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Hermes Agent can be operated in production with persistent state, supervised profile gateways, explicit API authorization, and isolated command execution. But its documented controls do not establish a supported multi-replica architecture or a measurable capacity ceiling. Build around one gateway per data directory, protect the gateway as privileged, and treat horizontal scaling as unverified until you validate a topology with current project guidance.
What a production-ready Hermes deployment needs
Hermes is more than an HTTP endpoint: its gateway can expose the agent’s tools, including terminal operations. A production deployment therefore needs deliberate boundaries around who can call the gateway, what commands the agent can run, where those commands execute, and how agent state is stored.
- Execution boundary: choose a terminal backend appropriate to the commands and trust level of each workload.
- Persistent state: protect Hermes configuration, credentials, sessions, memories, skills, scheduled jobs, and logs.
- Explicit authorization: require the API bearer key and restrict access to the gateway and its tools.
- Operational controls: apply resource limits, monitor logs, and use the documented supervision for profile gateways.
- Scale expectations: distinguish concurrency and profile management from a supported multi-replica or high-availability design.
Choose where terminal commands execute
Hermes configuration separates the agent’s location from the place its shell commands run. The available execution choices documented for Hermes are local, Docker, SSH, Modal, Daytona, and Singularity/Apptainer. They are not interchangeable security or performance options; select based on the boundary, persistence, and operating environment the workload needs.
| Execution choice | Documented boundary or use | Operational consideration |
|---|---|---|
| Local | Commands run directly on the host with the host user’s access; there is no isolation. | Use only when that host-level access is intentional. |
| Docker | Container isolation, with configurable CPU, memory, disk, and persistence settings. | Configure resource limits and decide explicitly which state and environment variables the container receives. |
| SSH | Commands run on a remote machine. | The remote machine is the execution boundary; manage its access and environment accordingly. |
| Modal or Daytona | Identified as isolated sandbox choices. | The documentation does not establish equivalent performance or security characteristics across backends. |
| Singularity/Apptainer | An option identified for HPC environments. | Choose it where the operating environment calls for that execution model. |
Do not assume that choosing a container automatically protects every secret or every tool. If credentials are forwarded into a terminal container, commands running there can see them. Keep docker_forward_env limited to only the variables a workload actually needs.
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Use Docker for a supervised, persistent deployment
The official Docker deployment runs Hermes with gateway run, mounts a persistent data directory, supports a restart policy, and can include the dashboard and container resource limits. Its documented data mount at /opt/data is the source of truth for Hermes state.
- Choose the command execution backend. Decide whether commands should run locally, in Docker, or through another supported backend based on the required trust boundary.
- Provide persistent storage. Mount the Hermes data directory so configuration and agent data survive container replacement or restart.
- Set operational limits. Configure container CPU, memory, disk, and persistence settings for the workload. The documentation describes these controls but does not provide a universal capacity value.
- Run the gateway under supervision. Use the documented Docker image and
gateway runsetup with a restart policy. The image supervises profile gateways, restarts a profile gateway after a crash, keeps rotated logs per profile, and restores gateways recorded as running after a container restart. - Plan recovery. Include the persistent data directory in backup, access-control, and restore planning; it contains sensitive application state, not disposable cache.
The Docker documentation recommends one container hosting all profiles by default. A separate container for a profile is an option when that workload needs its own CPU or memory isolation or an independently pinned image.
Protect Hermes state and avoid concurrent writers
The persistent Hermes data directory includes configuration and secrets, profiles, sessions, memories, skills, scheduled jobs, hooks, and logs. Restrict access to it and treat it as sensitive operational data. A backup plan should account for the data as a coherent set and include a tested way to restore it; the documentation does not specify a backup schedule or a particular backup product.
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Do not run two Hermes gateway containers against the same data directory at the same time. The official Docker documentation warns that session files and memory stores are not designed for concurrent write access. A shared mounted directory is therefore not a documented safe way to make multiple replicas.
Separate agents with profiles
A Hermes profile is a separate home for an agent’s configuration and data. Profiles separate configuration, environment credentials, personality, memories, sessions, skills, scheduled jobs, and gateway state. Use profiles when agents need distinct state rather than relying on names or prompts to keep their data apart.
The documented multi-profile gateway can serve profiles with their own credentials and state. In the official Docker image, profile gateways are supervised within the container. Choose a separate container only for a concrete boundary such as workload-level resource isolation or independent image pinning, and manage its data directory without concurrent gateway writers.
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Expose the API deliberately
The Hermes API server presents an OpenAI-compatible HTTP endpoint. It requires a configured provider and tool backends to perform useful work. Its documented default bind address is 127.0.0.1, its default port is 8642, and API_SERVER_KEY is required for every deployment, including one bound to loopback.
Because the endpoint can invoke the agent’s tools, including terminal commands, treat access as access to a privileged system rather than as ordinary application traffic.
- Use a strong bearer key and store it securely.
- Do not expose the endpoint broadly without deliberate network and access controls.
- If browser access is explicitly enabled, keep allowed CORS origins narrow.
- Configure explicit user or command allowlists rather than allowing every user or command by default.
The server’s max_concurrent_runs setting caps simultaneous runs on that API server. It is an execution control, not a throughput promise: the documentation gives no benchmark for requests per second, latency under load, users per instance, or CPU and memory requirements by workload.
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Apply the documented security controls
Use defense in depth rather than treating one container boundary or one API key as a complete security design. The official security checklist recommends explicit allowlists, isolated command execution, resource limits, secure API-key storage, DM pairing where appropriate, periodic review of command allowlists, a deliberate messaging working directory, non-root gateway execution, log monitoring, and keeping security updates current.
The Docker hardening example also drops Linux capabilities, prevents privilege escalation, caps process count, and constrains temporary filesystems. These are documented controls to evaluate for the deployment; they do not replace a threat-model review or checking the effective configuration. Likewise, forwarding a credential into a terminal container makes it available to commands in that container, so the container boundary does not shield an intentionally injected secret.
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Hermes documents useful operations controls: Docker resource limits, an API-server concurrency cap, supervised gateways for multiple profiles in the official Docker image, and profile-level state separation. Those features help manage a deployment, but none establishes horizontal scaling or a capacity estimate.
The available documentation does not establish a supported load-balanced multi-replica API topology, coordination among replica processes, a shared state backend safe for concurrent gateway writes, failover semantics, benchmarked throughput, or capacity recommendations. The explicit warning about concurrent writes to a shared data directory rules out assuming that a generic stateless-service replication pattern is safe.
For a production design, start with the documented single-container, multi-profile arrangement where it fits; use resource limits and the concurrency cap as controls, not as performance forecasts. If you need high availability or horizontal scale, get current project guidance and validate the proposed topology before relying on it. Do not equate adding containers with adding safe capacity.
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