Overview

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🐍 Python Client SDK

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Python Client SDK

• Model onboarding and schema definition
• Production event publishing (batch and streaming)
• Baseline dataset management
• Alert configuration
• Custom metrics and segments

• ML model monitoring (drift, performance, data quality)
• Production data ingestion
• Creating monitoring dashboards
• Configuring alerts for model issues

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🎯 Agentic AI SDKs

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Fiddler Evals SDK

• Pre-built evaluators (faithfulness, toxicity, coherence, etc.)
• Custom evaluation functions
• Experiment tracking and comparison
• Dataset management for test sets

• LLM output quality assessment
• A/B testing prompts and models
• Regression testing for LLM changes
• Custom evaluation metrics

import fiddler as fdl

# Initialize evaluator
evaluator = fdl.AnswerRelevance()

# Run evaluation
result = evaluator.evaluate(
    question="What is Fiddler?",
    answer="Fiddler is an AI observability platform."
)

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Fiddler OTel SDK

• Framework-agnostic tracing for any Python LLM or agent code
• @trace decorator and manual span wrappers (chain, generation, tool)
• Conversation and session attribute tracking
• Canonical Fiddler span attributes and span types
• Custom span processor and JSONL capture for offline analysis

• Instrumenting apps not built on LangChain, LangGraph, or Strands
• Adding spans around arbitrary functions and workflows
• Shared tracing primitives across Fiddler’s framework SDKs

from fiddler_otel import FiddlerClient, trace

# Initialize once at startup (see FiddlerClient reference for options)
client = FiddlerClient()

# Capture a Fiddler span around any function
@trace
def generate_answer(question: str) -> str:
    return call_llm(question)

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Fiddler LangGraph SDK

• Automatic LangGraph instrumentation
• Distributed tracing for agent workflows
• Span attributes for nodes and edges
• Conversation and session tracking

• Debugging multi-step agent workflows
• Performance analysis of agent chains
• Monitoring production LangGraph applications
• Understanding agent decision paths

from fiddler.langchain import LangGraphInstrumentor

# Instrument your LangGraph app
instrumentor = LangGraphInstrumentor()
instrumentor.instrument()

# Your LangGraph code runs normally
# Traces are automatically sent to Fiddler

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Fiddler LangChain SDK

• Automatic instrumentation of LangChain V1 agents
• Drop-in agent middleware emitting LLM, tool, and chain spans
• Retrieval context attachment via set_llm_context
• Span and session attribute helpers

• Monitoring production LangChain agents
• Debugging multi-step chains and tool calls
• Tracking retrieval context on LLM spans

from fiddler_langchain import FiddlerLangChainInstrumentor

# Call once before creating your agents
FiddlerLangChainInstrumentor().instrument()

# Agents created with langchain.agents.create_agent are now traced

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Fiddler Strands SDK

• Strands Agent instrumentation
• Session and conversation tracking
• Span attributes for agent actions
• Integration with Fiddler platform

• Monitoring Strands production agents
• Debugging Strands Agent workflows
• Tracking agent performance metrics
• Session-based analysis

from fiddler.strands import StrandsAgentInstrumentor

# Instrument Strands Agent
instrumentor = StrandsAgentInstrumentor(
    model_id="my-strands-agent"
)
instrumentor.instrument()

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Fiddler OTel JS SDK

• OpenTelemetry tracing for TypeScript and JavaScript apps
• Isolated tracer provider with OTLP HTTP export to Fiddler
• Manual span helpers (agent, generation, tool)
• Span attribute and token-usage conventions

• Instrumenting Node.js LLM and agent applications
• Capturing traces from non-LangChain TypeScript code
• Adding spans around arbitrary functions and workflows

import { FiddlerClient } from '@fiddler-ai/otel';

// Initialize once at startup (see FiddlerClient reference for options)
const client = new FiddlerClient();

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Fiddler LangGraph JS SDK

• Automatic instrumentation of LangGraph JS via the callback manager
• Trace capture for agentic workflows
• Conversation and session attribute tracking
• LLM context helpers

• Monitoring production LangGraph JS agents
• Debugging agent workflows in Node.js
• Conversation- and session-level analysis

import { LangGraphInstrumentor } from '@fiddler-ai/langgraph';

// Instrument once at startup; LangGraph runs export to Fiddler
new LangGraphInstrumentor().instrument();

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Fiddler LangChain JS SDK

• Automatic trace capture for LangChain JS applications
• No changes to existing chains
• Conversation and session attribute tracking
• LLM context helpers

• Monitoring production LangChain JS applications
• Adding observability to existing chains
• Conversation- and session-level analysis

import { LangChainInstrumentor } from '@fiddler-ai/langchain';

// Instrument once; existing chains are traced with no further changes
new LangChainInstrumentor().instrument();

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🌐 REST API

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REST API Reference

• Non-Python integrations (Java, Go, JavaScript, etc.)
• Custom CI/CD pipelines
• Integration with existing monitoring systems
• Webhook-based automation

# Publish events to Fiddler
curl -X POST https://app.fiddler.ai/api/v1/events 
  -H "Authorization: Bearer fid_..." 
  -H "Content-Type: application/json" 
  -d '{
    "project": "fraud-detection",
    "model": "fraud_model_v1",
    "events": [...]
  }'

• Environments - Environment management
• Jobs - Async job tracking
• Model API - Model management
• Custom Metrics - Metric definitions
• Explainability - SHAP explanations
• File Upload - Baseline and artifact uploads
• Projects - Project management
• Baselines - Baseline datasets
• Alert Rules - Alert configuration
• Segments - Segment management
• Events - Event publishing

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Guardrails API Reference

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🚀 Getting Started

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Choose Your SDK

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Installation

# Python Client SDK
pip install fiddler-client

# Evals SDK
pip install fiddler-evals

# OTel SDK (framework-agnostic instrumentation)
pip install fiddler-otel

# LangGraph SDK
pip install fiddler-langgraph

# LangChain SDK
pip install fiddler-langchain

# Strands SDK
pip install fiddler-strands

# OTel JS SDK
npm install @fiddler-ai/otel

# LangGraph JS SDK
npm install @fiddler-ai/langgraph

# LangChain JS SDK
npm install @fiddler-ai/langchain

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📚 Related Documentation

• Developer Guides - Quick starts and tutorials
• Integrations - Connect with your ML stack
• Product Documentation - Platform features and concepts

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💡 Common Workflows

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ML Model & LLM App Monitoring Workflow

1. Install Python Client SDK
2. Define model schema
3. Upload baseline dataset
4. Publish production events
5. Configure alerts

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LLM Experiments Workflow

1. Install Fiddler Evals SDK
2. Create a test dataset with the Dataset API
3. Define evaluators (built-in or custom)
4. Run experiments and analyze results

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Agent Monitoring Workflow

1. Install the SDK for your framework — LangGraph, LangChain, Strands, or a JavaScript SDK (OTel JS, LangGraph JS, LangChain JS)
2. Instrument your agent application
3. Deploy to production
4. View traces and analytics in the Fiddler platform

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📖 Additional Resources

• GitHub Examples - Sample code and notebooks
• SDK Changelog - Latest SDK updates
• Support Portal - Enterprise support
• Community - Join our Slack community