
Lintang developed token-level observability for large language model interactions in the All-Hands-AI/agent-sdk repository, focusing on backend development and event-driven programming using Python. They designed and implemented the TokenEvent data model and processing logic to capture token IDs for both prompts and responses, enabling detailed tracking and analysis of LLM interactions. This work laid the foundation for reinforcement learning experiments by consolidating token-event handling within the SDK, supporting future analytics and optimization efforts. Lintang’s contribution addressed the need for granular data collection, facilitating data-driven improvements in cost, latency, and performance for machine learning workflows within the project.
November 2025: Delivered TokenEvent tracking for LLM interactions in the All-Hands-AI/agent-sdk, enabling token-level observability and better RL analysis. Implemented TokenEvent data model and processing to capture token IDs for prompts and responses, facilitating data-driven optimization and reinforcement learning workflows.
November 2025: Delivered TokenEvent tracking for LLM interactions in the All-Hands-AI/agent-sdk, enabling token-level observability and better RL analysis. Implemented TokenEvent data model and processing to capture token IDs for prompts and responses, facilitating data-driven optimization and reinforcement learning workflows.

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