trulens-apps-langgraph 1.5.2

Library to systematically track and evaluate LangGraph based applications.

trulens-apps-langgraph

TruLens integration for LangGraph applications. This package provides comprehensive instrumentation and evaluation capabilities for LangGraph-based multi-agent workflows.

Features

• Automatic Detection: TruGraph automatically detects LangGraph applications
• Combined Instrumentation: Inherits all LangChain instrumentation plus LangGraph-specific methods
• Multi-Agent Evaluation: Comprehensive evaluation capabilities for complex workflows
• Automatic @task Instrumentation: Automatically detects and instruments functions decorated with @task
• Smart Attribute Extraction: Intelligently extracts information from function arguments

Installation

pip install trulens-apps-langgraph

Quick Start

from langgraph.graph import StateGraph, MessagesState, END
from langchain_core.messages import HumanMessage
from trulens.apps.langgraph import TruGraph

# Create your LangGraph application
workflow = StateGraph(MessagesState)
workflow.add_node("agent", your_agent_function)
workflow.add_edge("agent", END)
workflow.set_entry_point("agent")
graph = workflow.compile()

# Automatically instrument with TruGraph
tru_app = TruGraph(graph, app_name="MyLangGraphApp")

# Use normally - all interactions are automatically logged
with tru_app as recording:
    result = graph.invoke({"messages": [HumanMessage(content="Hello!")]})

Automatic @task Instrumentation

TruGraph automatically instruments functions decorated with LangGraph's @task decorator by monkey-patching the decorator itself. This follows TruLens instrumentation patterns and ensures seamless integration:

from langgraph.func import task

@task  # Automatically instrumented by TruGraph when TruGraph is imported
def my_agent_function(state, config):
    # Your agent logic here
    return updated_state

How it works:

1. Decorator Monkey-Patching: TruGraph patches the @task decorator at import time
2. Intelligent Attribute Extraction: Automatically extracts information from function arguments:
   • Handles BaseChatModel and BaseModel objects
   • Extracts data from dataclasses and Pydantic models
   • Skips non-serializable objects like LLM pools
   • Captures return values and exceptions
3. No Code Changes Required: Works with existing @task decorated functions

This approach follows TruLens conventions and is more robust than scanning sys.modules.

Usage

See the TruLens documentation for complete usage instructions.