
Contact us at [email protected] or on Discord. Follow us on X (Twitter)

Open-source projects

Adala

Adala: Autonomous Data (Labeling) Agent framework

Category

General purpose, Build your own, Multi-agent

Description

Reliable agents: Built on ground truth data for consistent, trustworthy results.
Controllable output: Tailor output with flexible constraints to fit your needs.
Specialized in data processing: Agents excel in custom data labeling and processing tasks.
Autonomous learning: Agents evolve through observations and reflections, not just automation.
Flexible and extensible runtime: Adaptable framework with community-driven evolution for diverse needs.
Easily customizable: Develop agents swiftly for unique challenges, no steep learning curve.

Agent4Rec

Recommender system simulator with 1,000 agents

Category

General purpose, Build your own, Multi-agent

Description

Agent4Rec is a recommender system simulator that utilizes 1,000 LLM-empowered generative agents.
These agents are initialized from the MovieLens-1M dataset, embodying varied social traits and preferences.
Each agent interacts with personalized movie recommendations in a page-by-page manner and undertakes various actions such as watching, rating, evaluating, exiting, and interviewing.

AgentForge

LLM-agnostic platform for agent building & testing

Category

General purpose, Build your own, Multi-agent

Description

A low-code framework designed for the swift creation, testing, and iteration of AI-powered autonomous agents and Cognitive Architectures, compatible with various LLM models.
Facilitates building custom agents and cognitive architectures with ease.
Supports multiple LLM models including OpenAI, Anthropic's Claude, and local Oobabooga, allowing flexibility in running different models for different agents based on specific requirements.
Provides customizable agent memory management and on-the-fly prompt editing for rapid development and testing.
Comes with a database-agnostic design ensuring seamless extensibility, with straightforward integration with different databases like ChromaDB for various AI projects.

AgentGPT

Browser-based no-code version of AutoGPT

Category

General purpose

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

AgentPilot

Build, manage, and chat with agents in desktop app

Category

General purpose

Description

Integrated into Open Interpreter and MemGPT
Group chats feature

Agents

Library/framework for building language agents

Category

General purpose, Build your own, Multi-agent

Description

Long-short Term Memory: Language agents in the library are equipped with both long-term memory implemented via VectorDB + Semantic Search and short-term memory (working memory) maintained and updated by an LLM.
Tool Usage: Language agents in the library can use any external tools via function-calling and developers can add customized tools/APIs here.
Web Navigation: Language agents in the library can use search engines to navigate the web and get useful information.
Multi-agent Communication: In addition to single language agents, the library supports building multi-agent systems in which language agents can communicate with other language agents and the environment. Different from most existing frameworks for multi-agent systems that use pre-defined rules to control the order for agents' action, Agents includes a controller function that dynamically decides which agent will perform the next action using an LLM by considering the previous actions, the environment, and the target of the current states. This makes multi-agent communication more flexible.
Human-Agent interaction: In addition to letting language agents communicate with each other in an environment, our framework seamlessly supports human users to play the role of the agent by himself/herself and input his/her own actions, and interact with other language agents in the environment.
Symbolic Control: Different from existing frameworks for language agents that only use a simple task description to control the entire multi-agent system over the whole task completion process, Agents allows users to use an SOP (Standard Operation Process) that defines subgoals/subtasks for the overall task to customize fine-grained workflows for the language agents.

AgentVerse

Platform for task-solving & simulation agents

Category

General purpose, Build your own, Multi-agent

Description

Assembles multiple agents to collaboratively accomplish tasks.
Allows custom environments for observing or interacting with multiple agents.

AI Legion

Multi-agent TS platform, similar to AutoGPT

Category

Multi-agent, Build-your-own

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

Aider

Use command line to edit code in your local repo

Category

Coding, GitHub

Description

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
You can start a new project or work with an existing repo. And you can fluidly switch back and forth between the aider chat where you ask GPT to edit the code and your own editor to make changes yourself
Aider makes sure edits from you and GPT are committed to git with sensible commit messages. Aider is unique in that it works well with pre-existing, larger codebases

AIlice

Create agents-calling tree to execute your tasks

Category

General purpose, Personal assistant, Productivity

Description

"An Agent in the form of a chatbot independently plans tasks given in natural language and dynamically creates an agents calling tree to execute tasks.
There is an interaction mechanism between agents to ensure fault tolerance.
External interaction modules can be automatically built for self-expansion.

AutoGen

Multi-agent framework with diversity of agents

Category

General purpose, Build your own, Multi-agent

Description

A framework for developing LLM (Large Language Model) applications with multiple conversational agents.
These agents can collaborate to solve tasks and can interact seamlessly with humans.
It simplifies complex LLM workflows, enhancing automation and optimization.
It offers a range of working systems across various domains and complexities.
It improves LLM inference with easy performance tuning and utility features like API unification and caching.
It supports advanced usage patterns, including error handling, multi-config inference, and context programming.

AutoGPT

Experimental attempt to make GPT4 fully autonomous

Category

General purpose

Description

An experimental open-source attempt to make GPT-4 fully autonomous, with >140k stars on GitHub
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

Automata

Generate code based on your project context

Category

Coding

Description

Model: GPT 4
Automata takes your project as a context, receives tasks, and executes the instructions seamlessly.
Features
- Automata aims to evolve into a fully autonomous, self-programming Artificial Intelligence system.
- It's designed for seamless integration with all available agent platforms and LLM providers.
- Utilizes the novel code search algorithm, SymbolRank, and associated tools to build superior coding intelligence.
- Modular, fully configurable design with minimal reliance on external dependencies

AutoPR

AI-generated pull requests agent that fixes issues

Category

Coding, GitHub

Description

Triggered by adding a label containing AutoPR to an issue, AutoPR will:
- Plan a fix
- Write the code
- Push a branch
- Open a pull request

Autonomous HR Chatbot

Agent that answers HR-related queries using tools

Category

HR, Business intelligence, Productivity

Description

A prototype enterprise application - an Autonomous HR Assistant powered by GPT-3.5.
An agent that can answer HR related queries autonomously using the tools it has on hand.
Powered by GPT-3.5
Current tools assigned to the agent (with more on the way):
- Timekeeping Policy
- Employee Data
- Calculator

BabyAGI

A simple framework for managing tasks using AI

Category

General purpose

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

BabyBeeAGI

Task management & functionality BabyAGI expansion

Category

General purpose, Productivity

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

BabyCatAGI

BabyCatAGI is a mod of BabyBeeAGI

Category

General purpose

Description

Just 300 lines of code
This was built as a d 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

BabyDeerAGI

Mod of BabyAGI with only ~350 lines of code

Category

General purpose

Category

General purpose

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

BabyElfAGI

Mod of BabyDeerAGI, with ~895 lines of code

Category

General purpose

Description

Features
- Skills class allows for creation of new skills
- 'Dynamic task list' example with vector search
- Beta reflection agent
- Can read, write, and review its own code

BabyCommandAGI

Test what happens when you combine CLI and LLM

Category

General purpose, Coding

Description

gent designed to test what happens when you combine CLI and LLM, which are more traditional interfaces than GUI (created by @saten-private)
An AI agent based on @yoheinakajima's BabyAGI which executes shell commands
Automatic Programming, Successfully created an app automatically just by providing feedback. The procedure can be found here.
Automatic Environment Setup, Successfully installed a Flutter environment on Linux in a container, created the 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

BabyFoxAGI

Mod of BabyAGI with a new parallel UI panel

Category

General purpose

Description

A mod of BabyElfAGI, in a series of mods w the naming of BabyAGI in alphabetical order
Self-improving task lists (FOXY method)
- By storing a final reflection at the end, and pulling the most relevant reflection to guide future runs, BabyAGI slowly generates better and better tasks lists
Novel Chat UI w parallel tasks
- You can chat w BabyAGI! It has an experimental UI where the chat is separate from the tasks/output panel, allowing you to request multiple tasks in parallel
- The Chat UI can use a single skill quickly, or chain multiple skills together using a tasklist
New skills
- 🎨 DALLE skill with prompt assist
- 🎶 Music player w Deezer
- 📊 Airtable search (add your own table/base ID)
- 🔍 Startup Analyst (example of beefy function call as a skill)
It’s own README

BambooAI

Data exploration and analysis for non-programmers

Category

Data analysis

Description

BambooAI runs in a loop (until user decides to end it).
Allows mixing of different models with different capabilities, token costs and context windows for different tasks.
Maintains the memory of previous conversations.
Builds the prompts dynamically utilising relevant context from Pinecone vector DB.
Offers a narrative or asks follow up questions if required.
For codified responses, the task is broken down into a list of steps and a pseudo-code algorithm is built.
Based on the algorithm, it ises the python code for dataset analysis, modeling or plotting.
Debugs the code which then executes, auto-corrects if needs to, and displays the output to user.
Ranks the final answers, and asks user for feedback.
Builds a vector DB knowledge-base, based on the rank and the user feedback.

BeeBot

Early-stage project for wide range of tasks

Category

General purpose, Productivity

Description

"BeeBot is currently a work in progress and should be treated as an early stage research project. Its focus is not on production usage at this time."

Blinky

An open-source AI debugging agent for VSCode

Banner

Category

Coding, Debugging

Description

Blinky is an open-source AI debugging agent for VSCode that uses LLMs to help identify and fix backend code errors (inspired by SWE-agent).
Blinky leverages the VSCode API, Language Server Protocol (LSP), and print statement debugging to triangulate and address bugs in real-world backend systems.

Bloop

AI code search, works for Rust and Typescript

Category

Coding

Description

A GPT-4 powered semantic code search engine that uses an AI agent
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)

BondAI

Code interpreter with CLI & RESTful/WebSocket API

Category

Coding

Description

A highly capable, autonomous AI Agent with an easy to use CLI, RESTful/WebSocket API, Pre-built Docker image and a robust suite of integrated tools.
Support for all GPT-N, Embeddings and Dall-E OpenAI Models
Support for Azure OpenAI Services
Easy to use SDK for integration into any application
Powerful Code Interpreter capabilities
Powerful data query capabilities via Postgres DB integration
Pre-built Docker image provides safe execution environment for code generation/execution
Support for telephony applications (via BlandAI)
Support for stock trading (via Alpaca Markets)
Integrates with Gmail and Google Search
Easy to install pip install bondai
To start the CLI just run bondai
To start the RESTful/WebSocket API just run bondai --server

bumpgen

AI agent that keeps npm dependencies up-to-date

demo

Category

Coding

Description

Put dependency management and upgrades on autopilot
bumpgen BUMPs an npm package's version up then GENerates the code fixes for breaking changes
Supports gpt-4-turbo
Easy install > npm install -g bumpgen
Easy start > bumpgen @tanstack/react-query 5.28.14

Cal.ai

Open-source scheduling assistant built on Cal.com

Category

Productivity

Description

Cal.ai can book meetings, summarize your week, and find time with others based on natural language.
Responds flexibly to unseen tasks eg. "move my second-last meeting to tomorrow morning".
Uses GPT-4 and LangChain Agent Executor under the hood.
GitHub

CAMEL

Architecture for “Mind” Exploration of agents

Category

General purpose

Description

CAMEL is an open-source library designed for the study of autonomous and communicative agents. 1)AI user agent: give instructions to the AI assistant with the goal of completing the task.

AI assistant agent: follow AI user’s instructions and respond with solutions to the task

CAMEL also has an open-source community dedicated to the study of autonomous and communicative agents

ChatArena

A chat tool for multi agent interaction

Category

Design, Build-your-own, SDK for AI apps, Multi-agent

Description

ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs. ChatArena provides:
A general framework for building interactive environments for multiple large language models (LLMs).
A collection of pre-built or community-created environments.
User-friendly interfaces with both Web UI and commandline interfaces.

ChatDev

Communicative agents for software development

Category

Coding, Multi-agent

Description

ChatDev is a virtual software company driven by a multitude of intelligent agents assuming different roles such as CEO, CPO, CTO, programmer, reviewer, tester, and art designer, each represented by unique icons.
These agents collaborate in a structured organizational environment, fulfilling the company's mission to "revolutionize the digital world through programming." They engage in functional seminars focusing on design, coding, testing, and documentation.
ChatDev aims to provide an accessible, modular, and extensible platform based on large language models, facilitating the study of collective intelligence in a controlled setting.
The framework allows for extensive customization, empowering users to tailor the software development process, define phases, and establish specific roles within the virtual company.
ChatDev is committed to open-source principles, encouraging contributions from the community and sharing advancements transparently.

ChemCrow

LangChain agent for chemistry-related tasks

Category

Science, Chemistry

Description

ChemCrow is an open source package for the accurate solution of reasoning-intensive chemical tasks
It integrates 13 expert-design tools to augment LLM performance in chemistry and demonstrate effectiveness in automating chemical tasks
Built with Langchain
The LLM is provided with a list of tool names, descriptions of their utility, and details about the expected input/output. It is then instructed to answer a user-given prompt using the tools provided when necessary. The instruction suggests the model to follow the ReAct format - Thought, Action, Action Input, Observation. One interesting observation is that while the LLM-based evaluation concluded that GPT-4 and ChemCrow perform nearly equivalently, human evaluations with experts oriented towards the completion and chemical correctness of the solutions showed that ChemCrow outperforms GPT-4 by a large margin. This indicates a potential problem with using LLM to evaluate its own performance on domains that requires deep expertise. The lack of expertise may cause LLMs not knowing its flaws and thus cannot well judge the correctness of task results. (Source: Weng, Lilian. (Jun 2023). LLM-powered Autonomous Agents". Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.)

Clippy

Agent that can plan, write, debug, and test code

Category

Coding

Description

The purpose of Clippy is to elop code for or with the user.
It can plan, write, debug, and test some projects autonomously.
For harder tasks, the best way to use it is to look at its work and provide feedback to it.

CodeFuse-ChatBot

Agent serving entire SW development lifecycle

Category

Coding

Description

An intelligent assistant serving the entire software development lifecycle, powered by a Multi-Agent Framework, working with DevOps Toolkits, Code&Doc Repo RAG, etc.

Cody by ajhous44

Query and navigate your codebase

Category

Coding

Description

An AI assistant designed to let you interactively query your codebase using natural language.
By utilizing vector embeddings, chunking, and OpenAI's language models, Cody can help you navigate through your code in an efficient and intuitive manner.

Cody by Sourcegraph

Agent that writes code and answers your questions

Category

Coding

Description

An AI code assistant from Sourcegraph that writes code and answers questions for you by reading your entire codebase and the code graph.

Continue

Open-source autopilot for software development

Category

Coding

Description

An open-source autopilot for software development—bring the power of ChatGPT to VS Code
Features:
- Answer coding questions
- Edit in natural language
- Generate files from scratch

CrewAI

Framework for orchestrating role-playing agents

Category

Build-your-own, SDK for agents, Multi-agent

Description

Cutting-edge framework for orchestrating role-playing, autonomous AI agents.
By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Crew AI is a multi-agent framework built on LangChain, aiming to empower engineers to harness the collective power of AI agents. In contrast to traditional automation methods, Crew AI introduces a new approach to collaborative decision-making, enhanced creativity, and solving complex problems.
The design philosophy of Crew AI advocates simplicity through modularity. Its main components include agents, tools, tasks, processes, and crews. Each agent is akin to a team member, possessing specific roles, background stories, goals, and memories. Through modular design, we make the intricate world of AI agents accessible, manageable, and more engaging.

data-to-paper

AI-driven research from data to human-verifiable research papers

Category

Science, Research, Multi-agent

Description

data-to-paper is a framework for systematically navigating the power of AI to perform complete end-to-end scientific research, starting from raw data and concluding with comprehensive, transparent, and human-verifiable scientific papers.

Towards this goal, data-to-paper systematically guides interacting LLM and rule-based agents through the conventional scientific path, from annotated data, through creating research hypotheses, conducting literature search, writing and debugging data analysis code, interpreting the results, and ultimately the step-by-step writing of a complete research paper.

The data-to-paper framework is created as a research project to understand the capacities and limitations of LLM-driven scientific research, and to develop ways of harnessing LLM to accelerate research while maintaining, and even enhancing, key scientific values, such as transparency, traceability and verifiability, and while allowing scientist to oversee and direct the process [see also: living guidelines].

Databerry

(Pivoted to Chaindesk) No-code chatbot building

Category

Build-your-own

Description

A super-easy no-code platform for creating AI chatbots trained on your own data
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

DemoGPT

Generates demo of a new app (of any purpose)

Category

Build-your-own, General purpose

Description

DemoGPT leverages the power of Language Models (LLMs) to provide fast and effective demo creation for applications.
Automates the prototyping process, making it more efficient and saving valuable time.
Understands and processes the given prompts to generate relevant applications.
Integrated with LangChain for generating application code through iterative parsing of LangChain's documentation with a "Tree of Transformations" (ToT) approach.
The roadmap for DemoGPT includes constant updates and improvements based on user feedback and real-world application, working towards refining the technology and solving the hallucination problem.
"We are planning to introduce features that will further enhance the application generation process, making it more user-friendly and efficient."

DevGPT

Team of virtual developers

Category

Coding, Multi-agent

Description

"Tell your AI team what microservice you want to build, and they will do it for you. Your imagination is the limit!!
Welcome to Dev-GPT, where we bring your ideas to life with the power of advanced artificial intelligence! Our automated development team is designed to create microservices tailored to your specific needs, making your software development process seamless and efficient. Comprised of a virtual Product Manager, Developer, and DevOps, our AI team ensures that every aspect of your project is covered, from concept to deployment.

Devika

Agentic AI Software Engineer

Category

Coding, general purpose

Description

Devika is an Agentic AI Software Engineer that can understand high-level human instructions, break them down into steps, research relevant information, and write code to achieve the given objective.
Devika aims to be a competitive open-source alternative to Devin by Cognition AI.

Devon

Open-source Devin alternative

Category

Coding, general purpose

Description

Open-source alternative to Devin by Entropy research

DevOpsGPT

AI-Driven SW Development Automation Solution

Category

Coding

Description

Welcome to the AI Driven Software Development Automation Solution, abbreviated as DevOpsGPT. We combine LLM (Large Language Model) with DevOps tools to convert natural language requirements into working software. This innovative feature greatly improves development efficiency, shortens development cycles, and reduces communication costs, resulting in higher-quality software delivery.

Features and Benefits

Improved development efficiency: No need for tedious requirement document writing and explanations. Users can interact directly with DevOpsGPT to quickly convert requirements into functional software.
Shortened development cycles: The automated software development process significantly reduces delivery time, accelerating software deployment and iterations.
Reduced communication costs: By accurately understanding user requirements, DevOpsGPT minimizes the risk of communication errors and misunderstandings, enhancing collaboration efficiency between development and business teams.
High-quality deliverables: DevOpsGPT generates code and performs validation, ensuring the quality and reliability of the delivered software.
[Enterprise Edition] Existing project analysis: Through AI, automatic analysis of existing project information, accurate decomposition and development of required tasks on the basis of existing projects.
[Enterprise Edition] Professional model selection: Support language model services stronger than GPT in the professional field to better complete requirements development tasks, and support private deployment.
[Enterprise Edition] Support more DevOps platforms: can connect with more DevOps platforms to achieve the development and deployment of the whole process.

dotagent

Deploy agents on cloud, PCs, or mobile devices

Category

Build-your-own

Description

An agent management system that facilitates the creation of robust AI applications and experimental autonomous agents through a rich suite of developer tools.
Enables the deployment of agents across multiple platforms including cloud, PCs, or mobile devices, and extends functionality through Python or plain English integrations.
Advances prompt engineering with a powerful prompt compiler, offering a higher degree of control over Language Models, significantly optimizing the response generation process.
Allows seamless export of agents into portable files for execution in any environment, along with an optional Agentbox feature for optimized computing resource management within a sandboxed environment.

Eidolon

Multi Agent SDK with pluggable, modular components

Category

Build-your-own (agent-builing frameworks and platforms), SDK for AI apps

Description

Eidolon is an open source SDK for AI agents

English Compiler

Converting markdown specs into functional code

Category

Coding

Description

OC AI based Compiler, for converting english based markdown specs, into functional code
"We know that all great™ projects start with awesome™ detailed functional specifications. Which is typically written in English, or its many other spoken language alternatives.
So what if, instead of writing code from functional specs, we simply compile it directly to code?
Into a future, where we replace nearly everything, with just written text."

evo.ninja

AI agent that adapts its persona to achive tasks

Category

General purpose, Research, Multi-agent

Description

What makes evo.ninja special is that it adapts itself in real-time, based on the tasks at hand.
Evo utilizes pre-defined agent personas that are tailored to specific domains of tasks.
Each iteration of evo's execution loop it will select and adopt the persona that fits the task at hand best.

FastAgency

The fastest way to deploy multi-agent workflows

Category

Build-your-own (agent-builing frameworks and platforms), SDK for AI apps, Multi-agent, Supports open-source models

Description

"FastAgency is an open-source framework designed to accelerate the transition from prototype to production for multi-agent AI workflows.
For developers who use the AutoGen framework, FastAgency enables you to seamlessly scale Jupyter notebook prototypes into fully functional, production-ready applications.
With multi-framework support, a unified programming interface, and powerful API integration capabilities, FastAgency streamlines the deployment process, saving time and effort while maintaining flexibility and performance.

Flowise

Low code Agent builder

Category

Build-your-own (agent-builing frameworks and platforms)

Description

Flowise is an open source low-code tool for developers to build customized LLM orchestration flow & AI agents

Friday

AI developer assistant for Node.js

Category

Coding

Description

A developer assistant able to make whole nodejs project with unlimited prompts
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.

GeniA

Engineering platform engineering AI team member

Category

Coding

Description

GeniA is able to work along side you on your production enviroment, executing tasks on your behalf in your dev & cloud environments, AWS/k8s/Argo/GitHub etc.
Allows you to enhance the platform by integrating your own tools and APIs.
Slack App Bot integration.
Supports GPT-3.5 & GPT-4.

Godmode

Inspired by AutoGPT and BabyAGI, with nice UI

Category

General purpose

Description

Godmode is a project inspired by Auto-GPT and BabyAGI, conducting various kinds of tasks via nice UI
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

GPT Discord

The ultimate AI agent integration for Discord

Category

Content creation, Productivity, General purpose, Discord

Description

GPT Discord is a robust, all-in-one GPT interface for Discord.
GPT Discord supports everything from multi-modality image understanding, code interpretation, advanced data analysis, Q&A on your own documents, internet-connected chat with Wolfram Alpha and Google access, AI-moderation, image generation with DALL-E, and much more!
Featuring code execution and environment manipulation by E2B
LLMs/model providers supported:
- OpenAI models

GPT Engineer

Generates entire codebase based on a prompt

Category

Coding

Description

GPT Engineer is an AI agent that generates an entire codebase based on a prompt.

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/rerun_edited_message_logs.py

GPT Migrate

Migrate codebase between frameworks/languages

Category

Coding

Description

GOT Migrate easily migrates your codebase from one framework or language to another.

Pick from different LLMs
Ability to allow GPT Migration to generate and run unit tests for the new codebase
Ability to select source and target language of the migration
Ability to customize the agent's workflow (setup -> migrate -> test)
GPT Migrate team is working on adding benchmarks for the agent

GPT Pilot

Code the entire scalable app from scratch

Category

Coding

Description

GPT Pilot is an AI agent that codes the entire app as you oversee the code being written

Dev tool that writes scalable apps from scratch while the developer oversees the implementation
A research project to see how can GPT-4 be utilized to generate fully working, production-ready, apps
The main idea is that AI can write most of the code for an app (maybe 95%) but for the rest 5%, a developer is and will be needed until we get full AGI

GPT Researcher

Agent that researches entire internet on any topic

Category

Research, Science

Description

GPT Researcher is a GPT-based autonomous agent that does online comprehensive research on any given topic

Can produce detailed, factual and unbiased research reports
Offers customization options for focusing on relevant resources, outlines, and lessons
Addresses issues of speed and determinism, offering a more stable performance and increased speed through parallelized agent work, as opposed to synchronous operation
Inspired by AutoGPT and the Plan-and-Solve paper
The main idea is to run "planner" and "execution" agents, whereas the planner generates questions to research, and the execution agents seek the most related information based on each generated research question

GPT Runner

Agent that converses with your files

Category

Research, Science

Description

Conversation with your files which selected by you, no embedding, no vector database!
It's also a AI Prompt Storybook. You can use it to manage some AI preset with your team. It support any IDE and language developer. We provide cli to run web and VSCode extension, Jetbrains plugin is coming soon.
Private first, all data is local.
We support both OpenAI and Anthropic (Claude-2)
It support support for multiple languages.

GPTSwarm

Language Agents as Optimizable Graphs

Category

Build-your-own (agent-builing frameworks and platforms), General purpose, Multi-agent

Description

🐝 GPTSwarm is a graph-based framework for LLM-based agents, providing two high-level features:
- It lets you build LLM-based agents from graphs.
- It enables the customized and automatic self-organization of agent swarms with self-improvement capabilities.
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases. We unify these approaches by describing LLM-based agents as computational graphs. Each node implements a function to process multimodal data or query other LLMs. Each edge describes the information flow between operations and agents. Graphs can be recursively combined into larger composite graphs representing hierarchies of inter-agent collaboration. Our novel automatic graph optimizers (1) refine node-level LLM prompts (node optimization) and (2) improve agent orchestration by changing graph connectivity (edge optimization). Experiments demonstrate that our framework can be used to efficiently develop, integrate, and automatically improve diverse LLM agents.

IX

Agents building, debugging, and deploying platform

Category

Build your own, Multi-agent

Description

IX is a platform for building, debugging, and deploying collaborative Agents and cognitive workflows. -IX is a LangChain-based agent platform that includes all the tools to build and deploy fleets of agents that collaborate to complete tasks. IX is both an editor and a runtime. The editor is a no-code graph style editor for the design of agents, chains, tools, retrieval functions, and collaborative workflows.

Intuitive graph style no-code editor.
Horizontally scaling agent worker fleet.
Multi-user, multi-agent chat interface.
Smart input auto-completes @mentions and {file} references.
Supports Chroma and other vector databases for document search.
Supports OpenAI API, Anthropic, PaLM, and LLama based models.
Component library is easily extended.
Powered by LangChain

JARVIS

System that connects LLMs with the ML community

Category

General purpose

Description

JARVIS is a system to connect LLMs with the ML community.

Task Planning: Using ChatGPT to analyze the requests of users to understand their intention, and disassemble them into possible solvable tasks.
Model Selection: To solve the planned tasks, ChatGPT selects expert models hosted on Hugging Face based on their descriptions.
Task Execution: Invokes and executes each selected model, and returns the results to ChatGPT.
Response Generation: Use ChatGPT to integrate the prediction of all models, and generate responses.

Langroid

Multi-agent framework for building LLM apps

Category

General purpose, Build your own

Description

Langroid is an intuitive, lightweight, extensible and principled Python framework to easily build LLM-powered applications. You set up Agents, equip them with optional components (LLM, vector-store and methods), assign them tasks, and have them collaboratively solve a problem by exchanging messages. This Multi-Agent paradigm is inspired by the Actor Framework (but you do not need to know anything about this!).

Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

Works with most commercial/remote and open/local LLMs.
Set up Multi-agent, multi-LLM system: use stronger LLMs for agents requiring strong reasoning and instruction-following, and delegate simpler tasks to weaker/local LLMs.
Supports OpenAI function-calling as well as native equivalent called ToolMessage, which works with LLMs that do not have built-in function-calling. Simply specify structure as a (nested) Pydantic object.
Batteries-included: vector-databases for RAG (Retrieval-Augmented Generation), caching, logging/observability.
Specialized agents available: DocChatAgent, SQLChatAgent, TableChatAgent (for tabular data, e.g. csv/dataframes).
DocChatAgent handles text, PDF, Docx files/URLS, and has state-of-the art techniques for retrieval combining lexical and semantic search.
Documentation: https://langroid.github.io/langroid/

Lemon Agent

Plan-Validate-Solve agent for workflow automation

Category

Productivity, Coding

Description

Lemon agent is a Plan-Validate-Solve (PVS) Agent for accurate, reliable and reproducable workflow automation

A standalone supervised Plan and Solve Agent specialized on performing read and write operations on various tools like GitHub, HubSpot or Airtable (ACL 2023 Paper "Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models")
Separation of tasks and human-in-the-loop interactions: Lemon Agent is currently holding a Planner Agent and a Solver Agent to keep the agents focussed and increase accuracy. We are planning on adding additional agents real soon. In addition, Lemon Agent will ask for approval at relevant workflow steps to make sure the intended actions are executed.
Unlimited configuration options: Lemon Agent gives you unlimited configuration options (see example here) when defining your workflow. For instance, you can tell Lemon Agent to ask for permission before executing a workflow step or to drop a 🧔‍♀️ dad joke every time the model executes a workflow step.
UI flexibility: Build any UI on top or engage with Lemon Agent via the built-in CLI.
[Soon] Model & framework agnostic operations: Lemon Agent is a standalone agent, but can easily be integrated into frameworks like LangChain and be used with any model.
Bonus: Identify weak spots in your agent’s decision-making capabilities and move to a more deterministic behavior by further configuring your Lemon Agent workflows. (.html file that can be run without any additional installation)

LLM Agents

Library for building agents, using tools, planning

Category

Coding

Description

A minimalistic library for building agents that leverage large language models to automate tasks through a loop of commands and tool integrations.

Executing Python code in a REPL environment.
Conducting searches on Google and Hacker News.
Iterating through a cycle of Thought, Action, Observation, and New Thought based on the output of integrated tools.
Dynamically appending new information to the prompt for informed decision-making by the agent.

LLM Stack

No-code platform to build LLM Agents

Category

Build-your-own, no-code, web UI

Description

LLM Stack is a no-code platform to build LLM Agents, workflows and applications with your data
LLMStack supports all major model providers, like OpenAI, Cohere, Stability AI, Hugging Face, and more. Easily use these models to build powerful apps.
With LLM Stack, you can build generative AI agents like AI SDRs, Research Analysts, RPA Automations etc., without writing any code. Connect agents to your internal or external tools, search the web or browse the internet with agents.
LLMs/model providers supported
- OpenAI
- Cohere
- Stability AI
- Hugging Face

Local GPT

Chat with documents without compromising privacy

Category

Research, Data analysis, General purpose

Description

LocalGPT is an open-source initiative that allows you to converse with your documents without compromising your privacy. Inspired by privateGPT, allows using your own documents as an information source

Chat with your documents on your local device using GPT models. No data leaves your device and 100% private
With everything running locally, you can be assured that no data ever leaves your computer
Dive into the world of secure, local document interactions with LocalGPT
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

Loop GPT

Re-implementation of AutoGPT as a Python package

Category

General purpose

Description

Loop GPT is a re-implementation of the popular Auto-GPT project as a proper python package, written with modularity and extensibility in mind

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)!"

L2MAC

Agent framework able to produce large complex codebases and entire books

Category

Multi-agent, Coding, Build your own

Description

L2MAC is a multi-agent generation framework that, a single input prompt can generate an extensive unbounded output, such as an entire codebase or an entire book.

L2MAC can create near unbounded outputs that align exactly with the user input prompt over very long generation tasks
It achieves strong empirical performance of state-of-the-art generation for large codebase tasks and is in the top 3 for the HumanEval coding global benchmark. As L2MAC can detect invalid code and failing unit tests when generating code and automatically error corrects them.
Internally persists a complete file-store memory that enables LLM agents to read files and write to files, creating a large output over many iterations
It can be instructed to follow an exact prompt program
As it generates the output one part at a time, it enables an LLM with a fixed context token limit to be bypassed
The paper, peer-reviewed and recently accepted and published at ICLR 2024, introduces L2MAC.

Maige

Natural-language workflows for your GitHub repo.

Category

Coding, Productivity, Debugging, Multi-agent

Description

Maige is a codebase agent that runs when new issues and pull requests come up. Its core features are labelling, assigning, and answering questions.
Maige can search the entire codebase, spin up a sandbox to run scripts, and even write basic code.

Magick

AIDE for creating, deploying, monetizing agents

Category

Coding, SDK for agents, Build-your-own

Description

Magick is an AIDE for creating, deploying, scaling, and monetizing useful AI agents, and prompt chaining.

A full suite, model agnostic AIDE for creating, deploying, scaling, and monetizing useful AI agents, and prompt chaining.
Magick allows to build things like BabyAGI within an hour. You can watch the graph executing in real time, watch the thought process as it executes, and understand the flow.
"Visual development of autonomous agents is incoming. We have built Magick specifically for the rapid development of cognitive architecture and scalable event-driven autonomous agents."

MemFree

Open Source Hybrid AI Search Engine

Category

Open Source, AI Search, Build your own

Description

Open Source Hybrid AI Search Engine, Instantly Get Accurate Answers from the Internet, Bookmarks, Notes, and Docs.

One-Click Chrome Bookmarks Sync and Index
Support multiple traditional search engines as source
Self-hosted Super Fast Serverless Vector Database
Self-hosted Super Fast Local Embedding and Rerank Service
Full Code Open Source
One-Click Deployment On Production

MemGPT

Memory management system, providing context to LLM

Category

Memory management, Data analysis

Description

A system that intelligently manages different memory tiers in LLMs to effectively provide the extended context within the LLM's limited context window.
Chat with your data - talk to your local files or SQL database
Create perpetual chatbots with self-editing memory

Mentat

Assists you with coding task from command line

Category

Coding

Description

Mentat is the AI tool that assists you with any coding task, right from your command line. Unlike Copilot, Mentat coordinates edits across multiple locations and files. And unlike ChatGPT, Mentat already has the context of your project - no copy and pasting required!

MetaGPT

Agent framework returning Design, Tasks, or Repo

Category

Multi-agent, Coding, Build your own

Description

MetaGPT is a multi-agent framework that, given one line requirement, returns PRD, Design, Tasks, or Repo.

MetaGPT allows to assign different roles to GPTs to form a collaborative software entity for complex tasks
It takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.
Internally, MetaGPT includes product managers / architects / project managers / engineers
It provides the entire process of a software company along with carefully orchestrated SOPs. Code = SOP(Team) is the core philosophy
The paper about LLM-based multi-agent work spushes forward the idea of autonomous agents collaborating with each other to do more than one can on its own.
MetaGPT incorporates efficient human workflows as a meta programming approach into LLM-based multi-agent collaboration

Mini AGI

General-purpose agent based on GPT-3.5 / GPT-4

Category

General purpose

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

Multiagent Debate

Implementation of a paper on Multiagent Debate

Category

General purpose, Multi-agent

Description

Multiagent Debate is an implementation of the paper "Improving Factuality and Reasoning in Language Models through Multiagent Debate".

The paper illustrates how we may treat different instances of the same language models as a "multiagent society", where individual language model generate and critique the language generations of other instances of the language model
The authors find that the final answer generated after such a procedure is both more factually accurate and solves reasoning questions more accurately
Illustrating the quantitative difference between multiagent debate and single agent generation on different domains in reasoning and factual validity

Multi GPT

Experimental multi-agent system

Category

General purpose

Description

An experimental open-source attempt to make GPT-4 fully autonomous
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

MutahunterAI

MutahunterAI: Accelerate developer productivity and code security with our open-source AI

Category

Developer tools, Software security, Multi-agent, General purpose

Description

Use Mutahunter to generate unit tests for your codebase, that specifically target the code vulnerabilities. By targeting the exact weaknesses in the code, we boost developer productivity.
Unlike copilots which blindly generates test cases for your code, Mutahunter makes use of our mutation testing engine to generate unit tests that specifically target the vulnerabilities in your code
Features
- Support all major languages.
- We can be used locally or can be integrated into any CI/CD runner as part of your existing workflow
- You can use Mutahunter with your own LLM APIs for privacy.

NLSOM

Natural Language-Based Societies of Mind

Category

Science, Multimodal, Social, Multi-agent

Description

Natural Language-Based Societies of Mind - concept with societies and communities of agents
Concept, which contains societies and communities of agents
Agents can be either LLMs, NN-based experts, APIs and role-players. They all communicate in natural language.
To solve tasks, these agents use a collaborative "Mindstorm" process involving mutual interviews.
Additional components for NLSOM can be easily added in a modular way.
"What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle." — Marvin Minsky, The Society of Mind, p. 308

OpenAgents

Multi-agent general purpose platform

Category

General purpose

Description

OpenAgents is an Open Platform for Language Agents in the Wild, ChatGPT Plus Replica for Researchers, Developers, and General Users.

User-centric
- Chat Web UI
- Productive Agents
- Online Demo
Fully open-sourced
- Full-stack
- Easy deployment
Extensible
- LLMs
- Tools
- Agent methods

OpenAGI

R&D agents platform

Category

General purpose

Description

OpenAGI is an open-source AGI R&D platform that enables agents for both benchmark tasks and open-ended tasks

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

OpenDevin

OpenDevin: Code Less, Make More

Category

Coding, general purpose

Description

The OpenDevin project is born out of a desire to replicate, enhance, and innovate beyond the original Devin model.
By engaging the open-source community, we aim to tackle the challenges faced by Code LLMs in practical scenarios, producing works that significantly contribute to the community and pave the way for future advancements.

Open Interpreter

Code interpreter that lets LLMs execute code

Category

Coding

Description

Open Interpreter is an open-source interpreter that lets LLMs run code on your computer to complete tasks

Runs locally
Can for example summarize PDFs, visualize datasets, control your browser
Works from a ChatGPT-like interface in your terminal.

Pezzo

Development toolkit for prompt management & more

Category

Coding

Description

Pezzo is a development toolkit designed to streamline prompt design, version management, publishing, collaboration, troubleshooting, observability and more

"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.

Private GPT

Tool for private interaction with your documents

Category

Research, Data analysis

Description

Private GPT is A tool for private interaction with documents, without a need for internet connection

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

PromethAI

AI agent that helps with nutrition and other goals

Category

Productivity, General purpose

Description

"Personalized AI assistant that decomposes problems, offers solutions, and lets you use Agent actions to automate your flows"
Features
- Helps users reach a solution by decomposing their requests into categories with a set of options (cuisine -> European)
- Has a dynamic UX/UI that helps avoid prompting
- Voice input supported
- Provides users with results of their queries and automates actions around them
- Remembers your past preferences and uses them to optimize your choices
Tech
- Powered by Langchain, decomposable async prompts + vector DB + Redis cache
- App built with Flutter + Dart
  - Connected to Zapier NLP

React Agent

Open-source React.js Autonomous LLM Agent

Category

Coding

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!

Self-operating computer

Let multimodal models operate a computer

Category

Productivity, Research

Description

Using the same inputs and outputs as a human operator, the model views the screen and decides on a series of mouse and keyboard actions to reach an objective.

Smol developer

Your own junior AI developer, deployed via E2B UI

Category

Coding

Description

Smol is your own junior developer. Deployed in few seconds via e2b

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

Stackwise

VSCode extension that writes nodejs functions

Category

Tool for agents, Coding

Description

Stackwise is a VS Code extension that writes and imports nodejs functions so that you can write code without context switching

The open source function collection
Explain what you want a function to do, and AI builds it.
Stackwise is a VS Code extension that automatically writes and imports nodejs functions so that you can write code without context switching. No more hunting for documentation to integrate with APIs or back and forth with ChatGPT. Just pure functionality within your code!

Superagent

Tool that allows creating agents without coding

Category

Build-your-own, General purpose, Data analysis

Description

Superagent is not a single agent, but a tool that allows creating agents without coding

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

SuperAGI

Framework to develop and deploy AI agents

Category

General purpose

Description

SuperAGI is an open-source autonomous AI framework to enable development and deployment autonomous agents

An AI agent framework
Open source, but infrastructure is -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

Suspicion Agent

Paper on imperfect information games

Category

General purpose

Description

Playing Imperfect Information Games with Theory of Mind Aware GPT-4
The paper delves into the applicability of GPT-4's learned knowledge for imperfect information games

SWE Agent

Open-source Devin alternative

Category

Coding, general purpose

Description

This Devin alternative scores 12.3% on the FULL swe benchmark
"An open source Devin getting 12.29% on 100% of the SWE Bench test set vs Devin's 13.84% on 25% of the test set!"
SWE-agent works by interacting with a specialized terminal, which allows it to:
- 🔍 Open, scroll and search through files
- ✍️ Edit specific lines w/ automatic syntax check
- 🧪 Write and execute tests
This custom-built interface is critical for good performance. Simply connecting an LM to a vanilla bash terminal does not work well.
"Our key insight is that LMs require carefully designed agent-computer interfaces (similar to how humans like good UI design). E.g. When the LM messes up indentation, our editor prevents it and gives feedback."
SWE-agent was released by the Princeton NLP team.
What makes SWE-agent special is that it performs almost as well as Devin on the SWE-bench.
It is important to say that the performance varies based on the model used by the agent.
The changes and innovations in SWE-agent compared to Devin are:
- The code in SWE Agent is executed locally via Docker.
- It uses “Agent-Computer Interface” (ACI) - constraining the interface makes the agent easier to use for LMs. Only a few commants are allowed: run code, look for code, edit code and submit changes to GitHub.
Any code the agent writes goes through a syntax check (linter) before being submitted. If the syntax is incorrect, the agent gets feedback and is forced to redo the code.
The agent can only read 100 lines of code at a time, rather than the entire file. This makes it easier for the language model to understand the code.

Sweep

Github assistant that fixes issues & writes code

Category

Coding, GitHub

Description

Sweep is a Github assistant the helps fix small bugs and implement small features

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

Taxy AI

Taxy AI is a full browser automation

Category

General purpose, Productivity

Description

Taxy uses GPT-4 to control your browser and perform repetitive actions on your behalf
Currently it allows you to define ad-hoc instructions
In the future it will also support saved and scheduled workflows
Currently in an early stage with a waitlist

Teenage AGI

BabyAGI-inspired agent, can recall infinite memory

Category

General purpose

Description

A BabyAGI-inspired agent that can recall infinite memory, "thinks" before making action, and doesn't lose memory after being shutting down

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

UFO

A UI-Focused agent on Windows OS

Category

Multi-agent, GUI Agent

Description

Agent by Microsoft
UFO is a UI-Focused dual-agent framework to fulfill user requests on Windows OS by seamlessly navigating and operating within individual or spanning multiple applications.

Vanna.AI

Python-based AI SQL agent trained on your schema

Category

Coding, Debugging, Code migration, Data analysis

Description

Vanna is an Open-Source Python-based AI SQL agent trained on your schema that writes complex SQL in seconds

AI-driven business intelligence assistant
Vanna helps you generate and run accurate SQL for your database using LLMs via Retrieval-Augmented Generation
Vanna works in two easy steps - train a RAG "model" on your data, and then ask questions which will return SQL queries that can be set up to automatically run on your database
The Vanna Python package and the various frontend integrations are all open-source
Vanna’s capabilities are tied to the training data you give it. More training data means better accuracy for large and complex datasets
Your database contents are never sent to the LLM. The metadata storage layer only sees schemas, documentation, and queries
As you use Vanna more, your model continuously improves as we augment your training data

Voyager

LLM-powered lifelong learning agent in Minecraft

Category

General purpose

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
- 1. an ever-growing skill library of executable code for storing and retrieving complex behaviors
- 1. 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

Web3 GPT

Write & deploy smart contracts to EVM blockchains

Category

Blockchain, Coding, Generating apps, Smart contract

Description

Write and deploy smart contracts to EVM blockchains.
Connect wallet to manually deploy contracts, even from you old chats.
Enable Web2/3 users to interact with blockchains without a dedicated web3 wallet using account abstraction and a gas master account.
Leverage Chat-GPT to interact with and control Web3-GPT functionalities.

“Westworld” simulation

A multi-agent environment simulation library

Category

General purpose, Multi-agent

Description

A- multi-agent simulation library, with a goal to simulate and optimize systems and environments with multiple agents interacting

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)

WorkGPT

GPT agent framework for invoking APIs

Category

Coding

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

Wren

Natural Language Interface to Your Databases

Category

Coding, Data analysis, Productivity

Description

"WrenAI is an AI-powered data assistant designed to help you retrieve results and insights quickly and effortlessly, without the need for SQL coding. It's also open-source, which means you can customize and use it to suit your specific needs.

There are four key principles that we followed when developing WrenAI:

Explainability: We made sure that every SQL query generated by WrenAI is accurate, concise, and reliable, so you can trust the results it provides.
Interoperability: WrenAI lets you query data from multiple sources, regardless of different data formats and dialects. You can enjoy a standard interface across various sources, saving you time and hassle.
Interactive Experience: Our AI-powered assistant engages users in a dialogue, helping to clarify their queries, and refine results in real-time. You can interact with WrenAI in a natural and intuitive way.
Continuous Learning: WrenAI continually learns from its interactions with users, feedback, and query history. It incorporates new patterns, information, and data structures into its LLM knowledge base, ensuring that it gets better and more accurate over time."

XAgent

Experimental LLM agent that solves various tasks

Category

General purpose, Coding

Description

XAgent is an open-source experimental Large Language Model (LLM) driven autonomous agent that can automatically solve various tasks

Emergence & Autonomy: XAgent's autonomous operations transcend biases.
Safety & Operation: Secure execution within docker environments.
Expert-Knowledge Free: Effective operation without sole expert reliance.
Interface & Interaction: Interact via a user-friendly GUI or command line, while it adapts and collaborates.
Dual-loop Mechanism:
- Outer-Loop: Manages plans and task refinements.
- Inner-Loop: Dispatch, ReACT-based execution, feedback.
Universal Language - Function Calling:
- ToolAgent & ReACT: Optimal action series for subtasks.
Tools:
- 📝 File Editor
- 📘 Python Notebook
- 🌏 Web Browser
- 🖥️ Shell
- 🧩 Rapid API

yAgents

Capable of designing, coding and debugging tools

Category

Coding, Build-your-own

Description

yAgents is an Agent-Builder Agent made by Yeager.ai capable of designing, coding and debugging its own tools.

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

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