# Agent Retry Loop Auditor (`firstrate/agent-retry-loop-auditor`) Actor

Find AI-agent retry loops, repeated failed tool calls, error streaks, terminal failures, and recovery patterns in execution traces. Generates the smallest replay experiment to test a retry/validation/fallback change without claiming unverified savings.

- **URL**: https://apify.com/firstrate/agent-retry-loop-auditor.md
- **Developed by:** [First Rate](https://apify.com/firstrate) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$10.00 / 1,000 retry trace auditeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Agent Retry Loop Auditor

Find **retry loops, repeated failed tool calls, failure streaks, terminal failures, and recovery transitions** in AI-agent execution traces.

This Actor is deliberately narrower than a general observability platform. It answers one operational question:

> Is this agent repeatedly spending attempts on failure states that deserve a different retry, validation, fallback, or escalation policy?

It uses deterministic trace analysis — no LLM calls — and returns evidence plus the smallest replay experiment needed to test a fix. It never claims that a retry is wasteful merely because it repeated, and it never reports verified savings without a matched replay.

### Best for

- debugging AI agent retry loops
- analyzing failed LLM/tool traces
- LangGraph, LangChain, OpenTelemetry, or custom agent spans
- repeated tool failures
- terminal unresolved agent runs
- testing retry budgets and fail-fast policies
- finding upstream validation opportunities
- CI analysis before changing retry/fallback logic

### What it detects

For every trace:

- error/failure event count
- **immediate identical retry after failure** — same tool/operation and same normalized input
- repeated failure signatures across a trace
- longest consecutive error streak
- whether the final observed event is still a failure
- transitions from failure back to a non-error event
- bounded hypotheses for replay experiments

Every result includes:

```json
{
  "authority": {
    "retryPolicyChangeAuthorized": false
  }
}
```

because pattern evidence is not enough to prove that production retry behavior should change.

### Example input

```json
{
  "traces": [
    {
      "traceId": "checkout-agent-42",
      "spans": [
        {
          "name": "tool:lookup_order",
          "tool_name": "lookup_order",
          "tool_input": {"orderId":"123"},
          "statusCode": "ERROR"
        },
        {
          "name": "tool:lookup_order",
          "tool_name": "lookup_order",
          "tool_input": {"orderId":"123"},
          "statusCode": "ERROR"
        },
        {
          "name": "tool:lookup_order",
          "tool_name": "lookup_order",
          "tool_input": {"orderId":"123","fallback":true},
          "statusCode": "OK"
        }
      ]
    }
  ]
}
```

The first two events are an immediate same-input retry after a failure. The third changed the recovery strategy. The Actor reports the pattern and suggests replaying matched failures with an explicit precondition, backoff, changed input, fallback, or retry budget — it does not assume which intervention is correct.

### Supported trace shapes

You can send:

- `traces`: objects containing `spans`
- `traces`: AgentTrace-style objects with `llm_steps`
- direct trace/event-like objects
- `spans`: a flat span array grouped by `traceId`, `trace_id`, `sessionId`, or `session_id`

Common OpenTelemetry-style error and tool attributes are recognized.

### Why retry analysis matters

Retries are sometimes essential: rate limits clear, transient infrastructure recovers, and fallback paths succeed. But an agent that repeats an unchanged failing transformation can also burn latency, tokens, tool quotas, and money while making no progress.

The safe optimization sequence is therefore:

1. observe the failure signature;
2. identify repeated or terminal pressure;
3. propose the smallest changed retry/validation/fallback policy;
4. replay the same representative cases;
5. preserve terminal success and protected authority boundaries;
6. only then promote the policy.

### Related Actor

Use **Agent Trace Efficiency Auditor** for a broader scan of context, model switching, exact repeated transformations, human-boundary spans, and failure pressure. Use this Actor when the problem you want to inspect is specifically **retry and failure-loop behavior**.

### Pricing

The intended Store pricing is one small pay-per-event charge per trace audited. The Actor uses no paid model, browser, proxy, or external API.

### Search phrases

AI agent retry analyzer, agent retry loop, LLM retry debugging, agent failure trace, AI agent error analysis, repeated tool failure, LangGraph retry loop, tool retry waste, agent failure debugging, retry policy audit.

# Actor input Schema

## `traces` (type: `array`):

Trace objects. Supports objects with spans and AgentTrace-style llm\_steps, or direct event/span-like objects.

## `spans` (type: `array`):

Alternative flat span array. traceId/sessionId attributes are used to group spans.

## `repeatThreshold` (type: `integer`):

Minimum occurrences of the same failure signature before it is reported as a repeated failure cluster.

## `maxItems` (type: `integer`):

Maximum trace groups to audit in one run.

## Actor input object example

```json
{
  "traces": [
    {
      "traceId": "example-retry",
      "spans": [
        {
          "name": "tool:search",
          "tool_name": "search",
          "tool_input": {
            "q": "alpha"
          },
          "statusCode": "ERROR"
        },
        {
          "name": "tool:search",
          "tool_name": "search",
          "tool_input": {
            "q": "alpha"
          },
          "statusCode": "ERROR"
        },
        {
          "name": "tool:search",
          "tool_name": "search",
          "tool_input": {
            "q": "alpha revised"
          },
          "statusCode": "OK"
        }
      ]
    }
  ],
  "repeatThreshold": 2,
  "maxItems": 1000
}
```

# Actor output Schema

## `results` (type: `string`):

Machine-readable per-trace retry/failure analysis.

## `summary` (type: `string`):

Aggregate failure/retry counts.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("firstrate/agent-retry-loop-auditor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("firstrate/agent-retry-loop-auditor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call firstrate/agent-retry-loop-auditor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,firstrate/agent-retry-loop-auditor"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/7KeQJObJ5eqj6Crm5/builds/FvsFfklWayWaqSQgg/openapi.json