dspy-debugging-observability

# DSPy Debugging & Observability ## Goal Debug, trace, and monitor DSPy programs using built-in inspection, MLflow tracing, and custom callbacks for production observability. ## When to Use - Debugging unexpected outputs - Understanding multi-step program flow - Production monitoring (cost, latency, errors) - Analyzing optimizer behavior - Tracking LLM API usage ## Related Skills - Optimize programs: [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md) - Evaluate quality: [dspy-evaluation-suite](../dspy-evaluation-suite/SKILL.md) - Build agents: [dspy-react-agent-builder](../dspy-react-agent-builder/SKILL.md) ## Inputs | Input | Type | Description | |-------|------|-------------| | `program` | `dspy.Module` | Program to debug/monitor | | `callback` | `BaseCallback` | Optional custom callback (subclass of `dspy.utils.callback.BaseCallback`) | ## Outputs | Output | Type | Description | |--------|------|-------------| | `GLOBAL_HISTORY` | `list[dict]` | Raw execution trace from `dspy.clients.base_lm` | | `metrics` | `dict` | Cost, latency, token counts from callbacks | ## Workflow ### Phase 1: Basic Inspection with inspect_history() The simplest debugging approach: ```python import dspy dspy.configure(lm=dspy.LM("openai/gpt-4o-mini")) # Run program qa = dspy.ChainOfThought("question -> answer") result = qa(question="What is the capital of France?") # Inspect last execution (prints to console) dspy.inspect_history(n=1) # To access raw history programmati...