The Orchestrator

OpenHuman is an orchestrator, not a chatbot: durable agent graphs, visual workflows, sub-agent fleets, a split-brain always-on layer, and end-to-end encrypted agent-to-agent sessions, all in one coher

Most harnesses run one agent in one loop. OpenHuman is built as an orchestrator: a stack for coordinating many agents, over long horizons, across machines. It does this durably, observably, and under your control.

Five layers make that real:

1. Graphs, not loops

Every agent turn runs on tinyagents, our open-source graph engine. Multi-step work compiles to state-machine graphs with conditional routing: plan → execute ⇄ review → finalize for delegation, phase DAGs for multi-agent workflow runs, and map-reduce fan-out for parallel workers. All of it has durable checkpointing. A graph can pause mid-run (for your answer, for an approval, for a restart) and resume exactly where it stopped.

2. Sub-agent fleets that don't get lost

The orchestrator spawns specialized sub-agents (up to 3 levels deep), reuses compatible idle workers instead of re-spawning, and routes each to the right model tier: heavy reasoning for the core, a fast burst tier for low-context workers. Reliability is structural: a no-progress circuit breaker stops loops, and stuck children hand back a question (pause + resume on your answer) or an Incomplete root-cause summary, never silence. See the Agent Harness.

3. Workflows you can see

Workflows lift orchestration out of the chat: the agent proposes a typed graph of triggers, agents, tools and conditions; you review it on a canvas and save it. Runs are durable, approval-gated, and fully inspectable step-by-step, powered by open-source tinyflows.

4. An always-on split brain

Inbound traffic hits a fast reflex agent that triages in seconds and hands a deep reasoning core a concise brief; the core does the multi-step work and delegates to workers. The subconscious loop reviews compressed session history and injects steering directives, keeping the always-on layer aligned with your goals, while 20:1 compression keeps week-long sessions bounded.

5. Encrypted orchestration across machines

OpenHuman instances collaborate through tiny.place sessions secured with the Signal protocol: real end-to-end encryption, with keys derived on-device and never persisted. Pairing is consent-based and fails closed: an unlinked agent's message is just a message, never an instruction. Your agent can orchestrate other agents (and be orchestrated) without a server ever seeing plaintext.

Because the transport is plain Signal-encrypted messaging, the other side doesn't have to be OpenHuman. Connect Claude Code, Codex, OpenClaw, Hermes, or anything that can hold a session, and use OpenHuman as the conductor for all of your agents and tools.

What's next: RLMs

The direction we're building toward: Rhai-backed language workflows. These are agents that express orchestration as small programs in a sandboxed REPL, rather than a fixed graph, so control flow itself becomes something the model writes, inspects, and repairs. The graph engine, checkpointing, and trust model above are the substrate for it.

Why this differentiates

Single-agent harnesses (Claude Code, OpenClaw, Hermes)

OpenHuman

Execution model

One loop, one context

Compiled graphs, conditional routing, checkpoint/resume

Parallelism

Manual / plugin

Native sub-agent fleets, map-reduce fan-out, worker reuse

Automation

Scripts & cron

Visual, durable, approval-gated workflows

Always-on

None

Split-brain reflex + reasoning core, subconscious steering

Agent-to-agent