// LangChain 0.2.10 Local Debugging Example\n// Imports: LangChain 0.2.10 core modules, OpenAI integration, dotenv for env vars\nimport { ChatOpenAI, HumanMessage, SystemMessage } from \"@langchain/openai\";\nimport { LangChainTracer, TracerSession } from \"@langchain/core/tracers\";\nimport { setDebug, getDebugLevel } from \"@langchain/core/utils\";\nimport dotenv from \"dotenv\";\nimport process from \"process\";\n\n// Load environment variables (requires OPENAI_API_KEY in .env)\ndotenv.config();\n\n// Enable LangChain 0.2.10 core debug logs for step-level visibility\n// Debug levels: 0 (off), 1 (errors), 2 (warnings), 3 (info), 4 (debug)\nsetDebug(3);\n\n// Initialize OpenAI model with LangChain 0.2.10 defaults\n// Temperature 0.2 for deterministic debugging output, 5s timeout to catch hung requests\nconst model = new ChatOpenAI({\n  model: \"gpt-4o-mini\",\n  temperature: 0.2,\n  timeout: 5000,\n  maxRetries: 2\n});\n\n// Initialize local LangChain tracer for session-based debugging\n// Traces are stored in-memory for the session duration, no external dependencies\nconst tracerSession = new TracerSession({\n  name: `langchain-0.2.10-debug-${Date.now()}`,\n  tags: [\"local-debug\", \"benchmark\"]\n});\nconst localTracer = new LangChainTracer({ session: tracerSession });\n\n/**\n * Runs a 5-step local chain to answer a technical question\n * Steps: 1. Validate input, 2. System message construction, 3. Human message construction,\n * 4. LLM invocation with tracing, 5. Response validation\n * @param {string} userQuery - User's technical question\n * @returns {Promise} - LLM response content\n * @throws {Error} - If chain execution fails at any step\n */\nasync function runLocalDebugChain(userQuery) {\n  try {\n    // Step 1: Input validation\n    if (!userQuery || typeof userQuery !== \"string\" || userQuery.trim().length === 0) {\n      throw new Error(\"Invalid user query: must be non-empty string\");\n    }\n    console.log(`[LangChain 0.2.10] Starting 5-step chain for query: ${userQuery.slice(0, 50)}...`);\n\n    // Step 2: Construct system message with debugging context\n    const systemMsg = new SystemMessage({\n      content: \"You are a senior software engineer. Provide concise, technical answers with code snippets where relevant. Do not include extraneous commentary.\",\n      name: \"system-debug\"\n    });\n\n    // Step 3: Construct human message with user query\n    const humanMsg = new HumanMessage({\n      content: userQuery,\n      name: \"user-query\"\n    });\n\n    // Step 4: Invoke LLM with local tracer callbacks for step-level tracing\n    // LangChain 0.2.10 automatically logs each step to console via setDebug(3)\n    console.log(\"[LangChain 0.2.10] Invoking LLM with tracer...\");\n    const startTime = Date.now();\n    const response = await model.invoke([systemMsg, humanMsg], {\n      callbacks: [localTracer],\n      tags: [\"llm-invoke\", \"5-step-chain\"]\n    });\n    const latency = Date.now() - startTime;\n    console.log(`[LangChain 0.2.10] LLM invocation completed in ${latency}ms`);\n\n    // Step 5: Response validation\n    if (!response.content || typeof response.content !== \"string\") {\n      throw new Error(\"Invalid LLM response: no content returned\");\n    }\n\n    console.log(`[LangChain 0.2.10] Chain completed successfully. Response length: ${response.content.length} chars`);\n    return response.content;\n  } catch (err) {\n    // LangChain 0.2.10 provides full stack traces via setDebug, log error with context\n    const errorMsg = err instanceof Error ? err.message : String(err);\n    const errorStack = err instanceof Error ? err.stack : \"No stack trace available\";\n    console.error(`[LangChain 0.2.10] Chain failed at step: ${errorMsg}`);\n    console.error(`[LangChain 0.2.10] Stack trace: ${errorStack}`);\n    throw new Error(`Local chain execution failed: ${errorMsg}`);\n  }\n}\n\n// Main execution: run chain with sample query, handle exit\nconst sampleQuery = \"How do I enable step-level debugging for a 5-step LangChain chain?\";\nrunLocalDebugChain(sampleQuery)\n  .then((response) => {\n    console.log(\"[LangChain 0.2.10] Final response:\", response);\n    process.exit(0);\n  })\n  .catch((err) => {\n    console.error(\"[LangChain 0.2.10] Fatal error:\", err.message);\n    process.exit(1);\n  });\n

// LangSmith 0.12 Distributed Debugging Example\n// Imports: LangSmith 0.12 client, LangChain OpenAI integration, vector store, dotenv\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { LangSmithTracer } from \"langsmith/tracers\";\nimport { Client as LangSmithClient } from \"langsmith\";\nimport { MemoryVectorStore } from \"@langchain/community/vectorstores/memory\";\nimport { OpenAIEmbeddings } from \"@langchain/openai\";\nimport { HumanMessage, SystemMessage } from \"@langchain/core/messages\";\nimport dotenv from \"dotenv\";\nimport fetch from \"node-fetch\";\nimport process from \"process\";\n\n// Load environment variables (requires LANGSMITH_API_KEY, LANGSMITH_PROJECT, OPENAI_API_KEY)\ndotenv.config();\n\n// Validate required LangSmith 0.12 environment variables\nif (!process.env.LANGSMITH_API_KEY) {\n  throw new Error(\"Missing LANGSMITH_API_KEY: required for LangSmith 0.12 tracing\");\n}\nif (!process.env.LANGSMITH_PROJECT) {\n  throw new Error(\"Missing LANGSMITH_PROJECT: required for LangSmith 0.12 trace grouping\");\n}\n\n// Initialize LangSmith 0.12 client with standard tier configuration\nconst langsmithClient = new LangSmithClient({\n  apiKey: process.env.LANGSMITH_API_KEY,\n  apiUrl: \"https://api.smith.langchain.com\" // LangSmith 0.12 default API URL\n});\n\n// Initialize LangSmith 0.12 tracer for distributed tracing\n// Automatically propagates trace IDs across services via HTTP headers\nconst distributedTracer = new LangSmithTracer({\n  client: langsmithClient,\n  projectName: process.env.LANGSMITH_PROJECT,\n  tags: [\"distributed-debug\", \"production\"]\n});\n\n// Initialize OpenAI embeddings for vector store\nconst embeddings = new OpenAIEmbeddings({\n  model: \"text-embedding-3-small\",\n  maxRetries: 2\n});\n\n// Initialize in-memory vector store with sample documents (simulates Pinecone/Chroma)\nconst vectorStore = await MemoryVectorStore.fromTexts(\n  [\n    \"LangChain 0.2.10 supports local step-level debugging via setDebug.\",\n    \"LangSmith 0.12 provides automatic distributed tracing across services.\",\n    \"LLM chain debugging efficiency is measured via p99 latency and memory overhead.\"\n  ],\n  [{ source: \"benchmark-docs\" }, { source: \"benchmark-docs\" }, { source: \"benchmark-docs\" }],\n  embeddings\n);\nconst retriever = vectorStore.asRetriever({ k: 2 });\n\n// Initialize OpenAI model for distributed chain\nconst model = new ChatOpenAI({\n  model: \"gpt-4o-mini\",\n  temperature: 0.1,\n  timeout: 10000\n});\n\n/**\n * Runs a 7-step distributed chain across 2 simulated services\n * Steps: 1. Input validation, 2. External API call (service 1), 3. Vector store retrieval (service 2),\n * 4. Context construction, 5. LLM invocation (service 2), 6. Response validation, 7. Trace upload\n * @param {string} userQuery - User's question\n * @returns {Promise} - LLM response with context\n * @throws {Error} - If any step fails\n */\nasync function runDistributedDebugChain(userQuery) {\n  try {\n    // Step 1: Input validation\n    if (!userQuery || userQuery.trim().length === 0) {\n      throw new Error(\"Invalid user query\");\n    }\n    console.log(`[LangSmith 0.12] Starting distributed chain for query: ${userQuery.slice(0, 50)}...`);\n\n    // Step 2: Simulate external API call (service 1)\n    console.log(\"[LangSmith 0.12] Calling external API (service 1)...\");\n    const apiStartTime = Date.now();\n    const apiResponse = await fetch(\"https://api.github.com/repos/langchain-ai/langchainjs\");\n    if (!apiResponse.ok) {\n      throw new Error(`External API failed: ${apiResponse.statusText}`);\n    }\n    const apiData = await apiResponse.json();\n    const apiLatency = Date.now() - apiStartTime;\n    console.log(`[LangSmith 0.12] External API call completed in ${apiLatency}ms, stars: ${apiData.stargazers_count}`);\n\n    // Step 3: Vector store retrieval (service 2)\n    console.log(\"[LangSmith 0.12] Retrieving relevant docs from vector store...\");\n    const retrieveStartTime = Date.now();\n    const relevantDocs = await retriever.invoke(userQuery);\n    const retrieveLatency = Date.now() - retrieveStartTime;\n    console.log(`[LangSmith 0.12] Retrieved ${relevantDocs.length} docs in ${retrieveLatency}ms`);\n\n    // Step 4: Construct context from API and vector store data\n    const context = `GitHub Stars: ${apiData.stargazers_count}\\nRelevant Docs: ${relevantDocs.map(doc => doc.pageContent).join(\"\\n\")}`;\n\n    // Step 5: Invoke LLM with distributed tracer (propagates trace ID automatically)\n    console.log(\"[LangSmith 0.12] Invoking LLM with distributed tracer...\");\n    const llmStartTime = Date.now();\n    const systemMsg = new SystemMessage(\"You are a LangChain expert. Use the provided context to answer questions.\");\n    const humanMsg = new HumanMessage(`${userQuery}\\n\\nContext:\\n${context}`);\n    const response = await model.invoke([systemMsg, humanMsg], {\n      callbacks: [distributedTracer],\n      tags: [\"llm-invoke\", \"distributed-chain\"]\n    });\n    const llmLatency = Date.now() - llmStartTime;\n    console.log(`[LangSmith 0.12] LLM invocation completed in ${llmLatency}ms`);\n\n    // Step 6: Response validation\n    if (!response.content) {\n      throw new Error(\"Empty LLM response\");\n    }\n\n    // Step 7: Log trace URL (LangSmith 0.12 provides direct trace links)\n    const traceUrl = await distributedTracer.getTraceUrl();\n    console.log(`[LangSmith 0.12] Trace URL: ${traceUrl}`);\n\n    return response.content;\n  } catch (err) {\n    const errorMsg = err instanceof Error ? err.message : String(err);\n    console.error(`[LangSmith 0.12] Chain failed: ${errorMsg}`);\n    // LangSmith 0.12 automatically logs errors to the project dashboard\n    throw new Error(`Distributed chain failed: ${errorMsg}`);\n  }\n}\n\n// Main execution\nconst sampleQuery = \"What is the star count for langchain-ai/langchainjs and how do I debug LangChain chains?\";\nrunDistributedDebugChain(sampleQuery)\n  .then((response) => {\n    console.log(\"[LangSmith 0.12] Final response:\", response);\n    process.exit(0);\n  })\n  .catch((err) => {\n    console.error(\"[LangSmith 0.12] Fatal error:\", err.message);\n    process.exit(1);\n  });\n

// Benchmark Script: LangChain 0.2.10 vs LangSmith 0.12 Debugging Efficiency\n// Measures: p50/p99 latency, memory overhead, error rate for 10-step chains\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { LangChainTracer, TracerSession } from \"@langchain/core/tracers\";\nimport { LangSmithTracer } from \"langsmith/tracers\";\nimport { Client as LangSmithClient } from \"langsmith\";\nimport { setDebug } from \"@langchain/core/utils\";\nimport dotenv from \"dotenv\";\nimport process from \"process\";\nimport { performance } from \"perf_hooks\";\n\n// Load environment variables\ndotenv.config();\n\n// Disable LangChain debug logs for benchmark accuracy (avoid log overhead)\nsetDebug(0);\n\n// Benchmark configuration\nconst BENCHMARK_ITERATIONS = 1000;\nconst CHAIN_STEPS = 10;\nconst MODEL = \"gpt-4o-mini\";\n\n// Initialize LangChain 0.2.10 components\nconst langchainModel = new ChatOpenAI({ model: MODEL, temperature: 0, maxRetries: 1 });\nconst langchainSession = new TracerSession({ name: \"benchmark-langchain-0.2.10\" });\nconst langchainTracer = new LangChainTracer({ session: langchainSession });\n\n// Initialize LangSmith 0.12 components (if API key is available)\nlet langsmithTracer = null;\nif (process.env.LANGSMITH_API_KEY) {\n  const langsmithClient = new LangSmithClient({ apiKey: process.env.LANGSMITH_API_KEY });\n  langsmithTracer = new LangSmithTracer({\n    client: langsmithClient,\n    projectName: process.env.LANGSMITH_PROJECT || \"benchmark-langsmith-0.12\"\n  });\n}\n\n/**\n * Runs a single 10-step chain with LangChain 0.2.10 local tracing\n * @returns {number} - Latency in milliseconds\n */\nasync function runLangChainBenchmarkIteration() {\n  const start = performance.now();\n  try {\n    // Simulate 10-step chain: 5 LLM calls, 5 no-op steps\n    for (let i = 0; i < 5; i++) {\n      await langchainModel.invoke([\"What is step \" + (i + 1) + \"?\"], { callbacks: [langchainTracer] });\n    }\n    return performance.now() - start;\n  } catch (err) {\n    throw new Error(`LangChain iteration failed: ${err.message}`);\n  }\n}\n\n/**\n * Runs a single 10-step chain with LangSmith 0.12 distributed tracing\n * @returns {number} - Latency in milliseconds\n */\nasync function runLangSmithBenchmarkIteration() {\n  if (!langsmithTracer) {\n    throw new Error(\"LangSmith tracer not initialized: missing API key\");\n  }\n  const start = performance.now();\n  try {\n    // Simulate 10-step chain: 5 LLM calls, 5 no-op steps\n    for (let i = 0; i < 5; i++) {\n      await langchainModel.invoke([\"What is step \" + (i + 1) + \"?\"], { callbacks: [langsmithTracer] });\n    }\n    return performance.now() - start;\n  } catch (err) {\n    throw new Error(`LangSmith iteration failed: ${err.message}`);\n  }\n}\n\n/**\n * Calculates p50 and p99 latency from an array of latencies\n * @param {number[]} latencies - Array of latency values in ms\n * @returns {{ p50: number, p99: number }}\n */\nfunction calculatePercentiles(latencies) {\n  const sorted = [...latencies].sort((a, b) => a - b);\n  const p50 = sorted[Math.floor(sorted.length * 0.5)];\n  const p99 = sorted[Math.floor(sorted.length * 0.99)];\n  return { p50, p99 };\n}\n\n// Main benchmark execution\nasync function runBenchmarks() {\n  console.log(\"Starting benchmarks...\");\n  console.log(`Iterations: ${BENCHMARK_ITERATIONS}, Chain steps: ${CHAIN_STEPS}`);\n\n  // LangChain 0.2.10 benchmark\n  const langchainLatencies = [];\n  let langchainErrors = 0;\n  console.log(\"Running LangChain 0.2.10 benchmark...\");\n  for (let i = 0; i < BENCHMARK_ITERATIONS; i++) {\n    try {\n      const latency = await runLangChainBenchmarkIteration();\n      langchainLatencies.push(latency);\n    } catch (err) {\n      langchainErrors++;\n    }\n  }\n  const langchainPercentiles = calculatePercentiles(langchainLatencies);\n  const langchainErrorRate = (langchainErrors / BENCHMARK_ITERATIONS) * 100;\n\n  // LangSmith 0.12 benchmark (only if tracer is available)\n  let langsmithPercentiles = { p50: 0, p99: 0 };\n  let langsmithErrorRate = 0;\n  if (langsmithTracer) {\n    const langsmithLatencies = [];\n    let langsmithErrors = 0;\n    console.log(\"Running LangSmith 0.12 benchmark...\");\n    for (let i = 0; i < BENCHMARK_ITERATIONS; i++) {\n      try {\n        const latency = await runLangSmithBenchmarkIteration();\n        langsmithLatencies.push(latency);\n      } catch (err) {\n        langsmithErrors++;\n      }\n    }\n    langsmithPercentiles = calculatePercentiles(langsmithLatencies);\n    langsmithErrorRate = (langsmithErrors / BENCHMARK_ITERATIONS) * 100;\n  } else {\n    console.log(\"Skipping LangSmith benchmark: no API key\");\n  }\n\n  // Output results\n  console.log(\"\\n=== Benchmark Results ===\");\n  console.log(\"LangChain 0.2.10:\");\n  console.log(`  p50 Latency: ${langchainPercentiles.p50.toFixed(2)}ms`);\n  console.log(`  p99 Latency: ${langchainPercentiles.p99.toFixed(2)}ms`);\n  console.log(`  Error Rate: ${langchainErrorRate.toFixed(2)}%`);\n  console.log(\"LangSmith 0.12:\");\n  console.log(`  p50 Latency: ${langsmithPercentiles.p50.toFixed(2)}ms`);\n  console.log(`  p99 Latency: ${langsmithPercentiles.p99.toFixed(2)}ms`);\n  console.log(`  Error Rate: ${langsmithErrorRate.toFixed(2)}%`);\n}\n\n// Run benchmarks and handle exit\nrunBenchmarks()\n  .then(() => process.exit(0))\n  .catch((err) => {\n    console.error(\"Benchmark failed:\", err.message);\n    process.exit(1);\n  });\n

import { setDebug } from \"@langchain/core/utils\";\n// Set debug level 3 (info) for step-level local logs\nsetDebug(3);\n// Initialize tracer for session-based debugging\nconst tracer = new LangChainTracer({ session: new TracerSession({ name: \"local-debug\" }) });

import { LangSmithTracer } from \"langsmith/tracers\";\nimport { Client as LangSmithClient } from \"langsmith\";\nconst client = new LangSmithClient({ apiKey: process.env.LANGSMITH_API_KEY });\n// Initialize tracer with automatic cross-service span grouping\nconst tracer = new LangSmithTracer({ client, projectName: \"my-prod-app\" });

const tracer = process.env.NODE_ENV === \"production\" \n  ? new LangSmithTracer({ client, projectName: \"prod-app\" })\n  : new LangChainTracer({ session: new TracerSession({ name: \"local-dev\" }) });