Messages (Anthropic)

POST https://gateway.mytokengate.com/v1/messages

curl --request POST \
  --url https://gateway.mytokengate.com/v1/messages \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "claude-sonnet-4-6",
    "messages": [
      {
        "role": "user",
        "content": "What opportunities and challenges will the AI industry face in 2025?"
      }
    ],
    "max_tokens": 8192
  }'

Authorization: Bearer YOUR_API_KEY

{
  "model": "claude-sonnet-4-6",
  "system": "You are a helpful coding assistant.",
  "messages": [...],
  "max_tokens": 4096
}

{
  "system": [
    {
      "type": "text",
      "text": "You are a helpful assistant with access to the following documents...",
      "cache_control": { "type": "ephemeral" }
    }
  ]
}

{ "role": "user", "content": "Hello!" }

{
  "role": "user",
  "content": [
    {
      "type": "image",
      "source": {
        "type": "base64",
        "media_type": "image/png",
        "data": "<base64-encoded-image>"
      }
    },
    {
      "type": "image",
      "source": {
        "type": "url",
        "url": "https://example.com/photo.jpg"
      }
    },
    { "type": "text", "text": "Describe this image." }
  ]
}

{
  "role": "user",
  "content": [
    {
      "type": "document",
      "source": {
        "type": "base64",
        "media_type": "application/pdf",
        "data": "<base64-encoded-pdf>"
      }
    },
    { "type": "text", "text": "Summarize this document." }
  ]
}

{
  "role": "assistant",
  "content": [
    { "type": "text", "text": "Let me check the weather for you." },
    {
      "type": "tool_use",
      "id": "toolu_abc123",
      "name": "get_weather",
      "input": { "location": "Paris", "unit": "celsius" }
    }
  ]
}

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_abc123",
      "content": "22°C and sunny in Paris",
      "is_error": false
    }
  ]
}

{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "The response text..."
    }
  ],
  "model": "claude-sonnet-4-6",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 2095,
    "output_tokens": 503,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0
  }
}

import anthropic
 
client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://gateway.mytokengate.com/v1"
)
 
with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Hello!"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

1. message_start          → Message metadata (id, model, role, initial usage)
2. content_block_start   → A new content block begins (text, tool_use, or thinking)
3. content_block_delta   → Incremental content (text_delta, input_json_delta, thinking_delta)
4. content_block_stop    → Content block ends
5. message_delta          → Stop reason and final output token usage
6. message_stop           → Stream ends

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    thinking={"type": "enabled", "budget_tokens": 10000}
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if event.delta.type == "thinking_delta":
                print(f"[Thinking] {event.delta.thinking}")
            elif event.delta.type == "text_delta":
                print(event.delta.text, end="")

{
  "name": "get_weather",
  "description": "Get current weather for a city",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City and state"
      },
      "unit": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"]
      }
    },
    "required": ["location"]
  }
}

import anthropic
import json
 
client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://gateway.mytokengate.com/v1"
)
 
tools = [
    {
        "name": "get_weather",
        "description": "Get current weather for a city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }
    }
]
 
# Step 1: Model decides to call a tool
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "How is the weather in Paris?"}],
    tools=tools
)
 
# Step 2: Extract tool call and execute
tool_use_block = next(b for b in response.content if b.type == "tool_use")
tool_result = get_weather(**tool_use_block.input)
 
# Step 3: Send tool result back
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "How is the weather in Paris?"},
        {"role": "assistant", "content": response.content},
        {
            "role": "user",
            "content": [{
                "type": "tool_result",
                "tool_use_id": tool_use_block.id,
                "content": tool_result
            }]
        }
    ],
    tools=tools
)
 
print(response.content[0].text)

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 16000,
  "thinking": { "type": "enabled", "budget_tokens": 10000 },
  "messages": [{"role": "user", "content": "Solve this math problem step by step..."}]
}

{
  "content": [
    {
      "type": "thinking",
      "thinking": "Let me analyze this step by step..."
    },
    {
      "type": "text",
      "text": "Based on my analysis..."
    }
  ],
  "stop_reason": "end_turn"
}

{
  "system": [
    {
      "type": "text",
      "text": "You are a helpful assistant with access to these documents: ...",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What does the document say about X?",
          "cache_control": { "type": "ephemeral" }
        }
      ]
    }
  ]
}