AutoGPT
The 2023 autonomous-agent project, rebuilt as a block-based visual builder with a hosted twin
What is AutoGPT?
An agent here is a graph of blocks — one block calls a service, transforms data, prompts a model or branches — assembled on a canvas rather than written in code, then run on demand, on a schedule or from a trigger. One repository holds both the free self-hosted platform and the code behind the paid hosted one, and that is the catch: everything under the platform directory is PolyForm Shield, which permits use but forbids offering it as a competing product.
What can you do with AutoGPT?
- Assemble an agent on a canvas — The builder is a drag-and-drop surface: you place blocks for the actions you need — an API call, a data transform, a model prompt, a conditional — and the wiring between them decides what runs when, with no code required to reach a working agent.
- Run it without watching it — An agent runs on demand, on a schedule, or from an external trigger. That is the substantive break from the original 2023 loop, which only made progress while you sat in front of it.
- Start from an agent someone else built — A marketplace of pre-built agents can be installed into your own library and then edited, so the first version rarely has to start from an empty canvas.
- Self-host with your own model keys — An installer script brings the platform up on your own infrastructure; you supply the model provider keys and operate the deployment, and no licence fee is charged for that path.
- The original 2023 agent is kept, not continued — The autonomous loop that made the name is preserved in the classic directory under MIT, but development moved to the platform, so it is there as history rather than as the recommended starting point.
Before you choose AutoGPT
- Everything under the platform directory — the builder and server that make up the current product — is PolyForm Shield rather than open source: it allows internal use but forbids offering it as a competing service.
- Anyone arriving from the 2023 autonomous-agent fame meets a different product; the loop that earned the name now sits in the classic directory and is no longer where the work happens.
Star history
21 Aug to 28 Aug · +244
Frequently asked questions
Is AutoGPT free for commercial use?
AutoGPT is published under MIT / PolyForm Shield 1.0.0, a source-available licence rather than OSI-approved open source — read its terms before relying on it commercially.
How can AutoGPT be deployed?
AutoGPT is available as Self-hosted / Managed cloud.
Documentation
Reproduced from the Significant-Gravitas/AutoGPT README, published under UNKNOWN — read the LICENSE file. Read the original ↗
The open-source platform for AI agents
AutoGPT lets you build, deploy, and run AI agents that carry out complete workflows. Describe an outcome in plain English or shape every step in the visual builder, then run the agent on demand, on a schedule, or from a trigger.
185,000+ GitHub stars. Cited by:
| “Next frontier of prompt engineering imo: ‘AutoGPTs’.” | Andrej Karpathy, founding member of OpenAI |
| “If you have a phone you can run AutoGPT. You don’t even need to learn how to code.” | Amjad Masad, co-founder & CEO of Replit |
| “AutoGPT might be the next big step in AI.” | Lior Alexander, CEO of AlphaSignal |
Four surfaces, one platform
Get started
AutoGPT Platform — public, hosted, and managed
The hosted Platform is publicly available. We manage the infrastructure, model access, credentials, reliability, and updates so you can focus on the work your agents perform.
Get started on AutoGPT Platform →
- AutoPilot, Agents, Marketplace, and Build
- 45+ connected platforms and hundreds of AI models
- No model API keys or infrastructure setup
- Agents that run on demand, on schedules, and from triggers
The hosted Platform is a paid service with usage-based agent runs. Compare plans and pricing →
Self-host AutoGPT
[!NOTE] Self-hosting is the free path. You provide the infrastructure and model API keys, and you maintain the deployment. If you want zero setup, use the managed Platform.
macOS and Linux:
curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.shWindows PowerShell:
powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"Managed Platform vs. self-hosting
| AutoGPT Platform | Self-hosted | |
|---|---|---|
| Access | Public signup | Clone and install |
| Cost | Paid plan plus agent usage | No license fee; pay your own infrastructure and model providers |
| Setup | Managed | Docker and configuration required |
| Model access | Built in | Bring your own API keys |
| Updates and operations | Managed by AutoGPT | Managed by you |
| Core builder and agent runtime | Included | Included |
| Data and infrastructure control | Hosted by AutoGPT | Runs on your infrastructure |
| Support | Plan-dependent | Community support |
Both paths use the same repository. Choose the managed Platform when you want agents running immediately; self-host when infrastructure control matters more than operational convenience.
Why the hosted Platform is paid
Every agent run consumes real model usage, compute, storage, secrets management, and operational support. The managed Platform covers that infrastructure and funds continued development of the open-source project.
Self-hosting remains available without a license fee for people and teams that want to provide and operate those resources themselves.
What you can automate
| Area | Example |
|---|---|
| Executive operations | Prepare a daily brief from internal and external signals |
| Sales | Research every account before tomorrow’s meetings |
| Marketing | Turn a launch brief into campaign drafts across channels |
| Engineering | Triage incidents and start with a likely cause |
| Customer support | Draft replies, collect context, and flag escalations |
| Research | Monitor sources and return structured reports when something changes |
Integrations
Connect the apps that are yours. AutoGPT provides access to hundreds of AI models and connects agents to 45+ platforms, including:
Gmail · Google Calendar · Google Docs · Google Sheets · GitHub · Slack · Discord · Notion · HubSpot · Linear · Airtable · Jira · Salesforce · Stripe · Webflow
Community and support
| Resource | Link |
|---|---|
| Discord | Join the AutoGPT community |
| Documentation | docs.agpt.co |
| Bug reports | GitHub Issues |
| Feature requests | GitHub Discussions |
| Contributing | CONTRIBUTING.md |
AutoGPT Classic
Looking for the original standalone AutoGPT agent? It remains available in classic/ under the MIT License.
Other options in Workflow & Low-code
hermes-agentTools & IntegrationsHermes Agent is Nous Research's own agent: a terminal UI plus a single gateway process that carries the same conversation into Telegram, Discord, Slack, WhatsApp, Signal and Email, with a cron scheduler, isolated subagents, and seven terminal backends from local and Docker to Modal and Vercel Sandbox. Its distinguishing piece is a closed learning loop — the agent curates its own memory on periodic nudges, creates skills after complex tasks, and searches past sessions through FTS5 with LLM summarization — which means the state that makes it useful accumulates under `~/.hermes`, outside your version control, and needs occasional pruning. It is an assistant you run and configure rather than a library you build on: the README documents `hermes` subcommands and slash commands, not an embedding API, so it is a poor fit if you wanted an agent loop to call from your own code. Putting a shell-capable agent behind chat platforms also makes the command-approval and DM-pairing settings load-bearing rather than optional.
n8nWorkflowConnects hundreds of services as workflows you draw on screen — each step in the flow is a node — and increasingly puts an AI agent node inside those workflows. The licence is the thing to check first: it is source-available under the Sustainable Use License, not OSI-approved open source. Internal use and self-hosting are fine; reselling it as a hosted service is not.
LangflowWorkflowUseful for sketching an agent visually and for showing non-engineers what a pipeline actually does, then exporting it as an API. Visual editors get unwieldy as branching grows, so treat it as a design and demo surface rather than the final home for complex logic.