Find where your AI workflow is burning tokens for no reason.
Debug retry storms, repeated calls, dead branches, and prompt waste before costs spiral out of control.
Works with OpenAI · Claude · LangChain · custom agents
Retry Storms
Rate-limited calls spawning cascading retries without backoff limits.
Repeated Model Calls
Same prompt or context re-submitted with identical inputs across loop iterations.
Prompt Bloat
Token counts growing each iteration because context is appended but never pruned.
Dead Branches
Fallback paths activated on every run because the primary path is permanently broken.
Silent Failures
Exceptions caught and discarded. The agent continues as if nothing happened.
Hidden Cost Leaks
No exit conditions. Loops that run until forced timeout, burning budget silently.
Paste a workflow trace. Get an instant diagnosis.
Drop in raw logs from any AI workflow. LOOP identifies waste patterns, scores severity, and gives you specific fixes.
Load the sample log and click Analyze to see a diagnostic report.
Three steps from messy logs to a clean diagnosis.
Paste your logs
Drop in raw output from OpenAI, Claude, LangChain, AutoGen, or any custom agent loop. No formatting required.
raw stdout · JSON traces · LangSmith exports · custom logs
Detect waste patterns
LOOP parses the trace for retry cascades, duplicate invocations, token drift, silent exceptions, and loops without exit conditions.
retry storms · repeated calls · prompt bloat · dead branches
Fix the broken flow
Every finding ships with a specific, actionable recommendation. The exact node, branch, or call that needs to change.
severity rating · root cause · recommended fix · cost saved
Debugging AI workflows is a tax on every team shipping agents.
The current tooling gap: you can observe model calls, but you can't quickly identify which ones were wasteful and why.
Token waste is invisible by default.
The model doesn't tell you when you're sending it the same context it already processed. Retries don't announce themselves. By the time you notice costs, the pattern has been running for days.
Failures in agent loops are often silent.
A tool call fails. The exception is caught and logged nowhere useful. The agent continues on stale data, making decisions that look valid until you trace the full execution path.
Logs are messy. Debugging takes too long.
Agent logs mix model output, tool calls, retries, and framework noise. Reading them line by line to find where a workflow broke is expensive engineering time.
LOOP turns traces into a diagnosis.
Instead of scrolling through logs, you get a structured report: what failed, why, where waste begins, and exactly what to change.
Questions.
Focused on what actually matters to developers building with AI.
Any raw text output from an AI workflow. This includes stdout from Python agent scripts, JSON traces from LangChain or LangSmith, raw OpenAI API logs, Anthropic Claude completions, AutoGen conversation history, or custom logging output.
Yes. LOOP is model-agnostic. The patterns it detects — retry storms, repeated tool calls, prompt bloat, missing stop conditions — occur regardless of which model is underneath.
Not in the current demo. LOOP is a trace analysis tool — you paste logs from a completed or in-progress run and get a static diagnostic report.
The demo runs entirely in your browser — no logs leave your machine. A connected version with native integrations is on the roadmap.
No. The demo performs all analysis locally in the browser. Nothing is sent to any server, stored, or logged externally.
Yes. If your agent writes readable logs with model calls, tool invocations, retries, and iteration markers, LOOP can detect waste patterns in them.