# GraphWorkflow

**GraphWorkflow** orchestrates tasks as a Directed Acyclic Graph (DAG), ensuring strict order and dependency management for multi-step Web3 operations. Designed for workflows requiring sequential execution (e.g., compliance → FX → payments), it eliminates errors from misordered steps while enabling parallel processing where possible. Ideal for cross-chain payrolls, regulatory adherence, and multi-protocol DeFi strategies.

***

#### **Core Components**

**1. DAG Engine**

* **Nodes**: Tasks (e.g., compliance check, currency conversion).
* **Edges**: Dependencies between tasks (e.g., "FX" requires prior "Compliance" approval).

**2. Parallel Execution Layer**

* Runs non-dependent tasks simultaneously (e.g., bridge to Ethereum and Polygon).

**3. Fault Handler**

* Retries failed nodes.
* Escalates unresolved failures to Manual Review Agents.

**4. ZK-Audit Layer**

* Generates proofs for each step’s completion.

***

#### **Workflow**

```mermaid
flowchart TD  
    Start[Start Workflow] --> A[Compliance Check]  
    A --> B[FX Conversion]  
    B --> C{Bridge Funds}  
    C -->|Ethereum| D[Execute Payment L1]  
    C -->|Polygon| E[Execute Payment L2]  
    D --> F[ZK-Audit]  
    E --> F  
    F --> End[End Workflow]  
```

***

#### **Logic Code Example**

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

class CrossChainPayrollFlow(GraphWorkflow):  
    def __init__(self):  
        super().__init__()  
        self.executor = ERC7645()  
        self.validator = ZKValidator()  
        self.add_nodes(["compliance", "fx", "bridge", "execute_l1", "execute_l2", "audit"])  
        self.add_edges([  
            ("compliance", "fx"),  
            ("fx", "bridge"),  
            ("bridge", "execute_l1"),  
            ("bridge", "execute_l2"),  
            ("execute_l1", "audit"),  
            ("execute_l2", "audit")  
        ])  

    def task_compliance(self, params: Dict):  
        # Validate addresses against OFAC  
        flagged = self.executor.screen_addresses(params["recipients"])  
        return {"valid_recipients": [r for r in params["recipients"] if r not in flagged]}  

    def task_fx(self, data: Dict):  
        # Convert EUR to USDC via Optimized DEX Route  
        rate = self.executor.fetch_rate("EUR/USDC")  
        usdc_amount = data["amount"] * rate  
        return {"usdc_amount": usdc_amount, "chain": "ethereum"}  

    def task_bridge(self, data: Dict):  
        # Bridge funds to L2 if gas spikes  
        if data["chain"] == "ethereum" and self.executor.gas_price > 50:  
            return {"chains": ["polygon"]}  
        return {"chains": [data["chain"]]}  

    def execute_workflow(self, task: Dict):  
        try:  
            results = {}  
            results["compliance"] = self.task_compliance({"recipients": task["recipients"]})  
            results["fx"] = self.task_fx({"amount": task["amount"]})  
            results["bridge"] = self.task_bridge(results["fx"])  
            
            # Parallel execution  
            tx_hashes = []  
            for chain in results["bridge"]["chains"]:  
                if chain == "ethereum":  
                    tx_hash = self.executor.transfer_l1(results["fx"]["usdc_amount"])  
                    results["execute_l1"] = tx_hash  
                else:  
                    tx_hash = self.executor.transfer_l2(results["fx"]["usdc_amount"])  
                    results["execute_l2"] = tx_hash  
                tx_hashes.append(tx_hash)  

            # Final audit  
            proof = self.validator.generate_proof(results)  
            results["audit"] = proof  
            return results  
        except Exception as e:  
            self.retry_or_escalate(e, task)  

# Usage  
workflow = CrossChainPayrollFlow()  
result = workflow.execute_workflow({  
    "recipients": ["0xEmp1", "0xEmp2"],  
    "amount": 50000.0  # EUR  
})  
```

***

#### **Key Features**

**1. Conditional Branching**

```python
if self.executor.gas_price > 50:  
    self.add_edge("bridge", "execute_l2")  
else:  
    self.add_edge("bridge", "execute_l1")  
```

**2. Parallel Node Execution**

Run cross-chain payments simultaneously:

```python
self.enable_parallel_nodes(["execute_l1", "execute_l2"])  
```

**3. Versioned Workflows**

Roll back to prior DAG versions during errors:

```python
self.restore_version("v2.1")  
```

***

#### **Enterprise Use Cases**

**1. Multi-Chain Payroll Processing**

* **Steps**: Compliance (OFAC) → FX → Bridge → Execute on Ethereum/Polygon → Audit.
* **Result**: 89% faster than manual workflows.

**2. DeFi Collateral Management**

* Liquidate undercollateralized loans after sequential checks.

***

#### **Performance Metrics**

| **Metric**                | **Result**        |
| ------------------------- | ----------------- |
| Success Rate              | 99.4%             |
| Execution Time (10 Steps) | 6.8s              |
| Error Recovery            | 93% auto-resolved |

***

#### **Implementation Guide**

**1. CLI Deployment**

Create a payroll workflow:

```bash
agentgpt-cli create-graphworkflow \  
  --name "EnterprisePayroll" \  
  --nodes compliance,fx,bridge,execute,audit \  
  --edges compliance:fx,fx:bridge,bridge:execute  
```

**2. Best Practices**

* Pre-validate node dependencies with `validate_dag()`.
* Use idempotent tasks for retries (e.g., nonce-aware transactions).

***

#### **Limitations**

* **Complex Setup**: Requires precise DAG design.
* **State Management**: Large workflows need distributed caching.


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