Monitoring Claude Code: Gaining Deeper Insights with OpenTelemetry and SigNoz
Introduction to Monitoring Claude Code
In the rapidly evolving landscape of artificial intelligence, AI coding assistants like Claude Code have become indispensable tools for modern development teams. These powerful assistants streamline workflows, accelerate development cycles, and enhance productivity. However, as these AI tools become more integrated into daily operations, the need for robust monitoring solutions becomes paramount. Without adequate visibility, understanding the true impact, performance, and cost associated with these tools can be challenging. This is where observability platforms, particularly those leveraging open standards like OpenTelemetry, come into play. SigNoz, an open-source observability platform, offers a compelling solution for gaining deep insights into Claude Code usage. By implementing OpenTelemetry instrumentation, teams can collect critical telemetry data—traces, metrics, and logs—providing real-time visibility into performance, identifying bottlenecks, and managing costs effectively. This approach not only aids in debugging and optimization but also fosters more resilient and efficient adoption of AI in development workflows.
Why Monitor Claude Code? The Need for Observability
Claude Code, while powerful, can quickly become a "black box" if its usage and performance are not actively monitored. Teams often know it's being used, but lack clarity on crucial aspects such as the volume of usage, the effectiveness of its assistance, and the associated costs. Without telemetry, it's difficult to ascertain whether Claude Code is delivering tangible value or simply existing as an underutilized resource. Robust monitoring transforms Claude Code from an opaque tool into a measurable component of the engineering ecosystem. By tracking key metrics and logs within platforms like SigNoz, organizations can answer critical questions that directly link usage to demonstrable value. This includes understanding total token consumption and its financial implications, analyzing user engagement through sessions, conversations, and requests, and gaining visibility into quota usage to prevent hitting limits. Furthermore, monitoring performance trends, such as command duration and request success rates, ensures that developers receive timely and reliable responses. Insights into developer behavior, such as preferred terminals or decision-making patterns when accepting or rejecting suggestions, and the distribution of model usage (e.g., Sonnet vs. Opus), provide a holistic view. Ultimately, this data transforms Claude Code from a generic AI tool into a transparent, optimizable asset that can be strategically integrated into developer workflows, justifying its adoption and enabling fine-tuning for maximum impact.
OpenTelemetry and SigNoz: The Observability Power Duo
To effectively monitor Claude Code, a combination of OpenTelemetry and SigNoz provides a powerful and flexible observability stack. OpenTelemetry, an open-source observability framework, offers a standardized way to instrument applications to generate, collect, and export telemetry data. For Claude Code, this means that every request, session, and token consumed can be meticulously traced and exported. This granular level of detail allows for surfacing information on request latency, success rates, and the specific models or terminals driving activity, effectively demystifying the AI assistant's operations. SigNoz, on the other hand, is an open-source application performance monitoring (APM) tool designed to ingest and visualize this telemetry data. It provides a unified platform where metrics, traces, and logs converge, offering a comprehensive view of application health and performance. By integrating Claude Code with OpenTelemetry and exporting the data to SigNoz, teams gain the ability to build custom dashboards that track essential metrics like token usage and costs over time, analyze request patterns per user and terminal type, monitor command durations and success rates, and understand model distribution. This synergy between OpenTelemetry's data collection capabilities and SigNoz's visualization and analysis features creates a complete observability solution, turning raw usage data into actionable intelligence.
Practical Implementation: Instrumenting Claude Code
Implementing observability for Claude Code involves configuring the tool to send telemetry data using OpenTelemetry exporters. This process typically begins by enabling telemetry within the Claude Code environment. For users operating within VS Code, this can be achieved by launching the editor with specific environment variables set, such as CLAUDE_CODE_ENABLE_TELEMETRY=1, alongside variables defining the OpenTelemetry exporters (e.g., OTEL_METRICS_EXPORTER=otlp, OTEL_LOGS_EXPORTER=otlp) and the endpoint for the observability backend (e.g., OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest.). Similar configurations apply when using Claude Code directly from the terminal, where the same environment variables can be set before launching the claude command. For administrators managing Claude Code deployments, telemetry can be centrally configured via a managed settings file, ensuring consistent data collection across an organization. This file, located at specific paths depending on the operating system (e.g., /Library/Application Support/ClaudeCode/managed-settings.json on macOS), allows for environment variables to be set with high precedence, preventing user overrides. The configuration allows for flexibility, supporting various exporters like console, OTLP/gRPC, and Prometheus, and enabling different endpoints for metrics and logs. Once configured, Claude Code activity automatically generates and exports logs and metrics to the designated SigNoz instance, making them available for analysis in the SigNoz Cloud interface under the logs and metrics tabs, respectively. Detailed views of individual logs and metrics, including their associated attributes, provide granular insights into user prompts, API requests, and performance data.
Understanding Your Telemetry Data: Metrics and Logs
Once Claude Code is successfully integrated with OpenTelemetry and SigNoz, a rich stream of telemetry data becomes available for analysis. Understanding these metrics and logs is crucial for deriving meaningful insights. The metrics provide quantitative data on usage and performance. Key metrics include claude_code.session.count, indicating the frequency of CLI sessions; claude_code.lines_of_code.count, measuring the volume of code modifications influenced by Claude; and claude_code.pull_request.count and claude_code.commit.count, which track contributions to the development lifecycle. Financial and efficiency metrics like claude_code.cost.usage (cost per session) and claude_code.token.usage (tokens consumed) are vital for budget management. Behavioral metrics such as claude_code.code_edit_tool.decision (accept vs. reject) and claude_code.active_time.total (session engagement depth) offer insights into developer trust and interaction patterns. Logs, conversely, provide qualitative context and detailed event information. The claude_code.user_prompt event logs developer queries, revealing the nature and frequency of requests. claude_code.tool_result events detail the outcomes of executed tools, including success, failure, and developer decisions, highlighting reliability and trust. claude_code.api_request and claude_code.api_error events offer granular data on Claude's backend interactions, including model used, cost, duration, token counts, and error messages, which are critical for debugging and performance tuning. Finally, claude_code.tool_decision events track user acceptance or rejection of automated suggestions, providing a measure of confidence in Claude's output. Visualizing these metrics and logs in SigNoz transforms raw data into a narrative of how Claude Code is used, its impact on development, and its overall efficiency.
From Data to Dashboards: Visualizing Claude Code Insights
The true power of observability lies in its ability to translate raw telemetry data into actionable insights through visualizations. SigNoz provides a robust platform for creating dashboards that offer a clear, real-time view of Claude Code
AI Summary
This article provides a technical guide on implementing OpenTelemetry and SigNoz for monitoring Claude Code, Anthropic's AI coding assistant. It emphasizes the importance of observability in understanding the performance, usage, and cost of AI tools within development workflows. The guide details the process of instrumenting Claude Code by wrapping API calls with tracing spans and configuring exporters to send data to SigNoz. Key benefits highlighted include gaining visibility into token usage, session activity, performance trends, and cost-effectiveness, positioning SigNoz as an open-source alternative to proprietary solutions. Challenges such as the initial setup complexity and potential for noisy data are acknowledged, with recommendations for pilot projects and gradual expansion. The article also touches upon future directions, including the need for enhanced observability for compliance and security in regulated industries. Ultimately, it presents a maturing approach to AI monitoring, empowering developers to maximize Claude Code's potential while maintaining robust oversight and ensuring resilient AI adoption.