Principle:Truera Trulens LangGraph Agent Construction
| Knowledge Sources | |
|---|---|
| Domains | Agent_Architecture, LLM_Applications |
| Last Updated | 2026-02-14 08:00 GMT |
Overview
A graph-based agent construction pattern that defines stateful, multi-step LLM workflows with conditional branching and tool integration.
Description
LangGraph Agent Construction uses a directed graph abstraction to define agent workflows. The StateGraph builder allows defining:
- Nodes: Individual processing steps (LLM calls, tool invocations, routing logic)
- Edges: Connections between nodes (static or conditional)
- State: Shared data structure passed between nodes
After defining the graph structure, .compile() produces a Pregel object (or CompiledStateGraph) that can be invoked with initial state.
This is an external library pattern — TruLens does not define LangGraph itself, but instruments and evaluates the compiled graphs.
Usage
Use this principle when building multi-step agentic workflows that require conditional routing, tool use, or iterative processing. Construct the graph using LangGraph's StateGraph API, then compile it before wrapping with TruGraph.
Theoretical Basis
LangGraph implements a Directed Graph Execution Model where:
- Nodes are functions that transform shared state
- Edges define execution order and conditional routing
- The compiled graph executes as a Pregel-style message-passing system
Pseudo-code Logic:
# Abstract agent graph construction
graph = StateGraph(AgentState)
graph.add_node("agent", call_llm)
graph.add_node("tools", call_tools)
graph.add_conditional_edges("agent", should_continue)
compiled = graph.compile()
result = compiled.invoke(initial_state)