A linter for agent runs— it reads the execution trace of a tool-calling agent (what itactually did) and flags structural bugs deterministically, with the exact evidence and a CI exit code. It runsafterthe 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
.- Deterministic rules catch
structural defects, not whether the final answer was correct. - 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
). - The recovery scorecard needs labeled task outcomes (success oracles); without them it measures
behavioral recovery only ("did not crash"), a weaker claim than correctness. - 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.
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.
| Rule | Finding | Tiers |
|---|---|---|
| R1 | schema violation — args fail the tool's JSON Schema | hard_defect |
| R2a | tool returned an error | hard_event (structured signal) / candidate (heuristic) |
| R2b | an errored result's value reused by a later side-effecting call | hard_defect / candidate |
| R3 | hallucinated argument — value not derivable from provenance | candidate ; hard_defect if the field is annotated provided |
| R4 | loop — N identical no-progress calls (polls/retries excluded) | candidate |
| R5 | redundant call — identical call + identical result, no mutation between | candidate |
| R6 | malformed arguments — the emitted tool-call arguments are not valid JSON | hard_defect |
| R7 | unknown tool — a call to a tool absent from the declared toolset (possible hallucinated tool) | candidate |
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.
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}
}
}
}
A tool can also declare what failure looks like in its result, so a domain failure returned as
a transport success (HTTP 200 carrying {"status": "declined"}
) is caught structurally instead of slipping through:
{
"tools": {
"charge_card": {
"metadata": {
"side_effecting": true,
"failure_when": {"pointer": "/status", "in": ["declined", "failed"]}
}
}
}
}
failure_when
is a JSON Pointer into the result plus a match (in
/ equals
/ exists
); a match
is a structured error for R2 (feeding R2a and, on reuse into a side-effecting call, R2b). A
side-effecting tool with no failure_when
and an unclassifiable result is suppressed with a reason — never counted as a clean pass.
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.
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_id
s) 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).
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.
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)
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.