How to Set Up OpenClaw for Multiple Users on a Team

GitHub issue #8081 asked for built-in multi-user permission management with role-based access control. The maintainers closed it as "not planned." Yet 65% of OpenClaw users operate in enterprise sectors, according to a 2026 analysis by FatJoe, where multiple people need access to the same agent infrastructure. The gap between single-operator design and team reality is where this guide lives.

OpenClaw's security model is explicit about its assumptions. The official documentation states that auth profiles are per-agent, each stored in a dedicated directory under the agent's own state folder. The Gateway trusts whoever has the auth token. There is no concept of "users" with different permission levels inside a single Gateway.

That does not mean teams cannot use OpenClaw. It means the team architecture has to be intentional. Instead of adding users to a shared instance, you create one agent per team member (or per role) and use OpenClaw's binding system to route each person's messages to their own isolated agent. Each agent gets a separate workspace, separate credentials, and separate session history. The Gateway stays single-operator, but each operator's scope is contained.

Teams deploying OpenClaw for multiple people need to address four areas: credential isolation so one person's API keys stay separate from another's, network access so the Gateway is not exposed without authentication, audit visibility so you can trace which agent took which action, and output management so the work each agent produces can be collected and shared. The rest of this guide walks through each one.

Most existing guides about OpenClaw teams conflate two different problems. Multi-agent means running multiple AI instances inside one Gateway, each with its own persona, model configuration, and skill set. Multi-user means multiple human beings interacting with those agents, each expecting isolated sessions, private credentials, and controlled access. The first is an OpenClaw feature. The second is an architecture pattern you build on top of it.

OpenClaw's multi-agent documentation defines an agent as the full per-persona scope: a workspace directory with configuration files, a state directory for authentication and model settings, and a separate session store for chat history. The documentation warns against reusing directories across agents because it causes auth and session collisions.

For a team, this isolation is the foundation. When your marketing lead and your engineering manager both need to interact with an OpenClaw-powered agent, you do not give them the same agent with different permissions. You create two agents, each with a workspace tuned to that person's context, credentials scoped to that person's tools, and its own session history. The Gateway routes incoming messages to the right agent based on bindings, which match channel accounts and peer identifiers to specific agent IDs.

This pattern has a practical benefit beyond security. Each team member's agent can run a different model, a different skill set, and a different persona. The marketing lead's agent can run a model optimized for writing with content-creation skills enabled. The engineering manager's agent can run a reasoning-focused model with code execution tools. Same Gateway, completely different agent configurations.

Start with the OpenClaw guided setup if this is a fresh installation. The onboarding process walks through model selection, workspace creation, Gateway settings, and your first channel integration. Once the default agent is running, use the agent management tools to add a new agent for each team member.

Adding an agent creates an isolated directory with its own workspace, state, and session storage. Each agent's workspace is where you place persona files, skill configurations, and context documents that define what the agent knows and how it behaves. After adding agents, verify that each one appears with its expected binding configuration, then restart the Gateway to pick up the changes. Consult the OpenClaw multi-agent documentation for exact commands and configuration syntax, since these can change between releases.

Collect every agent's output in one shared workspace

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With agents created, the next step is telling the Gateway which messages go to which agent. OpenClaw's binding system matches incoming messages to agents through a priority hierarchy documented in the multi-agent routing guide. The system checks seven levels in order: exact peer match (a specific DM or group ID), parent peer match (thread inheritance), guild or team plus roles (for Discord and Slack), guild or team ID alone, account ID match, channel-level wildcard match, and finally the default agent fallback.

For most teams, the simplest approach is binding each team member's messaging account to their corresponding agent. If your team uses Telegram, each person's Telegram user ID maps to a specific agent. If you use Discord, you can bind by guild roles so that everyone with the "engineering" role routes to the engineering agent and everyone with the "marketing" role routes to the marketing agent.

Platforms that support multiple accounts (WhatsApp, Telegram, Discord, Signal) use accountId to identify each login. One Gateway can host multiple phone numbers or bot accounts without session mixing, which means you can give each team member their own dedicated channel into the system.

Isolated agents produce isolated output. Each agent writes files to its own workspace, stores session history in its own directory, and has no visibility into what the other agents have produced. For a team, this creates a collection problem: how do you gather the work from five separate agents into one place where the whole team can see it?

Local file storage works for a single operator but breaks down for teams. You could use rsync or a shared network drive, but neither gives you version history, access controls, or the ability for agents to query each other's output. Cloud storage services like Google Drive, Dropbox, or S3 solve the collection problem but add manual steps: someone has to move files from each agent's workspace to the shared location.

Fast.io eliminates that manual step. Each agent connects to the same Fast.io workspace through the MCP server, which exposes 19 consolidated tools for file operations, workspace management, and AI queries over Streamable HTTP at /mcp. When an agent finishes a task, it uploads the output directly to a shared workspace. The rest of the team sees it immediately, with version history and audit trails built in.

The practical setup is straightforward. Create a shared workspace on Fast.io, give each agent MCP access with its own API credentials, and configure upload destinations by workspace path. Engineering output goes to one folder, marketing output to another, and both teams can search across everything using Intelligence Mode, which auto-indexes uploaded files for semantic search and RAG queries with citations.

For teams that want an agent to build something and then hand it to a human, ownership transfer lets the agent create workspaces, populate them with files, and transfer ownership to a team member while retaining admin access for future updates. The human gets a polished, shareable workspace. The agent retains the ability to update it. Both sides work from the same source of truth.

The free tier includes 50GB of storage, 5,000 AI credits per month, and 5 workspaces with no credit card or trial expiration. For a small team running OpenClaw, that covers the output storage layer without adding cost to the stack.