tracelint

A linter for agent runs — it reads the execution trace of a tool-calling agent (what it actually did) and flags structural bugs deterministically, with the exact evidence and a CI exit code. It runs after the run, on the trace — not on your code — and no second model ever judges it.

tracelint reads a tool-calling agent's trace and reports structural defects — schema-violating tool calls, ignored tool errors, hallucinated arguments, loops, and redundant calls — each with the exact trace lines as evidence, and returns a CI exit code. It also ships a fault injector and a per-fault recovery scorecard.

Model-as-judge detection of these defects is unreliable (published trace-error benchmarks show low localization accuracy). Many of these defects are structurally decidable and need no judge — that is the entire premise of this tool. No second model ever judges the trace.

View the live demo report — the constructed validation suite (one planted instance of every defect, clean controls, and legitimate-but-suspicious cases) plus the robust-vs-buggy recovery scorecard, generated by tracelint demo.

Limitations (read first)

1. Deterministic rules catch structural defects, not whether the final answer was correct.
2. Hallucinated-argument, loop, and redundant-call findings are candidates unless structurally proven — legitimate value transforms and intentional retries can trip them; each is shown with its evidence for human review, never asserted as a verdict. High-confidence hallucination detection requires the tool schema to declare field origins (x-value-origin).
3. The recovery scorecard needs labeled task outcomes (success oracles); without them it measures behavioral recovery only ("did not crash"), a weaker claim than correctness.
4. A trace is only as complete as its instrumentation. A rule whose required field is missing is suppressed with a stated reason — tracelint never lints a partial trace as if complete.

Quick start

The demo runs a keyless validation suite and a recovery scorecard end to end — no API key, no model download:

pip install tracelint
tracelint demo --html demo.html

Lint a trace in CI:

tracelint check ./trace.json --tools ./tools.json     # exit 2 on a hard_defect

Exit codes: 0 clean · 2 a structurally-provable defect (hard_defect) · 3 an input error. Heuristic candidates never fail CI on their own; suppressions are disclosed but are not defects.

The rules

hard_event and hard_defect are orthogonal to the finding kind: a tool-error event is a hard_event from a structured status field but a candidate from an exception-like string in free-form content.

Input format

A trace is a JSON object (.json, or .jsonl for many):

{
  "run_id": "run-1",
  "steps": [
    {"type": "message", "role": "user", "content": "cancel order 4521 if it hasn't shipped"},
    {"type": "tool_call", "call_id": "c1", "name": "get_order_status", "args": {"order_id": "4521"}},
    {"type": "tool_result", "call_id": "c1", "content": {"status": "processing"}, "status": "ok"},
    {"type": "tool_call", "call_id": "c2", "name": "cancel_order",
     "args": {"order_id": "4521", "reason": "not_shipped"}}
  ],
  "final": "Order 4521 has been cancelled."
}

tools.json supplies the ground truth the rules check against:

{
  "tools": {
    "cancel_order": {
      "schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                 "required": ["order_id"]},
      "metadata": {"side_effecting": true}
    }
  }
}

The rules run against one canonical trace schema; a thin adapter translates each source's format into it, so the rules never change. Built in: from_openai_messages (OpenAI chat message lists), from_langfuse_trace (a Langfuse trace's observations), and from_otel_spans (OpenTelemetry / OpenInference — the universal standard, so it reaches Arize Phoenix, OpenLLMetry, Langfuse-via-OTel, and datasets like TRAIL, not just one vendor). See examples/langfuse_cookbook.py to lint the traces you already collect in Langfuse and write findings back as scores.

On real traces: the adapters are validated against live data, not just the spec — from_langfuse_trace on real Langfuse v4 runs, and from_otel_spans on real TRAIL benchmark traces, where tracelint deterministically localized real tool errors, a malformed tool call, and excessive-retry loops with no model in the loop. Real exports vary, so a new source may need a small adapter tweak — and when a field a rule needs is absent, that rule suppresses (says so) rather than guessing, so an unhandled quirk degrades safely instead of producing a wrong result. More adapters are future work.

Lint the traces you already collect

check reads native tracelint JSON by default, but --format points it straight at the traces your stack already emits — no manual schema conversion:

tracelint check spans.json    --format openinference   # OTel/OpenInference: Phoenix, OTLP, TRAIL
tracelint check messages.json --format openai          # an OpenAI chat message list
tracelint check trace.json    --format langfuse        # a Langfuse trace export

Most rules need no tool schemas, so this works keyless; add --tools tools.json to light up the schema-dependent rules (R1, and R3's high-confidence tier). A multi-trace input (a .jsonl file, a JSON array, or an OTLP export carrying several trace_ids) fans out to one report each. From the library, the same one-liner:

from tracelint import lint_otel_trace

report = lint_otel_trace(spans)   # spans: your OpenInference span export (a list of dicts)
print(report.exit_code)           # 0 or 2

See examples/lint_openinference_phoenix.py for an offline, keyless end-to-end run (Phoenix-shaped spans → findings, with and without a tool registry).

Straight from a running Arize Phoenix instance:

import phoenix as px
from tracelint import lint_otel_trace

spans = px.Client().get_spans_dataframe().to_dict("records")
print(lint_otel_trace(spans).exit_code)

Both Phoenix shapes are handled: the span-export JSON (top-level span_kind) and the get_spans_dataframe() records (attributes as attributes.* columns).

Recovery scorecard

Measure how an agent behaves under injected faults, scored against deterministic success oracles:

tracelint scorecard --demo --faults timeout,error,rate_limit --runs 5

The baseline must satisfy the oracle first (else recovery is not measured). Each fault type reports a correctness-recovery rate with a Wilson confidence interval; with no oracle it falls back to behavioral recovery, labeled as weaker.

Library

from tracelint import lint_trace, default_rules, Trace, ToolRegistry

trace = Trace.load("trace.json")
registry = ToolRegistry.load("tools.json")
report = lint_trace(trace, default_rules(), registry)
print(report.exit_code)          # 0 or 2
for f in report.active_findings:
    print(f.rule, f.tier.value, f.summary)

Development

python -m pytest
ruff check src tests

The core is dependency-light (jsonschema + stdlib) and the whole test suite is deterministic and offline. A real OpenAI trace-generating agent lives behind the opt-in [real-agent] extra and is never part of the linter. Python 3.10–3.12.