agent-lightning

You optimize AI agent prompts with reinforcement learning by collecting execution traces and rewards, then running structured training loops to automatically improve agent policies and responses. Agent Lightning orchestrates this rollout and optimization workflow.

Automatic prompt optimization for production agents is the process of using execution traces and reward functions to iteratively refine agent instructions. Agent Lightning turns rollout data into measurable improvements without manual prompting adjustments.

You collect execution traces from AI agents by adding lightweight emission hooks that capture inputs, outputs, tool calls, and rewards. Agent Lightning instruments agents to record these execution spans for subsequent training workflows.

Yes, you can use OpenTelemetry for agent tracing and observability. Agent Lightning integrates OpenTelemetry to query execution traces, inspect rollout status, and validate training performance metrics across Python and JavaScript agent stacks.

Yes, reinforcement learning training for agents works with both JavaScript and Python stacks. Agent Lightning supports configurable runners, algorithm workers, and LightningStore coordination to manage training loops across these environments.

Training agents with automatic prompt optimization requires designing effective reward functions and managing rollout data via LightningStore. Limitations include the need for proper instrumentation and the complexity of configuring RL, APO, and supervised fine-tuning workers.