LangChain Integration Overview

Noveum Trace provides seamless integration with LangChain applications, automatically capturing detailed traces of your AI workflows without requiring code changes to your core logic. This integration helps you monitor, debug, and optimize your LangChain applications with comprehensive observability.

What You Get

• Automatic Tracing: Zero-code integration with LangChain components
• Complete Visibility: Track LLM calls, chains, agents, tools, and retrieval operations
• Performance Metrics: Monitor latency, token usage, and costs
• Error Tracking: Identify and debug issues in your AI workflows
• Cost Optimization: Analyze spending patterns and find cost-effective alternatives

Installation

pip install noveum-trace

Note: There's no special noveum-trace[langchain] package. The base noveum-trace package includes full LangChain support.

Quick Start

The simplest way to integrate Noveum Trace with LangChain is using the NoveumTraceCallbackHandler:

import os
import noveum_trace
from noveum_trace import NoveumTraceCallbackHandler
from langchain_openai import ChatOpenAI
 
# Initialize Noveum Trace
noveum_trace.init(
    api_key=os.getenv("NOVEUM_API_KEY"),
    project="customer-support-bot",
    environment="development"
)
 
# Initialize the callback handler
callback_handler = NoveumTraceCallbackHandler()
 
# Add to your LangChain components
llm = ChatOpenAI(callbacks=[callback_handler])

Integration Patterns

1. Basic LLM Calls

Trace individual LLM interactions with automatic context capture.

2. Chains

Monitor multi-step workflows and chain compositions.

3. Agents

Track agent decision-making processes and tool usage.

4. Tools

Monitor tool execution and performance.

5. Retrieval

Trace RAG pipeline components and retrieval quality.

Key Features

• Zero Configuration: Works out of the box with existing LangChain code
• Rich Context: Automatically captures inputs, outputs, and metadata
• Performance Insights: Detailed metrics on latency and resource usage
• Error Handling: Comprehensive error tracking and debugging information
• Cost Analysis: Track spending across different models and operations
• Manual Trace Control: Advanced control over trace lifecycle for complex workflows
• Custom Parent Relationships: Explicit parent-child span relationships with metadata
• LangGraph Integration: Full support for LangGraph routing decisions and node transitions
• Routing Decision Tracking: Capture and analyze conditional routing logic

Advanced Features

Manual Trace Control

For complex workflows, you can manually control trace lifecycle with start_trace() and end_trace() methods.

Custom Parent Span Relationships

Set explicit parent-child relationships between spans using metadata configuration:

metadata = {
    "noveum": {
        "name": "custom_span_name",
        "parent_name": "parent_span_name"
    }
}

LangGraph Integration

Full support for LangGraph workflows including:

• Node execution tracing
• Routing decision tracking
• State transition monitoring
• Custom event emission

Routing Decision Attributes

When tracking routing decisions, the following attributes are captured:

• Source and target nodes
• Decision reasoning and confidence
• State snapshots
• Alternative options

Next Steps

• Basic LLM Tracing - Start with simple LLM calls
• Chain Tracing - Monitor multi-step workflows

Need Help?

• Documentation: Browse our comprehensive guides
• Examples: Check out the integration examples directory for real-world implementations
• Community: Join our Discord for support and discussions
• Support: Contact our team for enterprise support