Buying snapshot
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| Decision point | What AutoGPT states |
|---|---|
Deployment | Hosted AutoGPT Platform with public signup, or self-hosted on your own infrastructure. Both paths use the same repository. |
Who runs it | Hosted: AutoGPT manages setup, updates and operations. Self-hosted: Docker and configuration required, and you maintain the deployment. |
Model access | Built in on the hosted Platform; bring your own API keys when self-hosting. Model families include Anthropic, OpenAI, Google, xAI, Meta, Mistral and Cohere. |
License | autogpt_platform/ is under Polyform Shield: free for personal and internal business use, but it cannot be sold as a competing hosted service. classic/ and everything else is MIT. |
Original agent | AutoGPT Classic, the original standalone agent, remains available under the MIT License, with Forge for building an agent and agbenchmark for benchmarking one. |
Support | Hosted Platform: plan-dependent. Self-hosted: community support. |
Our take
Choose the hosted Platform if you want agents working on recurring jobs right away and would rather not run models or servers yourself. Self-host when control over your own infrastructure matters more than convenience, and your use fits the platform license.
Evaluate AutoGPT
Comparisons
Where it stands out
Start with a conversation. AutoGPT says no workflow canvas is required: you brief Otto or a ready-made Expert in plain English. The visual builder is still there when you want to define the steps yourself.
Spending guardrails. You can set a spending approval threshold for each Expert, cap LLM spend on any task with
max_budget_usd, and see the cost of every task and every block.Human approval gates. You can place approval gates anywhere in a workflow, including nested workflows; execution pauses for human review, then resumes.
Broad integrations. The pricing page lists 200+ integration blocks, including Slack, Gmail, GitHub, HubSpot, Notion, and Pinecone and Mem0 for memory. You can also import automations from n8n, Make.com and Zapier.
Schedules, triggers and an API. Workflows run on a cron schedule with timezone awareness, from a generic webhook or from GitHub events. A public REST API can create workflows, and SDK clients are generated from its OpenAPI spec.
Product screenshot
Trade-offs to weigh
Team features are still coming. Multi-user workspaces, admin controls and roles, and seat management are listed for the Team plan, which is marked coming soon. Pro is described as "for individuals ready to put an AI team to work".
Self-hosting gives up managed services. AutoGPT marks hosted billing, managed credentials, hosted email delivery and publishing to its marketplace as services it runs for you. A Linux and macOS single-container installer is coming with the next appliance release; until then, and on Windows, you follow the manual self-hosting guide.
Output still needs review. AutoGPT says AI output still needs your judgment. A scheduled Expert may need details, connected services and your instructions before it can run.
Pricing and plan conditions
The hosted subscription covers Otto and Expert chat. Workflow runs are pay-as-you-go from a pre-paid credit wallet on every plan, with on-demand top-up and auto-refill with configurable minimums, so you pay only for the runs you use. Hiring an Expert has no separate charge.
Eligible new accounts can try Pro free for 7 days. A card is required, trial usage limits apply, and canceling ends trial access; eligibility and exact offer terms are confirmed at checkout.
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| Plan | Published price | What it adds |
|---|---|---|
Pro | $50.00/month billed monthly, or $510/year on annual billing (equivalent to $42.50/month) | Access to virtually any leading AI model; AI experts for research, content and operations; Otto chat and the visual workflow builder; your documents and connected apps; expert activity, results and spending controls; scheduled workflows and recurring routines |
Max | $320/month, billed monthly | Everything in Pro, plus 8.5x Pro usage (Otto Chat allowance 8.5x Pro's), 5x file storage, early access to features, priority support, and help driving the roadmap for new features |
Team | Coming soon; contact sales | Collaboration and approvals, centralized billing, security and compliance options, secure access for shared integrations, and custom usage |
Self-hosted | No license fee | You pay your own infrastructure and model providers; Otto Chat allowance is unlimited with your own LLM keys |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
1.2k GitHub commits 90d187.8k GitHub stars
See all signals from 5 sources
Alternatives to AutoGPT
The reviewed substitutes for AutoGPT among the agent frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- LangGraph
- An autonomous agent that works through a task step by step can be built with LangGraph or with AutoGPT. LangGraph gives developers low-level control over agent state, cycles and approvals, while AutoGPT is an open-source project geared toward ready-made assistants that automate digital workflows with less custom code.Applies to: Choosing between these two for the ai agent frameworks decision.
- Agno
- AutoGPT and Agno help teams create AI agents that carry out multi-step tasks on their own. AutoGPT is an open-source project aimed at assistants that automate everyday digital workflows, while Agno is a developer framework for multi-agent systems with memory, knowledge and guardrails, plus the AgentOS runtime for production.Applies to: Choosing between these two for the ai agent frameworks decision.
- LangChain
- Both LangChain and AutoGPT are open-source projects for making LLM-driven agents that work toward a goal. LangChain is a developer toolkit of components, chains and integrations with a platform for testing and deployment, while AutoGPT focuses on ready-to-run autonomous assistants that streamline a user's digital workflow.Applies to: Choosing between these two for the ai agent frameworks decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- AutoGen
- Microsoft's framework for building multi-agent conversational AI systems with customizable and composable agents.
- CrewAI
- AutoGPT is used rather than CrewAI for digital workflow automation workloads built around intelligent assistants and hosted automation credits. **AutoGPT** focuses on intelligent assistants that streamline digital workflows, with a self-hosted open-source option and hosted cloud plans.
- Haystack
- For a team setting up autonomous AI agents, AutoGPT and Haystack are both open-source starting points. AutoGPT is known for agents that pursue a goal step by step and for assistants that automate digital workflows, while Haystack gives engineers modular building blocks for production agents and retrieval-augmented generation.Applies to: When the goal is an agent that works through a multi-step task on its own.
- MetaGPT
- Both build applications where a model calls tools and takes several steps, from different starting points: framework breadth, explicit orchestration, role decomposition or an SDK inside an existing application. Teams pick one to build on, and the choice follows the shape of the workflow rather than a feature count.Applies to: Choosing the framework an agent or multi-step LLM application will be built on.
- Semantic Kernel
- Microsoft's open-source SDK for integrating LLMs into applications with AI agents, planners, and plugin architecture.Applies to: where a hosted automation product is the preferred operating model.