Monitor and evaluate your agentic AI applications with Fiddler’s native SDKs and framework integrations. From auto-instrumented LangGraph agents to Strands agent applications, Fiddler provides comprehensive observability for the next generation of AI systems.Documentation Index
Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt
Use this file to discover all available pages before exploring further.
Why Agentic Observability Matters
Agentic AI systems—autonomous agents that reason, plan, and coordinate—introduce exponential complexity compared to traditional AI applications:- 26x more monitoring resources required than single-agent systems
- Non-deterministic behavior makes traditional debugging approaches inadequate
- Multi-step workflows require hierarchical tracing across agents, tools, and LLM calls
- Cascading failures demand root cause analysis across distributed agent architectures
Native SDKs
Fiddler-built and maintained instrumentation libraries for production-grade agentic observability.Fiddler LangGraph SDK
Auto-instrument LangGraph applications with OpenTelemetry-based tracing. Best for: LangChain LangGraph agent applications with complex multi-agent workflows Key Features:- Automatic span creation for agent steps, tool calls, and LLM requests
- Hierarchical tracing across Application → Session → Agent → Span levels
- Zero-configuration setup with one environment variable
- Full context preservation for debugging non-deterministic behavior
Strands Agents SDK
Native integration for Strands Agents applications. Best for: Teams building agents with the Strands framework Key Features:- Purpose-built for Strands agent architecture
- Seamless integration with Strands agent runtime
- Multi-agent coordination tracking
- Platform-agnostic deployment (works on AWS, custom infrastructure, etc.)
Fiddler Evals SDK
LLM experiments framework with pre-built evaluators and custom eval support. Best for: Offline evaluation of LLM applications and agentic workflows Key Features:- 14+ pre-built evaluators (faithfulness, toxicity, PII, coherence, etc.)
- Custom evaluator framework for domain-specific metrics
- Batch evaluation for datasets
- Integration with the Fiddler platform for tracking and comparison
Platform SDKs
Core API access for building custom integrations and monitoring workflows.Python Client SDK
Comprehensive Python client for all Fiddler platform capabilities. Best for: Custom integrations, ML model monitoring, programmatic access to Fiddler features Key Features:- Full API coverage for ML and LLM monitoring
- Dataset uploads, model publishing, event ingestion
- Alert configuration, dashboard management
- Custom metrics and enrichments
REST API
Complete HTTP API for language-agnostic platform access. Best for: Non-Python environments, webhook integrations, custom tooling REST API Reference →Advanced Integrations
OpenTelemetry Integration
Direct OTLP integration for custom agent frameworks and multi-framework environments. Best for: Multi-framework environments, custom agentic frameworks, advanced users requiring full instrumentation control Key Features:- Vendor-neutral telemetry using OpenTelemetry standards
- Manual span creation for complete control over instrumentation
- Multi-framework support for custom and emerging agent frameworks
- Compatible with existing OpenTelemetry infrastructure
- Attribute mapping to Fiddler semantic conventions
When to Use OpenTelemetry vs SDKsUse OpenTelemetry integration for advanced use cases requiring manual control. For LangGraph and Strands applications, we recommend using the dedicated SDKs for easier setup and automatic instrumentation.
Framework Support
While Fiddler provides native SDKs for LangGraph and Strands, agentic applications can be monitored regardless of framework:Supported Frameworks & Tools
AI Agent Frameworks:- LangGraph - Native SDK with auto-instrumentation ✓
- LangChain - Compatible via LangGraph SDK or Python Client
- Other agentic frameworks - Monitorable via OpenTelemetry integration
- OpenAI SDK - Track via Python Client or custom instrumentation
- Anthropic SDK - Monitor Claude API calls via Python Client
- Strands Agents - Native Strands Agents SDK ✓
- OpenTelemetry - Full OTLP support for custom instrumentation
- Custom Tracing - Python Client API for framework-agnostic monitoring
Integration Selector
Not sure which SDK to use? Here’s a quick decision guide:| Your Use Case | Recommended Integration | Why |
|---|---|---|
| LangGraph agent application | LangGraph SDK | Auto-instrumentation, zero config, hierarchical tracing |
| Strands Agents | Strands Agents SDK | Purpose-built for Strands framework |
| LLM experiment workflows | Evals SDK | Pre-built evaluators, batch processing, tracking |
| Custom agentic framework | OpenTelemetry Integration | Standards-based tracing, manual control, multi-framework |
| Multi-framework environment | OpenTelemetry Integration | Universal compatibility, unified observability |
| Traditional ML monitoring | Python Client | ML-specific features, drift detection, explainability |
Getting Started
Quick Start Paths
-
LangGraph Applications
Full LangGraph Quick Start →
-
Strands Agents
What’s Next?
- Agentic Observability Concepts - Understand the agent lifecycle and monitoring approach
- Agentic Observability Quick Start - Complete setup guide
- Trust Service Overview - Learn about the evaluation platform powering Fiddler