looptrip 0.1.2

Deterministic, framework-agnostic detector of multi-agent coordination pathologies that trips at iteration 2

looptrip

Deterministic, framework-agnostic detection of multi-agent coordination pathologies — caught at iteration 2, not on the invoice.

looptrip watches a multi-agent run as a stream of normalized events and flags the coordination pathologies that make agent systems burn money and spin: duplicate-work loops, ping-pong / livelock, deadlock, and non-termination. It is detection-first — it works over data you already have (OpenTelemetry GenAI spans, or a CAST cast.db) — and deterministic / zero-LLM: the same event stream always yields the same verdict. looptrip is an observer, never a gate; it reports, it never blocks.

This release (0.1.1) ships full pathology coverage (duplicate-work, ping-pong / livelock, deadlock, non-termination), configurable sensitivity controls, counterfactual-replay attribution (via the attribute subcommand), and the cast.db adapter with reproducible proof on real data — plus OpenTelemetry support (Phase 4): an offline adapter (OTelSpanAdapter from flat span dicts and OTLP/JSON exports) plus a live LooptripSpanProcessor for in-flight detection, available in the looptrip[otel] extra. (0.1.0 was published before the Phase 4 code merged and shipped without the OTel modules; 0.1.1 is the first artifact to actually include them.)

The headline

On two real recorded multi-agent runaway sessions, a single workflow-subagent dispatch recurred 54 and 49 times with no progress between repeats. Tripping at the second dispatch — the first repeat — instead of letting the loop run to exhaustion would have saved:

session	runaway loop	dispatches	trip point	saved
2e6c0288	workflow-subagent	54	dispatch #2	$320.16
da27b414	workflow-subagent	49	dispatch #2	$472.80
			total	$792.96

Reproduce it yourself — no database required, the data is a committed fixture:

pip install -e .
looptrip proof

Why "iteration 2"

Native runaway guards are blunt total-step counters that trip at N=10–25 — after the waste has compounded. looptrip's trip is a safety predicate keyed on the pathology signature: no signature (agent, tool, args_hash) may recur without an intervening progress delta. The instant a signature is seen a second time (within a configurable input-token tolerance, with no progress marker between), it fires — before the third wasted turn and the O(N²) context-cost compounding. "2" is the default threshold, not a magic number. The approach (signature-keyed detection with configurable thresholds) is what matters — the detector itself is not the moat; the durable asset is standards engagement — adopting the upstream OpenTelemetry GenAI gen_ai.agent.handoff.* convention and contributing the agent-loop pathology layer (pending-wait and loop-termination semantics) that looptrip uniquely detects.

The worst real runaways are the hardest to catch: a workflow-subagent loop emits no structured handoff contract at all. So looptrip detects from the (agent, ts) repeat signal plus input-token variance alone; any handoff metadata only enriches the signal — it is never required.

Usage

looptrip proof                                  # reproduce the $792.96 headline on the committed fixture
looptrip scan fixture:<session_id>              # scan a session from the packaged fixture
looptrip scan cast-db:<session_id>              # scan a live cast.db session (CAST hosts only)
looptrip scan --all fixture:<session_id>        # run all four detectors (adds a 'kind' column)
looptrip attribute fixture:<session_id>         # attribute pathologies to decisive handoffs (overdetermined = no single one)
looptrip --version

How it works

1. One normalized event — (agent, tool, args_hash, ts, handoff_state) plus optional cost/token metadata. An adapter maps each source's fields onto this schema, so detection logic never touches source-specific span-attribute renames.
2. Detection-first — Phase 1 ships a cast.db adapter. Phase 4 (now shipped) adds an offline OTel adapter (OTelSpanAdapter ingesting flat span dicts and OTLP/JSON/JSONL exports) and a live LooptripSpanProcessor for in-flight pathology detection in the looptrip[otel] extra. Because agent_runs carries no per-dispatch args, the adapter sets args_hash=None and detection leans on the token-variance signal.
3. Stdlib state machine — the detector groups events by signature and trips on the 2nd same-signature occurrence with no progress delta. The core is stdlib-only; OpenTelemetry is an optional [otel] extra, never imported by the detector.
4. False-positive control is first-class — a configurable input-token tolerance, a progress-delta marker, and an idempotent_agents allowlist keep legitimately-repeatable work (commits, reviews) from tripping. looptrip is meant to be run detect-then-print and dogfooded before any signal is trusted.

Honest framing

This project tries hard not to oversell:

• Attribution numbers. Published LLM-prompting baselines for "which handoff broke the run" sit around ~14% — but that is the prompting baseline; structured / deterministic methods reach 29–52%. Adding structure is the lever, and looptrip's deterministic replay (Phase 3) is the limit case of that frontier — not a fix for a permanent ceiling. We don't anchor to "14%."
• Cost numbers. The $792.96 here is verifiable from the committed fixture. Larger figures circulate — e.g. a widely-reported "$47K" agent-loop bill — but those are unverified, and we label them as such.
• Prior art. The market gap is real, but the durable asset is the standard, not the ~200-line detector. A direct competitor exists — Watchtower (MIT, LangGraph-only, trips at 3+ repeats, no handoff contract, no attribution). looptrip differentiates on framework-agnosticism, speed, and standards engagement with the OpenTelemetry GenAI agent-observability conventions — adopting the upstream gen_ai.agent.handoff.* handoff identity and contributing the agent-loop pathology layer it uniquely detects.

Roadmap

• Phase 1 — (SHIPPED) cast.db adapter + duplicate-work / iteration-2 detector + reproducible proof.
• Phase 2 — (SHIPPED) full pathology coverage (ping-pong / livelock, deadlock, non-termination) + sensitivity controls.
• Phase 3 — (SHIPPED) counterfactual replay attribution ("which handoff was decisive").
• Phase 4 — (SHIPPED) OpenTelemetry support: offline adapter (OTelSpanAdapter ingesting flat span dicts, OTLP/JSON, JSONL exports) and live LooptripSpanProcessor (in-flight pathology detection via on_start hooks, thread-safe, deduped) in the looptrip[otel] extra. Unit and synthetic testing complete; production multi-agent validation pending.
• Phase 5 — (SHIPPED) packaging (Claude Code plugin, Homebrew).
• Phase 6 — (SHIPPED) documentation (reference deep-dives, examples, architecture notes).
• Phase 7 — (repo work merged; upstream engagement in progress) OpenTelemetry GenAI agent-observability semantic-convention engagement: adopt the upstream gen_ai.agent.handoff.* handoff identity (semantic-conventions-genai) and contribute the pathology layer — a pending/blocking wait-for state and loop-termination (gen_ai.agent.finish_reason) semantics — with looptrip as the deterministic reference implementation.
• Phase 8 — (in progress) launch.

Documentation

• Proof — Reproduce the $792.96 headline. Evidence that the fixture is real and reproducible.
• Usage — CLI and library API reference, adapters, and configuration.
• Architecture — Detector design, event normalization, signature matching, and phase-by-phase roadmap.
• Adapters — Implementing a custom adapter for your event source (OTel spans, custom JSON, etc.).
• Testing — Test structure, mutation sanity, fixture integrity, and independent re-derivation.
• Framing — Attribution, cost baselines, related work (Watchtower), and the role of standards.
• Case Studies — Real runaways: workflow-subagent loops, deadlock scenarios, and non-termination traces.
• Contributing — How to contribute, issue triage, and development setup.

License

Apache-2.0. See LICENSE.