Tracing OpenAI agents with ClickStack

The blog post explores the integration of ClickHouse MCP Server with 12 popular AI agent frameworks, revealing that large language models vary significantly in handling ambiguous instructions. The author uses the OpenAI agents library for its superior tracing capabilities to analyze decision-making processes, particularly in determining the most popular GitHub projects monthly in 2025. By configuring an OpenAI agent with ClickHouse's SQL Playground, the study demonstrates tracing data loading into ClickHouse and visualization with HyperDX. The post delves into the tracing mechanism, from capturing and exporting traces to executing SQL scripts that populate ClickHouse tables, ultimately enabling detailed analysis of AI agent workflows. The article highlights the importance of observability in AI systems, providing insights into agents' reasoning and decision-making processes, which are crucial for debugging and benchmarking models. This comprehensive approach emphasizes the growing need for observability tools in AI, paralleling their established role in microservices.