# API

**1. Core Initialization Module**

**`agentgpt.__init__`**

*Bootstraps decentralized infrastructure and telemetry for enterprise-grade workflows*

**Imports & Execution**:

```python
import asyncio  
from agentgpt.telemetry import bootstrap_decentralized_nodes, enable_zk_auditing  

# Asynchronous node bootstrapping  
async def init():  
    with ThreadPoolExecutor(max_workers=3) as executor:  
        await asyncio.gather(  
            executor.submit(bootstrap_decentralized_nodes, chains=[1, 137, 42161]),  
            executor.submit(enable_zk_auditing)  
        )  

# Import critical modules  
from agentgpt.agents import *          # EVM/Solana agent implementations  
from agentgpt.contracts import *        # Smart contract schemas  
from agentgpt.zk import *               # Zero-knowledge proof utilities  
```

***

#### **2. Smart Contract Artifact Framework**

**2.1 `Artifact` Base Class**

*Abstract model for cross-chain transaction proofs and state transitions*

```python
from pydantic import BaseModel  
from typing import Optional  

class BlockchainArtifact(BaseModel):  
    artifact_id: str                    # CID for IPFS/Filecoin storage  
    tx_hashes: list[str]                # Multi-chain transaction IDs  
    zk_proof: Optional[str]              # Groth16 zk-SNARK proof  
    gas_metrics: dict                   # {"chain_id": {"used": int, "saved": float}}  

    def to_audit_json(self) -> str:  
        """Returns ZK-auditable JSON with Merkle roots"""  
        return f'{{"proof": "{self.zk_proof}", "cid": "{self.artifact_id}"}}'  
```

**2.2 `DeFiTransactionArtifact`**

*Tracks complex DeFi operations across protocols*

```python
class DeFiTransactionArtifact(BlockchainArtifact):  
    operations: list[dict]               # [{"protocol": "AAVE", "action": "borrow", ...}]  
    risk_scores: dict                   # AI-computed risk metrics  
    liquidation_protection: bool = True  

    def simulate_liquidation(self, price_drop: float) -> bool:  
        """Stress-test positions against market crashes"""  
        return any(op["liquidation_price"] > price_drop for op in self.operations)  
```

***

#### **3. Agent Orchestration**

**3.1 `BaseWeb3Agent` (Abstract Class)**

```python
from abc import ABC, abstractmethod  

class BaseWeb3Agent(ABC):  
    def __init__(self, chain_id: int, gas_strategy: str = "eco"):  
        self.gas_oracle = ChainlinkGasFeed(chain_id)  
        self.zk_enabled = True  

    @abstractmethod  
    async def execute(  
        self,  
        task: dict,                     # {"action": "swap", "params": {...}}  
        compliance_check: bool = True   # Auto-verify OFAC/sanctions  
    ) -> dict: ...                      # Returns tx_hash + metrics  
```

**3.2 `CrossChainSwapAgent`**

*Atomic swaps between 50+ chains*

```python
class CrossChainSwapAgent(BaseWeb3Agent):  
    def __init__(self, src_chain: int, dest_chain: int):  
        super().__init__(chain_id=src_chain)  
        self.bridge = LayerZeroBridge(dest_chain)  

    async def execute(self, task: dict) -> dict:  
        tx_data = await self._build_swap(task["params"])  
        return {  
            "src_tx": await self._submit(tx_data),  
            "dest_tx": await self.bridge.relay(tx_data),  
            "zk_proof": generate_cross_chain_proof(tx_data)  
        }  
```

***

#### **4. Workflow Automation**

**4.1 `DeFiWorkflow` Class**

```python
class DeFiWorkflow:  
    def __init__(self, agents: list, risk_threshold: float = 0.7):  
        self.agents = agents            # [KYC, Risk, Execution agents]  
        self.risk_engine = RiskModelV3(threshold=risk_threshold)  

    async def run(self, task: dict) -> dict:  
        results = {}  
        for agent in self.agents:  
            result = await agent.execute(task)  
            if self.risk_engine.evaluate(result):  
                results[agent.__class__.__name__] = result  
            else:  
                raise RiskThresholdExceededError(result)  
        return self._package_results(results)  
```

***

#### **5. Real-World Use Cases**

**5.1 Multi-Chain Payroll Execution**

```python
kyc_agent = KYCAgent(chain_id=137)  
fx_agent = FXAgent(pairs=["EUR/USDC", "GBP/USDT"])  
payment_agent = BatchPaymentAgent(chain_ids=[1, 137, 42161])  

workflow = DeFiWorkflow(agents=[kyc_agent, fx_agent, payment_agent])  
result = await workflow.run({  
    "action": "payroll",  
    "amount": "1_000_000",  
    "currency": "EUR",  
    "recipients": "ipfs://QmXy.../employees.csv"  
})  
```

*Output*:

```json
{  
  "ExecutionTime": "12.7s",  
  "GasSaved": "42%",  
  "ZKProof": "0x891f...d21a"  
}  
```

**5.2 DAO Governance Proposal**

```python
class DAOVotingAgent(BaseWeb3Agent):  
    def __init__(self, contract_address: str):  
        super().__init__(chain_id=1)  
        self.contract = Web3Contract(contract_address)  

    async def submit_vote(self, proposal_id: int, choice: int):  
        return await self.execute({  
            "action": "vote",  
            "params": {  
                "proposal": proposal_id,  
                "choice": choice,  
                "voter": "0x..."  
            }  
        })  
```

***

#### **6. Compliance & Monitoring**

**6.1 `SanctionsCompliance` Module**

```python
def validate_address(address: str) -> dict:  
    """Checks 50+ global sanction lists in real-time"""  
    return requests.post(  
        f"{API_ENDPOINT}/compliance",  
        json={"address": address},  
        headers={"Authorization": f"Bearer {SANCTIONS_API_KEY}"}  
    ).json()  
```

**6.2 Transaction Simulation**

```python
async def simulate_tx(chain_id: int, tx_data: dict) -> dict:  
    """Dry-run with 98% accurate gas estimation"""  
    async with Web3AsyncProvider(chain_id) as provider:  
        return await provider.simulate(  
            tx_data,  
            block_count=12               # Check next 12 blocks for MEV risks  
        )  
```

***

#### **7. Performance Metrics**

| **Component**       | Throughput | Accuracy | Avg. Cost |
| ------------------- | ---------- | -------- | --------- |
| Cross-Chain Swaps   | 18 tx/s    | 99.1%    | $0.23     |
| Compliance Checks   | 420 req/s  | 100%     | $0.001    |
| ZK Proof Generation | 3.2/s      | -        | $0.0004   |
| Risk Modeling       | 84 ms      | 92.4%    | N/A       |

***

#### **8. API Endpoints**

| Endpoint            | Method | Parameters             | Example cURL                                         |
| ------------------- | ------ | ---------------------- | ---------------------------------------------------- |
| `/workflows/deploy` | POST   | JSON config            | `curl -X POST -d @config.json`                       |
| `/tx/simulate`      | POST   | `chain_id`, `tx_data`  | `curl -d '{"chain":1,"tx":{...}}'`                   |
| `/compliance/check` | GET    | `address`              | `curl api.agent-gpt.org/v3/compliance?address=0x...` |
| `/zk/generate`      | POST   | `artifacts` (CID list) | `curl -d '{"cids":["Qm..."]}'`                       |

***

#### **9. Best Practices**

**9.1 Gas Optimization**

```python
class OptimalGasScheduler:  
    def __init__(self, chains=[1, 137]):  
        self.oracles = {chain: ChainlinkGasFeed(chain) for chain in chains}  

    async def get_optimal_time(self) -> dict:  
        return {  
            chain: await oracle.predict_optimal_window()  
            for chain, oracle in self.oracles.items()  
        }  
```

**9.2 Error Handling**

```python
try:  
    await agent.execute(task)  
except Web3TransactionError as e:  
    if e.code == "OUT_OF_GAS":  
        agent.adjust_gas(multiplier=1.4)  
        agent.retry()  
    elif e.code == "SANCTIONED_ADDRESS":  
        compliance_alert.send(task["params"]["recipient"])  
```

***

#### **10. Limitations**

* **Cross-Chain Finality**: 3A-chain: 2.1s avg., Polygon: 4.8s
* **ZK Proof Overhead**: Adds 320-450ms per workflow step
* **MEV Risks**: 0.8% sandwich attack probability on Ethereum Mainnet


---

# 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/full-api-reference/api.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.