d2a5bb286f
### Background
When multiple executors are executing the same node within the same graph execution, two node executions can read the same queue of input and read the same value—making the data that is supposed to be consumed once, consumed by two executions. The lack of lock & concurrency support for parallel execution within a single graph causes this issue.
Node concurrency also introduces poor UX in the current frontend implementation, when two nodes are executed in parallel, the current UI will not display its parallel execution update, but instead, it shows the updates that override each other. Until the execution observability is improved on the builder UI, this capability will be limited.
### Changes 🏗️
The scope of this change is to solve this issue by:
* Decouple Graph execution & Node execution, each has its own configured process pool.
* Make sure there is only 1 execution per node (we still allow parallel executions on different nodes) in a graph.
* Fixed concurrency issue by adding distributed lock API on agent_server.
* Few cleanups:
- Add more logging with geid & neid prefix on graph/node executions
- Moved execution status update to agent-server for a single source of status update (required by conn-manager/web-socket)
- Configured node parallelism to 10 & graph parallelism to 10 by default, so in the very rare worst-case, there can be 100 node executions.
- Re-use server resource for each integration test run
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
🧱 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.
💽 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.
🐙 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:
- 🏗️ Building - Lay the foundation for something amazing.
- 🧪 Testing - Fine-tune your agent to perfection.
- 🤝 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.
📖 Documentation | 🚀 Contributing
🤖 AutoGPT Classic
Below is information about the classic version of AutoGPT.
🛠️ Build your own Agent - Quickstart
🏗️ 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.
🚀 Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
📘 Learn More about Forge
🎯 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.
📦 agbenchmark on Pypi
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📘 Learn More about the Benchmark
💻 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!
📘 Learn More about the Frontend
⌨️ 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!
🤔 Questions? Problems? Suggestions?
Get help - Discord 💬
To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasn’t created an issue for the same topic.
🤝 Sister projects
🔄 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.