The problem

AI agents get stuck. They run the same command again and again, re-read the same file, or hit the same error, and they keep spending tokens until a step or budget limit finally stops them.

The usual fixes are blunt: a fixed step limit, or a rule like "stop after the same action 3 times". Set them tight and you kill runs that would have succeeded. Set them loose and you pay for every loop.

The idea

LoopBrake watches a run one step at a time and gives each step a stuck score. When the score crosses a stop line, it stops the run and says why.

The stop line is not guessed. It is set from your own past successful runs, so that at most a chosen share of good runs, for example 5 in 100, would ever cross it. That is a statistical guarantee (split-conformal calibration), not a tuned target.

In v1 the stuck score is simply how long the run has gone on. Two experiments (below) tested smarter scores, and neither beat it on agents they were not tuned on. The stuck signals (repeats, nothing new, same error again) still run, but only to explain why a stopped run looked stuck.

flowchart LR
    A[Agent takes a step] --> B[Stuck score]
    B --> C{Above the stop line?}
    C -- no --> A
    C -- yes --> D[Stop the run and say why]
    E[(Your past successful runs)] -. set the stop line .-> C

What a stop looks like

A real run from the public SWE-bench data (GPT-5-mini), replayed through LoopBrake. The agent kept searching the same files for a function and found nothing new:

swe-gpt5mini · matplotlib__matplotlib-25775 · stopped at step 27 of 72 · saved 2,023,115 tokens (84%)
Reason: repeating in 5 of last 5 steps (similar to step 19); nothing new in 2 of last 5 steps (0 of 21 output lines new)

  23  sed -n '760,1080p' lib/matplotlib/backend_bases.py
  24  grep -n "def set_antialiased" -n lib/matplotlib/backend_bases.py || true
  25  sed -n '892,932p' lib/matplotlib/backend_bases.py
  26  sed -n '1,220p' lib/matplotlib/patches.py
  27  grep -n "set_antialiased" -n lib -R || true

More in eval/results/kill-stories.md.

Use it

Install. pip install loopbrake, or uv add loopbrake. Until the first PyPI release, use pip install git+https://github.com/SahilSelokar/LoopBrake. No other packages are needed.

1. Set your stop line from your own past runs. It needs at least 19 successful runs (at α 5%); with fewer, LoopBrake only watches.

loopbrake calibrate ~/.claude/projects/<your-project>/ --project my-agent   # your Claude Code history
loopbrake calibrate my_runs.jsonl --project my-agent                         # or recorded runs

2. Add it to your agent loop.

import loopbrake

with loopbrake.start(project="my-agent") as brake:
    for action, result in my_agent_steps():          # your loop
        decision = brake.step(action, result)
        if decision.stop:
            print(decision.reason); break

3. See how it's doing.

loopbrake status --project my-agent        # runs watched, stops, and stops you marked as mistakes
loopbrake feedback <run-id> --mistaken     # tell LoopBrake a stop was wrong

Claude's agent toolkit: pip install "loopbrake[agent-sdk]", then ClaudeAgentOptions(hooks=loopbrake.agent_sdk.hooks(project="my-agent")).

Everything stays on your machine, in ~/.loopbrake. LoopBrake never uses the network. If something inside it fails, it switches to watching only. It never crashes or stops your agent because of its own problem.

Phase 1 results

Before building the product, we tested the idea offline. We replayed 2,979 recorded runs from six agent groups, on coding tasks (SWE-bench Verified) and customer-service tasks (τ-bench).

Verdict: NO-GO. The cheap stuck signals do not clearly beat a calibrated step limit. Following the plan set in advance, a second experiment tested a progress judge (below). For comparison, FailFast, a trained monitor, reports 14.6–20.4% saved at 5% of good runs stopped. Its threshold was set on the same data it reports on, though, so that 5% is a target, not a guarantee.

Full report: eval/results/results.md

Progress judge: also NO-GO

Counting repeats is not the same as understanding a step, so the second experiment asked a fast hosted decision model, Jev (jev-1.13.0), about every step: did this move the agent closer to finishing? and what kind of step was it? Savings are counted net: every token the judge reads is subtracted. The method was chosen on one agent and committed (0827ece) before any other agent was judged.

What we learned:

• A threshold tuned on one agent did not travel. The chosen method asked the judge only when the cheap score passed a level set on Devstral's long runs. On the other agents that level was almost never reached (0–0.3% of steps), so the method rarely stopped anything.
• A judge on every step is expensive. On short customer-service tasks it used about as many tokens as the agent itself.
• Even ignoring its cost, the judge did not spot stuck runs better than counting steps: for example 7.3% vs 9.3% of tokens saved on GPT-5-mini.
• The guarantee held on every group. The whole experiment took 73,812 judgments, for about $3.91.

Full report: eval/results/judge/results.md

How the results are kept honest

• Chosen in advance. The method was picked using one agent's runs and committed before any test runs were scored (commits 8b8eaf3 and, for the judge, 0827ece).
• Mistakes stay visible. The first results were committed exactly as they came out, including a flaw in how runs were split (e655b96). The fix is a separate, documented commit (76e8cdc), with before-and-after numbers in CORRECTIONS.md.
• Tested on unseen runs. The stop line is always checked on runs it was not set from.
• Reproducible. Every number above comes from eval/results/ and comes out identical on every run.

How the guarantee works

1. Each step gets a stuck score. A run's score is the highest score it reaches.
2. Take n past successful runs and sort their scores. The stop line is the k-th smallest score, where k = ⌈(n + 1)(1 − α)⌉ and α is the share of good runs you accept losing (for example 5%).
3. A new successful run then crosses the stop line with probability at most α.

With α = 5%, LoopBrake needs at least 19 past successful runs. With fewer, it only watches and never stops anything.

The guarantee holds when new runs look like the past ones (same agent, same kind of tasks), and when the past runs come from different tasks. We learned the second condition the hard way: see CORRECTIONS.md.

Reproduce

Requires uv and Python 3.11+.

git clone https://github.com/SahilSelokar/LoopBrake && cd LoopBrake
uv run pytest                        # the test suite
uv run python eval/fetch.py          # one-time download, about 450 MB, into ~/.loopbrake/data
uv run python eval/run.py --final    # about 1 minute; writes eval/results/

The progress judge needs a Typesafe API key in TYPESAFE_API_KEY (or in ~/.loopbrake/typesafe_key). Judging every public step costs about $4:

uv run python eval/tasks.py                              # task texts for the judge
uv run python eval/judge.py --group swe-devstral         # one group at a time; resumable
uv run python eval/judge_eval.py --final                 # reads stored answers only; writes eval/results/judge/

Repository layout

src/loopbrake/   scoring core: stuck signals, stop-line rule, run readers (standard library only)
eval/            the experiments: fetch.py downloads the data, run.py replays runs, judge.py asks the
                 progress judge, judge_eval.py scores its answers; results/ holds the published numbers
specs/           design: constitution, roadmap, and the spec, plan, research and tasks of each experiment
tests/           tests, including a check of the guarantee on simulated data
liveness.py      the original naive rule, kept as the baseline

Roadmap

The full plan is in specs/roadmap.md.

Status

v0.1.0 is built and tested. The first PyPI release comes once publishing is set up; until then, install from GitHub (see "Use it"). Its stop rule is a stop line on run length, set from your own past successful runs, with a guaranteed limit on stopping good runs.

License

MIT. One test fixture is a trimmed τ-bench run, used under τ-bench's own MIT license (see tests/fixtures/NOTICE.md).