savi-loop-guard

A zero-dependency Python library that detects when an AI agent is stuck in a loop: calling the same tool over and over, or firing calls far faster than any real workflow would. Drop it into your own agent code. No account, no API key, no network call, ever.

Built by SAVI as a standalone, dependency-free package, so you can detect these patterns in your own agent code without an account, an API key, or a dependency on SAVI's platform.

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Install

pip install savi-loop-guard

No dependencies. Nothing else gets installed alongside it.

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Quick start

Record each call as your agent makes it, then check for loops whenever you want (after every call, on a timer, whatever fits your loop):

from loop_guard import LoopGuard, CallEvent
from datetime import datetime, timezone

guard = LoopGuard()

guard.record(CallEvent(
    span_id="call_1",
    agent_id="doc-extractor",
    timestamp=datetime.now(timezone.utc),
    tool_call="search_web",
))

issues = guard.check()
for issue in issues:
    print(issue["type"], issue["agent_id"])

An issue looks like:

{"type": LoopType.VELOCITY, "agent_id": "doc-extractor", "elapsed_s": 12.4, "call_count": 6}
# or
{"type": LoopType.STRUCTURAL, "agent_id": "doc-extractor",
 "tool_call": "search_web", "tool_variants": ["search_web", "search_web_v2"], "call_count": 7}

Prevent the call instead of just observing it

check_before_call() records the event and raises immediately if it would trip a threshold, for callers who want to stop the loop rather than find out about it afterwards:

from loop_guard import LoopGuard, CallEvent, LoopDetected

guard = LoopGuard()

try:
    guard.check_before_call(event)
except LoopDetected as e:
    print(f"Blocked: {e.loop_type}")  # "velocity_loop" or "structural_loop"
    print(e.details)                  # the same dict check() would have returned

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What it detects

Two independent checks:

• Velocity loop: more than 5 calls from the same agent_id within a 30-second window.
• Structural loop: the same tool called more than 5 times, with fuzzy matching so a broken agent can't dodge detection by alternating between near-identical tool names (search_web vs search_web_v2 vs web_search count as the same tool if their name tokens overlap enough).

All four numbers are configurable:

guard = LoopGuard(
    velocity_window_seconds=30,
    velocity_call_limit=5,
    structural_call_limit=5,
    tool_fuzzy_similarity_threshold=0.70,
)

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How this compares

A few other standalone Python packages exist for this: agent-loop-detector, agent-loop-guard, agentguard-kit. All of them, like savi-loop-guard's record()/check() API, are post-hoc/observational; they analyze calls after they happen. savi-loop-guard adds check_before_call() on top for callers who want to prevent the call rather than just observe it, which none of those currently offer.

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Known limitations

LoopGuard keeps every recorded event in memory for the life of the instance; nothing is ever pruned automatically. That's deliberate: structural-loop detection is a total call count with no time bound by design (a tool called 6 times over 3 hours is still a loop, not just a tool called 6 times in 30 seconds), so silently dropping "old" events would blind it to exactly the slow, steady loops it exists to catch.

In practice this means: for a short-lived task, a single LoopGuard() is fine as-is. For a long-running process, create a fresh LoopGuard() per logical unit of work (e.g. per agent run) rather than holding one open indefinitely, so memory doesn't grow without bound.

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License

MIT. See LICENSE.