# Show HN: Tracelint – a linter for AI agent traces, no LLM judge

> Source: <https://github.com/AshwinUgale/tracelint>
> Published: 2026-08-18 14:49:47+00:00

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](https://langfuse.com) 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](https://huggingface.co/datasets/PatronusAI/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:

``` python
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](https://phoenix.arize.com) instance:

``` python
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.

``` python
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.