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Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

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Complete SDK documentation and REST API reference for Fiddler AI Observability Platform.

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🐍 Python Client SDK

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Python Client SDK

Official Python SDK for comprehensive ML and LLM observability - monitor traditional ML models and LLM applications. Key Features:
  • Model onboarding and schema definition
  • Production event publishing (batch and streaming)
  • Baseline dataset management
  • Alert configuration
  • Custom metrics and segments
Use Cases:
  • ML model monitoring (drift, performance, data quality)
  • Production data ingestion
  • Creating monitoring dashboards
  • Configuring alerts for model issues
View Full Documentation → View Usage Guides →

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🎯 Agentic AI SDKs

SDKs for monitoring, evaluating, and testing LLM applications and AI agents.

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Fiddler Evals SDK

Evaluate and test LLM outputs with built-in and custom metrics. Key Features:
  • Pre-built evaluators (faithfulness, toxicity, coherence, etc.)
  • Custom evaluation functions
  • Experiment tracking and comparison
  • Dataset management for test sets
Use Cases:
  • LLM output quality assessment
  • A/B testing prompts and models
  • Regression testing for LLM changes
  • Custom evaluation metrics
Quick Start: 
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."
)
View Full Documentation →

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Fiddler LangGraph SDK

PyPI Monitor LangGraph agents with distributed tracing and observability. Key Features:
  • Automatic LangGraph instrumentation
  • Distributed tracing for agent workflows
  • Span attributes for nodes and edges
  • Conversation and session tracking
Use Cases:
  • Debugging multi-step agent workflows
  • Performance analysis of agent chains
  • Monitoring production LangGraph applications
  • Understanding agent decision paths
Quick Start: 
from fiddler.langchain import LangGraphInstrumentor

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

# Your LangGraph code runs normally
# Traces are automatically sent to Fiddler
View Full Documentation →

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Fiddler Strands SDK

PyPI Monitor Strands Agents with native instrumentation. Key Features:
  • Strands Agent instrumentation
  • Session and conversation tracking
  • Span attributes for agent actions
  • Integration with Fiddler platform
Use Cases:
  • Monitoring Strands production agents
  • Debugging Strands Agent workflows
  • Tracking agent performance metrics
  • Session-based analysis
Quick Start: 
from fiddler.strands import StrandsAgentInstrumentor

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

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🌐 REST API

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REST API Reference

Complete HTTP API documentation for programmatic access to the Fiddler platform. Use Cases:
  • Non-Python integrations (Java, Go, JavaScript, etc.)
  • Custom CI/CD pipelines
  • Integration with existing monitoring systems
  • Webhook-based automation
Quick Start (cURL): 
# 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": [...]
  }'
View Full REST API Documentation → API Guides:

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Guardrails API Reference

API endpoints for Fiddler Trust Service guardrails.

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🚀 Getting Started

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Choose Your SDK

Your Use CaseRecommended SDK
Monitor ML/LLM and platform adminPython Client SDK
Evaluate LLM outputsFiddler Evals SDK
Monitor LangGraph agentsFiddler LangGraph SDK
Monitor Strands AgentsFiddler Strands SDK
Non-Python integrationREST API

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Installation

Python SDKs: 
# Python Client SDK
pip install fiddler-client

# Evals SDK
pip install fiddler-evals

# LangGraph SDK (https://pypi.org/project/fiddler-langgraph/)
pip install fiddler-langgraph

# Strands SDK (https://pypi.org/project/fiddler-strands/)
pip install fiddler-strands
REST API: No installation required - use any HTTP client.

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📚 Related Documentation

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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 LangGraph SDK or Strands SDK
  2. Instrument your agent application
  3. Deploy to production
  4. View traces and analytics in the Fiddler platform

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📖 Additional Resources