Galea vs. LangSmith vs. Braintrust vs. Arize
Four tools look at the same agent workflow. Three say clean. Galea catches the fabricated citation. Here's why the approaches differ.
| Capability | Galea | LangSmith | Braintrust | Arize |
|---|---|---|---|---|
| Trace-level investigation | ✓ Investigator agent walks each run | ✗ Span viewer only | ✗ Eval pass/fail only | ✗ Embedding drift only |
| Customer-specific priorities | ✓ 10 configurable axes | ✗ Generic metrics | ✗ Generic scoring | ✗ Generic thresholds |
| Blame attribution | ✓ Causal blame per agent | ✗ No blame model | ✗ No blame model | ✗ No blame model |
| Eval generation from incidents | ✓ Finding → durable eval | ✗ Manual eval creation | Partial — dataset management | ✗ Manual |
| Framework-neutral | ✓ Any runtime via adapters | LangChain-first | Framework-agnostic | Framework-agnostic |
| Audit export | ✓ Signed export per workflow | ✗ No audit chain | ✗ No audit chain | ✗ No audit chain |
| Fabricated citation detection | ✓ Replay + evidence matching | ✗ Not detected | ✗ Not detected | ✗ Not detected |
| Primary use case | Investigation + optimization | Tracing + debugging | Evals + experimentation | ML observability + drift |
Galea vs LangSmith
LangSmith gives you a span viewer — it shows what happened. Galea gives you an investigator — it explains whether what happened was good. LangSmith is LangChain-first; Galea works across any runtime. LangSmith shows you spans returned OK; Galea catches the fabricated citation inside those spans.
Galea vs Braintrust
Braintrust focuses on eval management and experimentation — measuring whether your model outputs match expected results. Galea starts from production traces and investigates against your company's priorities. Braintrust says "5/5 passed." Galea says "the citation was fabricated — here's the agent at fault."
Galea vs Arize
Arize is built for ML observability — embedding drift, feature importance, model performance monitoring. Galea is built for agent workflows — multi-step, multi-model, multi-framework runs where the question isn't "did the model perform well?" but "did the workflow produce a correct, safe, compliant result?"
Want to see how Galea investigates your workflow?
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