Overview
AutoGPT is a platform for creating, deploying, and managing continuous AI agents that automate multi-step workflows. You build an agent by connecting blocks, where each block performs a single action, and chain them together into a workflow that can run on its own and be triggered by external sources.
It's aimed at developers and technical builders who want to run agents themselves. You can self-host the platform with Docker for free, or join the waitlist for the cloud-hosted beta. A low-code Agent Builder, workflow management, deployment controls, and monitoring are included, plus a library of pre-built agents you can run as-is.
As a general agent framework, AutoGPT covers the full lifecycle: design the workflow in the frontend, run it on the AutoGPT Server, and watch its performance through built-in monitoring and analytics.
What it does
- Block-based agent builder: connect blocks that each perform one action into a full workflow
- Low-code frontend for designing, configuring, and editing agents without deep boilerplate
- AutoGPT Server runs agents continuously and lets them be triggered by external sources
- Marketplace and library of pre-built, ready-to-run agents
- Deployment controls to manage agents from testing through production
- Monitoring and analytics to track agent performance over time
Getting started
Self-hosting AutoGPT is a Docker-based setup. The quickest path is the official one-line install script, which installs dependencies, configures Docker, and launches a local instance. You'll need Docker, git, Node.js, and npm installed first.
Check system requirements
You need Docker Engine 20.10+, Docker Compose 2.0+, Git 2.30+, and Node.js 16+ with npm 8+. Recommended hardware is 4+ CPU cores, 8GB+ RAM, and at least 10GB of free storage.
Run the one-line setup (macOS/Linux)
This downloads and runs the official install script, which sets up dependencies, configures Docker, and launches your local instance.
curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.shRun the one-line setup (Windows)
On Windows, use the PowerShell command instead. Windows 10/11 with WSL2 is required.
powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"Build your first agent
Once the local instance is running, open the frontend and use the Agent Builder to connect blocks into a workflow, or pick a pre-configured agent from the library. See the official docs for building custom blocks.
Commands and code are distilled from the project's own documentation — always check the official repo for the latest.
When to use it
- Automating multi-step content workflows, such as reading trending topics and generating short-form videos from them
- Monitoring a source (like a YouTube channel) and transcribing, summarizing, then posting highlights to social media
- Running long-lived agents that operate continuously and respond to external triggers
- Prototyping agent workflows with a low-code block builder before deploying to production
How AutoGPT compares
AutoGPT alongside other open-source agent frameworks & builders tools AI/TLDR tracks, ranked by GitHub stars.
| Tool | Stars | What it does |
|---|---|---|
| DeepSeek Harness | ★ 229k | DeepSeek AI's open-source agent harness (dsh), built on Cordis, where models, tools, skills, sessions, sandboxes, storage and the UI are all plugins composed through profiles. |
| AutoGPT | ★ 187k | Build, deploy, and run continuous AI agents from reusable blocks |
| DeerFlow | ★ 82.6k | ByteDance's open-source super agent harness built on LangGraph: skills, sub-agents, sandboxes, a filesystem and long-term memory for long-horizon research, coding and content tasks. |
| nanobot | ★ 48.3k | Lightweight self-hosted personal AI agent framework in Python, with a WebUI, terminal and chat-app channels, tools, long-term memory, MCP and scheduled automations. |
| Agno | ★ 42.2k | A fast Python framework (formerly Phidata) for building agents with memory, tools, and multimodal inputs, plus a runtime for deploying them in production. |
| LangGraph | ★ 41.9k | A library from the LangChain team for building stateful, graph-based agent workflows with explicit control over steps, memory, and human-in-the-loop checkpoints. |
| AgentGPT | ★ 36.3k | AgentGPT lets you name a custom AI, give it a goal, and watch it plan tasks, run them, and learn from the results, all from a web browser. |
| STORM | ★ 31.4k | Stanford OVAL's LLM-powered knowledge curation system that researches a topic and generates a Wikipedia-style article with citations. |