Anthropic SDK Compatibility Guide - Infercom Documentation

The Infercom API supports Anthropic’s Messages API format (/v1/messages), enabling you to use the Anthropic Python SDK and compatible tooling with Infercom’s models. This is useful for applications and frameworks built around the Anthropic API, such as Claude Code, LangChain’s Anthropic provider, or custom agentic workflows.

The Anthropic SDK was designed for Claude models. When using it with Infercom, you’re accessing open-source models (like MiniMax, DeepSeek, Llama) through an Anthropic-compatible interface - not Claude itself.

Install the Anthropic SDK

pip install anthropic

Configure the client

Set the base_url to Infercom’s API and provide your Infercom API key.

Don’t have an Infercom API key? Get yours from the API keys and URLs page.

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.infercom.ai",
    api_key="your-infercom-api-key"
)

Basic usage

Non-streaming example

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.infercom.ai",
    api_key="your-infercom-api-key"
)

message = client.messages.create(
    model="MiniMax-M2.7",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "What is the capital of Germany?"}
    ]
)

print(message.content[0].text)

Streaming example

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.infercom.ai",
    api_key="your-infercom-api-key"
)

with client.messages.stream(
    model="MiniMax-M2.7",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a haiku about AI."}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

System prompts

Use the system parameter to provide instructions to the model.

message = client.messages.create(
    model="MiniMax-M2.7",
    max_tokens=1024,
    system="You are a helpful assistant that speaks like a pirate.",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

Multi-turn conversations

message = client.messages.create(
    model="MiniMax-M2.7",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "My name is Thomas."},
        {"role": "assistant", "content": "Nice to meet you, Thomas!"},
        {"role": "user", "content": "What is my name?"}
    ]
)

Tool use (function calling)

The Anthropic Messages API supports tool use for models that have function calling capabilities.

Tool use works with MiniMax-M2.7 and gpt-oss-120b. Other models may not reliably call tools. See Function calling for model-specific guidance.

Defining tools

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.infercom.ai",
    api_key="your-infercom-api-key"
)

message = client.messages.create(
    model="MiniMax-M2.7",
    max_tokens=200,
    tools=[
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and country, e.g. Munich, Germany"
                    }
                },
                "required": ["location"]
            }
        }
    ],
    messages=[
        {"role": "user", "content": "What's the weather in Munich?"}
    ]
)

print(message.content)

If the model decides to use the tool, the response will include a tool_use content block:

[
  {
    "type": "tool_use",
    "id": "call_abc123",
    "name": "get_weather",
    "input": {"location": "Munich, Germany"}
  }
]

Providing tool results

After executing the tool, send the result back to continue the conversation:

message = client.messages.create(
    model="MiniMax-M2.7",
    max_tokens=200,
    tools=[
        {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "input_schema": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    ],
    messages=[
        {"role": "user", "content": "What's the weather in Munich?"},
        {
            "role": "assistant",
            "content": [
                {
                    "type": "tool_use",
                    "id": "call_abc123",
                    "name": "get_weather",
                    "input": {"location": "Munich, Germany"}
                }
            ]
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "tool_result",
                    "tool_use_id": "call_abc123",
                    "content": "Sunny, 22°C"
                }
            ]
        }
    ]
)

print(message.content[0].text)

Async usage

import anthropic
import asyncio

async def main():
    client = anthropic.AsyncAnthropic(
        base_url="https://api.infercom.ai",
        api_key="your-infercom-api-key"
    )

    message = await client.messages.create(
        model="MiniMax-M2.7",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": "Hello!"}
        ]
    )
    print(message.content[0].text)

asyncio.run(main())

Using curl

You can also call the Messages API directly with curl:

curl https://api.infercom.ai/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-infercom-api-key" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "MiniMax-M2.7",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Supported parameters

Parameter	Type	Description
`model`	string	Required. The model to use (e.g., `MiniMax-M2.7`)
`messages`	array	Required. Array of message objects with `role` and `content`
`max_tokens`	integer	Required. Maximum tokens to generate
`system`	string	System prompt for the model
`temperature`	number	Sampling temperature (0.0-2.0). No default, unlike the Anthropic API, which defaults to 1.0.
`top_p`	number	Nucleus sampling parameter
`top_k`	integer	Top-k sampling parameter
`stop_sequences`	array	Custom stop sequences
`stream`	boolean	Enable streaming responses
`tools`	array	Tool definitions for function calling
`tool_choice`	object	Control tool usage (`auto`, `any`, or specific tool)

Unsupported features

The following Anthropic-specific features are not supported:

Extended thinking (thinking parameter)
Prompt caching (cache_control)
Vision/image inputs
PDF file inputs
Citations
Server-side tools (web search, code execution)
Batch API

Differences from Anthropic’s API

Aspect	Anthropic	Infercom
Models	Claude (Opus, Sonnet, Haiku)	Open-source models (MiniMax, DeepSeek, Llama, Gemma)
Base URL	`https://api.anthropic.com`	`https://api.infercom.ai`
API key header	`x-api-key`	`x-api-key` (same)
Version header	Required: `anthropic-version`	Supported but optional

When to use Anthropic vs OpenAI compatibility

Use case	Recommended API
Existing Anthropic SDK code	Anthropic Messages API (`/v1/messages`)
Claude Code, LangChain Anthropic	Anthropic Messages API (`/v1/messages`)
OpenAI SDK code	OpenAI Chat Completions API (`/v1/chat/completions`)
Agentic workflows, coding tools	Responses API (`/v1/responses`)
New projects	Any - all three APIs work with the same models

OpenAI compatibility - Using the OpenAI SDK
Function calling - Detailed guide on tool use
Responses API - Agentic workflows with structured outputs
Supported models - Available models and capabilities

Get started

Models

Features

Agentic Coding

Build

Resources

Implement Anthropic-Compatible Features - Developer Guide

Install the Anthropic SDK

Configure the client

Basic usage

Non-streaming example

Streaming example

System prompts

Multi-turn conversations

Tool use (function calling)

Defining tools

Providing tool results

Async usage

Using curl

Supported parameters

Unsupported features

Differences from Anthropic’s API

When to use Anthropic vs OpenAI compatibility

​Install the Anthropic SDK

​Configure the client

​Basic usage

​Non-streaming example

​Streaming example

​System prompts

​Multi-turn conversations

​Tool use (function calling)

​Defining tools

​Providing tool results

​Async usage

​Using curl

​Supported parameters

​Unsupported features

​Differences from Anthropic’s API

​When to use Anthropic vs OpenAI compatibility

​Related documentation

Install the Anthropic SDK

Configure the client

Basic usage

Non-streaming example

Streaming example

System prompts

Multi-turn conversations

Tool use (function calling)

Defining tools

Providing tool results

Async usage

Using curl

Supported parameters

Unsupported features

Differences from Anthropic’s API

When to use Anthropic vs OpenAI compatibility

Related documentation