# 🔮 AI Agents

*Welcome to our list of AI autonomous agents. Your favorite one is missing? Add them via pull request. Discussion and feedback appreciated :heart:* 

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## [AgentGPT](https://agentgpt.reworkd.ai/)
A browser-based implementation of AutoGPT, accessible via a no-code platform
<details>

### How it works
- A no-code platform
- Assigning a goal to the agent, witnessing its thinking process, and formulation of an execution plan and taking actions accordingly

### Features
- Uses OpenAI **functions**
- Supports gpt-3.5-16k, pinecone and pg_vector databases

### Links
- [Website](https://agentgpt.reworkd.ai/)
- [GitHub](https://github.com/reworkd/AgentGPT)

### Stack
- Frontend: NextJS + Typescript
- Backend: FastAPI + Python
	- DB: MySQL through docker with the option of running SQLite locally

### Links
- [Documentation](https://docs.reworkd.ai/)
-  [GitHub](https://github.com/reworkd/AgentGPT)
</details>



## [AI Legion](https://gpt3demo.com/apps/ai-legion)
Similar in spirit to AutoGPT and Baby AGI, but written in TypeScript
<details>

- Author: eumemic
- [Website](https://gpt3demo.com/apps/ai-legion)
- [GitHub](https://github.com/eumemic/ai-legion)
- [Twitter](https://twitter.com/dysmemic)
</details>

## [AIrplane](https://www.airplane.dev/) 
Turning APIs, SQL queries, and scripts into apps for the entire team


<details>

### How it works
- A developer-centric approach to building internal UIs and workflows

### 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
- [Documentation](https://docs.airplane.dev/)
- [Twitter](https://twitter.com/AirplaneDev)


</details>

## [Aomni](https://www.aomni.com/)
An AI agent specifically designed for research
<details>

### How it works
- 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](https://discord.com/invite/a367ncqEsm)

</details>



## [AutoGPT](https://autogpt.net/) 

<details>

- A lot like BabyAGI combined with LangChain tools.
- Can execute many commands such as Google Search, browse websites, write to files, and execute Python files
- [GitHub](https://github.com/Significant-Gravitas/Auto-GPT)
</details>
 
## [BabyAGI](https://github.com/yoheinakajima/babyagi)
<details>

- Default model is OpenAI GPT3-turbo
- Paper: [Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications](https://yoheinakajima.com/task-driven-autonomous-agent-utilizing-gpt-4-pinecone-and-langchain-for-diverse-applications/)
- The main idea behind this system is that it 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

</details>


## [BabyCatAGI](https://replit.com/@YoheiNakajima/BabyCatAGI)
BabyCatAGI is a mod of BabyBeeAGI, which is a mod of OG BabyAGI. BabyCatAGI is at 300 lines of code.
<details>

</details>

## [BitBuilder](BitBuilder)

<details>
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## [Bloop](https://bloop.ai/)

<details>
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## [Butternut AI](Butternut.ai)

<details>
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## [Camel](https://github.com/camel-ai/camel)
Communicative Agents for “Mind” Exploration of Large Scale Language Model Society

<details>
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## [Cognosys](https://cognosys.ai)
Web based version of AutoGPT/babyAGI

<details>

- Friendly UI for building AI agents
- Author: Sully Omarr
- Twitter: https://twitter.com/SullyOmarr

</details>

 
## [Databerry](https://www.databerry.ai/)


<details>
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## [Factory](https://www.factory.ai/)

<details>
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## [Fixie](https://www.fixie.ai/)

<details>
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## [Friday](https://github.com/amirrezasalimi/friday/)
A developer assistant able to make whole nodejs project with unlimited prompts
<details>

### How it works
- 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](https://twitter.com/amirsalimiiii)

</details>
 
 
## [GitWit](https://www.gitwit.dev/)

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## [Grit](https://www.grit.io/)

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## [HayStack Agent](https://docs.haystack.deepset.ai/docs/agent)

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## [Hex Magic](https://hex.tech/product/magic-ai/)

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## [Heymoon.ai](https://heymoon.ai/)
Personal assistant for life: to keep you on top of your calendar, tasks and information

<details>

### Features
- Personal assistant for life: to keep you on top of your calendar, tasks and information. Was at Llama event demo


</details>

 
## [Hyperwrite](https://www.hyperwriteai.com/)
Your personal AI writing assistant

<details>
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## [Jarvis]()

<details>
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## [LangChain - Agents Round](https://blog.langchain.dev/agents-round/)

<details>
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## [LastMile AI](https://lastmileai.dev/)

<details>
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## [Loop GPT](https://github.com/farizrahman4u/loopgpt/tree/main)
 
 <details>
- 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
- 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)!
	</details>
 
## [Minion AI](https://minion.ai/)

<details>
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## [MultiOn](https://multion.ai/)

<details>
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## [Naut ai](https://www.naut.ai/)

<details>
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## [Pezzo](https://www.pezzo.ai/)

<details>
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## [Saga](https://saga.so/)

<details>
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## [Second](https://www.second.dev/)

<details>
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## [Smol developer](https://github.com/smol-ai/developer)

<details>
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## [Superagent](https://www.superagent.sh/)</details>

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## [Sweep](https://sweep.dev/)

<details>
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## [Teenage AGI](https://github.com/seanpixel/Teenage-AGI/blob/main/README.md#experiments)


<details>

### How it works
- 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

**Here is what 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

- Created by [@sean_pixel](https://twitter.com/sean_pixel)
! 

</details>


## [“Westworld” simulation](https://theolvs.github.io/westworld/)
Westworld is a multi-agent simulation library, its goal to simulate and optimize systems and environments with multiple agents interacting.
<details>

- 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


[Underlying paper - Generative Agents](https://arxiv.org/abs/2304.03442)
- 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

</details>


### Links
- [GitHub](https://github.com/TheoLvs/westworld)
- [Documentation](https://theolvs.github.io/westworld/ )

### Languages
- The library is available on PyPi via
pip install westworld
- [Javascript version (being developed)](https://github.com/TheoLvs/westworldjs)

### Current 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)

</details>




## [Yourgoal]()

Swift implementation of BabyAGI.

<details>
Author: PJ Gray
Twitter: https://twitter.com/pj4533
</details>

<br>

## :wave: Wanna discuss AI agents and more?

- [Hit us up on discord](https://discord.gg/5GmKg5Uz)
- [Pick a date for call in our calendar](https://calendly.com/tereza-tizkova/30min)
- Write us at hello@e2b.dev

<br>

# 💪 Contributors 💪 

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