# TaskWeaver

**Architecture Overview**

```mermaid
sequenceDiagram  
    participant User as User/DApp  
    participant Agent as AgentGPT Cortex  
    participant TW as TaskWeaver Planner  
    participant SC as Smart Contract Assembly  

    User->>Agent: "Global Supply Chain Optimization"  
    Agent->>TW: Request Priority Task Graph  
    TW-->>Agent: Atomic Workflow Nodes (ERC-7683)  
    Agent->>SC: Deploy Incentivized Tasks  
    SC-->>SC: Self-Executing Task Chains  
    SC-->>User: ERC-7645 Completion NFT  
    Note left of TW: Zero human task assignment  
```

***

#### **1. Workflow Planning Engine**

**Dynamic Task Generation (Python)**

```python
from taskweaver import Planner  
from agentgpt.blockchain import ERC7645  

class WorkflowOrchestrator:  
    def __init__(self):  
        self.planner = Planner(strategy="monte_carlo_tree")  
        self.contract = ERC7645(chain="polygon")  

    async def generate_supply_chain_plan(self, goal: str):  
        task_graph = await self.planner.generate(  
            objective=goal,  
            constraints="budget<1000 AGPT; deadline<72h",  
            context="historical_shipment_data_v5"  
        )  
        return await self.contract.deploy_tasks(  
            task_graph.to_blockchain_format(),  
            incentive_model="first_validator_wins"  
        )  

    async def verify_completion(self, task_id: bytes32):  
        return self.contract.check_fulfillment(  
            task_id,  
            zk_proof=self.planner.generate_zkp(task_id)  
        )  
```

***

#### **2. Task Templating System**

**Declarative Workflow (YAML)**

```yaml
# supply_chain.yaml  
workflow:  
  - task: "inventory_replenishment"  
    type: "DEFI_ACTION"  
    params:  
      token: "AGPT"  
      threshold: 15000  
      action: "uniswap_v4_swap"  
    compensation: 0.4 AGPT  
    deadline: 3600  

  - task: "route_optimization"  
    type: "ML_MODEL"  
    params:  
      model: "graph_attention_v3"  
      precision: "int8"  
    validators: 3  
    bounty: 1.2 AGPT  
```

***

#### **3. Automated Compensation Protocol**

**Task Payment Contract (Solidity)**

```solidity
// contracts/TaskPayment.sol  
pragma solidity ^0.8.25;  

contract AutoBounty {  
    struct Microtask {  
        address creator;  
        uint256 bounty;  
        bytes32 proofStandard;  
        bool fulfilled;  
    }  

    mapping(bytes32 => Microtask) public tasks;  

    function createTask(  
        bytes32 proofType,  
        uint256 bounty  
    ) external payable {  
        bytes32 taskId = keccak256(abi.encode(block.timestamp, msg.sender));  
        tasks[taskId] = Microtask(msg.sender, bounty, proofType, false);  
        _lockFunds(bounty);  
    }  

    function claimBounty(bytes32 taskId, bytes calldata proof) external {  
        require(_validateProof(proof, tasks[taskId].proofStandard), "Invalid");  
        _releaseFunds(taskId, msg.sender);  
    }  
}  
```

***

#### **4. Workflow Monitoring**

```bash
agentgpt monitor taskweaver --filter "status=running"  
```

**Output**:

```
ACTIVE WORKFLOWS 12-MAR-2025  
┌──────────────────────────┬──────────┬────────────┬─────────────┐  
│ Workflow ID               │ Tasks    │ AGPT Pool  │ Progress    │  
├──────────────────────────┼──────────┼────────────┼─────────────┤  
│ SC-Optim-88A1            │ 142/209  │ 82.4 AGPT  │ 67%         │  
│ NFT-Mint-Batch-F5C       │ 38/38    │ 14.8 AGPT  │ Completed   │  
├──────────────────────────┼──────────┼────────────┼─────────────┤  
│ TOTAL                    │ 6 Active │ 294.2 AGPT │ 89% Avg     │  
└──────────────────────────┴──────────┴────────────┴─────────────┘  
Pending Verification: 12 Tasks  
```

***

#### **5. Autonomous Recovery Protocol**

**Fault-Tolerant Execution (Rust)**

```rust
// agentgpt-recovery/src/lib.rs  
pub async fn handle_task_failure(  
    task_id: &str,  
    error_code: u32  
) -> Result<ReplanAction, OrchestrationError> {  
    let health = check_node_health().await?;  
    if health.score < 0.4 {  
        redistribute_tasks(task_id).await?;  
        return Ok(ReplanAction::Rescheduled);  
    }  
    adjust_resource_allocation(task_id, 1.5).await?;  
    Ok(ReplanAction::Retried)  
}  
```

***

#### **Troubleshooting Matrix**

| **Failure Mode**    | **Resolution Protocol**                                       |
| ------------------- | ------------------------------------------------------------- |
| Task Stalling (>2h) | 1. Force-redistribute ▸ 2. Penalize node ▸ 3. Burn AGPT bonds |
| Validator Dispute   | 1. Swarm voting ▸ 2. Invoke Chainlink VRF                     |
| Gas Price Spike     | 1. Dynamic task merging ▸ 2. Layer2 fallback                  |
| ZK-Proof Mismatch   | 1. Generate alternate path ▸ 2. Manual override (DAO)         |

***

#### **Best Practices**

1. **Workflow Version Control**:

```bash
agentgpt taskweaver versioning \  
  --workflow-id SC-Optim-88A1 \  
  --snapshot-interval "every 100 tasks"  
```

2. **Resource Allocation Policy**:

```yaml
resource_management:  
  auto_scaling:  
    min_workers: 3  
    max_workers: 12  
    scale_up: "80% cpu_util"  
  gpu_prioritization:  
    - "reinforcement_learning_tasks"  
    - "3d_rendering"  
```

3. **Incentive Rebalancing**:

```solidity
function rebalanceBounty(bytes32 taskId, uint256 newBounty) external {  
    require(msg.sender == tasks[taskId].creator, "Unauthorized");  
    tasks[taskId].bounty = newBounty;  
    emit BountyAdjusted(taskId, newBounty);  
}  
```


---

# 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/integration-examples/taskweaver.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.