# SpreadSheetAgentGPT

**SpreadSheetAgentGPT** seamlessly bridges traditional spreadsheet workflows with decentralized Web3 execution. It enables enterprises to design, automate, and audit financial models (e.g., payrolls, treasury management) using familiar grid-based interfaces while executing actions cross-chain via smart contracts. Ideal for real-time APY calculations, revenue reporting, and inventory financing.

***

#### **Core Components**

**1. Grid Computation Engine**

* **Cell Formulas**: Supports Python/JavaScript syntax for dynamic calculations (e.g., `=ETH() * AAVE_APY()`).
* **Auto-Refresh**: Pulls real-time data from 40+ blockchains and Oracles (Chainlink, Pyth).

**2. Smart Contract Integration**

* Automates actions based on cell values (e.g., "Execute swap if Cell B2 > $1M").

**3. Compliance Layer**

* Auto-generates audit trails for financial models (ZK-proofs anchored to IPFS).

**4. Collaboration Hub**

* Multi-user editing with role-based permissions (viewer/editor/approver).

***

#### **Workflow**

```mermaid
flowchart LR  
    API[Data APIs] --> Sheet[Spreadsheet]  
    Sheet -->|Formulas| Compute[Compute Engine]  
    Compute -->|Triggers| SC[Smart Contracts]  
    SC -->|Result| Sheet  
    Sheet --> Audit[ZK-Audit Dashboard]  
```

***

#### **Logic Code Example**

```python
from agentgpt.architectures import SpreadSheetAgent  
from agentgpt.web3 import ERC7645, ZKValidator  
from typing import Dict, List  
import pandas as pd  

class TreasuryManagementSheet(SpreadSheetAgent):  
    def __init__(self):  
        super().__init__(  
            columns=["Asset", "APY", "TVL", "Action"],  
            data_sources=["chainlink", "aave", "uniswap"]  
        )  
        self.executor = ERC7645()  
        self.validator = ZKValidator()  

    def refresh_data(self):  
        # Fetch real-time APY and TVL  
        df = pd.DataFrame(self.sheet_data)  
        df["APY"] = [  
            self.query_apy(row["Asset"])   
            for index, row in df.iterrows()  
        ]  
        df["TVL"] = [  
            self.query_tvl(row["Asset"])   
            for index, row in df.iterrows()  
        ]  
        self.sheet_data = df.to_dict("records")  

    def evaluate_triggers(self):  
        # Execute trades based on spreadsheet logic  
        for row in self.sheet_data:  
            if row["Action"] == "Buy" and row["TVL"] > 1e6:  
                tx_hash = self.executor.swap(  
                    token_in="USDC",  
                    token_out=row["Asset"],  
                    amount=row["TVL"] * 0.1  # Allocate 10%  
                )  
                self.audit_log.append(tx_hash)  

    def sync_with_gsheets(self, sheet_id: str):  
        # Bi-directional sync with Google Sheets  
        import gspread  
        gc = gspread.service_account(filename="creds.json")  
        sh = gc.open_by_key(sheet_id)  
        worksheet = sh.sheet1  
        df = pd.DataFrame(self.sheet_data)  
        worksheet.update([df.columns.values.tolist()] + df.values.tolist())  

    def generate_audit_proof(self):  
        proof = self.validator.generate_proof({  
            "data_hash": self.compute_data_hash(),  
            "actions": self.audit_log  
        })  
        return proof  

# Usage  
sheet = TreasuryManagementSheet()  
sheet.sheet_data = [  
    {"Asset": "AAVE", "APY": None, "TVL": None, "Action": "Buy"},  
    {"Asset": "COMP", "APY": None, "TVL": None, "Action": "Hold"}  
]  
sheet.refresh_data()  
sheet.evaluate_triggers()  
sheet.sync_with_gsheets("1A3zB...")  
audit_proof = sheet.generate_audit_proof()  
```

***

#### **Key Features**

**1. Real-Time Cell Binding**

```python
# Bind cell B2 to ETH price  
self.bind_cell("B2", lambda: self.query_price("ETH/USD"))  
```

**2. Formula Templates**

```python
self.add_formula(  
    "C10",  
    "=SUM(DEXVolume(A2:A10)) * 0.0003",   
    description="DEX revenue share"  
)  
```

**3. Role-Based Access**

```python
sheet.grant_access("0xCFO", role="approver")  
sheet.grant_access("0xAnalyst", role="viewer")  
```

***

#### **Enterprise Use Cases**

**1. DeFi Yield Dashboard**

* Tracks 50+ pools across Ethereum/Polygon.
* Auto-rebalances via spreadsheet triggers.

**2. Enterprise Inventory Financing**

* **Cell Logic**: `=IF(Stock < 1000, "Loan Request", "Hold")`
* Auto-borrows USDC via AAVE when inventory drops.

***

#### **Performance Metrics**

| **Metric**        | **Result** |
| ----------------- | ---------- |
| Data Refresh Rate | 650ms/cell |
| Formula Accuracy  | 99.8%      |
| Audit Efficiency  | 92% faster |

***

#### **Implementation Guide**

**1. CLI Commands**

```bash
# Deploy a payroll spreadsheet  
agentgpt-cli create-spreadsheet \  
  --name "TreasuryTracker" \  
  --columns "Asset,APY,TVL,Action" \  
  --formulas "APY=query_apy(Asset);TVL=query_tvl(Asset)"  
```

**2. Best Practices**

* Use `volatile` functions sparingly to reduce gas costs.
* Schedule hourly snapshots for historical compliance.

***

#### **Limitations**

* **Complexity**: Formulas beyond 1,000 cells require sharding.
* **Latency**: Google Sheets sync adds 2–5s delay.


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