Moda Python SDK

Overview

The Moda Python SDK provides automatic instrumentation for your LLM applications with built-in conversation threading. Every LLM call is automatically tracked with a stable moda.conversation_id that groups multi-turn conversations together.

Installation

pip install moda-ai

This installs the core SDK along with instrumentation for OpenAI and Anthropic. For other providers, install the corresponding instrumentation package:

pip install moda-claude-agent-sdk  # Claude Agent SDK (Claude Code, custom agents)

Quick Start

import moda
from openai import OpenAI

moda.init("YOUR_MODA_API_KEY")

# Set conversation ID for your session (recommended)
moda.conversation_id = "session_" + session_id

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

moda.flush()

Initialization is synchronous in Python. Call moda.init(...) once at startup before making LLM calls to ensure instrumentation is active.

Prompt Management

Use moda prompts sync to version prompt files, then render them through the SDK:

rendered = moda.prompt("support.triage").render({
    "ticket": {"text": user_message},
})

moda.conversation_id = conversation_id
moda.user_id = user_id

client.chat.completions.create(
    model="gpt-4o",
    messages=rendered["messages"],
)

moda.prompt(...).render(...) attaches prompt metadata to OpenTelemetry spans, including moda.prompt_key, moda.prompt_id, moda.prompt_version, and moda.prompt_version_id. See Prompt Management for the code-first sync workflow.

Conversation Tracking

Setting Conversation ID (Recommended)

For production use, explicitly set a conversation ID to group related LLM calls:

import moda

moda.init("YOUR_MODA_API_KEY")

# Property-style (recommended)
moda.conversation_id = "support_ticket_123"

client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "I need help"}]
)

client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "I need help"},
        {"role": "assistant", "content": "I'd be happy to help..."},
        {"role": "user", "content": "Order #12345"}
    ]
)

# Both calls share the same conversation_id
moda.conversation_id = None  # clear when done

Setting User ID

Associate LLM calls with specific users for per-user analytics:

moda.user_id = "user_12345"

client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

moda.user_id = None  # clear when done

Scoped Context Managers

For scoped context (useful in request handlers):

from moda import set_conversation_id, set_user_id

# Group specific calls under a custom conversation ID
with set_conversation_id("support-ticket-123"):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "I need help"}]
    )

# Attach user attribution
with set_user_id("user-456"):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )

Direct Setters

For cases where context managers aren’t suitable (e.g., setting once at request start):

from moda import set_conversation_id_value, set_user_id_value

# Set values that persist until cleared
set_conversation_id_value("session-123")
set_user_id_value("user-456")

# Make multiple calls...
response1 = client.chat.completions.create(...)
response2 = client.chat.completions.create(...)

# Clear when done
set_conversation_id_value(None)
set_user_id_value(None)

Reading Current Context

from moda import get_conversation_id, get_user_id

# Get current values
current_conv = get_conversation_id()
current_user = get_user_id()
print(f"Conversation: {current_conv}, User: {current_user}")

Computing Conversation ID

You can manually compute a conversation ID from messages:

from moda import compute_conversation_id

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
]

# Returns conv_[16-char-hash] based on system prompt + first user message
conv_id = compute_conversation_id(messages)

Automatic Fallback

If you don’t set a conversation ID, the SDK automatically computes a stable one based on:

The first user message in the conversation
The system prompt (if present)

This works well for prototyping and simple use cases:

import moda
from openai import OpenAI

moda.init("YOUR_MODA_API_KEY")
client = OpenAI()

# Turn 1
messages = [{"role": "user", "content": "What is Python?"}]
response = client.chat.completions.create(model="gpt-4o", messages=messages)

# Turn 2 - automatically has the same conversation_id
messages.append({"role": "assistant", "content": response.choices[0].message.content})
messages.append({"role": "user", "content": "How do I install it?"})
response = client.chat.completions.create(model="gpt-4o", messages=messages)

# Both calls share the same conversation_id in Moda

For production applications, explicit conversation IDs are recommended as they provide:

Predictable grouping regardless of message content
Integration with your existing session/thread identifiers
Easier debugging and correlation with your application logs

Configuration

Environment Variables

Variable	Description
`MODA_API_KEY`	Your Moda API key
`MODA_BASE_URL`	Custom ingest endpoint (optional)
`MODA_HEADERS`	Custom headers sent with telemetry requests (optional)

Programmatic Configuration

import moda

moda.init(
    api_key="YOUR_MODA_API_KEY",
    app_name="my-chatbot",           # Optional: name your application
    endpoint="https://custom.endpoint/v1/traces"  # Optional: custom endpoint
)

Advanced Configuration

The SDK supports additional configuration options:

import moda

moda.init(
    api_key="YOUR_MODA_API_KEY",
    app_name="my-app",

    # Enable/disable instrumentation
    enabled=True,

    # Send spans immediately instead of batching
    disable_batch=False,

    # Additional resource attributes
    resource_attributes={
        "deployment.environment": "production",
    },

    # Filter which providers are instrumented (see below)
    instruments=None,                 # Set of instruments to enable (None = all)
    block_instruments=None,           # Set of instruments to disable

    # Additional headers sent with telemetry requests
    headers={},
)

Filtering Instruments

Control which LLM providers are instrumented:

from moda import Instruments
import moda

# Only instrument OpenAI
moda.init(
    api_key="YOUR_KEY",
    instruments={Instruments.OPENAI}
)

# Instrument everything except Anthropic
moda.init(
    api_key="YOUR_KEY",
    block_instruments={Instruments.ANTHROPIC}
)

The Instruments enum supports a wide range of providers and frameworks:

Category	Instruments
LLM Providers	`OPENAI`, `ANTHROPIC`, `COHERE`, `MISTRAL`, `GROQ`, `OLLAMA`, `BEDROCK`, `VERTEXAI`, `TOGETHER`, `REPLICATE`, `SAGEMAKER`, `WATSONX`, `GOOGLE_GENERATIVEAI`, `WRITER`, `ALEPHALPHA`
Frameworks	`LANGCHAIN`, `LLAMA_INDEX`, `HAYSTACK`, `CREWAI`, `OPENAI_AGENTS`, `MCP`, `AGNO`
Vector Databases	`CHROMA`, `MILVUS`, `PINECONE`, `QDRANT`, `WEAVIATE`, `LANCEDB`, `MARQO`

Supported Providers

The SDK automatically instruments:

Provider	Package
OpenAI	`moda-openai` (included with moda-ai)
Anthropic	`moda-anthropic` (included with moda-ai)
Claude Agent SDK	`moda-claude-agent-sdk` (install separately)

OpenRouter Support

OpenRouter provides access to multiple LLM providers through a unified API. Since OpenRouter uses an OpenAI-compatible interface, it works automatically with the Moda SDK:

import moda
from openai import OpenAI

moda.init("YOUR_MODA_API_KEY")

# Configure OpenAI client to use OpenRouter
openrouter = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="YOUR_OPENROUTER_API_KEY",
    default_headers={
        "HTTP-Referer": "https://your-app.com",  # Optional: for rankings
        "X-Title": "Your App Name",               # Optional: for rankings
    },
)

moda.conversation_id = "openrouter_session_123"

# Use any model available on OpenRouter
response = openrouter.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",  # Or any OpenRouter model
    messages=[{"role": "user", "content": "Hello!"}]
)

# Also works with OpenAI models via OpenRouter
gpt_response = openrouter.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

OpenRouter model names use the format provider/model-name. See the OpenRouter models page for all available models.

Verifying Data in Moda

After setting up, verify that data is flowing correctly:

Make a few LLM calls
Call moda.flush() to ensure data is sent
Check the Moda dashboard, conversations should appear within seconds
Verify that multi-turn conversations share the same conversation ID

Data Captured

The SDK captures:

Attribute	Description
`moda.conversation_id`	Stable ID grouping multi-turn conversations
`moda.user_id`	User identifier (when set)
`llm.vendor`	LLM provider (e.g., “openai”, “anthropic”)
`llm.request.type`	Request type (e.g., “chat”, “completion”)
`llm.request.model`	Requested model name
`llm.response.model`	Actual model used in response
`llm.prompts`	User and system messages
`llm.completions`	Assistant responses
`llm.usage.prompt_tokens`	Input token count
`llm.usage.completion_tokens`	Output token count
`llm.usage.total_tokens`	Total token count
`llm.usage.reasoning_tokens`	Reasoning token count (extended thinking)

API Reference

Core Functions

Function	Description
`moda.init(api_key, **options)`	Initialize the SDK
`moda.flush()`	Force flush pending telemetry

Context Properties

Property	Description
`moda.conversation_id`	Get/set global conversation ID
`moda.user_id`	Get/set global user ID

Context Managers

Function	Description
`set_conversation_id(id)`	Context manager for scoped conversation ID
`set_user_id(id)`	Context manager for scoped user ID

Direct Functions

Function	Description
`set_conversation_id_value(id)`	Set conversation ID without context manager
`set_user_id_value(id)`	Set user ID without context manager
`get_conversation_id()`	Get current conversation ID
`get_user_id()`	Get current user ID
`compute_conversation_id(messages)`	Compute ID from message history

Troubleshooting

Conversation IDs not grouping correctly?

Use explicit moda.conversation_id instead of relying on auto-compute
If using auto-compute, ensure the first user message stays the same across turns
Check if system prompts are changing between calls

Data not appearing in Moda?

Call moda.flush() before your program exits
Check that your API key is correct
Verify network connectivity to the ingest endpoint

Import errors?

Make sure you installed moda-ai
Check that your Python version is 3.10 or higher

Getting Started

Ingestion

Frameworks

Prompt Management

Harness

Data API

Moda Python SDK

Overview

Installation

Quick Start

Prompt Management

Conversation Tracking

Setting Conversation ID (Recommended)

Setting User ID

Scoped Context Managers

Direct Setters

Reading Current Context

Computing Conversation ID

Automatic Fallback

Configuration

Environment Variables

Programmatic Configuration

Advanced Configuration

Filtering Instruments

Supported Providers

OpenRouter Support

Verifying Data in Moda

Data Captured

API Reference

Core Functions

Context Properties

Context Managers

Direct Functions

Troubleshooting

​Overview

​Installation

​Quick Start

​Prompt Management

​Conversation Tracking

​Setting Conversation ID (Recommended)

​Setting User ID

​Scoped Context Managers

​Direct Setters

​Reading Current Context

​Computing Conversation ID

​Automatic Fallback

​Configuration

​Environment Variables

​Programmatic Configuration

​Advanced Configuration

​Filtering Instruments

​Supported Providers

​OpenRouter Support

​Verifying Data in Moda

​Data Captured

​API Reference

​Core Functions

​Context Properties

​Context Managers

​Direct Functions

​Troubleshooting

Overview

Installation

Quick Start

Prompt Management

Conversation Tracking

Setting Conversation ID (Recommended)

Setting User ID

Scoped Context Managers

Direct Setters

Reading Current Context

Computing Conversation ID

Automatic Fallback

Configuration

Environment Variables

Programmatic Configuration

Advanced Configuration

Filtering Instruments

Supported Providers

OpenRouter Support

Verifying Data in Moda

Data Captured

API Reference

Core Functions

Context Properties

Context Managers

Direct Functions

Troubleshooting