Skip to content
← Interfaces with External Systems Security Considerations →

Debugging and Profiling Dana Programs

(This document is a placeholder and needs to be populated with details about tools and techniques for debugging and profiling Dana code.)

Key Aspects to Cover:

Debugging

  • Error Messages:
  • Clarity and helpfulness of error messages from parser and interpreter.
  • Stack traces: How they are presented, level of detail.
  • Logging:
  • The log() function as the primary way to trace execution.
  • Configuring log levels or output streams.
  • REPL for Debugging: Using the REPL to inspect state, test snippets, and experiment.
  • Variable Inspection: How to inspect variable values at runtime (e.g., via log() or future debugging tools).
  • __dana_debug_mode (Conceptual):
  • A system variable system:__dana_debug_mode (boolean) that, if true, could enable more verbose logging, additional checks, or expose more internal state.
  • How IPV and other components might alter behavior in debug mode.
  • Step-through Debugger:
  • Is a traditional step-through debugger (breakpoints, step-over, step-in) planned?
  • If so, integration with IDEs (e.g., VS Code via Debug Adapter Protocol).
  • Challenges for a language like Dana with potential LLM calls.
  • Post-mortem Debugging: Examining state after an error.

Profiling

  • Motivation: Identifying performance bottlenecks, especially in complex agent logic or interactions with external resources (LLMs).
  • Metrics: What to measure? (e.g., execution time of functions, resource call latency, token usage for LLMs).
  • Tools:
  • Built-in profiling capabilities?
  • Integration with existing Python profiling tools (if Dana calls Python-backed functions/resources).
  • __dana_profile_mode (Conceptual):
  • A system variable system:__dana_profile_mode (boolean or string indicating profile level) to enable collection of performance metrics.
  • How the interpreter and resource calls would record timing/usage data.
  • Output: How profiling data is presented (e.g., summary report, flame graphs).

Visualization

  • Visualizing execution flow, especially for complex agent interactions or IPV decision making.
  • Visualizing context changes in SandboxContext.

Self-reflection: For early stages, robust logging and informative error messages are key. A full debugger is a significant undertaking. Profiling, especially for LLM interactions, will be important for optimization.