OpenClaw and Hermes Agent are open source, self-hosted AI assistants you can message through Telegram, WhatsApp, or Slack. Both write their own skills as they work.
Their architectures differ. OpenClaw routes chats to named agents, while Hermes Agent organizes assistants into independent profiles that learn from repeated tasks. Both support personal and shared deployments. The comparison starts with how each organizes agents and their state.
Copy link to headingWhat are the key differences between OpenClaw and Hermes Agent?
OpenClaw is an open source gateway that connects chat apps to one or more agents. Hermes Agent is Nous Research's open source agent that builds skills and memory from use and can improve at repeated tasks. OpenClaw organizes assistants through named agents and channel bindings. Hermes uses independent profiles that can share a messaging gateway.
Architecture and hosting defaults determine what an agent can access on your machine. Choose those before comparing channels or models.
Copy link to headingArchitecture and control model
OpenClaw supports multiple named agents; Hermes supports multiple independent profiles. OpenClaw's Gateway manages sessions, routing, and channel connections, using bindings to send each inbound chat to a named agent. Hermes uses the same agent core across its interfaces, so the CLI, desktop app, and chat apps share memory and skills within a profile. One OpenClaw process can serve a family WhatsApp group and a separate coding agent. Hermes can serve separate profiles through a multiplexing gateway.
Copy link to headingSkill and memory learning
Both agents learn automatically by default, but their review controls differ. OpenClaw's Skill Workshop runs in auto mode and reviews a conversation after 10 accumulated model iterations. It archives learned skills that have been unused for 30 days on supported runtimes; automatic cleanup is disabled for the Codex runtime.
Hermes defaults skills.write_approval and memory.write_approval to false, allowing post-turn reviews to save memories and patch skills without approval. OpenClaw v2026.11.2 offers auto and off learning modes, with saved versions and Undo. Hermes can hold tool-mediated writes for approval, including writes made during your turn.
Copy link to headingChannels and model access
Model access is a useful distinction because both agents support the major chat apps. In the documented OpenClaw setup, AI Gateway routes only models listed in the provider's models array. You maintain that list as you add models. Hermes fetches AI Gateway's live catalog, filtered to language models that support tool calling, so new models appear without a config change. Channel totals are less comparable because the projects count vendors, authentication routes, and plugin types differently.
Copy link to headingDefault safety posture
Neither agent's default local configuration isolates shell commands. A sandbox backend may isolate only part of the agent. OpenClaw's built-in sandboxing is off by default; enabling it moves tool execution to a separate process while the Gateway stays on the host. Hermes defaults to the local terminal backend. A remote backend moves supported shell and file operations off the host, but the agent process stays there. For full containment, Hermes's security guidance recommends wrapping the whole agent process in a sandbox.
Copy link to headingDiving into OpenClaw for teams routing agents across channels
OpenClaw runs a long-running Gateway process on a laptop, server, or Docker host to keep configured agents reachable from chat. Agents can answer through that Gateway only while it is running, so host uptime limits their availability.
The project uses calendar-versioned releases with stable and beta channels. Its Sandbox guide specifies at least 2 GB of memory for the Gateway.
Copy link to headingOpenClaw pros:
Larger GitHub following: As of November 9, 2026, OpenClaw's repository has more GitHub stars and forks than Hermes Agent's. Community skills are shared through ClawHub, the project's registry.
Nonprofit governance: The OpenClaw Foundation, an independent, donation-funded 501(c)(3), stewards the project.
Swappable runtimes: OpenClaw separates agent runtimes, including Claude CLI and Codex, from hosted and local model providers. You can configure those layers separately.
Separate state per agent: Each agent has its own workspace,
agentDir, and session store. Conversations and memory stay distinct by default, but this separation is not a security boundary.
Copy link to headingOpenClaw cons:
Gateway security history: The 2026.2.14 release fixed a node-host approval bypass involving mismatched command arguments and exec allowlists. Affected older releases need upgrading.
Breaking cleanups: The 2026.10.1 release removed a retired configuration route. Control UI clients from 2026.6.1 stopped working until upgraded.
Unvetted community skills: ClawHub skills can direct the agent to use its available tools and permissions. Review each skill before enabling it.
Best for: Households or teams that want one always-on hub serving several people or chats, each with a separately configured agent.
Copy link to headingWhere OpenClaw stands out
You can add assistants to the same Gateway by configuring channel routing, access controls, and hosting resources for additional users. Each agent's SOUL.md, AGENTS.md, and optional USER.md define its persona and instructions. Adding an agent does not require another OpenClaw installation.
The roadmap keeps core lean and assigns many optional capabilities to plugins and ClawHub. Core handles routing; plugins add integrations. Skill maintenance depends on the install source: openclaw skills update refreshes ClawHub installs, while Git and local skills need reinstalling.
Copy link to headingExploring Hermes Agent as an OpenClaw alternative
Hermes Agent is an MIT-licensed agent that can refine saved skills through reuse and build a user profile across sessions.
Its built-in memory is deliberately limited. MEMORY.md defaults to 2,200 characters and USER.md to 1,375. The agent must curate those files rather than log everything in them, limiting their contribution to each session's prompt cost.
Copy link to headingHermes Agent pros:
Active development: The repository receives frequent commits and offers canary builds, so fixes can be tested before the next stable release.
Learning in core: The learning loop, skill curator, and approval queue require no extra installation. Write approval and model-based skill consolidation need configuration.
Cross-platform desktop app: The desktop app runs on Apple Silicon Macs, Windows, and Linux.
Guarded email intake: Unless you opt out, the email adapter drops mail that fails sender authentication. It checks the sender's domain using a pinned receiving-server authentication identity and properly handled authentication headers.
Copy link to headingHermes Agent cons:
Release regressions: The v0.22.2 notes say v0.22.1 shipped from the wrong commit and could stall the Microsoft Store app. Test upgrades before adopting them.
Features moving out of core: The v0.22.5 release moved
hermes mcp serveand the Singularity backend into plugins. Upgrades may require plugin installation or configuration changes.Skill scanning off by default: The scanner for agent-written skills,
skills.guard_agent_created, is off by default. Those writes receive its checks only when you enable it.
Best for: One person or a small team that wants assistants to improve at repeated tasks, with separate profiles as needed and versions pinned and tested before upgrades.
Copy link to headingWhere Hermes Agent stands out
Hermes's review queue can stage every skill_manage write, including writes made during your turn. OpenClaw v2026.11.2 instead uses saved versions and Undo. Setting skills.write_approval: true stages changes from both foreground turns and background reviews, and pending writes survive restarts. Review them with /skills pending and /skills diff <id>, then use /skills approve <id> or /skills reject <id>. Separately, the curator moves inactive agent-created skills from active to stale to archived.
Memory loads once per session. Hermes adds MEMORY.md and USER.md to the system prompt at session start. A committed memory write reaches disk immediately but enters the system-prompt snapshot only in the next session; unapproved writes remain staged. This keeps the model's prefix cache intact.
Copy link to headingOpenClaw vs Hermes Agent: which fits your workflow?
How an install organizes agents, people, and channels helps determine which project fits your workflow.
Copy link to headingOpenClaw for multiple agents across channels
OpenClaw fits when another person or chat needs a named agent behind the same Gateway. At that point, distinguish state separation from security isolation.
Per-agent workspaces separate memory and sessions, but they are not a security boundary. Unless execution is isolated, local commands still use the host account's permissions. A family chat and a coding agent may therefore access the same files.
Copy link to headingHermes Agent for one learning assistant
Hermes Agent fits when an assistant should turn repeated work into reusable procedures. Independent profiles support additional assistants. A saved skill can reduce the reasoning and tokens needed for repeat tasks. Background learning reviews consume tokens, while the default curator prunes inactive skills without model calls. Optional model-based consolidation can run for minutes; a cheaper auxiliary model can reduce that cost.
Test the same task before and after the agent saves a skill, comparing correctness, token spend, and review effort. Lower token usage is not a saving if the result requires costly corrections.
Copy link to headingHow Vercel isolates and meters Hermes and OpenClaw
Both agents can execute commands with their host account's permissions and spend on models unattended. Vercel provides execution isolation and model spending controls. OpenClaw can route model calls through AI Gateway and run its entire Gateway inside Vercel Sandbox. Hermes added both integrations in August 2026.
Copy link to headingKeep agent commands off your laptop with Vercel Sandbox
An always-on agent takes instructions from chat messages and, when configured locally, can run shell commands as its host account. A bad command or poisoned skill can access your home directory, private keys, and cloud credentials if the account and configured tools permit it. Vercel Sandbox runs that work in a Firecracker lightweight virtual machine (microVM) with its own kernel, filesystem, and network.
OpenClaw's Sandbox guide places the entire Gateway and its commands in a single sandbox. Hermes's Sandbox backend moves supported shell commands, file operations, and code execution into a microVM. Its agent process, plugins, and Model Context Protocol (MCP) servers remain on the Hermes host. Isolation limits what commands can access; it does not validate a learned skill's instructions. Skill review remains necessary.
Copy link to headingLimit where an injected agent can reach with the Sandbox firewall
Both agents read untrusted chats and web pages. A prompt injection may instruct either agent to contact an attacker's server or send a readable key. The Sandbox firewall supports allow-all, deny-all, and custom policies, including domain allowlists. Policies can change at runtime without restarting sandboxed processes, and every firewall feature is available on Hobby. Credential brokering adds API keys to matching outbound requests, so brokered secrets need not enter the sandbox.
OpenClaw's guide applies a restricted policy that allowlists ai-gateway.vercel.sh, web.whatsapp.com, and mmg.whatsapp.net, subject to documented domain-filtering limitations. Hermes creates a sandbox for each task and exposes settings for images, resources, persistence, and authentication. The Sandbox firewall controls only traffic originating inside that sandbox. Host-side agent, plugin, and MCP traffic remains outside its scope.
Copy link to headingRun both agents on one key with AI Gateway
Trying two agents can mean separate provider accounts, keys in separate config files, and bills to reconcile. AI Gateway lets them share one key, model catalog, and spend view. Use separate keys when each agent needs its own budget.
OpenClaw connects with npx vercel@latest ai-gateway setup --agent openclaw, which provisions a key and writes the config. The documented OpenClaw setup routes only declared models. Use --agent hermes for the Hermes setup, which enables live discovery of tool-calling models and their pricing. Hermes identifies its requests for attribution in analytics.
Copy link to headingCap each agent's overnight spend with AI Gateway budgets
A learning loop or retry cycle can consume tokens all night before anyone checks the dashboard. AI Gateway budgets set soft spending caps for a team, project, API key, or team member. Budgets reset daily, weekly, monthly, or never. Once spend reaches the limit, AI Gateway rejects further requests in that scope with HTTP 402. The request that crosses the limit still completes, so total spend can exceed the budget.
Give OpenClaw and Hermes separately budgeted keys to limit each agent's model spend. Shared team, project, or user budgets can still affect both, and agents should back off after budget rejections. Default API key budgets also cover keys created later. Budgets exclude bring-your-own-key (BYOK) spend, so use AI Gateway credits for usage you want counted toward the limits.
Copy link to headingResume an agent's workspace with persistent sandboxes
An agent stopped by a session limit should be able to resume from its saved skills and memory. Persistent sandboxes are the default in current Sandbox SDKs. They snapshot the filesystem on stop and restore it on resume. Snapshots expire 30 days after their last use by default. Sessions run for up to 45 minutes on Hobby and 24 hours on Pro and Enterprise, with a fresh limit after each stop and resume.
OpenClaw's Gateway process does not survive a stop. Resume the sandbox and restart the Gateway to restore access to its saved workspace; continuous operation across session limits requires lifecycle management. With terminal.container_persistent: true, the default, Hermes snapshots each task's sandbox filesystem during cleanup and restores it in a later sandbox for the same task. Running processes and shell state do not persist.
Copy link to headingChoose your agent, then run it isolated on Vercel
OpenClaw fits households or teams that route chats to named agents. Hermes Agent fits users who want assistants to learn from repeated work, with independent profiles for separate assistants. Choose based on how each organizes those assistants and how much time you can spend reviewing learned skills. Before either reads its first message, decide where commands run and how much model usage it can incur.
Vercel provides these controls when configured:
A microVM for execution in Vercel Sandbox: The OpenClaw Gateway or Hermes terminal backend executes commands in an isolated microVM. Host files remain separate unless copied in or otherwise made accessible. Credentials copied into the sandbox remain accessible there.
An egress allowlist in the Sandbox firewall: A configured policy restricts sandbox traffic according to its filtering rules. It does not control Hermes components running on the host.
One model key in AI Gateway: One setup command connects either agent, so trying the second does not require another provider account.
Per-agent spend caps with AI Gateway budgets: Separate keys give each agent a soft spending cap. Shared budgets can still reject requests from both.
Workspaces that survive restarts with persistent sandboxes: Restarting the Gateway in a resumed sandbox restores access to saved skills and memory. The process itself must start again.
Follow the OpenClaw setup or Hermes setup to connect to AI Gateway, or browse agent templates for starting points.
Copy link to headingFrequently asked questions about Hermes vs OpenClaw
Copy link to headingIs Hermes better than OpenClaw?
Neither is better for every workflow. Hermes Agent suits repeated tasks and reviewable learning; OpenClaw suits a shared hub with named-agent routing. Both support multiple assistants. This comparison does not establish a benchmark winner, so test each on a week of your own tasks.
Copy link to headingCan you use Hermes and OpenClaw together?
Yes. Run them independently or migrate supported settings between them. Hermes's migration plugin imports OpenClaw instructions and skills. Allowlisted secrets transfer only when the import uses --migrate-secrets; first-run setup can offer that import with the flag included after preview and confirmation. OpenClaw's openclaw migrate hermes imports in the other direction. Import onboarding requires a fresh OpenClaw setup, while standalone migration supports existing installations with backup and overwrite controls.
Copy link to headingWhich is cheaper to run, Hermes or OpenClaw?
Neither is inherently cheaper. Token usage, background learning, tools, hosting, and storage all affect the bill. OpenClaw's Gateway runs continuously. Hermes creates task sandboxes on demand, but its messaging gateway still needs a running host process. Compare identical tasks on the same model and comparable hosting, including background work and storage, in the total.