Anthropic SDK

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  "mcpServers": {
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What is Anthropic SDK?

The Anthropic SDK is the official Python client for Anthropic’s Claude models, supporting messages, streaming, and tool use. Respan can auto-instrument all Anthropic calls for tracing, route them through the Respan gateway, or both.

Setup

Install packages

Tracing
Gateway
Both

OpenInference (Recommended)
Traceloop

pip install respan-ai openinference-instrumentation-anthropic anthropic python-dotenv

pip install respan-ai opentelemetry-instrumentation-anthropic anthropic python-dotenv

pip install anthropic python-dotenv

OpenInference (Recommended)
Traceloop

pip install respan-ai openinference-instrumentation-anthropic anthropic python-dotenv

pip install respan-ai opentelemetry-instrumentation-anthropic anthropic python-dotenv

Set environment variables

Tracing
Gateway
Both

export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

No ANTHROPIC_API_KEY needed — the Respan gateway handles provider authentication.

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

No ANTHROPIC_API_KEY needed — the Respan gateway handles provider authentication.

Initialize and run

Tracing
Gateway
Both

OpenInference (Recommended)
Traceloop

import os
from dotenv import load_dotenv

load_dotenv()

from anthropic import Anthropic
from respan import Respan
from openinference.instrumentation.anthropic import AnthropicInstrumentor

# Initialize Respan with Anthropic instrumentation
respan = Respan(instrumentations=[AnthropicInstrumentor()])

# Calls go directly to Anthropic, auto-traced by Respan
client = Anthropic()

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)
respan.flush()

import os
from dotenv import load_dotenv

load_dotenv()

from anthropic import Anthropic
from respan import Respan

# Auto-discover and activate all installed instrumentors
respan = Respan(is_auto_instrument=True)

# Calls go directly to Anthropic, auto-traced by Respan
client = Anthropic()

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)
respan.flush()

import os
from dotenv import load_dotenv

load_dotenv()

from anthropic import Anthropic

# Route all LLM calls through the Respan gateway
client = Anthropic(
    api_key=os.getenv("RESPAN_API_KEY"),
    base_url="https://api.respan.ai/api/anthropic/",
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)

OpenInference (Recommended)
Traceloop

import os
from dotenv import load_dotenv

load_dotenv()

from anthropic import Anthropic
from respan import Respan
from openinference.instrumentation.anthropic import AnthropicInstrumentor

# Initialize Respan with Anthropic instrumentation
respan = Respan(instrumentations=[AnthropicInstrumentor()])

# Route all LLM calls through the Respan gateway
client = Anthropic(
    api_key=os.getenv("RESPAN_API_KEY"),
    base_url="https://api.respan.ai/api/anthropic/",
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)
respan.flush()

import os
from dotenv import load_dotenv

load_dotenv()

from anthropic import Anthropic
from respan import Respan

# Auto-discover and activate all installed instrumentors
respan = Respan(is_auto_instrument=True)

# Route all LLM calls through the Respan gateway
client = Anthropic(
    api_key=os.getenv("RESPAN_API_KEY"),
    base_url="https://api.respan.ai/api/anthropic/",
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)
respan.flush()

View your trace

Open the Traces page to see your auto-instrumented LLM spans.

This step applies to Tracing and Both setups. The Gateway-only setup does not produce traces.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate (e.g. `AnthropicInstrumentor()`).
`is_auto_instrument`	`bool \| None`	`False`	Auto-discover and activate all installed instrumentors via OpenTelemetry entry points.
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag (e.g. `"production"`).

Attributes

Attach customer identifiers, thread IDs, and metadata to spans.

In Respan()

Set defaults at initialization — these apply to all spans.

from respan import Respan
from openinference.instrumentation.anthropic import AnthropicInstrumentor

respan = Respan(
    instrumentations=[AnthropicInstrumentor()],
    customer_identifier="user_123",
    metadata={"service": "chat-api", "version": "1.0.0"},
)

With propagate_attributes

Override per-request using a context manager.

from respan import Respan, workflow, propagate_attributes
from openinference.instrumentation.anthropic import AnthropicInstrumentor

respan = Respan(
    instrumentations=[AnthropicInstrumentor()],
    metadata={"service": "chat-api", "version": "1.0.0"},
)

@workflow(name="handle_request")
def handle_request(user_id: str, question: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_001",
        metadata={"plan": "pro"},  # merged with default metadata
    ):
        message = client.messages.create(
            model="claude-sonnet-4-5-20250929",
            max_tokens=1024,
            messages=[{"role": "user", "content": question}],
        )
        print(message.content[0].text)

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.

Decorators

Use @workflow and @task to create structured trace hierarchies.

from respan import Respan, workflow, task
from openinference.instrumentation.anthropic import AnthropicInstrumentor
from anthropic import Anthropic

respan = Respan(instrumentations=[AnthropicInstrumentor()])
client = Anthropic()

@task(name="generate_outline")
def outline(topic: str) -> str:
    message = client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": f"Create a brief outline about: {topic}"},
        ],
    )
    return message.content[0].text

@workflow(name="content_pipeline")
def pipeline(topic: str):
    plan = outline(topic)
    message = client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=2048,
        messages=[
            {"role": "user", "content": f"Write content from this outline: {plan}"},
        ],
    )
    print(message.content[0].text)

pipeline("Benefits of API gateways")
respan.flush()

Examples

Basic message

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(message.content[0].text)

Streaming

with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a haiku about Python."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Tool calls

import json

tools = [
    {
        "name": "get_weather",
        "description": "Get the weather for a city.",
        "input_schema": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    }
]

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
)

if message.stop_reason == "tool_use":
    tool_block = next(b for b in message.content if b.type == "tool_use")
    result = f"Sunny, 72F in {tool_block.input['city']}"

    final = client.messages.create(
        model="claude-sonnet-4-5-20250929",
        max_tokens=1024,
        tools=tools,
        messages=[
            {"role": "user", "content": "What's the weather in Paris?"},
            {"role": "assistant", "content": message.content},
            {"role": "user", "content": [
                {"type": "tool_result", "tool_use_id": tool_block.id, "content": result}
            ]},
        ],
    )
    print(final.content[0].text)

Gateway features

The features below require the Gateway or Both setup from Step 3.

Switch models

Change the model parameter to use different Claude models through the same gateway.

# Claude Sonnet
message = client.messages.create(model="claude-sonnet-4-5-20250929", max_tokens=1024, messages=messages)

# Claude Haiku
message = client.messages.create(model="claude-3-5-haiku-20241022", max_tokens=1024, messages=messages)

# Claude Opus
message = client.messages.create(model="claude-3-opus-20240229", max_tokens=1024, messages=messages)

See the full model list.

Respan parameters

Pass additional Respan parameters via metadata under the respan_params key for gateway features.

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
    metadata={
        "respan_params": {
            "customer_identifier": "user_123",
            "metadata": {"session_id": "abc123"},
            "thread_identifier": "conversation_456",
        }
    },
)

See Respan parameters for the full list.

Overview

Agent Frameworks

LLM SDKs

Coding Agents

Vector DBs

Others

Model Providers

Anthropic SDK

What is Anthropic SDK?

Setup

Configuration

Attributes

In Respan()

With propagate_attributes

Decorators

Examples

Basic message

Streaming

Tool calls

Gateway features

Switch models

Respan parameters

Overview

Agent Frameworks

LLM SDKs

Coding Agents

Vector DBs

Others

Model Providers

​What is Anthropic SDK?

​Setup

​Configuration

​Attributes

​In Respan()

​With propagate_attributes

​Decorators

​Examples

​Basic message

​Streaming

​Tool calls

​Gateway features

​Switch models

​Respan parameters

What is Anthropic SDK?

Setup

Configuration

Attributes

In Respan()

With propagate_attributes

Decorators

Examples

Basic message

Streaming

Tool calls

Gateway features

Switch models

Respan parameters