AutoGPT

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

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JARVIS

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AgentGPT

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🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

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Quick Overview

AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. It is designed to autonomously achieve user-defined goals by breaking them down into tasks and executing them. AutoGPT can interact with various APIs and services to complete complex operations without constant human intervention.

Pros

Demonstrates the potential of AI for autonomous task completion
Highly customizable and extensible
Can handle a wide range of tasks, from research to coding
Active community and ongoing development

Cons

Requires API keys and can be costly to run due to API usage
May produce inconsistent or unexpected results
Potential for misuse or unintended consequences if not properly monitored
Still experimental and may have stability issues

Code Examples

# Example 1: Setting up a custom AI agent
from autogpt import AutoGPT

agent = AutoGPT(
    ai_name="ResearchAssistant",
    ai_role="An AI assistant specialized in academic research",
    ai_goals=[
        "Conduct a literature review on renewable energy",
        "Summarize key findings",
        "Identify gaps in current research"
    ]
)
agent.run()

# Example 2: Using AutoGPT for web scraping
from autogpt import AutoGPT

scraper_agent = AutoGPT(
    ai_name="WebScraper",
    ai_role="A web scraping specialist",
    ai_goals=[
        "Scrape product information from an e-commerce website",
        "Extract prices, titles, and descriptions",
        "Save data to a CSV file"
    ]
)
scraper_agent.run()

# Example 3: AutoGPT for code generation
from autogpt import AutoGPT

coder_agent = AutoGPT(
    ai_name="CodeGenerator",
    ai_role="An AI coding assistant",
    ai_goals=[
        "Create a simple Flask web application",
        "Implement user authentication",
        "Add a RESTful API endpoint"
    ]
)
coder_agent.run()

Getting Started

Clone the repository:

git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT

Install dependencies:
```
pip install -r requirements.txt
```

Set up environment variables:

cp .env.template .env
# Edit .env file with your API keys

Run AutoGPT:
```
python -m autogpt
```
Follow the prompts to set up your AI agent and define its goals.

Competitor Comparisons

JARVIS

23,607

JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf

Pros of JARVIS

More comprehensive multimodal capabilities, including vision and speech
Stronger focus on research and academic applications
Better integration with Azure services and Microsoft ecosystem

Cons of JARVIS

Less user-friendly for non-technical users
More complex setup and configuration process
Limited community support compared to AutoGPT's active open-source community

Code Comparison

JARVIS (Python):

from jarvis import JARVIS

jarvis = JARVIS()
response = jarvis.process_input("Analyze this image", image_path="example.jpg")
print(response)

AutoGPT (Python):

from autogpt import AutoGPT

agent = AutoGPT()
result = agent.run("Analyze the contents of example.jpg")
print(result)

Both projects aim to create autonomous AI agents, but JARVIS focuses more on multimodal interactions and research applications, while AutoGPT emphasizes general-purpose task automation and has a more active open-source community. JARVIS offers better integration with Microsoft services, but AutoGPT provides a more user-friendly experience for non-technical users. The code examples demonstrate the different approaches to processing inputs and executing tasks.

babyagi

20,171

Pros of BabyAGI

Simpler implementation, making it easier to understand and modify
Focuses on task management and execution, which can be beneficial for specific use cases
Lightweight and requires fewer dependencies

Cons of BabyAGI

Less feature-rich compared to AutoGPT
Limited to text-based interactions, lacking the ability to interact with external tools or APIs
May require more manual intervention for complex tasks

Code Comparison

BabyAGI:

def execute_task(self, objective: str, task: str) -> str:
    context = context_agent(query=objective, top_results_num=5)
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": f"You are an AI assistant tasked with {objective}. Your task: {task}"},
            {"role": "user", "content": f"Context: {context}\n\nTask: {task}"}
        ],
        temperature=0.7,
        max_tokens=2000
    )
    return response.choices[0].message['content']

AutoGPT:

def execute_command(self, command_name: str, arguments: Dict[str, str]) -> str:
    command = self.command_registry.get_command(command_name)
    if command:
        return command(**arguments)
    else:
        return f"Error: command '{command_name}' not found."

The code snippets highlight the different approaches: BabyAGI focuses on task execution using OpenAI's API, while AutoGPT employs a more modular command-based system.

AgentGPT

31,565

🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

Pros of AgentGPT

User-friendly web interface for easy interaction and task management
Supports multiple language models, including GPT-3.5 and GPT-4
Offers a more streamlined and focused approach to task execution

Cons of AgentGPT

Less customizable and extensible compared to AutoGPT
Limited integration with external tools and APIs
Fewer advanced features for complex task chains or autonomous operation

Code Comparison

AutoGPT:

def get_command(
    response: str,
    prompt: str,
    user_input: str,
    agent: Agent,
    ai_config: AIConfig,
) -> Command:
    # ... (code for command parsing and execution)

AgentGPT:

const executeTask = async (task: string): Promise<string> => {
  const response = await api.post("/api/agent/tasks", { task });
  return response.data.response;
};

The code snippets highlight the different approaches:

AutoGPT focuses on command parsing and execution within a more complex agent system
AgentGPT employs a simpler API-based task execution model

Both projects aim to create autonomous AI agents, but AutoGPT offers more flexibility and advanced features, while AgentGPT provides a more accessible and user-friendly experience.

langchain

93,526

🦜🔗 Build context-aware reasoning applications

Pros of langchain

More flexible and modular, allowing developers to build custom AI applications
Extensive documentation and examples for various use cases
Supports multiple language models and integrations with various tools

Cons of langchain

Steeper learning curve for beginners
Requires more manual configuration and coding compared to AutoGPT

Code Comparison

langchain:

from langchain import OpenAI, LLMChain, PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(input_variables=["product"], template="What is a good name for a company that makes {product}?")
chain = LLMChain(llm=llm, prompt=prompt)

print(chain.run("colorful socks"))

AutoGPT:

from autogpt.agents import Agent
from autogpt.config import Config

cfg = Config()
agent = Agent(cfg)
agent.start_interaction_loop()

The code snippets demonstrate the difference in approach between the two projects. langchain requires more explicit configuration and chaining of components, while AutoGPT provides a higher-level interface for autonomous agents.

minGPT

19,997

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

Pros of minGPT

Lightweight and easy to understand implementation of GPT
Excellent educational resource for learning about transformer architecture
Highly customizable and adaptable for various NLP tasks

Cons of minGPT

Limited in scope compared to AutoGPT's autonomous agent capabilities
Lacks advanced features like internet browsing and task planning
Requires more manual input and configuration for specific use cases

Code Comparison

minGPT:

class GPT(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
        self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
        self.drop = nn.Dropout(config.embd_pdrop)

AutoGPT:

class Agent:
    def __init__(self, ai_name, memory, full_message_history, next_action_count, command_registry, config, system_prompt, triggering_prompt):
        self.ai_name = ai_name
        self.memory = memory
        self.full_message_history = full_message_history
        self.next_action_count = next_action_count

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README

AutoGPT: Build, Deploy, and Run AI Agents

AutoGPT is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows.

Hosting Options

Download to self-host
Join the Waitlist for the cloud-hosted beta

How to Setup for Self-Hosting

[!NOTE] Setting up and hosting the AutoGPT Platform yourself is a technical process. If you'd rather something that just works, we recommend joining the waitlist for the cloud-hosted beta.

https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603

This tutorial assumes you have Docker, VSCode, git and npm installed.

ð§± AutoGPT Frontend

The AutoGPT frontend is where users interact with our powerful AI automation platform. It offers multiple ways to engage with and leverage our AI agents. This is the interface where you'll bring your AI automation ideas to life:

Agent Builder: For those who want to customize, our intuitive, low-code interface allows you to design and configure your own AI agents.

Workflow Management: Build, modify, and optimize your automation workflows with ease. You build your agent by connecting blocks, where each block performs a single action.

Deployment Controls: Manage the lifecycle of your agents, from testing to production.

Ready-to-Use Agents: Don't want to build? Simply select from our library of pre-configured agents and put them to work immediately.

Agent Interaction: Whether you've built your own or are using pre-configured agents, easily run and interact with them through our user-friendly interface.

Monitoring and Analytics: Keep track of your agents' performance and gain insights to continually improve your automation processes.

Read this guide to learn how to build your own custom blocks.

ð½ AutoGPT Server

The AutoGPT Server is the powerhouse of our platform This is where your agents run. Once deployed, agents can be triggered by external sources and can operate continuously. It contains all the essential components that make AutoGPT run smoothly.

Source Code: The core logic that drives our agents and automation processes.

Infrastructure: Robust systems that ensure reliable and scalable performance.

Marketplace: A comprehensive marketplace where you can find and deploy a wide range of pre-built agents.

ð Example Agents

Here are two examples of what you can do with AutoGPT:

Generate Viral Videos from Trending Topics
- This agent reads topics on Reddit.
- It identifies trending topics.
- It then automatically creates a short-form video based on the content.
Identify Top Quotes from Videos for Social Media
- This agent subscribes to your YouTube channel.
- When you post a new video, it transcribes it.
- It uses AI to identify the most impactful quotes to generate a summary.
- Then, it writes a post to automatically publish to your social media.

These examples show just a glimpse of what you can achieve with AutoGPT! You can create customized workflows to build agents for any use case.

Mission and Licencing

Our mission is to provide the tools, so that you can focus on what matters:

ðï¸ Building - Lay the foundation for something amazing.
ð§ª Testing - Fine-tune your agent to perfection.
ð¤ Delegating - Let AI work for you, and have your ideas come to life.

Be part of the revolution! AutoGPT is here to stay, at the forefront of AI innovation.

ð Documentation | ð Contributing

Licensing:

MIT License: The majority of the AutoGPT repository is under the MIT License.

Polyform Shield License: This license applies to the autogpt_platform folder.

For more information, see https://agpt.co/blog/introducing-the-autogpt-platform

ð¤ AutoGPT Classic

Below is information about the classic version of AutoGPT.

ð ï¸ Build your own Agent - Quickstart

ðï¸ Forge

Forge your own agent! – Forge is a ready-to-go toolkit to build your own agent application. It handles most of the boilerplate code, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from forge can also be used individually to speed up development and reduce boilerplate in your agent project.

ð Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.

ð Learn More about Forge

ð¯ Benchmark

Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.

ð¦ agbenchmark on Pypi | ð Learn More about the Benchmark

ð» UI

Makes agents easy to use! The frontend gives you a user-friendly interface to control and monitor your agents. It connects to agents through the agent protocol, ensuring compatibility with many agents from both inside and outside of our ecosystem.

The frontend works out-of-the-box with all agents in the repo. Just use the CLI to run your agent of choice!

ð Learn More about the Frontend

â¨ï¸ CLI

To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:

$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  agent      Commands to create, start and stop agents
  benchmark  Commands to start the benchmark and list tests and categories
  setup      Installs dependencies needed for your system.

Just clone the repo, install dependencies with ./run setup, and you should be good to go!

ð¤ Questions? Problems? Suggestions?

Get help - Discord ð¬

To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasnât created an issue for the same topic.

ð¤ Sister projects

ð Agent Protocol

To maintain a uniform standard and ensure seamless compatibility with many current and future applications, AutoGPT employs the agent protocol standard by the AI Engineer Foundation. This standardizes the communication pathways from your agent to the frontend and benchmark.

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of JARVIS

Cons of JARVIS

Code Comparison

Pros of BabyAGI

Cons of BabyAGI

Code Comparison

Pros of AgentGPT

Cons of AgentGPT

Code Comparison

Pros of langchain

Cons of langchain

Code Comparison

Pros of minGPT

Cons of minGPT

Code Comparison

Convert designs to code with AI

README

AutoGPT: Build, Deploy, and Run AI Agents

Hosting Options

How to Setup for Self-Hosting

ð§± AutoGPT Frontend

ð½ AutoGPT Server

ð Example Agents

Mission and Licencing

ð¤ AutoGPT Classic

ðï¸ Forge

ð¯ Benchmark

ð» UI

â¨ï¸ CLI

ð¤ Questions? Problems? Suggestions?

Get help - Discord ð¬

ð¤ Sister projects

ð Agent Protocol

Top Related Projects

Convert designs to code with AI

ð§± AutoGPT Frontend

ð½ AutoGPT Server

ð Example Agents

ð¤ AutoGPT Classic

ðï¸ Forge

ð¯ Benchmark

ð» UI

â¨ï¸ CLI

ð¤ Questions? Problems? Suggestions?

Get help - Discord ð¬

ð¤ Sister projects

ð Agent Protocol