01
One core agent, many surfaces
Hermes exposes a shared core across CLI, gateway, and API-style surfaces, so the same agent can operate across sessions instead of being trapped in a single demo interface.
Managed Hermes Agent Infrastructure
Managed Hermes Agent.
Operated by DreamBound.
DreamBound runs Hermes Agent as a managed operating layer for teams that want tools, memory, channels, automations, approvals, and model routing without self-hosting the gateway and runtime stack.
Model coverage runs from efficient options like NVIDIA Nemotron 3 Nano 30B, Gemini 3.1 Flashlight, QN 3.6+, and GPT-5.4 Nano / Mini to larger GPT-5.4, GPT-5.4 Pro, and Claude Opus 4.6.
Hermes Foundation
What Hermes Agent already gives us.
The open-source base is already broad: one core agent loop, 40+ tools, multi-channel delivery, MCP, memory, automation, and multiple execution backends. DreamBound sells the managed layer on top.
02
Channels and delivery already exist
Documented adapters include Telegram, Discord, Slack, WhatsApp, Signal, webhook-style delivery, and additional platform integrations through the gateway layer.
03
Tooling is broader than a chatbot shell
The repo documents 40+ tools and a registry-based tool system covering terminal, files, web, browser, code execution, memory, session search, delegation, and MCP connectivity.
04
It runs across multiple backends
Hermes can execute through local, Docker, SSH, Modal, Daytona, and Singularity-style environments. That matters because DreamBound can shape the runtime to your security and cost model.
05
Memory, search, and subagents are native
Hermes already ships persistent memory files, session persistence with search, optional user modeling, and isolated delegated subagents for bounded parallel work.
06
Automations belong in the same stack
Built-in cron scheduling means reports, audits, and recurring tasks can run inside the same managed system and deliver results back to the channels your team already uses.
Offer
What DreamBound actually manages.
The product is not a logo-swap around an open-source repo. The product is operating the Hermes stack for a team with real secrets, routing rules, uptime expectations, coding workflows, and workflow pressure.
01
Deployment and hosting
We run the stack on DreamBound infrastructure, configure providers and endpoints, secure credentials, and choose the right execution backend for your operating constraints.
02
Model routing from nano to frontier
We can route work across cheap and compact models for bulk execution, then escalate harder tasks to frontier reasoning and coding models when the job actually needs it.
03
Codex and Claude Code inside managed VMs
We can run Codex and Claude Code inside dedicated VMs, keep the environments persistent, and let our Hermes-based operator layer orchestrate when those coding agents should be invoked.
04
Tools and MCP wiring
We connect terminal, browser, file, web, code-execution, and MCP capabilities to the systems your team already uses instead of leaving Hermes as an isolated shell.
05
Memory and continuity
Hermes supports memory files, sessions, skills, search, and optional user modeling. We tune that layer so the system accumulates useful operational context instead of stale noise.
06
Cross-channel delivery
We operate the gateway, approvals, and channel routing so Hermes can live across CLI, messaging, and scheduled delivery without your team owning the glue code.
Managed Stack
Three layers, one operator surface.
We keep the open-source runtime, the managed production layer, and the improvement workflow separate so the stack can improve without collapsing into experimental chaos.
Foundation
Hermes Agent provides the core loop: channels, tools, memory, sessions, MCP, browser and web access, subagents, automation, and multi-provider execution.
Operation
DreamBound provides deployment, configuration, approvals, support, hardening, rollout discipline, monitoring, and ongoing tuning for the team using it.
Improvement
Hermes Agent Self-Evolution is positioned here as an offline improvement workflow for skills, with review and basic constraints, not as autonomous code mutation in production.
Evolution
Improvement belongs offline, under review.
The honest commercial use of Self-Evolution today is narrow and useful: improve skills offline, inspect the candidate changes, and promote them deliberately instead of letting production rewrite itself.
01
In-use learning
Hermes already has the right substrate for improvement work: skills, memory, and session continuity. That gives DreamBound a concrete base for targeted iteration instead of starting from zero.
02
Skill evolution
The implemented part today is Phase 1: offline `SKILL.md` evolution using GEPA with dataset generation from synthetic tasks, curated sets, or mined session history.
03
Guarded roadmap
Tool-description, prompt, and code evolution sit on the roadmap, but that is not what DreamBound should promise as shipped production behavior today.
Contact
If you want Hermes Agent without becoming its full-time operator, that is DreamBound.
We host it, wire it to your stack, harden it for production, and keep the operating layer healthy over time.