fiddler-otel 0.1.1

Core OpenTelemetry instrumentation library for Fiddler GenAI observability

fiddler-otel

Core OpenTelemetry instrumentation library for Fiddler GenAI observability. This is the foundation package used by all Fiddler framework integrations (fiddler-langgraph, fiddler-langchain).

Installation

pip install fiddler-otel

Quick Start

Using the @trace decorator

The simplest way to instrument any Python function — synchronous or async:

from fiddler_otel import FiddlerClient, trace

client = FiddlerClient(
    api_key="YOUR_API_KEY",
    application_id="YOUR_APPLICATION_ID",
    url="https://your-instance.fiddler.ai",
)

@trace(span_type="generation", name="my_llm_call")
def call_llm(prompt: str) -> str:
    # your LLM call here
    return response

@trace(span_type="chain")
async def run_pipeline(user_input: str) -> str:
    result = call_llm(user_input)
    return result

Using span wrappers manually

For fine-grained control over span attributes:

from fiddler_otel import FiddlerClient, FiddlerGeneration, FiddlerChain, FiddlerTool

client = FiddlerClient(
    api_key="YOUR_API_KEY",
    application_id="YOUR_APPLICATION_ID",
    url="https://your-instance.fiddler.ai",
)

with client.start_span("my_agent", span_type="chain") as chain_span:
    gen = FiddlerGeneration(client.start_span("llm_call"))
    with gen as span:
        span.set_input("What is the capital of France?")
        response = call_llm("What is the capital of France?")
        span.set_output(response)
        span.set_usage(input_tokens=10, output_tokens=5, total_tokens=15)

Multi-turn Conversations

Link multiple agent invocations into a single conversation:

from fiddler_otel import set_conversation_id
import uuid

set_conversation_id(str(uuid.uuid4()))

# All spans created after this call carry the same conversation_id
result = my_agent(user_message)

Local JSONL Capture

Capture all spans to a local file for debugging without sending to Fiddler:

client = FiddlerClient(
    api_key="...",
    application_id="...",
    url="...",
    jsonl_capture_enabled=True,
    jsonl_file_path="trace_data.jsonl",
)

Or via environment variables:

FIDDLER_JSONL_ENABLED=true \
FIDDLER_JSONL_FILE=trace_data.jsonl \
python my_agent.py

Console Tracing (Development)

Print all spans to stdout during development:

client = FiddlerClient(
    api_key="...",
    application_id="...",
    url="...",
    console_tracer=True,
)

Public API

Symbol	Description
FiddlerClient	Main client — configures the tracer and exports spans to Fiddler
FiddlerSpan	Base span wrapper with set_input, set_output, set_attribute
FiddlerGeneration	LLM span wrapper — adds set_usage, set_messages, set_context
FiddlerChain	Chain/pipeline span wrapper
FiddlerTool	Tool call span wrapper
trace	Decorator for automatic span creation around any function
get_current_span	Get the active Fiddler span inside a traced function
set_conversation_id	Link multiple invocations into one conversation
get_client	Return the global singleton FiddlerClient

Context Isolation

fiddler-otel uses its own isolated OpenTelemetry context that does not interfere with any existing global tracer in your application. If you already use OpenTelemetry for infrastructure tracing, Fiddler spans will not appear in your infrastructure traces and vice versa.

Framework Integrations

Package	Framework	Install
fiddler-langgraph	LangGraph + LangChain v0.x	pip install fiddler-langgraph
fiddler-langchain	LangChain V1 (create_agent)	pip install fiddler-langchain

Both integration packages depend on fiddler-otel and re-export its core symbols, so you typically only need to install the integration package for your framework.

Requirements

• Python 3.10+
• opentelemetry-api >= 1.27.0
• opentelemetry-sdk >= 1.27.0
• opentelemetry-exporter-otlp-proto-http >= 1.27.0
• pydantic >= 2.0