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Choose OpenClaw if you want a personal, always-on assistant that you reach through messaging apps and operate through your own gateway. Choose AutoGPT if you need visual, repeatable workflows connected to business software and launched on demand, on a schedule, or by triggers. Both can be self-hosted, but neither eliminates the work or cost of supplying infrastructure, maintaining the deployment, and paying for model access.
This guide explains how their architectures, setup requirements, integrations, licensing, privacy controls, and operating costs differ so you can select a realistic 2026 deployment rather than rely on a popularity list.
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OpenClaw and AutoGPT solve different problems
The names sound interchangeable because both products let you run AI agents under your control. Their primary interaction models are different, however. OpenClaw is organized around a gateway that connects conversations, tools, events, and AI agent harnesses. AutoGPT is organized around building and running agents that complete defined workflows.
OpenClaw: a self-hosted personal assistant and gateway
OpenClaw describes its Gateway as a local control plane for sessions, tools, events, and channel connections. Model and agent harnesses are installed as swappable plugins. In practical terms, you host one control point and connect the chat channels through which you want to delegate work.
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The project’s FAQ defines it as “an AI assistant you run on your own infrastructure – for yourself, or shared with your team.” That makes OpenClaw a natural fit for conversational delegation: send a request from a supported channel, let the gateway route it to the configured model and tools, and continue the conversation without opening a separate workflow editor.
OpenClaw’s repository says it has no paid tier or hosted service. You therefore choose the host, configure the channels and model provider, and handle updates and security yourself.
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AutoGPT: a platform for visual, outcome-oriented automation
AutoGPT’s official README presents a platform for building, deploying, and running agents that carry out complete workflows. Its surfaces include a plain-English AutoPilot, a visual Build interface, an agent library, and a marketplace. Agents can run on demand, on schedules, or when a trigger fires.
This model suits a team that wants to map a process into reusable blocks, connect services such as a calendar or CRM, and let an agent execute the same kind of outcome repeatedly. Conversation can still be part of a workflow, but the workflow canvas and its integrations are the organizing principle.
AutoGPT is available as a self-hosted platform and as a managed hosted service. The hosted option removes much of the infrastructure administration but is paid and uses usage-based agent runs.
At-a-glance comparison
| Decision area | OpenClaw | AutoGPT |
|---|---|---|
| Primary model | Personal assistant operated through a self-hosted gateway and messaging channels | Visual platform for building and running complete agent workflows |
| Typical invocation | Conversation in a connected channel; events and tools are handled by the gateway | Manual run, schedule, or trigger from an integrated service |
| Deployment choices | Documented installers for macOS, Linux, and Windows PowerShell/WSL2; package, Docker, and Nix paths | Self-hosted appliance operated with Docker on Linux containers, amd64 or arm64 |
| Model access | You configure a model provider; prompts are sent to the provider you select | You provide model API keys for self-hosting; the hosted service manages model access |
| Integrations emphasized | Channel plugins and tools, including Discord, Google Chat, iMessage, Matrix, Microsoft Teams, Signal, Slack, Telegram, WhatsApp, and Zalo | More than 45 connected platforms and hundreds of AI models, according to the current official README |
| Hosting economics | No paid tier or hosted service stated by the project; you pay for infrastructure and model usage | Self-hosting has infrastructure and model costs; a separate managed plan charges for usage-based runs |
| License information | Project documentation describes it as open source and says it is stewarded by the OpenClaw Foundation | autogpt_platform/ uses Polyform Shield; classic/ and other components use MIT |
| Best starting question | “Which channel should I message to delegate this?” | “Which repeatable outcome should this workflow deliver, and what should trigger it?” |
The table describes product emphasis, not a performance ranking. No cited apples-to-apples benchmark establishes that one is faster, more reliable, or more accurate than the other for every task.
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When OpenClaw is the better fit
You want an assistant that is always reachable
OpenClaw is designed for a gateway that stays available while you interact through chat services. That is useful when requests arrive irregularly: summarize a message, inspect a file, start a coding task, or ask the assistant to continue work from a previous conversation.
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The channel list in the project’s documentation includes Discord, Google Chat, iMessage, Matrix, Microsoft Teams, Signal, Slack, Telegram, WhatsApp, and Zalo. Availability still depends on the relevant plugin and your account configuration; a channel appearing in documentation does not mean it is enabled automatically on your host.
You prefer conversational delegation over a canvas
If your work changes from request to request and you do not want to model every variation as a workflow, the gateway approach reduces setup friction after the initial installation. You can keep the interaction in the channel your household or team already uses, while tools and model harnesses remain configured behind the gateway.
You need control over where the gateway runs
OpenClaw documents several installation routes rather than requiring one appliance format. You can use an installer on macOS or Linux, Windows PowerShell or WSL2, a package installation, Docker, or Nix. That range lets you match the deployment to a workstation, a small always-on server, a virtual private server, or network-attached storage that supports your chosen path.
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The repository states that OpenClaw has no paid tier or hosted service. That can simplify the purchasing decision, but it transfers responsibility to you: uptime, backups, access control, model-provider billing, plugin updates, and incident recovery are all your tasks.
When AutoGPT is the better fit
You are automating a defined business outcome
AutoGPT is built around agents that carry out complete workflows. A workflow can gather information, transform it, write to a business system, and notify a team. The visual Build surface helps you represent those steps and reuse the result instead of describing the process from scratch in a chat.
You need schedules and event triggers
AutoGPT supports runs on demand, on schedules, or from triggers. That matters for recurring work such as a daily report, a weekly data check, or a process that starts when a connected service emits an event. You can still invoke an agent manually while testing or handling an exception.
Your process spans many business tools
The official documentation lists more than 45 connected platforms, including Gmail, Google Calendar, Google Docs, Google Sheets, GitHub, Slack, Discord, Notion, HubSpot, Linear, Airtable, Jira, Salesforce, Stripe, and Webflow. It also advertises hundreds of AI models. The breadth is valuable when the workflow must move data between several services, although each connection still needs credentials, permissions, and its own failure handling.
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AutoGPT also sells a hosted platform. A hosted deployment manages infrastructure and model access for you, but it is not the self-hosted option and charges according to agent-run usage. Select it only if delegating operations is worth the recurring cost and the data-governance terms fit your requirements.
Self-hosting OpenClaw: what the setup involves
OpenClaw’s quick start provisions onboarding, a workspace, and the Gateway. The project documents more than one installation route, so choose the route that matches the machine you will keep available.
- Choose a host. Decide whether the gateway will run on your daily computer, an always-on mini PC, a VPS, or a NAS. Account for storage, network reachability, and who will administer the machine.
- Select an installation path. The documentation covers installers for macOS and Linux, Windows PowerShell and WSL2, package installation, Docker, and Nix. Use one method consistently so future updates and service management remain understandable.
- Run onboarding. The quick-start flow creates or selects a workspace and provisions the Gateway. Record the administrative account and any generated configuration securely.
- Connect the model provider. Supply the API credentials and model settings required by the provider you choose. Prompts leave your gateway for that provider unless you arrange a different model endpoint.
- Enable only the channels and plugins you need. Add messaging channels and tools one at a time, then test a short conversation before granting broader permissions.
- Harden the service. Restrict who can reach the gateway, protect credentials, and decide how logs and workspace data are backed up. An always-on assistant should not be exposed to the public internet without an access-control plan.
- Test recovery. Stop and restart the gateway, verify that sessions reconnect, and document how to restore the workspace and credentials if the host fails.
OpenClaw offers flexibility, but flexibility also creates more configuration choices. Treat the gateway as a service you operate, not as a desktop application that can be installed once and forgotten.
Self-hosting AutoGPT: Docker is the central requirement
AutoGPT’s official self-hosting guide describes an appliance that runs with a Docker CLI and daemon using Linux containers. Supported host architectures are amd64 and arm64. You supply the configuration and maintain updates.
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- Prepare a compatible machine. Install a current Docker CLI and ensure the Docker daemon is running in Linux-container mode. Confirm that the host is amd64 or arm64 and has enough disk, memory, and network capacity for the agents and services you plan to run.
- Obtain the appliance and configuration. Follow the official self-hosting instructions for the current release rather than copying an old compose file. Keep configuration files and secrets outside public repositories.
- Provide model credentials. Self-hosting requires model API keys supplied by you. Use a dedicated account or project where possible, set provider spending limits, and rotate keys if they are exposed.
- Start the containers and open the platform. Verify that the Docker services become healthy before building an agent. If a container exits, inspect its logs for missing variables, unavailable ports, permissions, or an unreachable dependency.
- Build a small workflow first. Use the Build surface to connect one trigger, one or two actions, and a visible result. Test with non-sensitive data before granting write access to production systems.
- Add integrations and schedules gradually. Authorize only the platforms required by the workflow, then test manual runs before enabling a recurring schedule or external trigger.
- Plan updates and rollback. The operator is responsible for maintenance. Back up configuration and workflow definitions, read release notes, and keep a known-good image or configuration so a failed update can be reversed.
Docker makes AutoGPT’s deployment more standardized than a collection of channel-specific installers, but it adds container administration. If nobody on your team is comfortable maintaining Docker, the hosted platform may be operationally simpler even though it is not free.
Model access, privacy, and the real cost of “free”
Both self-hosted options still need a model provider
Running software on your own computer does not run a capable language model for free. OpenClaw sends prompts to the model provider and chat platforms you configure. AutoGPT’s self-hosted documentation likewise requires you to provide model API keys. In both cases, inference charges depend on the provider, model, context size, and workload.
Infrastructure is an ongoing cost
Budget for the host, storage, backups, electricity or virtual-machine fees, network access, monitoring, and your time. A compact Mac mini or equivalent Docker-capable mini PC is a reasonable category for an always-on deployment, but available evidence does not establish a specific current model, RAM amount, or workload capacity. Choose hardware only after estimating the number of concurrent sessions, container services, retained files, and expected model traffic.
Privacy depends on the route your data takes
OpenClaw’s documentation says prompts go to the configured model provider and connected chat platforms. Its optional anonymous feature statistics are off by default. AutoGPT self-hosting lets you operate the infrastructure, but any external model or integration you authorize can still receive the data needed to perform a task. Review provider retention, integration scopes, logs, and backup locations before sending confidential material.
Hosted AutoGPT changes the cost and control balance
AutoGPT’s managed service handles infrastructure and model access, with usage-based agent-run charges. It can reduce maintenance work, but you give up some deployment control and must evaluate the service’s data handling and pricing for your workload. OpenClaw does not offer an equivalent hosted tier according to its repository.
Integrations: channels versus workflow connectors
OpenClaw’s strength is the conversation surface
OpenClaw’s channel plugins let people reach the assistant from familiar messaging systems. The gateway can coordinate sessions, tools, events, and agent harnesses behind those channels. This is well suited to personal automation and team delegation where the input is naturally a message and the response can remain conversational.
AutoGPT’s strength is process coverage
AutoGPT’s documented platform connections cover communication, documents, project management, customer systems, payments, development, and publishing. A workflow can therefore start in one system and finish in another without requiring a person to copy information between them. The trade-off is administration: every connector needs authorization, field mapping, error handling, and periodic verification when the external service changes.
Do not interpret “45-plus connected platforms” as 45 identical capabilities. Check whether the connector supports the exact read, write, search, file, or trigger operation your process needs, and whether your plan or self-hosted release includes it.
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OpenClaw governance
OpenClaw’s documentation describes the project as open source and says it is stewarded by the OpenClaw Foundation. Before deploying it for a team, identify the repository license for the version you will use, the contribution and release process, and who receives security reports.
AutoGPT’s split licensing
AutoGPT does not use one license for every directory. Its license file places autogpt_platform/ under Polyform Shield, which permits personal and internal business use but restricts selling it as a competing hosted service. The classic/ directory and other components use MIT. If you plan to modify, redistribute, embed, or offer the platform commercially, have counsel review the exact files and version involved.
Operational governance applies to both
- Assign an owner for model keys, integration credentials, updates, and backups.
- Use least-privilege scopes for chat, documents, source control, CRM, and payment systems.
- Keep audit logs long enough to investigate an incorrect or unauthorized agent action.
- Separate development workflows from production credentials and schedules.
- Define a human approval step for irreversible actions such as sending external messages, deleting records, or charging a payment method.
Common failure modes and practical fixes
The agent replies but cannot complete an action
Check whether the required plugin or connector is installed and authorized, not merely whether the model can describe the action. Confirm that the credential has the necessary scope and that the target account is the one you intended to connect.
A self-hosted service is unreachable
For OpenClaw, verify that the Gateway process is running, the host firewall permits the intended channel connection, and the channel plugin is enabled. For AutoGPT, check Docker daemon status, container health, exposed ports, and Linux-container mode before investigating the workflow itself.
Runs consume more money than expected
Set provider spending limits, select an appropriate model, cap retries where the platform allows it, and test with small inputs. Schedules and triggers can multiply usage silently, so review every recurring run and disable unused automations.
A workflow works manually but fails on a schedule
Scheduled runs often use a different environment from an interactive session. Verify that credentials are available to the runtime, file paths are persistent, tokens have not expired, and the schedule’s timezone matches the team’s expectation. Run the smallest scheduled test before enabling a high-frequency trigger.
An update breaks an existing deployment
Restore the last known-good image or package, then compare configuration changes and release notes. Keep backups of OpenClaw workspaces and AutoGPT workflow definitions, and avoid updating production immediately after a release without a rollback path.
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Pick OpenClaw when most answers are “yes”
- Your main interface should be a messaging app.
- You want one gateway for personal or small-team delegation.
- You value multiple installation routes and control over the host.
- Your tasks are variable conversations rather than fixed multi-step processes.
- You accept responsibility for model keys, updates, uptime, and security.
Pick AutoGPT when most answers are “yes”
- You can describe the desired result as a repeatable workflow.
- You need visual building blocks, schedules, or event triggers.
- Your process crosses several business platforms.
- You want an agent library or reusable workflow components.
- You can operate Docker on amd64 or arm64, or you prefer its managed hosted alternative.
Run a short proof of concept before committing
- Write down one measurable outcome, such as producing a report and saving it to a specified location.
- List every system, credential, trigger, approval, and data type involved.
- Test OpenClaw through the channel where users would actually send requests.
- Test AutoGPT with a small visual workflow and the same data restrictions.
- Record setup effort, failure recovery, model usage, and human review requirements. Do not treat this as a benchmark of the products; it is a fit test for your environment.
What the available evidence does—and does not—show
AutoGPT’s current official README advertises more than 45 connected platforms and hundreds of AI models. Its GitHub repository displayed approximately 187,000 stars when accessed in 2026; that number is a volatile snapshot of repository activity, not a measure of quality, reliability, or support.
OpenClaw’s documentation provides the clearest description of a self-hosted assistant and gateway, while AutoGPT’s documentation provides the clearest description of a workflow platform with both self-hosted and managed routes. No controlled comparative task-success test, independently verified setup-time study, or published head-to-head reliability figure establishes a universal winner.
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