Multi-agent LLM pipelines fail quietly — agents rephrasing the same argument forever, silent deadlocks, graphs that cycle without an exit. Most tools (LangSmith, AgentOps) show you this after the fact. Agent Watchdog intervenes at runtime, like a circuit breaker — watching message similarity and tool-call patterns to halt stuck LangGraph runs before you burn tokens. Built by PengKernel LLC: npm install agent-watchdog.
Hey Product Hunt 👋
I built Agent Watchdog after watching my own LangGraph pipelines quietly burn through tokens — two agents debating in circles, or a graph stuck waiting on a condition that never resolves. Standard tracing tools (LangSmith, AgentOps) are great for post-mortems, but I wanted something that intervenes while it's happening, not after.
It watches message similarity and tool-call patterns across a sliding window, and halts the run the moment it detects an argument loop, a repeated tool call, or a stuck iteration — before you're staring at a $40 API bill for a conversation going nowhere.
Just shipped a new version with tool-call loop detection and state-regression checks.
Would love feedback, especially from anyone running multi-agent systems in production — what failure modes have you hit that this doesn't catch yet?