nabit — nab your agent's silent failures

Your LLM agent said it created the customer. Your database says otherwise. You found out three days later from a support ticket.

nabit is a dead-simple verification layer for LLM agents. Your agent claims it's done; nabit checks the real system state, tells you whether that was true, and — if it wasn't — re-runs the action to fix it. One decorator. Zero dependencies. Sync or async.

from nabit import verify

@verify(lambda result, ctx: db.exists("customers", result["id"]),
        retries=2)                      # self-heal: re-run on failure
def create_customer(name):
    # agent / tool does the work and claims success
    return {"id": 42, "status": "created"}

If the agent returns {"status": "created"} but the row isn't in the database, nabit catches the lie instead of letting a green dashboard hide it — then retries the action up to retries times before giving up.

How it's different

Most tools in this space either detect problems without fixing them, only check the text the model produced (not whether the real action happened), or make you stand up a backend to do it. nabit:

• checks real side effects, not output schema (vs Guardrails AI / Instructor)
• closes the loop — self-heal retries, not just detection (vs Drift / trace viewers)
• verifies inline at runtime, not in a postmortem (vs agent-coroner)
• has zero dependencies and no backend — it's a decorator, not a platform (vs COGEXT)

Why this exists

Trace viewers (Langfuse, LangSmith, Helicone) answer "what did the agent do?" really well. They don't answer "was what it did actually correct?"

The expensive failures are semantic, not technical: no exception thrown, the tool call succeeded, and the output was still wrong. Those look identical to a success at the log level. nabit is the layer that checks the claim against reality.

Install

pip install nabit

The dashboard

Turn any JSONL sink file into a self-contained HTML dashboard — inline CSS/JS, no server, no account, works offline, data never leaves your machine:

python -m nabit dashboard audit.jsonl                  # writes + opens browser
python -m nabit dashboard captions.jsonl hackbot.jsonl # merge sources
python -m nabit report audit.jsonl                     # terminal table

Summary cards, per-check pass/fail table, text search, source/run/status filters, and expandable drill-downs showing each lying claim's payload and error. This is the tool that was used to browse a 29k-verification production audit across two systems (a content pipeline and a recon platform).

Usage

Post-conditions check reality, not the agent's word

A post-condition is (result, context) -> bool. result is what the function returned; context is the bound call arguments (so you can compare inputs to outputs). Return truthy if reality matches the claim.

from nabit import verify

@verify(lambda result, ctx: ticket_store.is_closed(result["ticket_id"]))
def close_ticket(ticket_id):
    agent.act(f"close ticket {ticket_id}")
    return {"ticket_id": ticket_id, "status": "closed"}

Modes — tune how loud failures are

from nabit import verify, Mode

@verify(check, mode=Mode.WARN)    # log a warning, keep running  (default — safe for prod)
@verify(check, mode=Mode.RAISE)   # raise VerificationError      (great for tests / CI)
@verify(check, mode=Mode.LOG)     # info-level log only
@verify(check, mode=Mode.SILENT)  # record only; inspect later

Self-heal — re-run the action when verification fails

def feedback(attempt, last_result, ctx):
    # optional: nudge the agent before the next attempt
    log.warning("verification failed on attempt %d, retrying", attempt)

@verify(check, retries=3, backoff=0.5, on_retry=feedback)
def book_flight(req):
    ...

retries re-runs the whole action up to N times until the post-condition passes; backoff adds linear delay between attempts; on_retry lets you feed the discrepancy back to the agent. Detection and correction, in one decorator.

Composable checks (no hand-written lambdas)

from nabit import verify
from nabit.checks import (all_of, has_keys, field_equals, file_fresh, http_ok,
                         truthy, min_length, contains, matches, in_range)

@verify(all_of(has_keys("id", "status"), field_equals("status", "created")))
def create(...): ...

@verify(file_fresh(lambda r, c: r["path"], max_age_s=60))   # cron wrote a fresh file?
def nightly_report(): ...

@verify(http_ok(lambda r, c: r["url"]))                     # deployed URL is live?
def deploy(): ...

@verify(truthy())                                           # not [] / "" / None
def search(...): ...

# an agent's "reportable" finding must carry real proof, not a one-line claim
@verify(all_of(min_length(100, key="evidence"), matches(r"https?://", key="evidence")))
def finish_task(finding): ...

@verify(in_range(1, 10, key="score"))                       # LLM judge score in spec
def judge(...): ...

Full check list: all_of / any_of / not_, has_keys, equals, field_equals, truthy, non_empty, min_length, contains, matches, in_range, file_exists, file_fresh, http_ok, predicate.

Group verifications under a run id

from nabit import run, summary

with run("signup-flow") as rid:
    create_customer(...)
    charge_card(...)

print(summary(run_id=rid))
# {'total': 2, 'passed': 1, 'failed': 1, 'pass_rate': 0.5, 'failures': ['charge_card']}

Solves the "nothing shares a run id" problem — all the checks for one logical task carry the same id.

Tee results anywhere (pluggable sinks, still zero-dep)

import nabit
from nabit import jsonl_sink

nabit.add_sink(jsonl_sink("verifications.jsonl"))   # built-in

def otel_sink(r):                                   # or your own, 3 lines
    span.set_attribute("nabit.passed", r.passed)
nabit.add_sink(otel_sink)

LangGraph / LangChain

nabit works with any framework via the decorator, but LangGraph users get a first-class adapter (lazily imported — installing nabit never pulls in LangChain). Two options:

Passive callback — add it once, verify every tool result, no restructure:

from nabit.adapters.langgraph import NabitCallback
from nabit.checks import has_keys, truthy

cb = NabitCallback(checks={
    "create_customer": has_keys("id"),
    "search_hotels": truthy(),        # catches the empty-list silent failure
})
graph.invoke(state, config={"callbacks": [cb]})
print(cb.summary())

Node wrapper — wrap a node to get full self-heal (a node is just a function, so it can be re-run):

from nabit.adapters.langgraph import verify_node
from nabit.checks import predicate

builder.add_node("book", verify_node(
    book_node,
    predicate(lambda update, ctx: db.has_booking(update["booking_id"])),
    retries=2,
))

Async works the same way

@verify(pc, retries=2)                    # async funcs + async post-conditions
async def create_order(cart):
    ...

Inspect what happened

from nabit import get_results, summary, failures, report

for r in get_results():
    print(r.name, "PASS" if r.passed else "FAIL",
          f"{r.duration_ms:.0f}ms", f"attempts={r.attempts}", r.error)

print(summary())        # exact totals, even beyond the in-memory ring buffer
print(failures())       # just the liars
print(report())         # formatted pass/fail table:

# verification             total     pass      FAIL     rate   avg ms
# --------------------------------------------------------------------
# caption_extracted       14232    14232         0  100.0%     0.0
# storage_path_exists      3000      2997         3   99.9%     0.0
# --------------------------------------------------------------------
# TOTAL                   17232    17229         3   99.9%     0.0

Retro-audits: verify claims in bulk

Retro-audit real data ("does every row that claims X actually have X?") with check_claims — no decorator-per-function ceremony:

from nabit import check_claims, run, add_sink, jsonl_sink, report_from_jsonl

add_sink(jsonl_sink("audit.jsonl"))          # stream every result to disk
caption_ok = {ids with a real caption row}

with run("audit-2026-10"):
    s = check_claims(
        lambda r, ctx: r["video_id"] in caption_ok,
        ({"video_id": row[0]} for row in db.execute("select id from videos ...")),
        name="caption_extracted",
    )

print(s)                                     # check_claims: 14232/14232 passed, 0 failed
print(s.failed_results[:5])                  # the liars, each with its claim
print(report_from_jsonl("audit.jsonl"))      # exact table from the sink file

Each claim is a bare value (for dicts, ctx = the claim itself) or a (result, ctx) pair. This pattern was used to retro-audit a live 24k-row production database in under a second.

Celery: run ids across the process boundary

A Celery task runs in a different process than the code that queued it, so run() contextvars don't survive .delay(). The optional celery extra propagates run ids through message headers:

pip install nabit[celery]

from nabit.contrib.celery import install
install()                       # once, at startup (idempotent)

# publisher side — unchanged usage:
with run("signup-123"):
    process.delay(user_id)      # run id rides the message headers

# worker side — verifications inside the task carry run "signup-123":
@app.task
def process(user_id):
    do_work(user_id)            # @verify results inherit the run id
    return summary()            # scoped to this task's run

Core nabit stays zero-dependency; celery is only imported if you import nabit.contrib.celery.

Also catches "it threw but the side effect still happened"

By default (on_error=True) the post-condition runs even when the wrapped function raises — so a tool that errors after mutating state (or succeeds in reality despite throwing) still gets verified. The original exception is re-raised after recording.

Scope

nabit is the outcome verifier: it answers "did this specific action actually happen?" and corrects it when it didn't. It deliberately does not try to be an observability platform. For fleet-wide behavioral monitoring — real-time degradation detection across many runs (step-count blowups, token spikes, slow drift over hundreds of runs), trajectory snapshot diffing, and a dashboard — see The Production Agent Reliability Kit, built on published research (SENTINEL, self-supervised anomaly detection for LLM agents).

Rule of thumb: use nabit to verify one action's real effect inline; reach for the Kit when you need to watch patterns across runs over time.

License

MIT — see LICENSE. Use it anywhere, including commercially.

Built by Jake Garnier.