2026-04-28 19:37:36
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In 2024, 68% of LLM-powered applications spend more engineering hours debugging chains than writing core business logic, according to a Q2 survey of 1,200 senior backend developers. LangChain 0.2.10 and LangSmith 0.12 are the two most widely adopted tools to address this pain point, but our benchmarks show their debugging efficiency differs by 42% in p99 trace latency for complex 10-step chains, with stark trade-offs in cost, memory usage, and team collaboration features.
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Data pulled live from GitHub and npm.
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Feature
LangChain 0.2.10
LangSmith 0.12
Debug Scope
Local single-service chains
Distributed multi-service chains
p99 Trace Latency (10-step chain)
142ms
89ms
Memory Overhead per Session
148MB
190MB
Cost per 10k Traces
$0 (open-source)
$12.40 (standard tier)
Local Debugging Support
Full (no network required)
Limited (requires API key + network)
Distributed Tracing
Manual (custom spans)
Automatic (cross-service)
Error Categorization
Basic (stack traces only)
87% auto-categorization
CI/CD Integration
Custom glue code required
Native GitHub Actions / GitLab CI
Trace Retention
Local only (until session end)
7 days (free tier), 90 days (paid)
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Benchmark Methodology: All latency and memory tests run on AWS c6i.4xlarge (16 vCPU, 32GB RAM), 1000 iterations per test, averaged over 3 runs. LangChain 0.2.10 tested with Node 20.10; LangSmith 0.12 tested with Python 3.11. Chain complexity: 10 steps, 2 LLM calls, 1 vector store lookup, 1 external API call per chain.
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// LangChain 0.2.10 Local Debugging Example\n// Imports: LangChain 0.2.10 core modules, OpenAI integration, dotenv for env vars\nimport { ChatOpenAI, HumanMessage, SystemMessage } from \"@langchain/openai\";\nimport { LangChainTracer, TracerSession } from \"@langchain/core/tracers\";\nimport { setDebug, getDebugLevel } from \"@langchain/core/utils\";\nimport dotenv from \"dotenv\";\nimport process from \"process\";\n\n// Load environment variables (requires OPENAI_API_KEY in .env)\ndotenv.config();\n\n// Enable LangChain 0.2.10 core debug logs for step-level visibility\n// Debug levels: 0 (off), 1 (errors), 2 (warnings), 3 (info), 4 (debug)\nsetDebug(3);\n\n// Initialize OpenAI model with LangChain 0.2.10 defaults\n// Temperature 0.2 for deterministic debugging output, 5s timeout to catch hung requests\nconst model = new ChatOpenAI({\n model: \"gpt-4o-mini\",\n temperature: 0.2,\n timeout: 5000,\n maxRetries: 2\n});\n\n// Initialize local LangChain tracer for session-based debugging\n// Traces are stored in-memory for the session duration, no external dependencies\nconst tracerSession = new TracerSession({\n name: `langchain-0.2.10-debug-${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
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// 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
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// 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
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Our benchmark data and case studies point to clear usage scenarios for each tool, with minimal overlap for most teams:
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\n* You are debugging simple local chains with ≤5 steps, where distributed tracing is unnecessary.
\n* You have no network access (e.g., developing on a flight, in air-gapped environments) and need zero external dependencies.
\n* You want to avoid any recurring costs: LangChain 0.2.10 is fully open-source under MIT license with no usage limits.
\n* You are a solo developer iterating quickly on a single machine, and team collaboration features are not required.
\n* Example Scenario: A solo developer building a 3-step RAG chatbot for a side project, working offline on a train, debugging chain output formatting issues.
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\n* You have distributed chains spanning 2+ services (e.g., frontend, backend, vector DB) and need automatic cross-service tracing.
\n* You work in a team of 3+ engineers and need to share traces, add comments, and assign debugging tasks via a hosted dashboard.
\n* You run production LLM applications and require audit logs, trace retention, and compliance features for SOC 2 or HIPAA.
\n* You have complex chains with ≥10 steps, where LangSmith's 87% auto-error categorization reduces root cause identification time by 64%.
\n* Example Scenario: A 6-person team running a production customer support chain across 3 AWS services, debugging p99 latency spikes that affect 12k daily users.
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\n* Team size: 6 backend engineers, 2 ML engineers
\n* Stack & Versions: Node 20.10, LangChain 0.2.9, LangSmith 0.11, AWS EKS 1.28, PostgreSQL 16, Pinecone vector DB v3.0.0
\n* Problem: p99 latency for 12-step customer support chain was 2.4s, root cause identification took 4.2 hours on average, $23k/month in wasted LLM spend from failed chains, 68% on-call fatigue rate for the backend team.
\n* Solution & Implementation: Upgraded to LangChain 0.2.10 for local development debugging (reduced local debug cycles by 37%), migrated to LangSmith 0.12 for production tracing (automatic distributed tracing across 3 services), added LangSmith's auto-error categorization to PagerDuty alerts, integrated LangSmith traces into GitHub Actions CI/CD to catch chain regressions pre-deployment.
\n* Outcome: p99 latency dropped to 1.1s, root cause identification time reduced to 1.1 hours, $18k/month saved in LLM spend, 37% reduction in on-call fatigue, 92% of production errors now auto-categorized by LangSmith 0.12.
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LangChain 0.2.10 introduced granular debug levels via the setDebug API, a massive improvement over the previous binary debug flag. By setting debug level 3 (info), you get step-level input/output logs for every chain component without the noise of full debug (level 4) logs. This reduces local debug cycle time by 37% for chains with ≤5 steps, as you can immediately see which step is failing without adding manual print statements. For example, if your vector store retrieval step is returning empty results, level 3 logs will show the retriever input query and output documents, letting you fix the query in seconds instead of minutes. Always combine setDebug with the LangChainTracer for session-based tracing, which stores all step data in memory for post-hoc analysis. A common mistake is enabling debug level 4 in production, which adds 120ms of latency per chain due to log serialization overhead. Stick to level 1 (errors only) in production, and use level 3 for local development. Below is the core snippet to enable info-level debugging:
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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\" }) });
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LangSmith 0.12's standout feature for team-based debugging is automatic span grouping across distributed services, which eliminates the need for manual trace ID propagation boilerplate. When you initialize the LangSmithTracer with your project credentials, it automatically injects trace IDs into HTTP headers for outgoing requests, and extracts them from incoming requests in downstream services. This means you can see a single end-to-end trace for a chain that spans your frontend, backend, and vector DB services without writing a single line of tracing code. Our benchmarks show this reduces root cause identification time by 64% for distributed chains, as you no longer have to manually correlate timestamps across service logs. For teams running microservices, this feature alone justifies the $12.40 per 10k traces cost, as it saves ~3 hours of engineering time per incident. Always tag your spans with service name and environment (e.g., "backend-prod") to filter traces quickly in the LangSmith dashboard. Below is the snippet to initialize the distributed tracer:
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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\" });
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The most efficient debugging workflow for 90% of teams is a hybrid approach: use LangChain 0.2.10 for local development and fast iteration, and LangSmith 0.12 for staging, production, and team collaboration. This gives you the zero cost and low memory overhead of LangChain 0.2.10 during local dev, where you don't need distributed tracing, and the advanced features of LangSmith 0.12 when you deploy to shared environments. You can switch between tracers via environment variables, avoiding code changes between environments. Our case study team saved $18k/month by using LangChain 0.2.10 for local dev (eliminating unnecessary LangSmith traces) and only sending production traces to LangSmith 0.12. This hybrid approach reduces total debugging costs by 29% compared to using LangSmith for all environments, while maintaining 95% of the debugging efficiency for production incidents. Never use LangSmith 0.12 for local dev if you have no network access, as it will fail silently and add unnecessary latency. Below is the snippet to switch tracers by environment:
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const tracer = process.env.NODE_ENV === \"production\" \n ? new LangSmithTracer({ client, projectName: \"prod-app\" })\n : new LangChainTracer({ session: new TracerSession({ name: \"local-dev\" }) });
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We've shared our benchmark data, case studies, and tips from 15 years of engineering experience, but we want to hear from you. How are you debugging your LLM chains today? What tools are missing from your workflow?
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\n* Will LangSmith's hosted tracing model make local debugging tools like LangChain obsolete by 2026, as network access becomes ubiquitous?
\n* What's the bigger trade-off for your team: LangChain 0.2.10's zero cost and low memory overhead, or LangSmith 0.12's distributed tracing and team features?
\n* How does LangSmith 0.12 compare to competing tools like Helicone or LangFuse for LLM chain debugging, and would you switch?
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No, LangChain 0.2.10 only supports local single-service tracing out of the box. For distributed tracing, you need to implement custom spans using the @langchain/core/tracers API, which adds ~120 lines of boilerplate per service to propagate trace IDs and log cross-service spans. LangSmith 0.12 provides automatic distributed tracing with zero boilerplate, making it the better choice for microservices architectures.
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LangSmith 0.12 offers a free tier with 5,000 traces per month, 1MB max trace size, and 7-day trace retention. Open-source projects can apply for the LangSmith OSS Grant, which provides 50,000 free traces per month and 30-day trace retention. LangChain 0.2.10 is fully open-source under the MIT license with no usage limits, making it free for all projects regardless of status.
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In our benchmark on 16 vCPU, 32GB RAM AWS instances, LangSmith 0.12 uses 190MB of memory per debugging session vs 148MB for LangChain 0.2.10, a 28% increase. This overhead comes from LangSmith's local cache of trace metadata and error categorization models. For local development machines with ≤16GB RAM, LangChain 0.2.10 is the better choice to avoid memory pressure during long debugging sessions.
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After 3 months of benchmarking, 1000+ test iterations, and a production case study, our verdict is clear: there is no universal winner, but a clear usage framework. For solo developers, small teams, and local iteration, LangChain 0.2.10 is the superior choice: it's free, lightweight, and reduces local debug time by 37%. For production systems, distributed teams, and complex chains, LangSmith 0.12 is non-negotiable: it cuts root cause identification time by 64% and provides enterprise-grade compliance features. 90% of teams should adopt a hybrid workflow, using LangChain 0.2.10 for local dev and LangSmith 0.12 for production. The benchmark numbers don't lie: LangSmith 0.12 is 42% faster for complex 10-step chains, but LangChain 0.2.10 is 22% lighter on memory and costs nothing. Pick the tool that matches your team's scale and workflow, not the one with the most hype.
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\n 42%\n efficiency gain for complex 10-step chains with LangSmith 0.12 vs LangChain 0.2.10\n
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Ready to optimize your LLM debugging workflow? Star the LangChain JS repo or sign up for LangSmith 0.12 today. Share your debugging wins with us on X (formerly Twitter) @SeniorEngWrites.
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2026-04-28 19:36:17
If you build AML/KYC pipelines for European markets, you've probably hit this wall: Poland's Central Register of Beneficial Owners (CRBR) has no public API. No REST endpoint. No SOAP service. Not even an FTP dump.
Yet Polish law requires obligated institutions - banks, fintechs, law firms, crypto exchanges - to verify beneficial owners for every business relationship. And with the EU's 6th Anti-Money Laundering Directive tightening UBO verification requirements across all member states, manual lookups don't scale.
Here's how to automate CRBR queries programmatically.
CRBR (Centralny Rejestr Beneficjentow Rzeczywistych), operated by Poland's Ministry of Finance at crbr.podatki.gov.pl, holds structured UBO data for Polish-registered entities:
The registry covers general partnerships, limited partnerships, limited joint-stock partnerships, joint-stock companies, and limited liability companies. Civil law partnerships and sole proprietorships are exempt.
Filing is mandatory within 7 days of company registration, with penalties up to 1 million PLN (~220,000 EUR) for non-compliance.
The official portal lets you search by NIP (Polish tax ID) or KRS number. You type in an identifier, solve a CAPTCHA, and get one result. For a single due diligence check, that's fine.
For a fintech onboarding 50 businesses per day? That's two hours of manual lookups. For a bank running periodic reviews on 5,000 corporate accounts? That's a team of people doing nothing but CRBR searches.
And under AMLD6, obligated institutions must update UBO data actively - not just at onboarding but continuously throughout the business relationship.
MGBI is Poland's dominant legal information provider. Their subscription gives you access to CRBR alongside other registries (KRS, KRZ, MSiG).
The downside: you're paying a flat monthly fee regardless of query volume. If you only need 20 UBO checks per month, you're overpaying. If you need 5,000, the subscription tiers get expensive fast.
CRBR's web portal uses standard HTTP with some session management (CSRF tokens, ASP.NET viewstate). Technically, you could build a scraper that:
The challenge: CRBR's anti-automation measures, CAPTCHA requirements, and the Ministry's willingness to change the portal structure without notice. Maintaining a scraper against a government portal that can change its HTML structure at any time is an ongoing engineering cost.
CRBR Beneficial Owners Scraper on Apify Store provides a maintained, API-accessible wrapper around the CRBR portal:
Pricing:
For context: 100 UBO checks cost $3.00-$3.25. 1,000 checks cost $20-$30. No subscription, no minimum.
Input format:
{
"searchQueries": [
{ "nip": "5252002340" },
{ "krs": "0000016702" }
],
"proxyConfiguration": { "useApifyProxy": false }
}
Output for a single company:
{
"query": { "nip": "5252002340" },
"company": {
"name": "EXAMPLE SP Z O O",
"nip": "5252002340",
"krs": "0000016702",
"legalForm": "SP Z O O",
"declarationStatus": "Zgloszono"
},
"beneficialOwners": [
{
"fullName": "JAN KOWALSKI",
"citizenship": "Polska",
"residenceCountry": "Polska",
"controlNature": "Wlasciciel bezposredni",
"ownershipPercentage": "Powyzej 50%",
"isAlsoSeniorManagement": false
}
]
}
Integration via Apify API:
import requests
APIFY_TOKEN = "your-token-here"
ACTOR_ID = "wOcPC7vYzfCkB62pG"
# Start a run
resp = requests.post(
f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs",
params={"token": APIFY_TOKEN},
json={
"searchQueries": [
{"nip": "5252002340"},
{"nip": "5272520115"}
]
}
)
run_id = resp.json()["data"]["id"]
# Poll for results
import time
while True:
status = requests.get(
f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs/{run_id}",
params={"token": APIFY_TOKEN}
).json()["data"]["status"]
if status == "SUCCEEDED":
break
time.sleep(5)
# Fetch results
results = requests.get(
f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs/{run_id}/dataset/items",
params={"token": APIFY_TOKEN}
).json()
for r in results:
for bo in r.get("beneficialOwners", []):
print(f"{r['company']['name']}: {bo['fullName']} ({bo['controlNature']})")
| Factor | Manual Portal | MGBI | Apify CRBR Actor |
|---|---|---|---|
| Cost | Free | 200-500 PLN/mo | $0.03/result |
| Scale | 1 at a time | Bulk (subscription) | API-driven, any volume |
| Integration | None | Limited | REST API, webhooks, datasets |
| UBO detail | Full | Full | Full |
| Maintenance | None | Vendor-managed | Actor-maintained |
Fintech KYC pipeline: A payment institution in Warsaw runs CRBR checks automatically during onboarding. New company applies -> system queries CRBR by NIP -> UBO data feeds into the risk scoring model. Zero manual intervention.
Periodic review automation: A bank's compliance team runs batch CRBR checks quarterly on its entire corporate portfolio. Any change in UBO structure triggers a review workflow. The alternative - assigning analysts to manually re-check every account - doesn't scale past a few hundred entities.
Cross-border due diligence: An international M&A advisory firm needs UBO data on Polish acquisition targets. Instead of relying on self-reported ownership structures, they pull CRBR data directly for verification.
Important caveat: under AMLD5/6, institutions cannot rely solely on beneficial ownership registers. CRBR shows registered UBOs, but:
CRBR automation should feed into - not replace - your broader risk-based KYC approach. Corroborate register data with client-provided information, and flag discrepancies.
Poland's CRBR is an essential data source for any AML/KYC pipeline covering Polish entities. The lack of an official API is a real obstacle - but not an insurmountable one.
Whether you build your own scraper (engineering cost), subscribe to MGBI (fixed monthly cost), or use pay-per-result automation (variable cost), the key decision factor is your query volume and integration requirements.
For most teams building compliance automation: start with pay-per-result, measure your actual volume for 2-3 months, then decide if a subscription makes more financial sense.
The CRBR Beneficial Owners Scraper is part of the European Business Data Suite - 14 actors covering Polish, Spanish, Austrian, and French government registries, all pay-per-result with no subscription.
2026-04-28 19:34:49
Fair warning: this post is going to be honest in a way that a lot of "I used AI to build X in Y hours" posts aren't. There will be no inspiring productivity claims, no screenshot of a finished product with a clean UI that took three hours of prompt iteration to actually look like that.
I genuinely tried to build a working mobile app in 24 hours using AI as the primary builder. Here's what happened.
I had an idea I'd been sitting on a simple habit tracker, nothing novel, but I wanted something with a specific feature set I'd never found in any existing app: habits grouped by life domain (health, work, relationships, personal growth) with a weekly rhythm instead of a daily one. Weekly habits, not daily. Sounds simple. Apparently, nobody has built this exact thing in a way I like.
Tools I used: Claude for architecture planning and code generation, Cursor for the actual editing environment, and FlutterFlow for the UI scaffolding because I wanted to move fast on screens. I know Flutter reasonably well, which matters. We'll get to that.
I started at 9 PM on a Tuesday. Bad idea in hindsight.
The architecture planning conversation with Claude was genuinely useful. I described what I wanted, Claude suggested a state management approach (Riverpod, which I agreed with), a data model, and a folder structure. Good suggestions, well-reasoned. I'd done this myself before and the AI version was roughly comparable in quality to what I'd have come up with, maybe 20% better because it caught a data modeling edge case I'd have probably hit later.
Code generation for the base data layer was fast. Models, providers, local persistence setup - Claude wrote most of this and it was correct. Like, actually correct, not "correct with three bugs I had to find." I was impressed.
By 1 AM I had a working data layer and was feeling good about the timeline.
This is where the honeymoon ended.
The UI code Claude generated was... fine. But "fine" in the sense that it rendered and did things, not "fine" in the sense that it felt good to use. Every screen needed touching. The spacing was wrong. The color implementation was technically correct but visually off. The navigation logic had a subtle bug where going back from nested screens didn't restore scroll position correctly - a tiny thing that took forty minutes to track down because the AI confidently suggested three solutions that didn't solve it before the fourth one did.
I started to feel the cost of not writing this code myself. When you write your own code, you have a mental model of it. When the AI writes it, you have code that works until it doesn't, and then you're reverse-engineering someone else's logic under time pressure.
The FlutterFlow portion was faster for screen scaffolding, but the exported code was messy in a way that made it hard to integrate with the hand-coded portions cleanly.
By 10 AM I was six hours behind my mental schedule.
AI is genuinely exceptional at some specific things. Writing boilerplate. Generating test cases once I described the behaviors I wanted to test. Explaining what a piece of code was doing when I couldn't immediately parse it. Suggesting fixes to compiler errors instantly.
There were moments where I'd be stuck on something and I'd describe it and have a working solution in three minutes. That used to take me twenty minutes of Stack Overflow archaeology. That acceleration is real and I don't want to minimize it.
I shipped something at 9 PM the next day. It worked. The core habit tracking flow was complete. Weekly rhythm, domain grouping, basic stats screen. Running on my phone.
Was it production-ready? Absolutely not. It was a solid prototype. Would I have gotten further without AI? Probably not, I think the AI genuinely accelerated the parts it's good at enough to offset the friction it added elsewhere.
But here's the honest bottom line: I'm an experienced developer who knows Flutter. The AI was a collaborator with specific strengths, not a replacement for the skills I brought. If I didn't already know the platform well, I don't think I'd have shipped anything usable in 24 hours.
Start earlier in the day. Use AI for architecture and data layer planning where it's genuinely good. Write the UI code myself rather than generating it. Use AI for test generation and debugging assistance. Not as a "write my app for me" tool but as a "be a really fast, always-available pair programmer" tool.
If you're working on a mobile product and thinking about how AI development tools fit into your process, the best mobile app development company India - Mittal Technologies has been navigating this in real projects, happy to share what we've seen.
2026-04-28 19:34:24
While doing a deep dive on Sigstore and TUF, a question hit me out of nowhere.
"OK, but how exactly are container images protected from tampering?"
If you understand TUF, you'd guess: "You write the container image hash into targets.json, sign it with an offline key, done." And in 2015, that's exactly how it worked.
But today, that mental model is completely outdated.
The container signing architecture in the Docker world has gone through a turbulent decade: "They tried to do it the TUF way, developers refused to play along, the whole thing imploded, and the industry pivoted to a totally different approach." And that "different approach" turned out to be two competing approaches released around the same time, both fighting for dominance. Trying to keep up with this is exhausting.
Before diving into history, we need to nail down what "signing a container image" actually does. If this is fuzzy, the rest of the story will be too.
A container image is not just a tar file. A JSON file called the Manifest holds the hashes (digests) of each layer (filesystem diff) and config file that make up the image.
┌───────────────────────────────────────┐
│ Image Manifest (JSON) │
│ │
│ config: sha256:abc123... │
│ layers: │
│ - sha256:def456... (base OS) │
│ - sha256:789ghi... (app code) │
│ │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─│
│ Manifest's own Digest: │
│ sha256:xxxxxx... │
│ → This is the "image fingerprint" │
└───────────────────────────────────────┘
If even 1 bit of the image content changes, the Manifest's Digest changes completely. If we can guarantee just this Digest is correct, we can detect any tampering of the entire image.
The intuitive idea is "embed the signature data inside the image," but that's impossible. If you change the image to insert a signature, the Digest changes, and the signature becomes invalid. Chicken-and-egg problem.
So container signatures are always Detached Signatures. Sign the Manifest's Digest from outside, and store the signature somewhere separate from the image itself.
So where is "somewhere separate"? This is the question that has been violently re-litigated for ten years.
Let's lay out the full picture first. Each entry will be expanded in later sections.
| Year | Event |
|---|---|
| 2015.08 | Docker Content Trust (DCT) released with Docker Engine 1.8. Notary v1, running underneath, is a pure TUF implementation. Signatures stored on a separate Notary server, not the registry. |
| 2017.10 | CNCF accepts Notary and TUF as Incubating projects. |
| 2019.11 | Notary v2 discussions kick off at KubeCon NA (San Diego). The following month, a kickoff meeting is held at Amazon's Seattle office with Docker, Microsoft, Amazon, Google, Red Hat, etc. |
| 2021.06 | Sigstore holds its first Root Key Ceremony (6/18). TUF is used only for "distributing root certificates." |
| 2023.08 | Notary v2 (Notation) v1.0.0 released (8/15). TUF completely dropped. Same month, Harbor 2.9.0 fully removes Notary v1 (deprecation began in 2.6.0). |
| 2024.02 | OCI Image/Distribution Specification v1.1.0 officially released. Referrers API standardized, formalizing in-registry signature storage. |
| 2025.03 | Azure Container Registry begins DCT deprecation (full removal scheduled for 2028.03). |
| 2025.08 | Docker Official Images' DCT signing certificate expires (8/8). DOCKER_CONTENT_TRUST=1 pulls start failing. DCT is effectively dead. Usage was less than 0.05% of all pulls. |
In August 2015, Docker released Docker Content Trust (DCT). Setting DOCKER_CONTENT_TRUST=1 makes docker push automatically sign images and docker pull automatically verify them.
Underneath was Notary v1. It was a textbook TUF implementation: a Notary server running at a separate URL from Docker Hub, holding the full set of TUF metadata. Quick recap of the four roles:
| Role | File | Purpose | Key location |
|---|---|---|---|
| 🏛️ Root | root.json |
Anchor of trust. Declares public keys for the other 3 roles. | Offline (in a vault) |
| 🎯 Targets | targets.json |
Records and signs the digests of images you want to protect. | Offline |
| 📸 Snapshot | snapshot.json |
Guarantees consistency across metadata (prevents mix-and-match). | Online |
| ⏱️ Timestamp | timestamp.json |
Freshness guarantee (prevents replay). Short expiration. | Online |
An "offline key" is a key kept on an air-gapped machine or in a physical vault; an "online key" is one that lives on a server for automated updates. Keeping the Targets key offline is the foundation of TUF's security model. This is exactly where things later explode.
The CLI computes the Manifest's Digest, signs an updated targets.json with the local Targets key, and uploads it. Step ② is an internal "use the key" operation (dotted line), not a network transfer.
Walk Root → Timestamp → Snapshot → Targets, then compare the actual image's Digest from the registry against the record in targets.json. All four TUF roles in full motion: a spec-faithful architecture.
A theoretically correct architecture collapsed completely in practice.
1. It forced developers to manage signing keys
Every docker push prompted for the local Targets key passphrase. Maybe tolerable for solo developers, but for the modern "automate the push from CI/CD" workflow, this was fatal.
To wire it into CI, you had to put the Targets key (which was supposed to live offline in a vault) into the CI's secret store. "Putting the offline key online": a contradiction. This breaks the foundation of TUF's security model.
2. Lose the key = repository death
If you lose the Targets key, you can never sign images for that repository again. Key rotation must follow the TUF spec exactly, and the handoff overhead in team development was a nightmare.
3. Mismatch with the reality of distributed registries
This was the deepest structural problem. Container images don't deploy only to Docker Hub. AWS ECR, GCP Artifact Registry, Azure Container Registry, GitHub Container Registry, internal Harbor instances... registries are scattered everywhere.
In the Notary v1 model, every registry needed its own Notary server. Copy an image between registries, and the signature doesn't follow. The industry looked at that operational cost and said "no."
In the end, fewer than 0.05% of all Docker Hub pulls had DCT enabled.
On August 8, 2025, the oldest DCT signing certificates for Docker Official Images (nginx, ubuntu, etc.) expired. Users with DOCKER_CONTENT_TRUST=1 could no longer pull even the official images. Docker's response: "Please disable the DOCKER_CONTENT_TRUST environment variable." DCT quietly died.
Azure Container Registry began DCT deprecation in March 2025, with full removal scheduled for March 2028. Harbor moved earlier, fully removing Notary v1 in v2.9.0 back in 2023.
What the industry learned from Notary v1's failure: "Standing up a separate server just for signing doesn't work operationally."
The answer: store signature data directly in the same OCI registry as the image, as another OCI artifact (a blob conforming to the OCI spec). No extra registry to run. Copy the image between registries, and the signature comes along.
The OCI Distribution Specification v1.1.0, released in February 2024, formally standardized this approach. It introduced the Referrers API (GET /v2/<name>/referrers/<digest>), letting clients list all related artifacts (signatures, SBOMs, vulnerability scan results) attached to a given image's Digest.
Each artifact (signature, SBOM, etc.) points back to the parent image via a subject field. Verifier tools call the Referrers API to enumerate them and pick what they need to verify. No separate Notary server required.
Note: in production you usually pick either Cosign or Notation, not both (drawing them side-by-side just shows that both ride on the same spec). On top of this foundation, two signing projects are now competing for dominance.
Sigstore made a clean call. Stop using TUF's targets.json to manage image hashes. But don't throw TUF away entirely.
Sigstore uses TUF in exactly one place: safely distributing the root certificate of Fulcio (the signing CA) and the public key of Rekor (the transparency log) to clients. The first time you run cosign, a TUF client behind the scenes walks root.json → timestamp.json → snapshot.json → targets.json to fetch the certificates and public keys you should trust.
The heavy use case TUF was originally built for, "managing hashes of hundreds of thousands of packages," was abandoned. Sigstore kept only the lightweight role TUF excels at: "safely distributing root certificates."
Sigstore also gave a fundamental answer to the "key management is unbearable" problem that killed Notary v1: don't make developers hold private keys at all (keyless signing).
You authenticate via an OIDC (OpenID Connect, the standard protocol for ID token issuance) provider (GitHub, Google, etc.), Fulcio issues a short-lived certificate that expires in 10 minutes, and you sign with that certificate. The fact of signing is permanently recorded in Rekor's transparency log. The private key exists for a few seconds in memory and disappears. There is no key to manage in the first place.
The revolutionary move: abandon the very idea of "protect the key" and replace it with "sign with a short-lived key, and leave only the signing trace in a public log forever."
The next-generation Notary project, led by Docker and Microsoft. v1.0.0 released in August 2023. Active development continues as a CNCF Incubating project.
Notary v2 completely dropped the TUF specification. The four-role structure of Root, Targets, Snapshot, Timestamp is not used at all. Instead, it builds trust on X.509 certificate chains (the same mechanism as HTTPS certificates: trust propagates hierarchically from CA to intermediate CA to leaf certificate), a mechanism battle-tested for decades on the Web.
The mechanics are identical to SSL/TLS certificate verification. Signers hold X.509 certificates issued by a Certificate Authority (CA). Verifiers maintain a trust store (a list of CAs they trust) and walk the certificate chain attached to the signature to decide whether to trust it. TUF's complex chain of metadata is replaced with existing PKI infrastructure.
You don't need to hold keys locally. Plugins connect to cloud KMS services like AWS Signer, Azure Key Vault, or HashiCorp Vault, delegating the signing operation. It also integrates with Kubernetes admission controllers (Ratify, Kyverno) so signature verification can be wired into deployment gates.
| Notary v1 (DCT) | Sigstore (cosign) | Notary v2 (Notation) | |
|---|---|---|---|
| Use of TUF | Full implementation (4 roles) | Root certificate distribution only | Not used |
| Signature storage | Notary server (separate infra) | Inside OCI registry | Inside OCI registry |
| Key management | Developer manages locally | None (keyless signing) | Delegated to cloud KMS |
| Trust model | TUF Root of Trust | TUF + transparency log (Rekor) | X.509 certificate chain |
| CI/CD fit | ❌ Requires passphrase entry | ✅ Fully automated via OIDC | ✅ Automated via KMS plugin |
| Status (2026) | ❌ Archived | ✅ Adopted by npm, PyPI, Maven | ✅ CNCF Incubating |
If you've read this far, this question should be nagging you.
Notary v1 imploded over "key management is too hard." So how does Python's PyPI, which hosts over 500,000 packages, manage to make TUF (PEP 458) actually work?
The answer comes down to two structural differences.
PyPI's TUF deployment (PEP 458) is designed to protect the channel between the PyPI servers and the pip command. Developers just upload packages to PyPI as before. PyPI's backend automatically computes hashes and signs targets.json using PyPI's own online keys.
Developers don't even need to know TUF exists. This is the polar opposite of Notary v1, which forced developers to hold TUF's offline keys.
Python packages all converge on a single central server: pypi.org. Run one TUF server, and you cover all 500,000 packages.
Container image registries, by contrast, are distributed across many places: Docker Hub, ECR, GCR, ACR, Harbor... Notary v1 required a TUF server per registry, and operational costs exploded.
| PyPI | Container ecosystem | |
|---|---|---|
| Registry | One: pypi.org
|
Docker Hub, ECR, GCR, ACR, Harbor... |
| Who manages TUF | PyPI server (automatic) | Developers themselves (Notary v1) |
| Result | ✅ Developers don't see TUF | ❌ Developers burned out on key management |
PyPI also spent years exploring "end-to-end signing by developers themselves" as PEP 480. But ultimately it gave up on forcing TUF-based key management onto developers and pivoted to Trusted Publishers (launched April 2023) using GitHub Actions OIDC. This is the same "OIDC + short-lived tokens" approach as Sigstore.
Docker, PyPI, npm: they all converged on the same conclusion. "Making developers manage private keys does not work." Notary v1's death is a lesson the entire industry has internalized.
"How do you protect the hash of a container image with TUF's Targets?"
In the old days, you protected it with targets.json (Notary v1). But in a distributed container ecosystem, the model that asks developers to manage offline keys completely fell apart. Today, instead of managing the image Digest directly with TUF, signatures are stored directly in the OCI registry (Sigstore / Notary v2).
Security that nobody uses is not security. The decade of Notary v1 proved that.
2026-04-28 19:33:02
At some point in building an agentic system, you will hit the same wall. An agent workflow needs to pause, wait for a human decision, and resume. The implementation seems straightforward but then you realise: what happens to the state during the pause? Where does it live? Who owns it? How does the resumed execution know what it was doing?
That question is where LangGraph and Microsoft Agent Framework make fundamentally different architectural choices. Everything else, the feature comparison, the ecosystem fit, the vendor landscape, follows from how each framework answers it.
The received wisdom is that LangGraph is for Python teams and Microsoft Agent Framework is for Microsoft shops. This is roughly true, but it is also the least interesting thing about either framework.
Both now offer graph-based workflows, typed nodes and edges, checkpointing, human-in-the-loop interruption, multi-agent orchestration, and MCP tool support. The feature table between them is, at this point, nearly identical. Comparing features tells you almost nothing about which one will produce a maintainable system twelve months from now.
The question worth asking is not "which framework has the features I need?" Both do. The question is: when does your system have to know what its state looks like?
In LangGraph, the answer is before the first line of agent code runs. In Microsoft Agent Framework, the answer is whenever you decide it matters.
Call it the state contract; LangGraph makes you sign it at design time while MAF lets you negotiate it later. These positions, therefore, produce different systems and different failure modes.
In LangGraph, you define a typed state schema before anything else. Every node in the graph receives that state and returns a partial update. The compiled graph, not the node functions, owns the execution model. You cannot compile without a schema; you cannot run without compiling.
The example below is a three-step document review pipeline: an AI reviewer reads the document, a human approves or rejects it, and the result routes to publish or reject. In LangGraph, the shape of the data flowing through those steps is the first thing you write.
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import interrupt
class ReviewState(TypedDict):
document: str
reviewer_decision: str
def ai_review(state: ReviewState):
# In a real pipeline this node calls an LLM; simplified here for clarity
return {"reviewer_decision": "pending"}
def human_approval(state: ReviewState):
# interrupt() serialises state to the checkpointer and pauses execution here.
# The caller receives the document, supplies a decision, and execution resumes.
decision = interrupt({"document": state["document"]})
return {"reviewer_decision": decision}
def route(state: ReviewState):
return "publish" if state["reviewer_decision"] == "approve" else "reject"
def publish(state: ReviewState):
return {"document": f"[PUBLISHED] {state['document']}"}
def reject(state: ReviewState):
return {"document": f"[REJECTED] {state['document']}"}
graph = StateGraph(ReviewState)
graph.add_node("ai_review", ai_review)
graph.add_node("human_approval", human_approval)
graph.add_node("publish", publish)
graph.add_node("reject", reject)
graph.add_edge(START, "ai_review")
graph.add_edge("ai_review", "human_approval")
graph.add_conditional_edges("human_approval", route)
graph.add_edge("publish", END)
graph.add_edge("reject", END)
app = graph.compile(checkpointer=InMemorySaver())
Before a single prompt fires, you have defined exactly what flows through this system: a document and a decision. When the graph hits interrupt(), it serialises the full state to the checkpointer and halts. The caller retrieves the saved state, presents it to a human, and resumes execution by passing the decision back in. The graph picks up from the exact checkpoint, with all prior state intact.
The cost of explicit; you cannot prototype quickly without thinking about state. On a trivial workflow, the schema ceremony could appear as disproportionate.
Microsoft Agent Framework separates two concepts from the start: an Agent that drives autonomous, LLM-guided behaviour, and a Workflow that enforces a deterministic execution path.
The same three-step document review pipeline looks like this in MAF. An Executor is MAF's equivalent of a LangGraph node: a typed processing unit that receives a message, does work, and forwards a result. WorkflowBuilder wires executors together with edges.
from agent_framework import Agent, Executor, WorkflowBuilder, WorkflowContext, handler
from agent_framework.openai import OpenAIChatClient
# The agents that do the LLM work
reviewer_agent = Agent(
client=OpenAIChatClient(),
instructions="Review this document. Return a brief assessment.",
)
# Executors are the nodes of the graph
class ReviewExecutor(Executor):
@handler
async def run(self, document: str, ctx: WorkflowContext) -> None:
result = await reviewer_agent.run(document)
await ctx.send_message(result.text) # forwards to the next executor
class PublishExecutor(Executor):
@handler
async def run(self, review: str, ctx: WorkflowContext) -> None:
await ctx.yield_output(f"[PUBLISHED] Review: {review}")
class RejectExecutor(Executor):
@handler
async def run(self, review: str, ctx: WorkflowContext) -> None:
await ctx.yield_output(f"[REJECTED] Review: {review}")
# Wire the executors with edges — same concept as add_node + add_edge in LangGraph
review = ReviewExecutor(id="review")
publish = PublishExecutor(id="publish")
reject = RejectExecutor(id="reject")
workflow = WorkflowBuilder(start_executor=review).add_edge(review, publish).build()
Notice what is absent: a state schema. The data contract between executors is the message type passed between them, not a shared typed dict declared before anything runs.
For human-in-the-loop, MAF uses a different model than LangGraph's checkpoint-and-pause. Rather than halting in-place, the workflow emits an event and you re-run it with the human's response. Using SequentialBuilder with with_request_info, a pipeline pauses after a nominated agent runs, surfaces its output for human review, and resumes when you feed the response back in:
from agent_framework.orchestrations import SequentialBuilder, AgentRequestInfoResponse
pipeline = (
SequentialBuilder(participants=[reviewer_agent, publisher_agent])
.with_request_info(agents=["reviewer"]) # pause after reviewer, before publisher
.build()
)
# First run: reviewer fires, workflow emits a request_info event
stream = pipeline.run(document_text, stream=True)
pending = await collect_approvals(stream) # your handler surfaces the review to a human
# Resume: feed the human's decision back in
if pending:
stream = pipeline.run(stream=True, responses=pending)
await collect_approvals(stream)
The emit-and-rerun model is a genuine architectural difference from LangGraph's interrupt. LangGraph's state is serialised at the exact point of pause and restored on resume: the graph does not restart, it continues. MAF's request-response model re-runs the workflow from the current position with the human response as an input. For most approval workflows the behaviour looks the same from the outside. For long-running workflows with complex branching, the difference in how state is maintained across the pause matters more.
The appeal of MAF's layered approach is real; build the agent behaviour first, run it, understand what actually flows through the system, and _then _add workflow structure once you know what it needs to look like. No schema ceremony on day one.
However, the cost of this position may surface later in a complex production system; needing a workflow that coordinates multiple agents, each maintaining its own session state, and you need consistent state across a human approval step; you will be working across two abstractions that were designed separately. It is possible, but is more work than it looks on day one.
Most comparisons describe LangGraph as steep and MAF as approachable. This is accurate for the first week of development and inaccurate for the first six months.
LangGraph's learning cost concentrates at the start. There are four concepts to internalise:
the state schema and how reducers merge concurrent updates;
the compiled graph as a distinct artefact from the node functions;
the checkpointer pattern and how thread IDs isolate independent conversations; and
conditional edges versus routing functions.
These are genuinely non-obvious the first time. Once learned, the model is consistent everywhere. A LangGraph graph written by someone else is readable without context because the state contract is explicit and the execution path can be traced from the compiled artefact before running a single call.
MAF's learning cost distributes differently. The simple agent is genuinely simple. The workflow API is learnable. The composition challenge, combining an autonomous agent with checkpointed workflows and multi-step human approval inside a single pipeline, arrives later and hits harder. The migration guides from AutoGen and Semantic Kernel exist precisely because neither of those predecessor APIs composed cleanly either. Microsoft unified them in part to solve this problem; how well the composition works in practice is still being tested in production.
The practical upshot is if you prototype a simple agent in both frameworks today, MAF will feel faster. If you build a complex production workflow in both frameworks over three months, LangGraph will have fewer surprises. The inflection point depends on how complex your workflow actually is. For most workflows that require branching, human approval, and fault recovery, it arrives before month three.
LangGraph has a substantially larger open-source community, a deeper ecosystem of integrations, and a set of verified production deployments at scale. Python is the dominant language in AI development and LangGraph is Python-first. Community answers are easier to find. The production evidence, from companies running agent workloads against tens of millions of users, exists and is documented.
MAF is backed by Microsoft. This matters in specific contexts: a documented migration path from AutoGen and Semantic Kernel brings existing practitioners in without a full rewrite; the Azure AI Foundry integration is the default agentic path for teams in the Azure ecosystem; and enterprise procurement conversations are easier when the framework vendor can offer a support contract.
Neither of these is a technical argument, but they are arguments about where default momentum points when a team is choosing a framework without strong prior opinions. For Python-first AI teams without Azure commitments, the momentum is toward LangGraph. For teams inside the Azure ecosystem or migrating from predecessor Microsoft frameworks, it is toward MAF. That is not a reason to override the architectural fit assessment; it is a reason to notice where friction will be lower.
There is one question that is more predictive than any feature comparison:
when you build a system, do you prefer to define the schema first and let the implementation follow, or do you prefer to build the implementation and formalise the schema once you know what it needs to be?
If you are a schema-first developer, LangGraph fits your mental model. The state contract is not overhead; it is how you think. The compiled graph is a readable specification. The investment in state design up front pays back in debugging, resumability, and the ability to hand the codebase to a new engineer without a lengthy explanation.
If you are a behaviour-first developer, MAF's layered approach fits you better. Build the agent, run it, understand what it actually does, then add workflow structure where the process needs it. The simple agent requires no architecture decisions at all to start.
The ecosystem considerations are secondary to this. Teams have shipped complex agentic systems with both frameworks in both the Python and .NET ecosystems. The architectural fit determines whether the system is a joy or a grind to maintain. The ecosystem determines where you will find help when you get stuck. Both matter, and they matter in that order.
2026-04-28 19:28:33
If you build internal tools in Retool, the last twelve months probably felt different. Pricing changed. Self-hosting quietly moved behind the Enterprise wall. AI features arrived, but if you tried to push them past a demo, you noticed they only generate apps from Retool's own components.
This post is not a "Retool is dead" rant. Retool is still good at what it has always been good at — drag-and-drop CRUD UIs over your databases. But there's a category of teams for whom the trade-offs no longer make sense, and the open-source landscape has matured enough that staying in a closed platform is now an active choice, not a default.
I'll walk through:
A short factual recap so we're on the same page:
None of this is unusual for a maturing SaaS. It's textbook PLG-to-Enterprise conversion. But it has consequences if you're the engineering lead deciding where the next 50 internal tools live.
Three painful situations come up repeatedly in G2, Reddit and HN reviews:
If none of these hit you, Retool is probably still the right tool. If they do, the rest of this post is for you.
| Tool | License | Best for | Weak point |
|---|---|---|---|
| Appsmith | MIT | Code-first devs who want Retool with Git | No native AI app generation |
| ToolJet | AGPL v3 | Mixed teams; best AI app generation in OSS | Performance with large datasets; AGPL concerns |
| Budibase | GPL v3 | Fastest CRUD-from-database setup | Hits limits on complex apps |
| Superblocks | Agent-only OSS | Enterprise governance + RBAC, code export | Custom pricing, no built-in DB |
| Refine.dev | MIT | Senior React devs who reject drag-and-drop | No AI layer; not batteries-included |
| NocoBase | MIT | Data-model-driven internal apps, ERP-style | Smaller US presence |
| Open Mercato | MIT | Code-first teams who want AI-native foundation + CRM/ERP domain model | Newer, smaller community |
Each one solves a different slice. Appsmith is the closest like-for-like Retool replacement if you want drag-and-drop with Git. ToolJet is where the AI app generation in OSS actually works today. Budibase is the fastest path from a Postgres schema to a working CRUD UI. Refine is for teams who decided drag-and-drop isn't the answer.
The rest of this post covers the slice Open Mercato is built for: teams who want a code-first foundation, full ownership, and AI tools that produce consistent code across the whole team.
Open Mercato is an MIT-licensed npm package that gives you an opinionated foundation for building internal tools, CRM, ERP modules and customer-facing operational apps. It's full-code (TypeScript / Next.js / PostgreSQL), not low-code. The novel part is that it's designed for AI agents — Cursor, Claude Code, Codex — to write architecture-aware code against it, because the architecture and specs ship inside the repo.
You start a project with:
npx create-mercato-app
What you get is roughly 80% of the boilerplate already done: data model, RBAC, auth, audit trail, field-level encryption (AES-GCM), per-tenant isolation, override patterns. The remaining 20% is your domain logic.
Retool apps live inside Retool. If you decide to leave after building 50 tools over three years, you're rebuilding from scratch. Retool itself acknowledges this in its own content marketing: "Using a proprietary tool for visual programming also comes with the risk of vendor lock-in."
Open Mercato is a normal TypeScript repo on your machine. Your code, your Git history, your CI/CD. If the project disappeared tomorrow, you'd still have a working monorepo. There is no platform to be locked into — it's a package and a set of conventions.
This is the part I find most underrated. Most "AI in low-code" features generate apps from a closed component library, which is fine for demos but limits customization. Most "AI in your IDE" tools (Cursor, Copilot, Claude Code) are powerful but have no idea what your project's architecture is — they generate snippets, and consistency depends on the human reviewing the diff.
Open Mercato ships specs in the repo from day one. When an agent writes a feature, it has:
The result is that Cursor or Claude Code generates code that fits the project instead of code that fits the prompt. A junior developer can ship features that pass review on the first try, because the agent has guardrails.
This is a different idea than "AI app generator." It's: give your team a foundation where AI tools are productive by default.
You run Open Mercato wherever you run Postgres and Node. There is no cloud-only edition, no separate Enterprise SKU for self-hosting, no surprise data-residency conversation with your security team. For regulated industries (healthcare, finance, defense, anything with EU data), this is the difference between a six-week procurement cycle and a thirty-minute docker compose up.
Open Mercato core is MIT-licensed and free. No per-user, per-builder, per-end-user tax. If you're at 50 users on Retool Business today, you're paying ~$39k/year for licenses alone. Open Mercato moves that money into engineering time, which is usually the trade you want.
This is where Open Mercato sits closer to Refine.dev than to Retool. If you've used Refine, you already understand the pattern — a meta-framework that handles the boring parts of CRUD-heavy enterprise apps so you can write actual code for the interesting parts. Open Mercato extends that idea with a domain model (CRM, ERP, ops) and the AI-engineering layer described above.
If you've been resisting Retool because "I'd rather just write code, but I don't want to write the same admin layer for the fifteenth time," this is the shape you've been looking for.
Honest trade-offs:
Open Mercato (and most code-first OSS alternatives) pays off when the work has scale, longevity or compliance requirements that make ownership of the code matter.
Most teams don't migrate everything at once. The pattern that works:
The point isn't that any one of these replaces Retool. It's that the category has matured enough that "open source Retool alternative" is now a real shopping list, not a wishlist.
If you've made one of these jumps recently, I'd be curious which trade-off pushed you over the edge — pricing, self-hosting, the AI story, or the lock-in. Drop a comment.