Use both. Here's where each one wins.
Observability tools see failures. Pisama acts on them. They are precondition layers; we are the action layer above. The comparison below is honest about where each one is stronger, not a zero-sum claim.
Pisama vs Braintrust
Braintrust is excellent at eval workflow: dataset versioning, scoring functions, regression dashboards, comparing model versions side-by-side. Pisama is failure detection: when an agent run goes wrong, name the failure and locate the step.
These are not competitors. Braintrust evaluates outputs against expected behavior; Pisama detects when execution itself misbehaves. Most teams shipping agentic systems need both.
- Best-in-class eval workflow UX (dataset diffs, scorer authoring)
- Tight CI integration: evals as part of the deploy gate
- Fast playground for prompt iteration
- 34 process-level structural detectors with published F1
- In-flight detection: failures caught while the agent is still running
- Multi-agent coordination, loops, persona drift. Braintrust scorers do not target these
At a glance
| Dimension | Braintrust | Pisama |
|---|---|---|
| Primary job | Output evaluation workflow | Process-level failure detection |
| When detection runs | Post-hoc against datasets | Synchronous, mid-execution |
| Author scorer how | Custom code or LLM judge per scorer | Pre-calibrated detector packs |
| Multi-agent depth | Trace UI; scorers per agent | Cross-agent detectors (coordination, loops) |
Recommendation
Braintrust for "did this prompt change regress the eval set?" Pisama for "did this run fail, and where?" Run both.
FAQ
- Is Pisama a Braintrust scorer?
- You can wrap Pisama detectors as Braintrust scorers. The detection happens locally; the result is a structured DiagnosisResult that maps cleanly to a Braintrust scorer output.