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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://docs.respan.ai/mcp"
    }
  }
}

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What is Together AI?

The Together AI SDK is the official Python client for Together’s inference platform, providing access to open-source models like Llama, Mistral, and more. Respan can auto-instrument all Together AI calls for tracing, route them through the Respan gateway, or both.

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Setup

1

Install packages

pip install respan-ai opentelemetry-instrumentation-together together python-dotenv
2

Set environment variables

export TOGETHER_API_KEY="YOUR_TOGETHER_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
3

Initialize and run

import os
from dotenv import load_dotenv

load_dotenv()

from together import Together
from respan import Respan

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

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

response = client.chat.completions.create(
    model="meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(response.choices[0].message.content)
respan.flush()
4

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.

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Configuration

ParameterTypeDefaultDescription
api_keystr | NoneNoneFalls back to RESPAN_API_KEY env var.
base_urlstr | NoneNoneFalls back to RESPAN_BASE_URL env var.
is_auto_instrumentbool | NoneFalseAuto-discover and activate all installed instrumentors via OpenTelemetry entry points.
customer_identifierstr | NoneNoneDefault customer identifier for all spans.
metadatadict | NoneNoneDefault metadata attached to all spans.
environmentstr | NoneNoneEnvironment tag (e.g. "production").

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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

respan = Respan(
    is_auto_instrument=True,
    customer_identifier="user_123",
    metadata={"service": "chat-api", "version": "1.0.0"},
)

​
With propagate_attributes

Override per-request using a context manager. 
from together import Together
from respan import Respan, workflow, propagate_attributes

respan = Respan(
    is_auto_instrument=True,
    metadata={"service": "chat-api", "version": "1.0.0"},
)
client = Together()

@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"},
    ):
        response = client.chat.completions.create(
            model="meta-llama/Llama-3-70b-chat-hf",
            messages=[{"role": "user", "content": question}],
        )
        print(response.choices[0].message.content)
AttributeTypeDescription
customer_identifierstrIdentifies the end user in Respan analytics.
thread_identifierstrGroups related messages into a conversation.
metadatadictCustom key-value pairs. Merged with default metadata.

​
Decorators

Use @workflow and @task to create structured trace hierarchies. 
from together import Together
from respan import Respan, workflow, task

respan = Respan(is_auto_instrument=True)
client = Together()

@task(name="generate_outline")
def outline(topic: str) -> str:
    response = client.chat.completions.create(
        model="meta-llama/Llama-3-70b-chat-hf",
        messages=[
            {"role": "user", "content": f"Create a brief outline about: {topic}"},
        ],
    )
    return response.choices[0].message.content

@workflow(name="content_pipeline")
def pipeline(topic: str):
    plan = outline(topic)
    response = client.chat.completions.create(
        model="meta-llama/Llama-3-70b-chat-hf",
        messages=[
            {"role": "user", "content": f"Write content from this outline: {plan}"},
        ],
    )
    print(response.choices[0].message.content)

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

​
Examples

​
Basic chat

response = client.chat.completions.create(
    model="meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(response.choices[0].message.content)

​
Gateway features

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

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Switch models

Change the model parameter to use 250+ models from different providers through the same gateway. 
# Together AI (Llama)
response = client.chat.completions.create(model="together_ai/meta-llama/Llama-3-70b-chat-hf", messages=messages)

# OpenAI
response = client.chat.completions.create(model="gpt-4.1-nano", messages=messages)

# Anthropic
response = client.chat.completions.create(model="claude-sonnet-4-5-20250929", messages=messages)
See the full model list.

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Respan parameters

Pass additional Respan parameters via extra_body for gateway features. 
response = client.chat.completions.create(
    model="together_ai/meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "Hello"}],
    extra_body={
        "customer_identifier": "user_123",
        "fallback_models": ["gpt-4.1-nano"],
        "metadata": {"session_id": "abc123"},
        "thread_identifier": "conversation_456",
    },
)
See Respan parameters for the full list.