LangGraph SDK Advanced

from fiddler_langgraph import FiddlerClient, LangGraphInstrumentor, trace, get_current_span

client = FiddlerClient(
    application_id="your-app-id",
    api_key="your-api-key",
    url="https://your-instance.fiddler.ai"
)

# Auto-instrument LangGraph nodes
instrumentor = LangGraphInstrumentor(client)
instrumentor.instrument()

# Add custom tracing to business logic outside LangGraph
@trace(name="validate_input", as_type="chain")
def validate_user_input(user_message: str) -> dict:
    span = get_current_span(as_type="chain")
    if span:
        span.set_input(user_message)

    result = {"valid": True, "sanitized": user_message.strip()}

    if span:
        span.set_output(result)
    return result

# Both auto-instrumented LangGraph spans and custom spans
# appear together in Fiddler's trace view
validated = validate_user_input(user_message)
result = agent.invoke({"messages": [{"role": "user", "content": validated["sanitized"]}]})

import asyncio
from fiddler_langgraph import trace, get_current_span

@trace(name="supervisor", as_type="chain")
async def supervisor_agent(state: dict) -> dict:
    """Top-level orchestrator — creates parent span."""
    span = get_current_span(as_type="chain")
    if span:
        span.set_attribute("agent_count", len(state.get("tasks", [])))

    # Child spans nest automatically under the supervisor span
    results = await asyncio.gather(
        research_agent(state),
        analysis_agent(state),
    )

    if span:
        span.set_output({"completed": len(results)})
    return {"results": results}

@trace(name="research_agent", as_type="chain")
async def research_agent(state: dict) -> dict:
    """Child span nested under supervisor."""
    span = get_current_span(as_type="chain")
    if span:
        span.set_attribute("data_sources", 3)
    # ... research logic ...
    return {"findings": "..."}

@trace(name="analysis_agent", as_type="chain")
async def analysis_agent(state: dict) -> dict:
    """Child span nested under supervisor."""
    span = get_current_span(as_type="chain")
    if span:
        span.set_attribute("analysis_type", "comparative")
    # ... analysis logic ...
    return {"analysis": "..."}

from fiddler_langgraph import FiddlerClient

client = FiddlerClient(
    application_id="your-app-id",
    api_key="your-api-key",
    url="https://your-instance.fiddler.ai"
)

# as_type="generation" returns a FiddlerGeneration wrapper with LLM-specific helper methods
with client.start_as_current_span("llm_call", as_type="generation") as gen:
    gen.set_model("gpt-4o")
    gen.set_system_prompt("You are a travel planning assistant.")
    gen.set_user_prompt(user_input)

    response = call_llm(user_input)

    gen.set_completion(response.content)
    gen.set_usage(response.usage.prompt_tokens, response.usage.completion_tokens)

# as_type="tool" returns a FiddlerTool wrapper with tool-specific helper methods
with client.start_as_current_span("search_hotels", as_type="tool") as tool:
    tool.set_tool_name("hotel_search")
    tool.set_tool_input({"city": "Paris", "dates": "2026-03-01"})

    results = search_hotels("Paris", "2026-03-01")

    tool.set_tool_output(results)

import os
from opentelemetry.sdk.trace import SpanLimits, sampling
from opentelemetry.exporter.otlp.proto.http.trace_exporter import Compression
from fiddler_langgraph import FiddlerClient

# Configure batch processing BEFORE initializing FiddlerClient
os.environ['OTEL_BSP_MAX_QUEUE_SIZE'] = '500'         # Increased from default 100
os.environ['OTEL_BSP_SCHEDULE_DELAY_MILLIS'] = '500'  # Faster export than default 1000ms
os.environ['OTEL_BSP_MAX_EXPORT_BATCH_SIZE'] = '50'   # Larger batches than default 10
os.environ['OTEL_BSP_EXPORT_TIMEOUT'] = '10000'       # Longer timeout than default 5000ms

# Increase span limits to capture more data
production_limits = SpanLimits(
    max_events=128,                   # Default: 32
    max_links=64,                     # Default: 32
    max_span_attributes=128,          # Default: 32
    max_event_attributes=64,          # Default: 32
    max_link_attributes=32,           # Default: 32
    max_span_attribute_length=8192,   # Default: 2048
)

# Sample 5-10% of traces to manage data volume
production_sampler = sampling.TraceIdRatioBased(0.05)

client = FiddlerClient(
    application_id=os.getenv("FIDDLER_APPLICATION_ID"),
    api_key=os.getenv("FIDDLER_API_KEY"),
    url=os.getenv("FIDDLER_URL"),
    span_limits=production_limits,
    sampler=production_sampler,
    compression=Compression.Gzip,
)

import os
from opentelemetry.exporter.otlp.proto.http.trace_exporter import Compression
from fiddler_langgraph import FiddlerClient

# Reduce batch delay for faster exports
os.environ['OTEL_BSP_SCHEDULE_DELAY_MILLIS'] = '100'  # Export every 100ms
os.environ['OTEL_BSP_MAX_EXPORT_BATCH_SIZE'] = '5'    # Smaller batches

client = FiddlerClient(
    application_id=os.getenv("FIDDLER_APPLICATION_ID"),
    api_key=os.getenv("FIDDLER_API_KEY"),
    url=os.getenv("FIDDLER_URL"),
    compression=Compression.Gzip,  # Still use compression
)

import os
from opentelemetry.sdk.trace import SpanLimits, sampling
from opentelemetry.exporter.otlp.proto.http.trace_exporter import Compression
from fiddler_langgraph import FiddlerClient

memory_constrained_limits = SpanLimits(
    max_events=16,                  # Minimal event capture
    max_links=16,                   # Minimal linking
    max_span_attributes=32,         # Reduced attributes
    max_event_attributes=16,        # Reduced event attributes
    max_link_attributes=16,         # Reduced link attributes
    max_span_attribute_length=1024, # Shorter attribute values
)

os.environ['OTEL_BSP_MAX_QUEUE_SIZE'] = '50'           # Smaller queue
os.environ['OTEL_BSP_MAX_EXPORT_BATCH_SIZE'] = '5'    # Smaller batches

client = FiddlerClient(
    application_id=os.getenv("FIDDLER_APPLICATION_ID"),
    api_key=os.getenv("FIDDLER_API_KEY"),
    url=os.getenv("FIDDLER_URL"),
    span_limits=memory_constrained_limits,
    sampler=sampling.TraceIdRatioBased(0.1),  # Sample 10%
    compression=Compression.Gzip,
)

import os
from fiddler_langgraph import FiddlerClient

# Capture everything with verbose debugging
# console_tracer=True prints spans to stdout AND continues to export to Fiddler (additive)
dev_client = FiddlerClient(
    application_id=os.getenv("FIDDLER_APPLICATION_ID"),
    api_key=os.getenv("FIDDLER_API_KEY"),
    url=os.getenv("FIDDLER_URL"),
    console_tracer=True,    # Also prints spans to console; does NOT disable OTLP export
    sampler=None,           # Capture 100% of traces
)

import os
from opentelemetry.sdk.trace import SpanLimits, sampling
from opentelemetry.exporter.otlp.proto.http.trace_exporter import Compression
from fiddler_langgraph import FiddlerClient

# Define production limits (see High-Volume Applications above for full example)
production_limits = SpanLimits(
    max_events=128, max_span_attributes=128, max_span_attribute_length=8192
)

# Optimized for performance and cost
prod_client = FiddlerClient(
    application_id=os.getenv("FIDDLER_APPLICATION_ID"),
    api_key=os.getenv("FIDDLER_API_KEY"),
    url=os.getenv("FIDDLER_URL"),
    sampler=sampling.TraceIdRatioBased(0.05),  # Sample 5%
    compression=Compression.Gzip,              # Reduce bandwidth
    span_limits=production_limits,             # Controlled limits
)

from fiddler_langgraph import set_llm_context, set_conversation_id
import uuid

# Set meaningful, searchable context labels
set_llm_context(model, 'Customer Support - Tier 1 - Billing Inquiries')
set_llm_context(model, 'Content Generation - Marketing Copy - Blog Posts')
set_llm_context(model, 'Data Analysis - Financial Reports - Q4 2025')

# Use structured conversation IDs with metadata
user_id = 'user-12345'
session_type = 'support'
timestamp = '2026-06-15'
conversation_id = f'{user_id}_{session_type}_{timestamp}_{uuid.uuid4()}'
set_conversation_id(conversation_id)

# Example: user-12345_support_2026-06-15_550e8400-e29b-41d4-a716-446655440000