Browser-based autonomous AI agents for any task.
Agents
AgentGPT is the browser-based descendant of the "give an autonomous agent a goal and watch it work" idea that AutoGPT introduced in 2023. Built by Reworkd, it landed a few weeks after AutoGPT and captured the audience that wanted the same experience without cloning a repository or running Python. You open agentgpt.reworkd.ai, type a name and a goal — "Research the top three CRM tools for early-stage SaaS teams" — and the agent plans its subtasks, executes them via LLM calls and web queries, and streams the reasoning back to you in real time.
The 2026 status is honest: AgentGPT is still online, still open-source on GitHub, still free at the entry tier, and still useful for quick exploratory agent runs. What it is not is a serious platform for building deployable agents. That was never really its bet. The bet was a zero-setup autonomous-agent playground that anyone with a browser could try, and on that bet it delivered — millions of runs, hundreds of thousands of GitHub stars, an active community, and a Reworkd product line that has since expanded beyond the original agent.
The distinctive positioning versus AutoGPT Platform, LangChain, and Dify is: AgentGPT is the fastest way to try the autonomous-agent experience, not the fastest way to ship one. Sign up, type a goal, watch the agent think. No infrastructure, no code, no wiring. The tradeoff is that everything past that initial experience — building complex agents, wiring custom tools, deploying to production — is thinner than the alternatives in this comparison.
The project is open-source (GPL-3.0), which matters if you want to self-host — you get the full agent runtime and web UI to run locally with your own API keys and no message limits.
Zero-Setup Agent Runs. The core value. Type a name, type a goal, hit deploy. The agent plans subtasks, executes them, and returns a result. This is what "autonomous agent" felt like the first time you tried it, packaged as a hosted webapp.
Task Planning and Execution. The agent decomposes the goal into subtasks in real time — you watch each new task appear, get executed by an LLM call or web search, and produce output. Every subtask shows its reasoning trace so you can see why the agent went where it went.
Web Search. Built-in web search means the agent can pull in external context — recent information, real facts, up-to-date sources. Combined with the LLM, this is what makes the agent's outputs feel useful for research-style goals.
BYO OpenAI Key. On the free tier, you use Reworkd's daily-limited runs; on any serious use, you plug in your own OpenAI API key and pay OpenAI directly for tokens. This unlocks the higher-quality models and removes the daily cap.
Self-Hostable. GPL-3.0 on GitHub. Docker Compose runs the full stack — Next.js frontend, agent runtime, database — on any machine. Bring your own OpenAI key, run unlimited agents.
Simple Preset Modes. Preset agent "modes" for different task types — research, brainstorm, plan, list generation. Each mode tweaks the underlying prompt structure to favor a certain shape of output. Not deep, but useful for quick jobs.
Save and Share Runs. Signed-in users can save agent runs, share URLs, and revisit later. Useful for research work you want to reference or share with a teammate.
Multi-Language Support. Localized UI in a handful of languages; the underlying LLM handles multilingual input/output as usual.
Web Interface First. No CLI, no SDK. Everything happens in the browser. That's a feature for the target user; it's a limitation for anyone wanting to embed the agent in a larger app.
AgentGPT's pricing story is simple: mostly free, self-host-friendly.
Free (Anonymous) — A small number of agent runs per day, using a shared underlying model tier. Enough to try the experience but not enough for real work.
Free (Signed in with BYO Key) — Sign in with an account and plug in your own OpenAI key. You pay OpenAI for tokens directly; AgentGPT's platform layer is free.
Pro (~$40/mo) — Reworkd's paid tier for AgentGPT. Higher run limits without a personal OpenAI key, priority model access, and additional tool integrations. Pricing has moved a few times since launch and the current sticker is on the site; the tier is aimed at hobbyists and casual users, not enterprises.
Self-Hosted (Free, GPL-3.0) — Clone the GitHub repo, run the Docker Compose stack, bring your OpenAI key. No message limits, no seat limits, your own infrastructure.
Reworkd Broader Products — Reworkd has expanded into other agent-related products (extraction agents, scrapers) with their own pricing. AgentGPT-the-product remains at the personal-scale pricing described above.
| Feature | Free (Anon) | Free (BYO Key) | Pro | Self-Host |
|---|---|---|---|---|
| Daily runs | Very limited | Your OpenAI cap | Higher | Unlimited |
| Model choice | Basic | Any OpenAI | Any OpenAI + priority | Any OpenAI |
| Save runs | No | Yes | Yes | Yes |
| Priority infra | No | No | Yes | Your infra |
| Starting price | $0 | $0 + OpenAI | ~$40/mo | $0 + infra + OpenAI |
The economically honest read: use the BYO-key path for anything beyond a first try, or self-host if you want to run without limits. The Pro tier is convenient but not essential; most users either use BYO-key on the hosted app or run Docker locally.
Pros.
Cons.
Curious first-time agent users. The archetype. Someone hears about "autonomous AI agents" and wants to see what they actually do. AgentGPT is the demo you point them at. Under a minute to a live run, output that captures the pattern honestly.
Quick exploratory research runs. Non-critical, hobby-scale research tasks — "what are the top three tools for X," "summarize the trade-offs of Y," "brainstorm marketing angles for Z." Fast, cheap with your own key, disposable output.
Educators demonstrating agent behavior. The visible task tree is a good teaching aid. Students can watch how the agent decomposes a problem, where it goes wrong, where it recovers, where it loops. Better classroom demo than most alternatives.
Privacy-first hobbyists via self-host. The GPL-3.0 codebase plus Docker deployment means you can run the whole stack in your homelab with your own OpenAI key or a local Ollama backend, no data touching Reworkd's servers.
Users who want a polished autonomous-agent UI without infrastructure. If AutoGPT Platform feels too much like software and Dify feels too much like enterprise, AgentGPT sits in the "just let me type a goal" niche, and it does that well.
Bad fit. Anyone building a product with an embedded agent — use LangChain, Dify, or AutoGPT Platform. Anyone needing custom tools or workflows — same. Anyone with a serious multi-agent problem — use CrewAI. Anyone with an enterprise compliance requirement — this isn't the product.
AutoGPT Platform. The other open-source autonomous-agent project, more mature as a builder. Visual block editor, scheduler, marketplace, active development. If you want to build agents rather than just run canned ones, AutoGPT is the closer analog.
Dify. The polished no-code agent and workflow platform. Not autonomous-agent-first, but faster to a real product. If your goal is "ship something to users," Dify is a better landing spot than AgentGPT.
LangChain / LangGraph. The code-first framework. No overlap with AgentGPT's zero-setup positioning, but the mature answer if you want to build actual agent products. LangChain tool page, full head-to-head in our agent framework showdown.
CrewAI. The multi-agent-by-role framework. Different pattern from AgentGPT's single-agent-plans-and-executes model. Worth evaluating if your task decomposes into distinct roles. See our CrewAI tool page.
GPT Researcher (open-source). Community project focused specifically on autonomous research agents. More specialized, better at long-form research reports than AgentGPT's general-purpose loop.
Honorable mentions: HuggingGPT-style agent experiments (research code, not products), MultiOn (browser-automation agent, different niche), and Cognosys (another browser-based autonomous agent tool with a similar zero-setup pitch).
Getting an AgentGPT run to complete takes about two minutes.
Step 1: Try the Hosted Version. Go to agentgpt.reworkd.ai. You can start without signing in — enter a name for your agent, enter a goal, hit Deploy. Watch the task tree fill in.
Step 2: Add Your OpenAI Key. For anything beyond the demo, sign in and add your OpenAI key in settings. This unlocks GPT-4-class models, removes the daily cap, and shifts your economics onto pay-as-you-go with OpenAI directly.
Step 3: Pick a Mode. The preset modes — research, brainstorm, plan — shape the prompt. Match the mode to the shape of output you want.
Step 4: Refine the Goal. The single biggest quality lever. A vague goal produces vague output. "Research CRM tools" is bad. "Compare Salesforce, HubSpot, and Pipedrive on pricing, ease of setup, and API quality for a 20-person SaaS team" is dramatically better. Treat goal-writing as prompt engineering, because that's what it is.
Step 5: Self-Host if You Want More. Clone the GitHub repo from Reworkd, run Docker Compose, and you have the full stack locally. Point it at your own OpenAI key or a local Ollama endpoint. No caps, full control, your data doesn't leave.
Practical tip: don't expect the output to be publication-ready. Treat AgentGPT as a first-draft generator that gets you 60% of the way there in five minutes. The last 40% is human work, and no autonomous agent in 2026 will do it well without significant scaffolding.
Is AgentGPT still active? Yes. The product is still online and the GitHub repository is maintained, though Reworkd's development focus has broadened to other agent-related products. Expect stable service, not aggressive new features.
Is it really open source? Yes, GPL-3.0 licensed on GitHub. You can self-host the full stack and modify it. GPL-3.0 is copyleft — commercial redistribution has license implications, but personal and internal use is unrestricted.
How does AgentGPT compare to AutoGPT? AgentGPT is a browser-based user-facing product with a polished UI; AutoGPT is a more serious platform for building custom agents. AgentGPT for demoing and trying; AutoGPT Platform for actually building.
Can I build custom tools for it? Only if you self-host and modify the source. There is no public plugin API for the hosted version. Custom-tool development means going into the codebase.
Which LLM does it use? OpenAI models by default. On self-host, you can point it at any OpenAI-compatible endpoint, including local Ollama for privacy.
Do I have to pay Reworkd? No. The BYO-key path uses the free hosted UI with your own OpenAI key. Self-hosting is entirely free. Pro is a convenience tier for people who don't want to manage their own key.
Is my data private? On the hosted version, your agent goals and outputs are stored by Reworkd. Read the privacy policy. On self-host, nothing leaves your infrastructure except the OpenAI API calls.
Can I embed it in my product? Not officially. There is no public API for the hosted version. If you need programmatic access, self-host and call the internal endpoints, understanding they're unstable and undocumented.
AgentGPT is the correct choice for one specific job: showing yourself, a student, or a curious friend what an autonomous AI agent actually feels like, without setting up any infrastructure. On that job it succeeds better than any of the more serious alternatives — the UI is polished, the task tree is legible, the zero-setup experience is unmatched. As a first-touch demo of the autonomous-agent pattern, it deserves its place.
For nearly every other use case, one of the alternatives is a better fit. If you want to build custom agents, use AutoGPT Platform for the open-source visual path, LangChain for the code-first path, or Dify for the fast-to-production hosted path. If your task genuinely maps onto multiple roles cooperating, use CrewAI. If you want a comparison across the whole category, our Dify vs LangChain vs CrewAI showdown covers it.
Start on the free hosted version to see the experience. If you like it and want to push further, either add your OpenAI key on the hosted app or self-host from the GitHub repo. Don't pay for Pro unless you specifically want the convenience — the BYO-key and self-host paths are strictly better economically. And don't mistake this for the platform you'll build a product on. It's the demo that inspires the product, and the actual product will live in one of the frameworks with a deeper builder story.