Anthropic API Migration Guide

Migrate from the Anthropic API to the Telnyx Inference API by changing the base URL and passing your Telnyx API key in an Authorization header — no code rewrites required.

Swap the base URL and pass your Telnyx API key as a Bearer token. That’s it.

The Telnyx Inference API now exposes an Anthropic-compatible Messages endpoint at POST /v2/ai/anthropic/v1/messages. It accepts the same request body as the Anthropic Messages API and returns the same response shape — including streaming via Anthropic SSE event types (message_start, content_block_start, content_block_delta, content_block_stop, message_delta, message_stop).

Authentication

The Anthropic SDK sends requests with an x-api-key header by default. Telnyx uses Authorization: Bearer <TELNYX_API_KEY> instead. Pass the Telnyx key as a default_headers override and set the SDK’s own api_key to any placeholder value — the gateway ignores it.

export TELNYX_API_KEY='KEY***'

import os
from anthropic import Anthropic

client = Anthropic(
    api_key="unused",  # placeholder — the gateway uses the Bearer header below
    base_url="https://api.telnyx.com/v2/ai/anthropic",
    default_headers={
        "Authorization": f"Bearer {os.environ['TELNYX_API_KEY']}",
    },
)

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: "unused", // placeholder — the gateway uses the Bearer header below
  baseURL: "https://api.telnyx.com/v2/ai/anthropic",
  defaultHeaders: {
    Authorization: `Bearer ${process.env.TELNYX_API_KEY}`,
  },
});

The Anthropic SDK requires api_key to be set to a non-empty string, even when you override auth via default_headers. Use any placeholder — the Telnyx gateway only reads the Authorization: Bearer header.

Quickstart

Python

import os
from anthropic import Anthropic

client = Anthropic(
    api_key="unused",
    base_url="https://api.telnyx.com/v2/ai/anthropic",
    default_headers={
        "Authorization": f"Bearer {os.environ['TELNYX_API_KEY']}",
    },
)

# Non-streaming
message = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    system="You are a friendly chatbot.",
    messages=[
        {"role": "user", "content": "Tell me about Telnyx"}
    ],
)
print(message.content[0].text)

JavaScript / TypeScript

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: "unused",
  baseURL: "https://api.telnyx.com/v2/ai/anthropic",
  defaultHeaders: {
    Authorization: `Bearer ${process.env.TELNYX_API_KEY}`,
  },
});

// Non-streaming
const message = await client.messages.create({
  model: "zai-org/GLM-5.2",
  max_tokens: 1024,
  system: "You are a friendly chatbot.",
  messages: [{ role: "user", content: "Tell me about Telnyx" }],
});
console.log(message.content[0].text);

curl

curl -sS -X POST "https://api.telnyx.com/v2/ai/anthropic/v1/messages" \
  -H "Authorization: Bearer $TELNYX_API_KEY" \
  -H "Content-Type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "zai-org/GLM-5.2",
    "max_tokens": 1024,
    "system": "You are a friendly chatbot.",
    "messages": [{"role": "user", "content": "Tell me about Telnyx"}]
  }'

Streaming

The endpoint streams Anthropic-format Server-Sent Events. Use the SDK’s built-in streaming just as you would with the native Anthropic API:

import os
from anthropic import Anthropic

client = Anthropic(
    api_key="unused",
    base_url="https://api.telnyx.com/v2/ai/anthropic",
    default_headers={
        "Authorization": f"Bearer {os.environ['TELNYX_API_KEY']}",
    },
)

with client.messages.stream(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    system="You are a friendly chatbot.",
    messages=[{"role": "user", "content": "Tell me about Telnyx"}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

const stream = await client.messages.stream({
  model: "zai-org/GLM-5.2",
  max_tokens: 1024,
  system: "You are a friendly chatbot.",
  messages: [{ role: "user", content: "Tell me about Telnyx" }],
});

for await (const event of stream) {
  if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
    process.stdout.write(event.delta.text);
  }
}

Tool Calling

Tool definitions and tool results follow the Anthropic tool use format:

import os
import json
from anthropic import Anthropic

client = Anthropic(
    api_key="unused",
    base_url="https://api.telnyx.com/v2/ai/anthropic",
    default_headers={
        "Authorization": f"Bearer {os.environ['TELNYX_API_KEY']}",
    },
)

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather in a given location.",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City and state, e.g. San Francisco, CA"},
            },
            "required": ["location"],
        },
    }
]

response = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Lisbon?"}],
)

# The model returns a tool_use content block
for block in response.content:
    if block.type == "tool_use":
        print(f"Tool: {block.name}")
        print(f"Input: {block.input}")

Extended Thinking

For models that support extended thinking (e.g. Claude reasoning models), pass the thinking parameter. On older SDK versions that reject unknown kwargs, use extra_body to forward it into the request JSON:

response = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Exprove que 17 é primo."}],
    extra_body={
        "thinking": {"type": "enabled", "budget_tokens": 2048},
    },
)

System Prompts

The system parameter can be a plain string or an array of content blocks, matching the Anthropic API format:

# String system prompt
response = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    system="You are a helpful assistant.",
    messages=[{"role": "user", "content": "Hello!"}],
)

# Array-style system prompt (cache_control, etc.)
response = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    system=[
        {"type": "text", "text": "You are a helpful assistant.", "cache_control": {"type": "ephemeral"}},
    ],
    messages=[{"role": "user", "content": "Hello!"}],
)

Models

Anthropic models are available under the anthropic/ prefix. See Available Models for the full list. Open-source models hosted on Telnyx (e.g. zai-org/GLM-5.2, moonshotai/Kimi-K2.6) also work through this endpoint — the request is translated to the OpenAI-compatible format internally and the response is translated back to the Anthropic shape.

Telnyx Extensions

The endpoint accepts several Telnyx-specific fields alongside the standard Anthropic request body:

Field	Type	Description
api_key_ref	string	Reference to an integration secret for external provider keys.
mcp_servers	array	List of MCP (Model Context Protocol) server configs to expose to the model.
fallback_config	object	Configuration for automatic model fallback when the primary model is unavailable.
billing_group_id	uuid	Billing group to associate with this request.
timeout	number	Request timeout in seconds (default: 300).
max_retries	integer	Maximum retry attempts for the request.
service_tier	string	Service tier for the request.

These fields pass through as extra body parameters in the SDK:

response = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
    extra_body={
        "billing_group_id": "6a09cdc3-8948-47f0-aa62-74ac943d6c58",
    },
)

Compatibility

Parameter	Telnyx	Anthropic
model	✅	✅
messages	✅	✅
max_tokens	✅	✅
system	✅	✅
stream	✅	✅
temperature	✅	✅
top_p	✅	✅
top_k	✅	✅
stop_sequences	✅	✅
metadata	✅	✅
tools	✅	✅
tool_choice	✅	✅
thinking	✅	✅
api_key_ref	✅	❌
mcp_servers	✅	❌
fallback_config	✅	❌
billing_group_id	✅	❌
timeout	✅	❌
service_tier	✅	❌