# TaskQueueAgentGPT

**TaskQueueAgentGPT** manages ordered execution of Web3 workflows with configurable prioritization, retries, and rate limiting. Designed for high-volume operations like enterprise payrolls, DeFi batch swaps, and NFT airdrops, it ensures reliable execution while preventing chain congestion. Combines FIFO/LIFO queues with dynamic priority adjustments based on gas fees and compliance requirements.

***

#### **Core Components**

**1. Queue Engine**

* **Priority Tiers**: Critical (real-time), High (CFO-approved), Medium (routine), Low (backlogs).
* **Persistence Layer**: Stores queued tasks on IPFS with recovery snapshots.

**2. Rate Limiter**

* Prevents RPC node throttling:

  ```python
  self.set_rate_limit(chain="ethereum", txs_per_min=450)  
  ```

**3. Retry Engine**

* **Exponential Backoff**: 1s → 5s → 25s.
* **Dead Letter Queue (DLQ)**: Stores permanently failed tasks for manual review.

**4. Parallel Executor**

* Processes non-conflicting tasks across multiple chains/agents.

***

#### **Workflow**

```mermaid
flowchart LR  
    User[User Submits Task] --> Queue[Priority Queue]  
    Queue -->|Order: Critical→Low| Process[Task Processor]  
    Process -->|Success| OnChain[On-Chain Execution]  
    Process -->|Failure| Retry[Retry Engine]  
    Retry -->|3 Attempts| DLQ[Dead Letter Queue]  
    OnChain --> Audit[ZK-Audit Logs]  
```

***

#### **Logic Code Example**

```python
from agentgpt.architectures import TaskQueueAgent  
from agentgpt.web3 import ERC7645, ZKValidator  
from typing import Dict, List  
import datetime  

class BatchPayrollQueue(TaskQueueAgent):  
    def __init__(self):  
        super().__init__(  
            priority_tiers=["critical", "high", "medium", "low"],  
            rate_limits={"ethereum": 300, "polygon": 1000}  
        )  
        self.executor = ERC7645()  
        self.validator = ZKValidator()  
        self.dlq = []  

    def enqueue_task(self, task: Dict):  
        # Auto-set priority based on amount  
        if task["amount"] > 1e6:  # > $1M  
            priority = "critical"  
        elif task["urgent"]:  
            priority = "high"  
        else:  
            priority = "medium"  
        self.add_to_queue(task, priority)  

    def process_tasks(self):  
        while not self.queue_empty():  
            task = self.dequeue_task()  
            try:  
                tx_hash = self.executor.transfer(  
                    chain=task["chain"],  
                    recipient=task["to"],  
                    amount=task["amount"]  
                )  
                self.validator.log_success(task["id"], tx_hash)  
            except Exception as e:  
                if task["retries"] < 3:  
                    self.retry_task(task, delay=5**task["retries"])  
                else:  
                    self.move_to_dlq(task, e)  

    def handle_dlq(self):  
        for task in self.dlq:  
            self.alert_admins(  
                f"Failed task: {task['id']}",  
                f"Error: {task['error']}"  
            )  

    def generate_audit_report(self) -> Dict:  
        return {  
            "success": self.validator.get_success_count(),  
            "failed": len(self.dlq),  
            "proof": self.validator.generate_batch_proof()  
        }  

# Usage  
queue = BatchPayrollQueue()  
queue.enqueue_task({  
    "id": "PAY-00345",  
    "to": "0xEmp12",  
    "amount": 5500.0,  
    "chain": "polygon",  
    "urgent": True  
})  
queue.process_tasks()  
print(queue.generate_audit_report())  
```

***

#### **Key Features**

**1. Priority Overrides**

```python
# CEO override for specific task  
queue.set_priority("PAY-00987", "critical")  
```

**2. Gas-Aware Ordering**

Reorder queue when gas dips below 20 Gwei:

```python
if self.gas_feed.ethereum < 20:  
    self.reorder_queue(strategy="gas")  
```

**3. Multi-Chain Fanout**

Process 500+ parallel tasks:

```python
self.enable_parallel_execution(workers=16)  
```

***

#### **Enterprise Use Cases**

**1. Global Payroll Processing**

* **Stats**: 220K transactions/day across 12 chains.
* **Execution**: Critical-tier salaries prioritized during gas spikes.

**2. DeFi Batch Liquidations**

* Queue liquidation tasks based on collateral health (e.g., 85% LTV).
* Auto-retry with backoff if RPC nodes fail.

***

#### **Performance Metrics**

| **Metric**         | **Result**       |
| ------------------ | ---------------- |
| Throughput (vCPUs) | 2,400 tasks/sec  |
| Avg. Latency       | 4.2s             |
| Dead Letter Rate   | 0.07%            |
| Cost Efficiency    | 58% vs real-time |

***

#### **Implementation Guide**

**1. CLI Deployment**

```bash
# Create payroll queue with rate limits  
agentgpt-cli create-taskqueue \  
  --name "PayrollQueue" \  
  --priorities critical,medium \  
  --limits "ethereum:300,polygon:1000"  
```

**2. Best Practices**

* Set medium priority for 90% of tasks to avoid gas wars.
* Schedule off-peak processing (2 AM UTC) for low-tier tasks.

***

#### **Limitations**

* **Latency**: Priority tiers add 0.3–1.8s overhead.
* **Persistence Cost**: IPFS storage costs $0.08/100K tasks.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://agent-gpt.gitbook.io/agent-gpt/agentgpt-architectures/architectures-available/taskqueueagentgpt.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.