Laminar's debugger already reruns traces and confirms fixes. Is that the same loop as Pisama's healing?

They share the rerun idea and differ in where it runs. Laminar's rerun executes in your development loop, driven by your coding agent, to confirm a fix while you iterate. Pisama's rerun is a server-side product capability: the platform applies the fix to a live workflow engine (production-verified on n8n), re-executes the failing unit, and re-runs detection to confirm the failure cleared. One is a debugging aid; the other is production fix verification.

Does Laminar publish detection accuracy?

Not as of July 2026. Signals are plain-English patterns evaluated per trace, with no published precision, recall, or F1. Pisama publishes per-detector F1 from a versioned calibration program: 7,212 traces, dataset fingerprints, and a public changelog.

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 Laminar

Name: Pisama
Author: Pisama

Laminar is an open-source (Apache 2.0) agent observability platform out of YC S24, with a Rust core, SOC 2 Type II, and first-class support for the OpenAI Agents SDK, Vercel AI SDK, Claude Agent SDK, and Pydantic AI. Its Signals feature runs plain-English failure patterns on every trace, and its debugger lets Claude Code, Cursor, or Codex re-run a trace from cached state and confirm a fix resolved the issue.

If you ship agents from Cursor or Claude Code, Laminar is built for you, and so is Pisama. The overlap is real. The difference is the evidence layer: Laminar describes failure patterns in plain English and publishes no accuracy numbers for them. Pisama names failures against a calibrated taxonomy and publishes per-detector F1 from a versioned calibration program.

The rerun loops also differ in kind. Laminar's rerun is a debugging aid inside your dev loop, driven by your coding agent. Pisama's rerun is a server-side production capability: apply the fix, re-execute the failing unit, re-detect, and only then trust it.

Where Laminar wins

Open source under Apache 2.0 with a real community; you can read and run the whole stack
SOC 2 Type II today (Pisama's audit is in progress, target Q4 2026)
First-class adapters for the OpenAI Agents SDK, Vercel AI SDK, Claude Agent SDK, and Pydantic AI, which are gaps in Pisama's adapter matrix today
The cached-state rerun debugger is a genuinely fast dev-loop tool for coding-agent users

Where Pisama wins

87 detectors (6 externally validated at production grade) with per-detector F1 published; Laminar publishes no accuracy numbers for Signals
Named MAST-aligned taxonomy: detections arrive as a specific failure mode with a calibrated confidence, not a pattern description
Multi-agent and sub-agent detectors: coordination breakdown, role usurpation, information withholding, silent cascade
Server-side healing verified by re-execution: auto-apply on n8n, then re-run the failing unit and re-detect. Laminar's rerun verifies a fix in your dev loop, not in production
Workflow-engine coverage: n8n, Dify, OpenClaw, where coding-agent-centric tooling does not reach

At a glance

Dimension	Laminar	Pisama
Detection	Signals: plain-English patterns, no published accuracy	Named detectors, F1 published per detector
Multi-agent failures	Not targeted	Coordination, role usurpation, withholding, silent cascade, by name
Fix loop	Coding agent reruns from cached state in your dev loop	Server-side apply (n8n), re-execute, re-detect
Compliance	SOC 2 Type II	In progress, target Q4 2026
License	Apache 2.0	MIT
Entry pricing	$0 / $30 / $150	$0 / $9 / $19

Externally validated at production grade: real-trace F1 0.80 or higher, precision 0.70 or higher, 30 or more external traces, external-grounded thresholds, and no per-difficulty blind spot (capability registry, external-only lane, 2026-06-14).

Recommendation

If your agents live in TypeScript on the OpenAI Agents SDK or Vercel AI SDK and you debug from Cursor or Claude Code, Laminar is on home turf and the observability is excellent. Run Pisama when you need to know what class of failure fired and how often the detector is right, when your system is multi-agent, or when the thing that broke is an n8n or Dify workflow. The two compose: Laminar for traces and the dev loop, Pisama for calibrated verdicts and verified fixes.

FAQ

Laminar's debugger already reruns traces and confirms fixes. Is that the same loop as Pisama's healing?: They share the rerun idea and differ in where it runs. Laminar's rerun executes in your development loop, driven by your coding agent, to confirm a fix while you iterate. Pisama's rerun is a server-side product capability: the platform applies the fix to a live workflow engine (production-verified on n8n), re-executes the failing unit, and re-runs detection to confirm the failure cleared. One is a debugging aid; the other is production fix verification.
Does Laminar publish detection accuracy?: Not as of July 2026. Signals are plain-English patterns evaluated per trace, with no published precision, recall, or F1. Pisama publishes per-detector F1 from a versioned calibration program: 7,212 traces, dataset fingerprints, and a public changelog.