Overview

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

🐍 Python Client SDK

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.

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

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

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

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:

• 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

Guardrails API Reference

API endpoints for Fiddler Trust Service guardrails.

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🚀 Getting Started

Choose Your SDK

Your Use Case	Recommended SDK
Monitor ML/LLM and platform admin	Python Client SDK
Evaluate LLM outputs	Fiddler Evals SDK
Monitor LangGraph agents	Fiddler LangGraph SDK
Monitor Strands Agents	Fiddler Strands SDK
Non-Python integration	REST API

Installation

Python SDKs:

# Python Client SDK
pip install fiddler-client

# Evals SDK
pip install fiddler-evals

# LangGraph SDK
pip install fiddler-langgraph

# Strands SDK
pip install fiddler-strands

REST API: No installation required - use any HTTP client.

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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

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

LLM Evaluation 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

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

• GitHub Examples - Sample code and notebooks

• SDK Changelog - Latest SDK updates

• Support Portal - Enterprise support

• Community - Join our Slack community