This tutorial demonstrates how to integrate Qualifire into your AI agent workflows to gain full logging, tracing, and insights using OpenTelemetry. By the end of this tutorial, you will be able to monitor your LangGraph agent's operations and understand its behavior.
This notebook walks you through integrating Qualifire with a LangGraph agent to achieve comprehensive observability, including logging, tracing, and insights via OpenTelemetry.
Modern AI applications increasingly rely on sophisticated, multi-step AI agents. These agents often involve multiple LLM calls, interactions with various tools, and complex decision-making processes. Gaining clear visibility into these intricate workflows is a significant challenge. On top of all of that you might also encounter hallucinations, poor tool selection quality and other AI related risks.
- End-to-End Tracing: Track every step of your agent's execution, from initial prompt to final output
- Real-Time Monitoring: Get immediate insights into your agent's performance and behavior
- Debug & Troubleshoot: Quickly identify and resolve issues in your agent's decision-making process
- Quality Assurance: Monitor for hallucinations and ensure high-quality tool selection
- OpenTelemetry Integration: Leverage industry-standard observability practices
- Tracing Setup: Implement distributed tracing to track agent workflows
- Logging Integration: Capture detailed logs of agent operations
- Performance Monitoring: Track response times and resource usage
- Quality Metrics: Measure and monitor agent decision quality
- Setup tracing and observability in your LangGraph agent
- Debug and troubleshoot your agent
- Get real-time agent observability using Qualifire
Start the hands-on tutorial here: agent-observability-with-qualifire.ipynb