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How to trace every LLM call to debug a run with Asamana

Inspect prompts, responses, tokens and latency for each stage of a run.

  • Content
  • Intermediate
  • 45 minutes
  • From the README
  • Any agent

Authorization notice. Asamana is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA developer whose story is behaving oddly and who needs to see exactly what the model was asked and returned at each stage.

The problemOdd character behavior is hard to diagnose without seeing the prompts, outputs and cost behind each cognitive stage.

The outcomeThe user can find the stage and LLM call behind a specific story event, along with its prompt, response, tokens and latency.

What you need first

  • Docker with Compose v2 installed (see the Installing Docker section in docs/deployment.md)
  • git to clone the repository
  • macOS is the recommended platform
  • An API key for an OpenAI-compatible LLM vendor, only if you want to create or run worlds
  • A token and spending budget, since each step makes several LLM calls per character

Steps

  1. Run ./deploy/asamana.sh start with the developer tools enabled
  2. Run a world for a few steps so that LLM calls are recorded
  3. Open the #/dev page and select the Trace view
  4. Filter to the character or step that produced the odd behavior
  5. Compare the prompt and response with the stage it belongs to, such as perception, motivation, decision, action or feedback

Prompt for your agent

In the repo https://github.com/dreamscroll-lab/asamana, run a world for 3 steps and open the Trace view. Report the stage name, token count and latency of the first recorded LLM call for one character. Verify that each recorded call is linked to a stage from the five-stage pipeline.

Caveat. Trace data is shown in the developer tools, which the README says are for local development, so treat recorded prompts as potentially containing generated content.

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