OpenClaw Multi-Agent Configuration

OpenClaw's Agent Model: Sessions vs True Parallel Agents

A session is conversation state routed from a DM, group, channel, cron job, webhook, or sub-agent run. Multiple sessions can belong to one agent and share that agent's workspace and auth profiles. Session isolation therefore separates context without necessarily separating files or credentials.

An OpenClaw agent is a larger boundary. Each configured agent can have its own workspace, agentDir, model registry, auth profiles, and session store. Current OpenClaw also supports sub-agents that run in parallel background sessions. The useful distinction is not “fake versus true” agents; it is session isolation, agent-state isolation, process/runtime isolation, and sandbox isolation.

Spawning Subagents: Configuration and Constraints

Sub-agents start through sessions_spawn. They use an isolated context by default unless the caller explicitly forks the current transcript. Each child has its own context and token usage, announces completion back to the requester, and operates under a restricted tool surface.

Configure a less expensive model for repetitive child work when appropriate, set sandbox requirements for untrusted tasks, and avoid forking a large conversation when a clear task brief is enough. Nested orchestration is configurable, but leaf agents do not automatically receive tools for spawning more children.

Task Delegation Patterns Across Multiple Sessions

Use delegation when tasks can be bounded and reconciled:

• one agent researches official documentation while another inspects local configuration;
• separate channel accounts route to dedicated support, operations, or personal agents;
• a coordinator assigns independent repository checks and merges evidence;
• a scheduled session gathers status while the main session handles interactive decisions.

Define the expected output, allowed tools, source of truth, and completion condition for every task. Parallel work adds coordination cost; do not split a small sequential action merely to create more agents.

Session Isolation: Preventing Agents From Interfering

Never reuse an agentDir across configured agents. OpenClaw warns that this causes auth and session collisions. Give each agent a separate workspace when files or instructions differ, then set sandbox and tool policy according to the risk of its channels.

For DMs, the default can share one session across senders. If several people can message an agent, use session.dmScope: "per-channel-peer" or the more specific account-scoped option. Group chats and rooms already receive their own session routing, but they may still share the agent's files unless sandboxing or workspaces separate them.

Performance Limits: How Many Agents Can One OpenClaw Instance Handle?

OpenClaw does not publish a single universal agent limit. Capacity depends on host memory, browser processes, channel connections, sandbox backend, tool workloads, model-provider concurrency, rate limits, context size, and token budget. A Gateway that routes several idle chat identities is different from one running many Chromium sessions and local commands.

Measure queue time, peak memory, provider errors, session-store growth, and tool contention under a representative load. Set sub-agent models and concurrency conservatively, and add capacity only after identifying the actual bottleneck.

When You Need More Than OpenClaw Can Offer

Choose a separate orchestrator or managed development platform when the workload needs stronger repository lifecycle controls, team governance, deterministic workflow graphs, independent infrastructure boundaries, or a product-specific review process. OpenClaw remains a good fit when the central requirement is an assistant reachable through many channels with flexible tools.

The decision should follow the boundary you need, not the number of agents shown in a UI. For tool interoperability, see OpenClaw MCP integrations. For host sizing, use OpenClaw system requirements.

In Verdent

OpenClaw Multi-Agent vs Verdent's Parallel Execution Architecture

OpenClaw uses one Gateway as a control plane for channels, sessions, tools, and configured agents. Verdent is a managed AI development environment oriented around software tasks and parallel development work. The products overlap at coordination but have different boundaries.

Verdent publishes this comparison. Its current documentation defines product behavior; test both products against the intended workload.

Verdent is relevant when parallelism is attached to a software-delivery process rather than a messaging Gateway. It does not turn OpenClaw sessions into Verdent agents or replace OpenClaw's channel configuration. Evaluate it as a separate managed development option.

Sources: OpenClaw multi-agent routing, sub-agents, session management, and OpenClaw security.