Clyro is a runtime governance layer for AI agents that monitors execution, enforces policies, and limits runaway behavior before failures escalate. It is aimed at teams building production agents with frameworks like LangGraph, CrewAI, Claude Agent SDK, Anthropic SDK, or custom Python callables.
Clyro wraps AI agents with runtime controls that monitor execution, enforce policy rules, and limit cost or step growth before a run goes off track. It is designed for production agent systems where the main concern is not model quality alone, but what the agent does while it is acting.
The product supports Python-based agent workflows and works with frameworks including LangGraph, CrewAI, Claude Agent SDK, Anthropic SDK, and generic Python callables. Teams can use it to add governance without changing the basic way they build agents.
Define rules for tool parameters with operators such as max value, equals, and allowlists. Policies can block violations instantly or log decisions for audit trails, and they can be managed in YAML or through the dashboard.
Every tool call is recorded with parameters, governance decisions, cost, and outcomes. Logs are stored in append-only JSONL format with sensitive field redaction and fail-open execution handling.
Clyro detects repeated tool-call patterns, such as identical calls in a sliding window, and stops runs before costs spiral. The CostTracker checks budgets before execution and tracks accumulated spend across the session.
The cloud plans include local replay, flame graphs, and trace comparison so teams can inspect how an agent reached a result and compare runs when behavior changes.
Starter and Team plans add drift detection, while the product also exposes an Agent Reliability Index with trends and alerts so teams can watch for changes in agent behavior over time.
Clyro can sit between an agent and an MCP server to apply default-deny server allowlists, parameter rules, loop detection, and cost ceilings to every tool call.
Teams shipping agents can wrap an existing agent and apply runtime controls such as step limits, cost ceilings, and policy checks before tool execution.
For agents that use the Model Context Protocol, Clyro adds server-scoped allowlists and parameter rules so tool access and write actions can be constrained centrally.
Shared replays, flame graphs, trace comparison, and full audit logs help teams understand what happened in a run and compare it against expected behavior.
Drift detection and ARI trends are aimed at teams that want to notice when agent behavior changes over time rather than discovering it after a visible incident.
Cloud policy sync, shared dashboards, RBAC, and alerts support teams that want to define governance once and apply it across multiple agents or environments.
It wraps an agent and evaluates tool calls before they execute, using controls such as policy checks, loop detection, cost limits, and step limits. It also logs execution context for review.
The source names LangGraph, CrewAI, Claude Agent SDK, Anthropic SDK, and a generic adapter for Python-based agents or callables.
No. The product is positioned as a prevention layer, so it blocks or bounds risky behavior at runtime rather than only showing what failed afterward.
Yes. The pricing page shows a free plan with usage limits, plus paid Starter, Team, and Enterprise options.
It adds default-deny governance for MCP tool calls, plus server-scoped allowlists, parameter rules, audit logging, loop detection, and cost ceilings.
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