Gate tool calls, messages, subagents, and sandbox access through formal ontological constraints. Audit every decision before your agent can affect files, networks, code, or people.
Everything you need to keep autonomous agents safe
Formal ontology constraints, not just pattern matching. Your agent's actions are validated against a real knowledge graph.
Every tool call passes through agent governance, classification, role checks, SHACL, policy, preferences, dependencies, temporal windows, and rate limits.
NemoClaw YAML policies become RDF constraints for network allowlists and filesystem boundary checks inside the same policy engine.
Append-only JSONL logs with machine-readable ontological justification for every allow and block decision.
Kill switches, delegation detection, role-based access control, subagent spawn checks, hierarchy-wide limits, and temporary scoped permissions.
Outbound messages are checked for sensitive data and contact rules; inbound messages are assessed for prompt-injection risk by channel trust level.
The knowledge graph feeds live constraints directly into the LLM's system prompt for better self-regulation.
SQLite-backed state keeps kill switches, rate-limit counters, and temporary permission grants intact across service restarts.
Pure plugin architecture. OpenClaw updates independently. No fork drift, no vendor lock-in.
Three steps between intent and execution
The AI agent requests a tool call, outbound message, or subagent spawn.
The request passes through 11 constraint checks against the ontology, policy graph, session state, and sandbox rules.
SafeClaw returns a decision with a formal, auditable justification.
SafeClaw evaluating tool calls in real time
Every tool call passes through these gates in order
Get governed in under a minute
Install the OpenClaw plugin and point it at our hosted service. No server setup needed.
That's it. safeclaw connect writes your key to ~/.safeclaw/config.json and verifies the connection.
Run the SafeClaw engine on your own infrastructure.