langchain4j/langchain4j
Feb 2026 – Feb 2026
1 Month active
Languages Used
Java
Technical Skills
JavaMicrometerOpenTelemetrySpring Bootbackend development
Susanne Pieterse
PROFILE
Susanne Pieterse
Developed and delivered a Chat Model Observability Metrics feature for the langchain4j/langchain4j repository, focusing on tracking token usage during chat model interactions. The solution involved implementing a specialized ChatModelListener that leverages Micrometer’s MeterRegistry to collect and expose detailed metrics for both requests and responses, adhering to OpenTelemetry Semantic Conventions for Generative AI. This work enhanced backend observability, enabling improved SLA tracking, performance tuning, and capacity planning for chat workflows. The implementation was completed in Java and Spring Boot, with comprehensive unit and integration testing and updated documentation to ensure maintainability and clarity for future development and monitoring efforts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
824
Activity Months1
Your Network
207 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — langchain4j/langchain4j: - Key feature delivered: Chat Model Observability Metrics (token usage). Implemented a specialized ChatModelListener using Micrometer MeterRegistry to collect and expose metrics for chat model requests and responses, aligned with OpenTelemetry Semantic Conventions for Generative AI. Enables token-level observability to drive performance monitoring and optimization. - Major bugs fixed: No major bugs fixed this month; effort focused on feature delivery and quality. - Overall impact and accomplishments: Strengthened observability for chat workflows, enabling better SLA tracking, performance tuning, and proactive capacity planning. lays groundwork for alerting and data-driven improvements across the chat model pathway. - Technologies/skills demonstrated: Micrometer, MeterRegistry, OpenTelemetry semantic conventions for Generative AI, Java, unit/integration testing, documentation practices. Commit reference for the delivered feature: 898beedd4db2342f2c1afd41add19c48f30a486a
1 Commits • 1 Features
Feb 1, 2026February 2026 — langchain4j/langchain4j: - Key feature delivered: Chat Model Observability Metrics (token usage). Implemented a specialized ChatModelListener using Micrometer MeterRegistry to collect and expose metrics for chat model requests and responses, aligned with OpenTelemetry Semantic Conventions for Generative AI. Enables token-level observability to drive performance monitoring and optimization. - Major bugs fixed: No major bugs fixed this month; effort focused on feature delivery and quality. - Overall impact and accomplishments: Strengthened observability for chat workflows, enabling better SLA tracking, performance tuning, and proactive capacity planning. lays groundwork for alerting and data-driven improvements across the chat model pathway. - Technologies/skills demonstrated: Micrometer, MeterRegistry, OpenTelemetry semantic conventions for Generative AI, Java, unit/integration testing, documentation practices. Commit reference for the delivered feature: 898beedd4db2342f2c1afd41add19c48f30a486a
February 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
JavaMicrometerOpenTelemetrySpring Bootbackend development
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