LLM Observability Stack

Complete integration of LangChain, LangGraph, and Langfuse for production-ready LLM applications with full observability.

When to Use

• "integrate langchain langgraph langfuse"
• "setup full LLM stack"
• "build LLM app with observability"
• "end-to-end LLM pipeline with tracing"
• "production LLM stack"
• "connect langchain with langgraph and langfuse"
• "fullstack LLM observability"
• "setup RAG agent with monitoring"

What It Covers

1. Architecture overview (LangChain + LangGraph + Langfuse)
   • How the three components connect
   • Data flow: user input -> LangGraph workflow -> LangChain chains/tools -> Langfuse tracing
   • When to use each component in the stack
2. Project structure recommendation
   • Directory layout for maintainable code
   • Separation of concerns: chains, graphs, tools, observability config
3. Setup all 3 components together
   • Combined dependency management
   • Environment configuration
   • Initialization order
4. Integrate LangChain with LangGraph
   • Using LangChain chains inside LangGraph nodes
   • Passing state between LangChain operations
   • Tool binding in graph workflows
5. Add Langfuse observability to the stack
   • Single Langfuse callback for the entire pipeline
   • Tracing across LangGraph state transitions
   • Cost and latency tracking end-to-end
6. Best practices for production
   • Error handling and fallback strategies
   • Async execution patterns
   • Resource cleanup and connection management

Architecture

User Input
    |
    v
+---------------+
|   LangGraph   |  <-- State management, workflow orchestration
|    (Graph)    |      Conditional edges, loops, branching
+---------------+
    |
    v
+---------------+
|   LangChain   |  <-- Chains, RAG, tools, LLM calls
|   (Chains)    |      Prompts, retrievers, output parsers
+---------------+
    |
    v
+---------------+
|   Langfuse    |  <-- Tracing, monitoring, evaluation
| (Observability)|     Spans, generations, scores, datasets
+---------------+

Component Responsibilities

Integration Points

1. LangChain + LangGraph: LangChain chains/tools are used as node functions in LangGraph
2. LangChain + Langfuse: Langfuse callback handler automatically traces all LangChain operations
3. LangGraph + Langfuse: Langfuse traces the full graph execution including state transitions
4. Full Stack: One Langfuse callback traces the entire pipeline from graph entry to final output

Templates

Examples

References

• references/integration-patterns.md — Common integration patterns and anti-patterns
• ../langchain/SKILL.md — LangChain individual skill (chains, RAG, tools)
• ../langgraph/SKILL.md — LangGraph individual skill (state graphs, agents)
• ../langfuse/SKILL.md — Langfuse individual skill (tracing, monitoring, evaluation)

Resources

• LangChain Documentation
• LangGraph Documentation
• Langfuse Documentation

Use When

The user wants to build a complete, production-ready LLM application using LangChain for chains/RAG, LangGraph for workflow orchestration, and Langfuse for observability. This is the go-to skill for full-stack LLM development with monitoring.