Hook Examplesv1.0.0

Security teams can use the before_tool_call hook to log every tool invocation with parameters and session context, creating a tamper-evident audit trail. The hook can also block dangerous operations like gateway, cron, or nodes tools in non-elevated sessions, matching regulatory requirements for access control. Additionally, the parameter validation example helps detect and block destructive shell commands before execution.

Platform engineers can use the before_model_resolve hook to automatically select the most appropriate language model based on the task type, such as using a large reasoning model for coding tasks and a smaller model for summarization. This reduces inference costs while maintaining quality for specialized workloads. The example shows switching between Ollama-hosted models based on message content.

Developers building complex multi-agent systems can leverage subagent_ended hooks to process and route subagent results to external systems, trigger follow-up actions, or aggregate outputs. The hook provides access to the event result, enabling custom logic after a subagent completes. This facilitates building scalable, event-driven agent pipelines.

Customer support organizations can implement message_sending hooks to cancel or modify outgoing messages that contain sensitive data or violate compliance policies. Combined with tool call auditing, this ensures that AI agents adhere to corporate communication guidelines. The hook system provides a centralized way to enforce governance across all chat sessions.

AI researchers and developers can use hooks to intercept and log tool calls during agent development, making it easy to debug and understand agent behavior. The blocking capability prevents accidental execution of dangerous commands during testing. This creates a safer sandbox for rapid iteration on agent capabilities.

Offer a managed service that provides pre-built hook configurations for common security policies, such as tool whitelisting, command pattern blocking, and audit logging. The service integrates with OpenClaw and other agent frameworks to ensure compliance without custom development. Revenue comes from subscription tiers based on the number of agents and hooks managed.

Sell a middleware plugin that intelligently routes requests to different LLM providers based on content, cost, and performance metrics. The plugin uses hooks like before_model_resolve to analyze incoming messages and select the optimal model, reducing costs for high-volume AI applications. Revenue is generated through a usage-based fee or a flat monthly license.

Build a no-code platform that allows businesses to design multi-step AI agent workflows with custom hooks for auditing, validation, and result routing. The platform provides visual hook configuration and pre-built templates for common use cases like data processing and customer support. Revenue comes from tiered subscriptions based on workflow complexity and execution volume.