agnost-ingestion

What This Skill Does

The agnost-ingestion skill is a specialized tool for developers and AI engineers to integrate Agnost AI analytics into their applications. It serves as a bridge between your AI agent or MCP server and the Agnost dashboard, enabling structured tracking of user interactions, latency, event logs, and performance metrics. By utilizing the provided SDKs for Python, TypeScript, and Go, you can ensure that your AI conversations are captured, stored, and visualized effectively, allowing for data-driven improvements to your AI models.

Installation

To integrate this skill, use the ClawHub command within your project directory: clawhub install openclaw/skills/skills/ajmeraparth132/agnost-ai

Once installed, verify the SDK dependencies for your specific environment:

• For Python: pip install agnost or uv add agnost
• For Node.js/TypeScript: npm install agnostai or pnpm add agnostai
• Ensure you have your Organization ID obtained from the Agnost dashboard before initializing the client.

Use Cases

1. Conversation Analytics: Tracking end-to-end user-AI exchanges to monitor quality, identify failure points, and optimize prompt responses.
2. MCP Server Monitoring: Managing performance analytics for Model Context Protocol servers to track tool call frequency and server latency.
3. User Interaction Tracking: Capturing custom events, such as feedback signals or UI interactions, to correlate them with specific AI session states.
4. Performance Auditing: Measuring the duration of AI generations using the begin() and end() pattern to detect performance bottlenecks in your backend.

Example Prompts

1. "Initialize the Agnost SDK in my existing Python FastAPI project and set up tracking for my chat completion endpoint."
2. "How do I instrument an MCP server using the agnost-mcp library to track tool call durations?"
3. "Show me the TypeScript implementation for capturing a custom feedback event after an AI response is generated."

Tips & Limitations

• Initialization Order: Always ensure agnost.init() is called at the application entry point before any other tracking methods are invoked to avoid data loss.
• Async Handling: When using the begin() and end() methods in asynchronous environments, ensure that context tracking is properly handled to avoid cross-pollination of session data.
• Data Privacy: Be mindful that sensitive user data sent to tracking endpoints is stored on Agnost servers; ensure you filter out PII before transmission if your compliance policy requires it.
• Network Dependency: This skill requires a persistent outbound connection to the Agnost API; ensure your firewall or egress rules allow traffic to api.agnost.ai.