3 步开始调用 Claude

pip install anthropic

from anthropic import Anthropic

client = Anthropic(
    base_url="https://cldapi.com",  # ← 改这里
    api_key="sk-cr-xxxxxxxxxxxxxx",
)

msg = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    messages=[{"role": "user",
               "content": "Hello, Claude"}],
)
print(msg.content[0].text)

# 1. 安装（需要 Node.js 18+）
npm install -g @anthropic-ai/claude-code

# 2. 在 ~/.zshrc 或 ~/.bashrc 追加（永久生效）
export ANTHROPIC_BASE_URL="https://cldapi.com"
export ANTHROPIC_AUTH_TOKEN="sk-cr-xxxxxxxxxxxxxx"

# 3. source 后在任意项目目录跑
source ~/.zshrc
claude

// macOS / Linux: ~/.config/opencode/opencode.json
// Windows:       %APPDATA%\opencode\opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "anthropic": {
      "options": {
        "baseURL": "https://cldapi.com/v1",
        "apiKey": "sk-cr-xxxxxxxxxxxxxx"
      }
    }
  }
}

# macOS / Linux: ~/.continue/config.yaml
# Windows:       %USERPROFILE%\.continue\config.yaml
models:
  - name: Claude Sonnet 4.6 via Relay
    provider: anthropic
    model: claude-sonnet-4-6
    apiBase: https://cldapi.com
    apiKey: sk-cr-xxxxxxxxxxxxxx
    roles: [chat, edit, apply]

export ANTHROPIC_API_BASE="https://cldapi.com"
export ANTHROPIC_API_KEY="sk-cr-xxxxxxxxxxxxxx"

# 在你的项目目录跑
aider --model anthropic/claude-sonnet-4-6

{
  "language_models": {
    "anthropic": {
      "api_url": "https://cldapi.com"
    }
  },
  "assistant": {
    "default_model": {
      "provider": "anthropic",
      "model": "claude-sonnet-4-6"
    }
  }
}

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "写一首关于咖啡的俳句"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

tools = [{
    "name": "get_weather",
    "description": "获取指定城市的当前天气",
    "input_schema": {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
    },
}]

resp = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "上海现在多少度?"}],
)

# 如果 stop_reason == "tool_use"，执行工具，把结果塞回去再调一次
if resp.stop_reason == "tool_use":
    tu = next(b for b in resp.content if b.type == "tool_use")
    result = my_get_weather(**tu.input)
    resp = client.messages.create(
        model="claude-sonnet-4-6", max_tokens=1024, tools=tools,
        messages=[
            {"role": "user", "content": "上海现在多少度?"},
            {"role": "assistant", "content": resp.content},
            {"role": "user", "content": [
                {"type": "tool_result", "tool_use_id": tu.id, "content": result},
            ]},
        ],
    )

import base64, httpx
img = base64.standard_b64encode(httpx.get("https://example.com/diagram.png").content).decode()

resp = client.messages.create(
    model="claude-sonnet-4-6", max_tokens=1024,
    messages=[{"role": "user", "content": [
        {"type": "image", "source": {
            "type": "base64",
            "media_type": "image/png",
            "data": img,
        }},
        {"type": "text", "text": "用三句话概括这张图"},
    ]}],
)

resp = client.messages.create(
    model="claude-sonnet-4-6", max_tokens=1024,
    system=[
        {"type": "text", "text": "你是一个资深 Rust 编译器开发者..."},
        {  # 长文档作为可缓存上下文
            "type": "text",
            "text": long_codebase,
            "cache_control": {"type": "ephemeral"},
        },
    ],
    messages=[{"role": "user", "content": "为什么我这段 lifetime 推断不通过?"}],
)
# 检查命中:
print(resp.usage.cache_read_input_tokens, resp.usage.cache_creation_input_tokens)