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.
Top Related Projects
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
🦜🔗 Build context-aware reasoning applications
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
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, 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.
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.
🤖 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.
🦜🔗 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.
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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AutoGPT: Build & Use AI Agents
AutoGPT is a powerful tool that lets you create and run intelligent agents. These agents can perform various tasks automatically, making your life easier.
How to Get Started
https://github.com/user-attachments/assets/8508f4dc-b362-4cab-900f-644964a96cdf
𧱠AutoGPT Builder
The AutoGPT Builder is the frontend. It allows you to design agents using an easy flowchart style. You build your agent by connecting blocks, where each block performs a single action. It's simple and intuitive!
Read this guide to learn how to build your own custom blocks.
ð½ AutoGPT Server
The AutoGPT Server is the backend. This is where your agents run. Once deployed, agents can be triggered by external sources and can operate continuously.
ð Example Agents
Here are two examples of what you can do with AutoGPT:
-
Reddit Marketing Agent
- This agent reads comments on Reddit.
- It looks for people asking about your product.
- It then automatically responds to them.
-
YouTube Content Repurposing Agent
- This agent subscribes to your YouTube channel.
- When you post a new video, it transcribes it.
- It uses AI to write a search engine optimized blog post.
- Then, it publishes this blog post to your Medium account.
These examples show just a glimpse of what you can achieve with AutoGPT!
Our mission is to provide the tools, so that you can focus on what matters:
- ðï¸ Building - Lay the foundation for something amazing.
- 𧪠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
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 template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from the forge.sdk
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.
Top Related Projects
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
🦜🔗 Build context-aware reasoning applications
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot