AgentGate vs OpenAI Guardrails

AgentGate

An agent or its host can send each tool call to one REST endpoint, or wrap an MCP server, and get allow, deny or ask from a local classifier and a YAML policy, with an audit log and approval queue. The project is three days old, auth is off by default, and no detection accuracy is published.

OpenAI Guardrails

MIT-licensed wrapper that adds twelve configurable checks to OpenAI client calls from one JSON file, with tool-level injection checks for the Agents SDK. The README labels it a preview at version 0.3.3, and by default a check that fails to run is reported as passed unless raise_guardrail_errors=True is set.

AgentGate

1. Create a session with the user's real goal first. The goal-alignment head scores every later call against it
2. Pass earlier tool results in history_override with untrusted: true for web or email content, or the injection head sees only the action
3. On ask, stop and tell the user the decision_id. Polling /v1/decisions/:id needs the dashboard key
4. Treat allow_with_checkpoint as allow and keep the checkpoint_id for an undo
5. Expect HTTP 404 with not_found for an unknown session and 401 when the owner has set an agent key

OpenAI Guardrails

1. Pass raise_guardrail_errors=True to the client. The default treats a check that failed to run as passed
2. Run python -m spacy download en_core_web_sm before using Contains PII, or client initialisation fails
3. Catch GuardrailTripwireTriggered, and append a user message to history only after the call returns without it
4. Use block=true for Contains PII in the output stage. Masking works only in the pre-flight stage
5. Keep stream=False where output must be checked before it is shown, and budget one extra model call per LLM-based check