# LiteLLM

**Architecture Blueprint**

```mermaid
flowchart TD  
    User[User Request] --> BOT[AgentGPT Bot]  
    BOT -->|Routing Logic| LIT[LiteLLM Proxy]  
    LIT --> LLM{{LLM Pool}}  
    LLM --> PAY[ERC-20 Payments]  
    PAY -->|Token Stream| LIT  
    LIT --> BOT  
    BOT --> BC[(Blockchain Audit)]  
    BC -->|Hashed Logs| IPFS  
    style LLM fill:#f9d,stroke:#333  
    style PAY fill:#7f7,stroke:#333  
```

***

#### **1. Cross-Provider LLM Configuration**

**Dynamic API Router (YAML)**

```yaml
# litellm-router.yaml  
routes:  
  - route: "mission_critical"  
    model: "gpt-4-turbo"  
    budget: 0.12 AGPT/1k tokens  
    providers:  
      - openai@az-westus  
      - anyscale@europe  
      - together-ai@fallback  

  - route: "cost_optimized"  
    model: "mixtral-8x22b"  
    budget: 0.0085 AGPT/1k tokens  
    providers:  
      - groq@L40S  
      - firework-ai  
      - deepinfra  

agentgpt:  
  blockchain:  
    payment_token: "AGPT"  
    dispute_window: 3600  # 1 hour  
```

***

#### **2. Autonomous Payment Workflow**

**Self-Billing Contract (Solidity)**

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

contract AutoPayLLM {  
    mapping(bytes32 => uint256) public apiCredits;  
    uint256 public constant TOKENS_PER_AGPT = 1250; // 1 AGPT = 1250 tokens  

    function executeLLMCall(  
        address user,  
        string calldata provider,  
        uint256 tokensUsed  
    ) external returns (uint256) {  
        bytes32 id = keccak256(abi.encode(user, provider));  
        uint256 agptCost = tokensUsed / TOKENS_PER_AGPT;  
        
        apiCredits[id] += agptCost;  
        _transferFrom(user, address(this), agptCost);  
        return agptCost;  
    }  

    function withdrawCredits(bytes32 id) external {  
        uint256 amount = apiCredits[id];  
        _transfer(msg.sender, amount);  
    }  
}  
```

***

#### **3. Fault-Tolerant Routing**

**Latency-Optimized Load Balancer (Python)**

```python
from litellm import Router  
from agentgpt.payments import TokenStream  

class AIFactor:  
    def __init__(self):  
        self.router = Router(  
            model_list=[  
                {"model_name": "gpt-4", "litellm_params": {"provider": "openai"}},  
                {"model_name": "claude-3", "litellm_params": {"provider": "anthropic"}},  
            ]  
        )  
        self.paymaster = TokenStream()  

    async def process_query(self, user: str, query: str):  
        try:  
            response = await self.router.acompletion(  
                model="gpt-4",  
                messages=[{"role": "user", "content": query}]  
            )  
            await self.paymaster.stream(  
                user,  
                provider=self.router.selected_provider,  
                tokens=response.usage.total_tokens  
            )  
            return response.choices[0].message.content  
        except Exception as e:  
            await self.router.failover()  
            return await self.process_query(user, query)  
```

***

#### **4. Real-Time Cost Dashboard**

```bash
agentgpt monitor litellm --granularity provider  
```

*Output*:

```
LLM USAGE 08-FEB-2025  
┌─────────────────┬───────────┬───────────┬───────────┐  
│ Provider        │ Requests  │ AGPT Cost │ Latency   │  
├─────────────────┼───────────┼───────────┼───────────┤  
│ OpenAI          │ 12,891    │ 182.34    │ 423ms     │  
│ Groq            │ 8,214     │ 49.27     │ 19ms      │  
│ Anthropic       │ 5,492     │ 94.15     │ 670ms     │  
├─────────────────┼───────────┼───────────┼───────────┤  
│ TOTAL           │ 26,597    │ 325.76 AGPT           │  
└─────────────────┴───────────┴───────────┴───────────┘  
Budget Remaining: 417.82 AGPT  
```

***

#### **5. Security & Compliance**

**Zero-Touch API Key Management**

```python
from agentgpt.vault import HSMEnclave  

class KeyOrchestrator:  
    def __init__(self):  
        self.enclave = HSMEnclave("litellm-keys")  
        self.rotator = KeyRotator(interval="24h")  

    def get_provider_key(self, provider: str) -> str:  
        encrypted_key = self.enclave.fetch_key(provider)  
        return self.rotator.decrypt_with_transaction(encrypted_key)  

    def rotate_all_keys(self):  
        for provider in registered_providers:  
            new_key = generate_secure_key()  
            self.enclave.store(provider, self.rotator.encrypt(new_key))  
```

***

#### **6. Cross-Chain Settlement**

**Layer 2 Payment Channels**

```yaml
payment_channels:  
  - provider: "openai"  
    chain: "arbitrum"  
    balance: "500 AGPT"  
    strategy: "optimistic_rollups"  
  - provider: "anthropic"  
    chain: "polygon"  
    balance: "300 AGPT"  
    strategy: "zk-rollup"  
auto_refill:  
  threshold: "50 AGPT"  
  amount: "200 AGPT"  
  gas_optimized: true  
```

***

#### **Troubleshooting Matrix**

| **Issue**           | **Resolution Protocol**                             |
| ------------------- | --------------------------------------------------- |
| Model Response Lag  | 1. Failover to Groq/CUDA next-gen GPU               |
| Billing Discrepancy | 2. Verify with Chainlink Oracle ▸ Reconcile via DAO |
| API Key Leak        | 3. Instantiate Enclave Rekey ▸ Blacklist old keys   |
| Rate Limit Hit      | 4. Distribute requests via IPFS+libp2p network      |

***

#### **Best Practices**

1. **Performance-Based Routing**:

```python
Router.add_rule(  
    "legal_docs",  
    condition=lambda q: "contract" in q,  
    targets=["claude-3-opus", "gpt-4-legal"]  
)  
```

2. **On-Chain Budget Caps**:

```solidity
function setModelBudget(  
    string calldata model,  
    uint256 dailyLimit  
) external onlyGovernance {  
    modelBudgets[keccak256(bytes(model))] = dailyLimit;  
}  
```

3. **Cold Storage Failover**:

```bash
agentgpt litellm backup --strategy glacier --location eu-central-1  
```


---

# 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/litellm.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.