Native Claude Format

curl -N -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "stream": true,
    "messages": [
      {"role": "user", "content": "Please give a brief introduction to artificial intelligence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai"
)

# Non-streaming
message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Please give a brief introduction to artificial intelligence"}
    ]
)
print(message.content[0].text)

# Streaming
with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Please give a brief introduction to artificial intelligence"}
    ]
) as stream:
    for text_block in stream.text_stream:
        print(text_block, end="")

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "system": [
      {"type": "text", "text": "You are a helpful assistant who is good at answering questions clearly."}
    ],
    "messages": [
      {"role": "user", "content": "What is machine learning?"}
    ]
  }'

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-5-5",
    "max_tokens": 16000,
    "thinking": {"type": "between_tools"},
    "output_config": {"effort": "high"},
    "messages": [
      {"role": "user", "content": "Introduce yourself in one sentence"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai"
)

with client.messages.stream(
    model="claude-opus-5-5",
    max_tokens=16000,
    thinking={"type": "adaptive", "display": "summarized"},
    output_config={"effort": "high"},
    messages=[
        {"role": "user", "content": "Give me a medium-difficulty geometry problem and explain the solution step by step"}
    ]
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if event.delta.type == "thinking_delta":
                # Thinking summary
                print(f"[Thinking] {event.delta.thinking}", end="")
            elif event.delta.type == "text_delta":
                # Final answer
                print(event.delta.text, end="")

# Only applies to Claude 4.5 and earlier models;
# returns 400 on Opus 4.7+, Fable, Opus 5/5.5, Sonnet 5/5.5, Haiku 5.5
with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=8192,
    thinking={"type": "enabled", "budget_tokens": 4096},
    messages=[
        {"role": "user", "content": "Give me a medium-difficulty geometry problem and explain the solution step by step"}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "tools": [
      {
        "type": "web_search_20250305",
        "name": "web_search"
      }
    ],
    "messages": [
      {"role": "user", "content": "What is the latest news about artificial intelligence?"}
    ]
  }'

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "tools": [
      {
        "type": "web_search_20250305",
        "name": "web_search",
        "max_uses": 5
      }
    ],
    "messages": [
      {"role": "user", "content": "Search for the current weather in Beijing"}
    ]
  }'

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "tools": [
      {
        "type": "web_search_20250305",
        "name": "web_search",
        "max_uses": 5,
        "user_location": {
          "type": "approximate",
          "timezone": "Asia/Shanghai",
          "country": "CN",
          "region": "Beijing",
          "city": "Beijing"
        }
      }
    ],
    "messages": [
      {"role": "user", "content": "What is the weather like in Shanghai today?"}
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai"
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    tools=[
        {
            "type": "web_search_20250305",
            "name": "web_search",
            "max_uses": 5
        }
    ],
    messages=[
        {"role": "user", "content": "What is the latest news about artificial intelligence?"}
    ]
)
print(message.content[0].text)

{
  "id": "msg_xxx",
  "content": [
    {
      "type": "tool_use",
      "id": "toolu_xxx",
      "name": "get_weather",
      "input": {"city": "Shanghai"}
    }
  ],
  "stop_reason": "tool_use"
}

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "tools": [...],
    "messages": [
      {"role": "user", "content": "What is the weather like in Shanghai?"},
      {
        "role": "assistant",
        "content": [
          {
            "type": "tool_use",
            "id": "toolu_xxx",
            "name": "get_weather",
            "input": {"city": "Shanghai"}
          }
        ]
      },
      {
        "role": "user",
        "content": [
          {
            "type": "tool_result",
            "tool_use_id": "toolu_xxx",
            "content": "{\"temp\":\"22°C\",\"condition\":\"Cloudy\",\"aqi\":53}"
          }
        ]
      }
    ]
  }'

curl -X POST "https://api.gravitex.ai/v1/messages" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "system": "You are a Python programming assistant",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Analyze this code:\n```python\n[large amount of code, at least 1024 tokens]\n```",
            "cache_control": {"type": "ephemeral", "ttl": "1h"}
          }
        ]
      },
      {
        "role": "assistant",
        "content": [
          {
            "type": "text",
            "text": "The main purpose of this code is...[detailed analysis]",
            "cache_control": {"type": "ephemeral", "ttl": "1h"}
          }
        ]
      },
      {
        "role": "user",
        "content": "How can I optimize the performance of this code?"
      }
    ]
  }'

from anthropic import Anthropic

client = Anthropic(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai"
)

# First request: create the cache
message1 = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a professional document analysis assistant...[long text content]",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "First question"}
    ]
)

print(f"Cache created: {message1.usage.cache_creation_input_tokens} tokens")
print(f"Cache read: {message1.usage.cache_read_input_tokens} tokens")

# Another request within 5 minutes: use the cache
message2 = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a professional document analysis assistant...[the same long text]",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[
        {"role": "user", "content": "Second question"}
    ]
)

print(f"Cache created: {message2.usage.cache_creation_input_tokens} tokens")
print(f"Cache read: {message2.usage.cache_read_input_tokens} tokens")

event: message_start
data: {"type":"message_start","message":{"id":"msg_xxx","type":"message","role":"assistant","content":[],"model":"claude-sonnet-4-5-20250929","stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":25,"output_tokens":0}}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Re"}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"ply"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"output_tokens":100}}

event: message_stop
data: {"type":"message_stop"}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"thinking_delta","thinking":"Let me think about this problem..."}}