Multi-LLM Adapter - 多 LLM 适配器#

统一接口调用多个 LLM 提供商，支持自动故障转移和负载均衡。

Version: 1.0.0

Features: 统一接口、多提供商支持、自动回退、负载均衡、流式响应

Purpose#

让 OpenClaw 能够:

• 用统一接口调用不同 LLM
• 自动在多个提供商间切换
• 比较不同模型的响应
• 降低单点故障风险

Quick Start#

1. 配置 Providers#

bash

# 设置 API Keys
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GEMINI_API_KEY=...
export OLLAMA_HOST=http://localhost:11434

2. 基本使用#

bash

# 使用 OpenAI
python3 scripts/main.py chat --provider openai --model gpt-4 "Hello"

# 使用 Claude
python3 scripts/main.py chat --provider anthropic --model claude-3-opus "Hello"

# 使用本地 Ollama
python3 scripts/main.py chat --provider ollama --model llama2 "Hello"

# 自动选择 (按优先级)
python3 scripts/main.py chat --auto "Hello"

3. 带 Tools 的调用#

bash

python3 scripts/main.py chat \
  --provider openai \
  --model gpt-4 \
  --tools tools.json \
  --message "What's the weather in Beijing?"

Python API#

python

from multi_llm_adapter import LLMClient, ProviderConfig

# 初始化客户端
client = LLMClient()

# 添加 providers
client.add_provider(ProviderConfig(
    name="openai",
    api_key="sk-...",
    model="gpt-4",
    priority=1
))

client.add_provider(ProviderConfig(
    name="anthropic",
    api_key="sk-ant-...",
    model="claude-3-opus",
    priority=2
))

# 简单调用
response = client.chat(
    messages=[{"role": "user", "content": "Hello"}],
    provider="openai"
)

# 自动选择 provider
response = client.chat_auto(
    messages=[{"role": "user", "content": "Hello"}]
)

# 带工具调用
response = client.chat(
    messages=[...],
    tools=[...],
    provider="anthropic"
)

# 流式响应
for chunk in client.chat_stream(messages=[...]):
    print(chunk.content, end="")

Supported Providers#

ProviderModelsFeaturesOpenAIGPT-4, GPT-3.5Tools, Vision, JSONAnthropicClaude 3, Claude 2Tools, VisionGoogleGemini Pro, UltraTools, VisionOllamaLlama, MistralLocal, ToolsAzureGPT-4, GPT-3.5EnterpriseAWS BedrockClaude, LlamaAWS NativeCohereCommand, GenerateRAGMistralMistral LargeOpenAI-compatible

CLI Reference#

Chat#

bash

# 基本对话
python3 scripts/main.py chat --message "Hello"

# 指定 provider
python3 scripts/main.py chat \
  --provider anthropic \
  --model claude-3-haiku \
  --message "Hello"

# 多轮对话
python3 scripts/main.py chat \
  --provider openai \
  --system "You are a helpful assistant" \
  --message "Hello" \
  --message "How are you?"

# 流式输出
python3 scripts/main.py chat \
  --provider openai \
  --message "Tell me a story" \
  --stream

Tools#

bash

# 使用工具
python3 scripts/main.py chat \
  --provider openai \
  --tools tools.json \
  --message "Search for Python tutorials"

# 查看工具调用结果
python3 scripts/main.py chat \
  --provider anthropic \
  --tools tools.json \
  --message "Get weather in Tokyo" \
  --execute-tools

Compare#

bash

# 比较多个模型的回答
python3 scripts/main.py compare \
  --providers openai,anthropic,google \
  --message "Explain quantum computing"

# 输出为 JSON
python3 scripts/main.py compare \
  --providers openai,ollama \
  --message "Hello" \
  --output json

Manage Providers#

bash

# 列出配置的 providers
python3 scripts/main.py providers list

# 测试连接
python3 scripts/main.py providers test --name openai

# 添加 provider
python3 scripts/main.py providers add \
  --name my-ollama \
  --type ollama \
  --host http://localhost:11434 \
  --model llama2

# 设置优先级
python3 scripts/main.py providers prioritize \
  --name openai \
  --priority 1

Configuration#

yaml

# config.yaml
providers:
  openai:
    api_key: ${OPENAI_API_KEY}
    model: gpt-4
    priority: 1
    timeout: 30

  anthropic:
    api_key: ${ANTHROPIC_API_KEY}
    model: claude-3-opus
    priority: 2
    timeout: 30

  ollama:
    host: http://localhost:11434
    model: llama2
    priority: 3
    timeout: 60

retry:
  max_attempts: 3
  fallback_enabled: true

load_balance:
  strategy: round_robin  # or priority, random

Advanced Features#

Fallback Chain#

python

# 按优先级自动回退
response = client.chat_with_fallback(
    messages=[...],
    fallback_order=["openai", "anthropic", "ollama"]
)

Load Balancing#

python

# 轮询多个相同 provider 的实例
client.enable_load_balancing("openai", [
    {"api_key": "key1"},
    {"api_key": "key2"}
])

Cost Tracking#

python

# 追踪调用成本
response = client.chat(...)
print(f"Cost: ${response.cost_usd}")
print(f"Tokens: {response.total_tokens}")

Architecture#

text

multi-llm-adapter/
├── SKILL.md
├── requirements.txt
├── lib/
│   ├── __init__.py
│   ├── client.py            # LLMClient 核心
│   ├── config.py            # 配置管理
│   ├── providers/           # Provider 实现
│   │   ├── __init__.py
│   │   ├── base.py          # BaseProvider
│   │   ├── openai.py
│   │   ├── anthropic.py
│   │   ├── gemini.py
│   │   ├── ollama.py
│   │   └── litellm.py       # LiteLLM 统一接口
│   ├── fallback.py          # 故障转移
│   ├── load_balancer.py     # 负载均衡
│   └── cost_tracker.py      # 成本追踪
├── scripts/
│   └── main.py              # CLI 入口
└── examples/
    ├── basic_chat.py
    ├── with_tools.py
    └── fallback_demo.py

Integration with OpenClaw#

python

from multi_llm_adapter import LLMClient

class MySkill:
    def __init__(self):
        self.llm = LLMClient()
        self.llm.load_config("config.yaml")

    def ask_llm(self, prompt: str):
        # 自动选择最佳 provider
        response = self.llm.chat_auto([
            {"role": "user", "content": prompt}
        ])
        return response.content

    def compare_models(self, prompt: str):
        return self.llm.compare(
            prompt,
            providers=["openai", "anthropic"]
        )

Use Cases#

1. 高可用 - Provider 故障自动切换
2. 成本控制 - 优先使用便宜模型
3. 性能优化 - 根据任务选择模型
4. A/B 测试 - 比较不同模型效果

License#

MIT License