This guide demonstrates how to build a sophisticated code generation system using LangGraph and HoneyHive tracing. The system combines Retrieval Augmented Generation (RAG) with self-correction capabilities to generate reliable code solutions. HoneyHive’s tracing capabilities provide comprehensive visibility into the entire process, making debugging and optimization easier than ever.

Overview

The system consists of several key components:

  • Documentation loading and processing (traced with HoneyHive)
  • LLM setup with structured output (monitored for performance)
  • A LangGraph workflow for code generation and validation (with detailed tracing)
  • HoneyHive tracing for monitoring and debugging (real-time insights)

Prerequisites

Before running this code, ensure you have the following:

  • Python 3.x
  • Required API keys:
    • HoneyHive API key (for comprehensive tracing)
    • OpenAI API key
    • Anthropic API key
  • Required packages:
    pip install langchain langgraph beautifulsoup4 honeyhive langchain-openai langchain-anthropic
    

Code Implementation

Environment Setup and Imports

import getpass
import os
import sys
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel, Field
from typing import List
from typing_extensions import TypedDict, Annotated
from langgraph.graph import END, StateGraph, START
from honeyhive import HoneyHiveTracer, trace

# Set up environment variables
os.environ["HONEYHIVE_API_KEY"] = "your honeyhive api key"
os.environ["HONEYHIVE_PROJECT"] = "your honeyhive project"
os.environ["HONEYHIVE_SOURCE"] = "your honeyhive source"
os.environ["HONEYHIVE_SESSION_NAME"] = "your session name"
os.environ["OPENAI_API_KEY"] = "your openai api key"
os.environ["ANTHROPIC_API_KEY"] = "your anthropic api key"

HoneyHive Tracing Setup

HoneyHive’s tracing setup provides comprehensive monitoring capabilities:

# Initialize HoneyHive tracer with detailed configuration
HoneyHiveTracer.init(
    api_key=os.environ.get("HONEYHIVE_API_KEY", "your honeyhive api key"),
    project=os.environ.get("HONEYHIVE_PROJECT", "your honeyhive project"),
    source="development",
    session_name="LangGraph Code Generation"
)

With this setup, you get:

  • Real-time monitoring of all traced functions
  • Detailed performance metrics
  • Error tracking and debugging
  • Session-based analytics
  • Custom metadata support

Documentation Loading

The system uses RecursiveUrlLoader with HoneyHive tracing to monitor documentation loading:

@trace
def load_documentation(url):
    """Load documentation from a URL"""
    print("---LOADING DOCUMENTATION---")
    loader = RecursiveUrlLoader(
        url=url, max_depth=20, extractor=lambda x: Soup(x, "html.parser").text
    )
    docs = loader.load()

    # Sort and concatenate documentation
    d_sorted = sorted(docs, key=lambda x: x.metadata["source"])
    d_reversed = list(reversed(d_sorted))
    concatenated_content = "\n\n\n --- \n\n\n".join(
        [doc.page_content for doc in d_reversed]
    )
    print("---DOCUMENTATION LOADED---")
    return concatenated_content

HoneyHive tracing here provides:

  • Loading time metrics
  • Document count tracking
  • Error handling for failed loads
  • Memory usage monitoring
  • URL accessibility tracking

Data Model

The system uses Pydantic with HoneyHive tracing for structured output validation:

class code(BaseModel):
    """Schema for code solutions to questions about HoneyHive."""
    prefix: str = Field(description="Description of the problem and approach")
    imports: str = Field(description="Code block import statements")
    code: str = Field(description="Code block not including import statements")

HoneyHive helps track:

  • Model validation success rates
  • Field completion rates
  • Schema compliance
  • Data quality metrics

LLM Setup

The system uses Claude with HoneyHive tracing for comprehensive LLM monitoring:

code_gen_prompt_claude = ChatPromptTemplate.from_messages([
    (
        "system",
        """<instructions> You are a coding assistant with expertise in HoneyHive. \n 
        Here is the LCEL documentation:  \n ------- \n  {context} \n ------- \n Answer the user  question based on the \n 
        above provided documentation. Ensure any code you provide can be executed with all required imports and variables \n
        defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block. \n
        Invoke the code tool to structure the output correctly. </instructions> \n Here is the user question:""",
    ),
    ("placeholder", "{messages}"),
])

@trace
def setup_llm():
    """Set up the LLM with structured output"""
    expt_llm_claude = "claude-3-7-sonnet-latest"
    llm_claude = ChatAnthropic(
        model=expt_llm_claude,
        default_headers={"anthropic-beta": "tools-2024-04-04"},
    )
    structured_llm_claude = llm_claude.with_structured_output(code, include_raw=True)
    return structured_llm_claude

HoneyHive provides:

  • LLM response time tracking
  • Token usage monitoring
  • Error rate tracking
  • Model performance analytics
  • Cost tracking per request

LangGraph Implementation

The system uses LangGraph with HoneyHive tracing for workflow monitoring:

  1. State Definition:
class GraphState(TypedDict):
    """Represents the state of our graph."""
    error: str
    messages: List
    generation: str
    iterations: int
  1. Graph Nodes with HoneyHive tracing:
  • generate: Monitors code generation performance
  • code_check: Tracks validation success rates
  • reflect: Measures improvement iterations
  • decide_to_finish: Tracks decision points
  1. Graph Construction with comprehensive tracing:
@trace
def build_graph():
    """Build the LangGraph for code generation"""
    graph_builder = StateGraph(GraphState)
    
    # Add nodes
    graph_builder.add_node("generate", generate)
    graph_builder.add_node("code_check", code_check)
    graph_builder.add_node("reflect", reflect)
    
    # Add edges
    graph_builder.add_edge(START, "generate")
    graph_builder.add_edge("generate", "code_check")
    
    # Add conditional edges
    graph_builder.add_conditional_edges(
        "code_check",
        decide_to_finish,
        {
            "generate": "generate",
            "reflect": "reflect",
            "finish": END,
        },
    )
    graph_builder.add_edge("reflect", "generate")
    
    return graph_builder.compile()

HoneyHive tracing provides:

  • Node execution time tracking
  • Edge traversal monitoring
  • State transition tracking
  • Error propagation analysis
  • Performance bottlenecks identification

Usage

To use the code generation system with HoneyHive monitoring:

if __name__ == "__main__":
    question = "How can I use HoneyHive tracing with LangGraph?"
    solution = solve_coding_question(question)
    
    print("\n=== FINAL SOLUTION ===")
    print(f"\n{solution.prefix}\n")
    print(f"IMPORTS:\n{solution.imports}\n")
    print(f"CODE:\n{solution.code}")

Key Features

  1. RAG Integration: HoneyHive traces document retrieval and processing
  2. Self-Correction: Monitors validation and improvement cycles
  3. Structured Output: Tracks schema compliance and data quality
  4. HoneyHive Tracing: Provides comprehensive monitoring and debugging
  5. Maximum Iterations: Tracks iteration counts and success rates

Best Practices

  1. Always set up proper environment variables before running
  2. Monitor the HoneyHive dashboard for:
    • Performance metrics
    • Error rates
    • Cost analysis
    • Usage patterns
  3. Adjust the max_depth parameter in RecursiveUrlLoader based on your needs
  4. Customize the reflection step based on your specific use case
  5. Implement proper error handling for production use

HoneyHive Dashboard Insights

The HoneyHive dashboard provides valuable insights:

  1. Performance Metrics:
    • Response times
    • Throughput
    • Resource usage
  2. Error Tracking:
    • Error rates
    • Stack traces
    • Error patterns
  3. Cost Analysis:
    • API usage costs
    • Resource consumption
    • Cost optimization opportunities
  4. Usage Patterns:
    • Peak usage times
    • Common operations
    • User behavior

Conclusion

This implementation provides a robust foundation for code generation with self-correction capabilities. The combination of LangGraph and HoneyHive tracing ensures reliable and monitored code generation processes. HoneyHive’s comprehensive tracing capabilities make it easier to:

  • Debug issues
  • Optimize performance
  • Track costs
  • Monitor quality
  • Scale the system