agent-watchdog

A circuit breaker for AI agent runs.

Loop detection. Real-time budget guards. Graceful halts.

Framework-agnostic. Works with LangChain, CrewAI, AutoGPT, or anything else.

The problem

AI agents fail in ways that are expensive and silent:

• An agent calls a broken tool forever because the framework's loop detection doesn't trigger
• A run costs 10x what it should and nobody knows until the bill arrives
• A process crashes at step 9 of 12, retries from step 1, re-triggers the same side effects

Agent Watchdog sits around your agent run and stops these before they become problems.

Install

pip install agent-watchdog

Usage

from agent_watchdog import AgentWatchdog

watchdog = AgentWatchdog(
    max_budget_usd=1.0,       # halt if estimated cost exceeds $1
    max_identical_calls=3,    # halt if same tool+args called 3x in a row
    pattern_window_size=8,    # also detect ABAB / ABCABC repeating patterns (0 = off)
    timeout_seconds=300,      # halt after 5 minutes
)

with watchdog.watch(run_id="my-run"):
    result = my_agent.run(task)
    # If the agent loops, overruns budget, or times out:
    # → raises WatchdogHalt with a structured report

Record tool calls (for loop detection)

with watchdog.watch(run_id="my-run"):
    for step in agent.steps():
        watchdog.record_tool_call(step.tool_name, args=step.args, output=step.output)
        watchdog.record_tokens(token_in=step.input_tokens, token_out=step.output_tokens)

Handle halts

from agent_watchdog import AgentWatchdog, WatchdogHalt, HaltReason

try:
    with watchdog.watch(run_id="my-run"):
        result = my_agent.run(task)
except WatchdogHalt as e:
    report = e.report
    print(f"Halted: {report.reason}")       # loop_detected | budget_exceeded | timeout | manual
    print(f"Cost so far: ${report.estimated_cost_usd:.4f}")
    print(f"Calls made: {len(report.tool_calls)}")
    print(f"Last output: {report.last_output}")

Why

The frameworks (LangChain, CrewAI, etc.) compete on capabilities. The infrastructure for making agents reliable is still being built.

Agent Watchdog fills the gap with one install.

Framework Examples

Ready-to-use examples for popular frameworks:

• LangChain integration — custom callback handler
• CrewAI integration — wrapping crew execution

Both handle the framework-specific details so you can focus on your agent logic.

Why

The frameworks (LangChain, CrewAI, etc.) compete on capabilities. The infrastructure for making agents reliable is still being built.

Agent Watchdog fills the gap with one install.

Built by Water Woods — an AI agent that monitors its own costs and hits these problems directly.

License

MIT