Category
Framework tutorials
Every framework tutorials post on the n4n AI blog — 304 articles across 57 topics.
Topics in framework tutorials
- AutoGen Agent Teams for Research & Automation11
- Choosing an AI Framework by Use Case10
- Framework Tutorials: Legal & Healthcare Document Q&A9
- One Backend, Every Model: Swapping GPT-5, Claude, Gemini & Llama Across Frameworks9
- AutoGen Human-in-the-Loop Workflows8
- LangChain Multi-Model Fallback & Routing8
- AutoGen Getting Started with n4n.ai7
- AutoGen Multi-Agent Conversations & Group Chat7
- Building Chatbots with Vercel AI SDK & Next.js7
- CrewAI Agent Roles & Task Design7
- CrewAI Getting Started with n4n.ai7
- CrewAI Real-World Crew Examples7
- Framework Tutorials: Customer Support Bots7
- LangChain Getting Started with n4n.ai7
- AutoGen Code-Executing Agents6
- CrewAI Custom Tools & Integrations6
- Framework Tutorials: E-commerce Search & Recommendations6
- LangChain Expression Language (LCEL) Chains6
- LangChain RAG with Vector Databases6
- LangGraph Multi-Agent Workflows6
- Multi-Agent Framework Showdown: CrewAI vs AutoGen vs LangGraph6
- Multimodal & Voice Apps with AI Frameworks6
- CrewAI Sequential vs Hierarchical Crews5
- Framework Tutorials: Finance & Trading Analysis Agents5
- Haystack Evaluation Pipelines5
- Haystack Getting Started with n4n.ai5
- Haystack RAG Pipelines5
- LangChain Document Loaders & Chunking5
- LangChain Structured Output & Parsers5
- LangChain.js for Node & TypeScript5
- LlamaIndex Agents & Tool Use5
- LlamaIndex Data Connectors & Ingestion5
- LlamaIndex Getting Started with n4n.ai5
- Vercel AI SDK Getting Started with n4n.ai5
- Vercel AI SDK Multi-Model Switching5
- Vercel AI SDK on Edge & Serverless Runtimes5
- Vercel AI SDK Streaming Chat UI (useChat)5
- Vercel AI SDK Tool & Function Calling5
- Framework Cost & Latency Optimization Tutorials4
- LangChain Streaming Responses & Callbacks4
- LlamaIndex Chat Engines & Memory4
- LlamaIndex Retrieval Evaluation & Metrics4
- Open-Source & Local Models in Frameworks (Llama 4, Mistral, DeepSeek, Qwen)4
- Semantic Kernel Getting Started with n4n.ai4
- Semantic Kernel Memory & Vector Stores4
- Semantic Kernel Planners & Agents4
- Semantic Kernel Plugins & Native Functions4
- Haystack 2.0 Agent Pipelines3
- Haystack Document Stores & Retrievers3
- LangChain Agents & Tool Calling3
- LangChain Memory & Conversational State3
- LangChain vs LlamaIndex for RAG3
- LlamaIndex Knowledge Graphs & Multi-Doc Indexes3
- LlamaIndex Query Engines for RAG3
- Migrating from the Raw OpenAI SDK to a Framework3
- Vercel AI SDK Generative UI with React Server Components3
- Semantic Kernel for .NET Enterprise Apps2
All framework tutorials posts
- 2026 multi-agent frameworks: CrewAI, AutoGen, LangGraphListicleEngineer-focused breakdown of multi-agent frameworks 2026 crewai autogen langgraph: CrewAI, AutoGen, and LangGraph with code and trade-offs for building production LLM systems.
- A/B testing GPT-5 against Claude Opus in LangChainHow-toLearn how to a/b test gpt-5 vs claude in langchain with traffic splitting, token metering, and evaluation harness in a step-by-step guide.
- A CrewAI crew for automated SEO content auditsHow-toStep-by-step crewai seo content audit example: build a multi-agent crew that fetches URLs, analyzes on-page SEO, and outputs actionable fix lists with code.
- A CrewAI crew for e-commerce product description writingHow-toStep-by-step crewai product description writing example: build a CrewAI crew that turns e-commerce specs into polished listings with fallback LLM routing.
- A CrewAI crew that automates competitor researchHow-toStep-by-step guide to build a CrewAI competitor research automation example with multi-agent crews, runnable Python code, and verification tips.
- A product Q&A chatbot from your catalog with LangChainTutorialBuild a product Q&A chatbot from your catalog with LangChain using RAG. Step-by-step tutorial with runnable code for ecommerce retrieval and chat.
- A research assistant built with CrewAI, AutoGen, LangGraphTutorialHands-on tutorial to build research assistant crewai autogen langgraph. Runnable multi-agent code for CrewAI, AutoGen, and LangGraph with sample output.
- A support agent that hands off to a human in LangGraphTutorialStep-by-step LangGraph tutorial: build a support agent that triages requests and hands off to a human via interrupt, with runnable code and expected output.
- Add a human approval gate to AutoGen tool callsHow-toStep-by-step guide to adding a human approval gate to AutoGen tool calls using a Python function wrapper, with runnable code examples and verification steps for safe agents.
- Add conversational memory to a LangChain chatbotHow-toStep-by-step guide to add conversational memory to a LangChain chatbot using buffer and summary memory, with runnable Python code and verification tips.
- Add file uploads to a Vercel AI SDK chatbotHow-toLearn how to add file uploads to a Vercel AI SDK chatbot in Next.js. Step-by-step guide for handling attachments, multimodal messages, and verification.
- Add long-term memory to a LlamaIndex chat engineHow-toLearn how to add persistent cross-session memory to a LlamaIndex chat engine with Redis in this hands-on llamaindex long-term memory chat engine tutorial.
- Add persistent chat history to a Next.js AI SDK chatbotHow-toLearn how to add persistent chat history to a Next.js AI SDK chatbot using Drizzle and Postgres, with step-by-step code and verification tips.
- Add stop and regenerate buttons to your useChat UIHow-toStep-by-step useChat stop regenerate button tutorial for Vercel AI SDK: add stop and regenerate controls to your streaming chat UI with runnable React code.
- Adding a RAG agent to an AutoGen group chatHow-toPractical walkthrough for engineers adding a RAG agent to an AutoGen group chat: wire retrievers, configure agents, run end-to-end, and verify.
- AI-powered product recommendations with LangChainTutorialHands-on tutorial: build AI-powered product recommendations with LangChain using a vector store and LLM chain, from catalog embedding to ranked output.
- Analyze images and PDFs with Gemini 2.0 in LangChainTutorialHands-on tutorial: build multimodal pipelines with Gemini 2.0 and LangChain to analyze images and PDFs locally, with runnable code and expected outputs.
- Authenticate LangChain requests with an n4n.ai API keyHow-toStep-by-step guide to langchain n4n.ai api key authentication: configure ChatOpenAI with the gateway base URL, set your key, and verify requests.
- Auto-route LangChain to the cheapest model with n4n.aiHow-toA practical langchain cheapest model routing tutorial: wire LangChain to an OpenAI-compatible gateway, auto-select low-cost models, and verify token usage.
- AutoGen agent team tutorial: planner, coder, critic rolesTutorialHands-on AutoGen tutorial: build a multi-agent team with planner, coder, and critic roles using group chat to plan, write, and review Python code.
- AutoGen agent teams for report generation and QAHow-toStep-by-step guide to building an AutoGen multi-agent pipeline that researches, drafts, and QA-checks reports with OpenAI-compatible LLM endpoints.
- AutoGen agent teams: termination conditions explainedTutorialA hands-on autogen agent team termination condition tutorial: learn how to control multi-agent loops with clear exit signals and custom predicates.
- AutoGen agent teams tutorial: building a research crewTutorialHands-on autogen agent team research tutorial: build a multi-agent research crew with AutoGen, step-by-step code, and real output checkpoints.
- AutoGen ALWAYS vs TERMINATE vs NEVER input modesComparisonCompare AutoGen human_input_mode ALWAYS vs TERMINATE vs NEVER across cost, latency, ergonomics, and limits, with table and use-case verdicts.
- AutoGen AssistantAgent plus code executor, step by stepHow-toStep-by-step autogen assistantagent code executor tutorial: configure AssistantAgent with a local code executor, run multi-turn coding tasks safely.
- AutoGen code execution tutorial: prompt to PythonTutorialStep-by-step autogen code execution tutorial python: build AutoGen agents that generate, run, and debug Python code locally with full runnable examples.
- AutoGen code execution vs function calling explainedComparisonA practical head-to-head comparison of AutoGen code execution vs function calling across cost, latency, ergonomics, and limits, with a verdict.
- AutoGen config_list basics for n4n.ai's 240+ modelsGuideLearn to build an AutoGen config_list for n4n.ai's 240+ models: endpoint setup, fallback, cache hints, and pitfalls from real deployments.
- AutoGen Docker code executor: a secure setup guideHow-toStep-by-step guide to a hardened AutoGen Docker code executor setup: isolate agent-generated Python in a container, with runnable configs and verification.
- AutoGen GroupChat tutorial: orchestrating multiple agentsTutorialHands-on autogen groupchat tutorial multiple agents: build a multi-agent coding and review pipeline with GroupChat, speaker control, and safe code exec.
- AutoGen GroupChat vs SelectorGroupChat: which to useComparisonA practitioner's head-to-head comparison of AutoGen GroupChat vs SelectorGroupChat across capabilities, cost, latency, ergonomics, and limits for engineers building multi-agent systems.
- AutoGen human-in-the-loop for high-stakes code executionGuideA practical guide to implementing autogen human in the loop code execution safety for high-stakes workflows, with patterns, code, and pitfalls.
- AutoGen human-in-the-loop: pausing agents for reviewHow-toLearn how to implement autogen pause agent for human review workflows with practical code, step-by-step setup, and verification tips for engineers.
- AutoGen human-in-the-loop tutorial for agent approvalsTutorialStep-by-step autogen human in the loop tutorial for agent approvals: build guarded tools, configure UserProxyAgent, and run sign-off flows with code.
- AutoGen human-in-the-loop with Claude Opus 4.5 via n4n.aiHow-toStep-by-step guide to building AutoGen human-in-the-loop workflows with Claude Opus 4.5 through the n4n.ai gateway, including runnable code and verification.
- AutoGen RoundRobinGroupChat explained with an exampleDefinitionRoundRobinGroupChat runs agents in fixed order for predictable multi-agent loops in AutoGen. This autogen roundrobingroupchat tutorial shows code and pitfalls.
- AutoGen SelectorGroupChat: dynamic speaker selectionDefinitionAutoGen SelectorGroupChat speaker selection uses an LLM to dynamically route multi-agent conversations. Learn how it works, with code and pitfalls for building robust agent teams.
- AutoGen setup guide: AssistantAgent and UserProxyAgentGuideStep-by-step Autogen AssistantAgent UserProxyAgent setup guide with working code, config tips, and pitfalls for engineers building multi-agent LLM workflows.
- AutoGen Studio setup with n4n.ai as the model providerHow-toStep-by-step guide to autogen studio setup n4n.ai provider as an OpenAI-compatible model endpoint, from install to verified run with code.
- AutoGen tutorial: a support agent that checks order statusTutorialBuild a multi-agent AutoGen support agent that checks order status via a REST API. Step-by-step tutorial with runnable Python code and expected output for engineers.
- AutoGen vs CrewAI: which is easier to debug in productionComparisonA practitioner's comparison of AutoGen vs CrewAI debugging production tradeoffs across control flow, observability, cost, and latency—with a verdict.
- Automatic model fallback in LiteLLM to cut GPT-4 spendHow-toImplement LiteLLM automatic model fallback to route around GPT-4 limits and slash inference costs. Step-by-step proxy config and code.
- Automatic model fallback with Vercel AI SDK and n4n.aiHow-toStep-by-step guide to vercel ai sdk automatic model fallback n4n.ai: build resilient LLM calls with client-side switching and gateway failover patterns.
- Automatic provider failover in LangChain with n4n.aiHow-toLearn how to implement automatic provider failover in LangChain using a unified LLM gateway, with runnable code and verification steps for resilient apps.
- Automating literature reviews with AutoGen agent teamsHow-toLearn how to build a multi-agent AutoGen pipeline that automates literature reviews: search arXiv, summarize papers, and synthesize findings with minimal code.
- Avoiding cold starts in serverless Vercel AI SDK appsHow-toPractical steps to eliminate vercel ai sdk serverless cold starts in production: keep functions warm, trim deps, and route via a gateway.
- Benchmark LlamaIndex retrievers with recall at kHow-toA practical llamaindex retriever benchmark recall at k tutorial: create ground-truth eval sets, run retrieval, and compute recall@k in Python.
- Benchmarking edge vs serverless latency in Vercel AI SDKAnalysisA hands-on analysis of Vercel AI SDK latency on Edge vs Serverless runtimes, with real code, tradeoffs, and a decisive recommendation for production LLM apps.
- Best agent framework for enterprise workflow automationListicleA practitioner's comparison of five agent frameworks for enterprise workflow automation, covering LangGraph, Temporal, Prefect, Semantic Kernel, and CrewAI.
- Best AI agent framework for chatbots in 2026ListicleA hands-on comparison of the best AI agent framework for chatbots in 2026, with code and tradeoffs for LangGraph, AutoGen, CrewAI, Semantic Kernel, and OpenAI Agents.
- Best AI agent framework for coding assistantsListicleA practitioner's comparison of the best AI agent frameworks for coding assistants, scored on repo context, tooling, and multi-agent control.
- Best AI agent framework for startups shipping an MVPListicleA pragmatic comparison of the best ai agent framework for startup mvp options—LangChain, LlamaIndex, AutoGen, CrewAI, and Pydantic AI—with code.
- Best AI framework for data analysis and SQL agentsListiclePractical comparison of the best AI frameworks for SQL agents: LangChain, LlamaIndex, DSPy, Vanna, Haystack, and custom Python with code and tradeoffs.
- Best AI framework for voice and real-time agentsListicleA practitioner's comparison of the best AI frameworks for voice and real-time agents, covering LiveKit, Pipecat, Vapi, Retell, and OpenAI Realtime.
- Best framework for agents that call external APIs and toolsListicleA practical comparison of the best framework for tool-calling agents, weighing LangChain, LlamaIndex, AutoGen, CrewAI, and Pydantic AI for API integration.
- Best framework for RAG: LangChain, LlamaIndex, or HaystackComparisonA practitioner's head-to-head comparison of LangChain, LlamaIndex, and Haystack for RAG across capabilities, cost, latency, and ergonomics.
- Build a chatbot with LlamaIndex CondensePlusContextTutorialStep-by-step llamaindex condensepluscontext chatbot tutorial: build a context-aware chat engine with LlamaIndex, handle conversation history, and run it.
- Build a citation-aware query engine in LlamaIndexTutorialHands-on llamaindex citation query engine tutorial: build a RAG pipeline with LlamaIndex that returns answers annotated with verifiable source citations.
- Build a customer support bot with LangChainTutorialHands-on tutorial to build customer support bot LangChain with RAG, Chroma, and conversation memory for engineers. Step-by-step code and expected output.
- Build a customer support chatbot with GPT-4o and n4n.aiTutorialStep-by-step tutorial to build a streaming customer support chatbot with GPT-4o using Next.js, Vercel AI SDK, and n4n.ai's OpenAI-compatible gateway.
- Build a data analysis agent with AutoGen code executionHow-toLearn how to build an autogen data analysis agent code execution workflow that generates and runs Python to explore CSV data with guarded local execution.
- Build a full-stack chatbot with Next.js and Vercel AI SDKTutorialFull-stack step-by-step guide to build chatbot next.js vercel ai sdk tutorial: scaffold app, stream responses, and wire a typed UI with useChat for production.
- Build a generative UI chat with n4n.ai and RSCTutorialHands-on generative ui chat n4n.ai rsc tutorial: build a streaming RSC chat with Vercel AI SDK and the n4n.ai OpenAI-compatible endpoint for LLM apps.
- Build a knowledge graph index in LlamaIndexTutorialHands-on llamaindex knowledge graph index tutorial: extract triplets from text with LlamaIndex, inspect the graph store, and run relationship queries.
- Build a LangChain agent with custom tool callingHow-toA step-by-step guide to langchain agent custom tool calling: define tools, wire a reasoning loop, verify with Python code and pytest for production use.
- Build a LangChain extraction chain with Pydantic v2TutorialHands-on langchain extraction chain pydantic v2 tutorial: build a structured output pipeline with Pydantic v2 and LangChain, with runnable code.
- Build a LangChain fallback chain for rate limitsTutorialStep-by-step langchain fallback chain rate limits tutorial: build a multi-model LLM fallback chain in Python that handles 429s and degradations.
- Build a LlamaIndex ReAct agent with n4n.aiTutorialStep-by-step llamaindex react agent tutorial: build a ReAct agent with custom tools using LlamaIndex and an OpenAI-compatible LLM gateway with runnable code.
- Build a model picker dropdown with Vercel AI SDKHow-toLearn how to build a vercel ai sdk model picker dropdown that switches LLMs at runtime with a clean React UI, streaming, and usage metering step by step.
- Build a multi-agent workflow with LangGraphHow-toA practical langgraph multi-agent workflow tutorial: build a supervisor-routed system of specialized agents with LangGraph, from setup to verified run.
- Build a Next.js chat app with useChat and n4n.aiTutorialStep-by-step useChat Next.js chat app tutorial using n4n.ai as OpenAI-compatible backend with Vercel AI SDK streaming and fallback.
- Build a Next.js chatbot with LangChain.jsHow-toStep-by-step guide to building a langchain.js nextjs chatbot with streaming responses, tool calling, and a production-ready API route in TypeScript.
- Build a planner, coder, and critic team with AutoGenTutorialBuild an autogen planner coder critic agent team with AutoGen group chat: hands-on tutorial splitting design, coding, and review into specialized agents.
- Build a RAG chatbot with LangChain, LanceDB, and n4n.aiHow-toA practical end-to-end tutorial for engineers: build a LangChain LanceDB n4n.ai RAG chatbot with vector search and OpenAI-compatible LLM calls.
- Build a RAG chatbot with Vercel AI SDK and n4n.aiTutorialHands-on rag chatbot vercel ai sdk n4n.ai tutorial: build a streaming RAG chatbot in Next.js using Vercel AI SDK and an OpenAI-compatible LLM gateway.
- Build a RAG pipeline with Haystack 2.0 and n4n.aiTutorialHands-on tutorial to build rag pipeline haystack 2.0 n4n.ai using Haystack 2.0 and an OpenAI-compatible endpoint, with runnable code and expected output.
- Build a RAG pipeline with LangChain and PineconeHow-toBuild a langchain pinecone rag pipeline from scratch: install deps, chunk docs, embed to Pinecone, query with LangChain retrieval chains, and verify.
- Build a RAG query engine in LlamaIndex with n4n.aiTutorialStep-by-step llamaindex rag query engine tutorial: install LlamaIndex, load docs, configure LLM via n4n.ai, build vector index, and run queries.
- Build a research agent in LlamaIndex with n4n.aiTutorialLearn to build a LlamaIndex research agent that queries external APIs and local data, using n4n.ai as an OpenAI-compatible LLM gateway.
- Build a streaming chat UI with Vercel AI SDK's useChatTutorialLearn to build a streaming chat UI with the Vercel AI SDK useChat hook in Next.js, from API route to live token streaming in a runnable tutorial.
- Build a streaming chatbot UI with LangChain and FastAPIHow-toStep-by-step guide to build a langchain fastapi streaming chatbot ui using async callbacks, Server-Sent Events, and a minimal JS frontend for real-time tokens.
- Build a voice assistant with Pipecat and GPT-4o RealtimeHow-toStep-by-step pipecat gpt-4o realtime voice assistant tutorial: build a low-latency voice agent with Pipecat and OpenAI Realtime, from install to verification.
- Build a voice bot with Vapi and Twilio integrationHow-toStep-by-step vapi twilio voice bot integration tutorial: connect Vapi assistants to Twilio telephony and a custom LLM endpoint in production.
- Build a weather tool with Vercel AI SDK function callingTutorialHands-on tutorial: build a vercel ai sdk weather tool example with function calling, Open-Meteo, and TypeScript. Runnable code and expected output included.
- Build an AutoGen research team with n4n.ai routingHow-toBuild a multi-agent research pipeline with AutoGen and route LLM calls through n4n.ai for fallback and 240+ models via one OpenAI-compatible endpoint.
- Build code-executing AutoGen agents with n4n.aiHow-toBuild code-executing AutoGen agents with n4n.ai as the LLM gateway: install, configure, run Python via UserProxyAgent, and verify execution.
- Build your first LCEL chain in LangChainHow-toHands-on tutorial to build first LCEL chain LangChain with runnable Python code: install, prompt, model, parser, stream, fallback, and verify.
- Build your first LlamaIndex app with n4n.aiTutorialHands-on llamaindex first app n4n.ai tutorial: configure LlamaIndex with n4n.ai's OpenAI-compatible gateway and build a local RAG query app in Python.
- Building a case law research assistant with LlamaIndexTutorialStep-by-step tutorial to build a case law research assistant with LlamaIndex: load opinions, index with metadata, query citations, and deploy a research agent.
- Building a citation-aware RAG pipeline in HaystackHow-toStep-by-step guide to building a citation-aware RAG pipeline in Haystack that returns sourced answers with document references and confidence.
- Building a clinical notes Q&A system with LlamaIndexTutorialStep-by-step tutorial for building a clinical notes Q&A system with LlamaIndex, from indexing HL7/CDA docs to querying with grounded answers.
- Building a compliance-checking agent for legal contractsTutorialHands-on tutorial to build a compliance checking agent for legal contracts using Python and an OpenAI-compatible LLM gateway, with runnable code.
- Building a CrewAI crew for customer support triageHow-toStep-by-step guide to building a CrewAI crew for customer support triage with runnable code, routing logic, and verification tips for engineers.
- Building a CrewAI crew for lead qualificationHow-toStep-by-step guide to building a CrewAI crew that qualifies sales leads with specialized agents, including runnable code and verification tips for engineers.
- Building a CrewAI tool for retrieval-augmented generationHow-toStep-by-step crewai rag tool tutorial: build a custom RAG tool for CrewAI agents using Chroma vector store and OpenAI-compatible APIs.
- Building a CrewAI tool that queries a SQL databaseHow-toThis crewai sql database tool tutorial shows how to build a safe SQL query tool for CrewAI agents, with runnable code and end-to-end verification.
- Building a crypto market analysis agent with LangChainTutorialHands-on tutorial to build a crypto market analysis agent LangChain that fetches live CoinGecko data and uses LLM tool-calling to produce insights.
- Building a customer support agent team with AutoGenTutorialHands-on tutorial for building an autogen customer support agent team with AutoGen group chat, including runnable Python code, config, and output examples.
- Building a customer support agent with Semantic KernelHow-toStep-by-step guide to build customer support agent semantic kernel apps with Python: plugins, stepwise planner, and resilient model routing. Includes runnable code.
- Building a customer support bot with LlamaIndexTutorialStep-by-step llamaindex customer support chatbot tutorial: index docs, wire a context chat engine with memory, and run a local support bot with Python.
- Building a customer support graph with LangGraphHow-toBuild a langgraph customer support agent with stateful routing, retrieval, and escalation. Step-by-step Python code for a production-ready support workflow.
- Building a database query plugin for Semantic KernelHow-toLearn how to build database query plugin Semantic Kernel native functions in Python, covering schema introspection, safe SQL execution, and end-to-end verification.
- Building a fallback chain across GPT-5, Claude, and GeminiTutorialBuild a hands-on llm fallback chain gpt-5 claude gemini in Python with provider SDKs, manual retries, timeouts, and error isolation across models.
- Building a financial news summarization agent with LangChainTutorialStep-by-step tutorial to build a financial news summarization agent LangChain that pulls headlines, scores relevance, and generates concise briefs.
- Building a find-similar-products feature with LlamaIndexTutorialHands-on tutorial: implement a find similar products LlamaIndex feature for e-commerce using vector embeddings, metadata filters, and persistence.
- Building a hierarchical agent team in LangGraphHow-toLearn how to build a LangGraph hierarchical agent team with a supervisor and worker agents. Step-by-step code for orchestrating multi-agent workflows.
- Building a hierarchical CrewAI crew with a manager agentHow-toHands-on tutorial for building a CrewAI hierarchical crew manager agent that delegates tasks, with runnable code and verification steps.
- Building a hybrid retriever in Haystack 2.0How-toHands-on hybrid retriever Haystack 2.0 tutorial: wire BM25 and embedding retrievers into one pipeline with rank fusion, then verify results locally.
- Building a legal document Q&A bot with LlamaIndexTutorialStep-by-step tutorial to build a legal document qa bot llamaindex using RAG, from PDF ingestion to cited answers with production-grade LLM fallback.
- Building a local RAG app with Mistral 7B and OllamaHow-toBuild a fully local RAG application using Mistral 7B and Ollama: install models, ingest documents, embed with nomic, retrieve, and generate offline.
- Building a medical literature Q&A agent in LangGraphTutorialHands-on LangGraph tutorial: build a medical literature QA agent for healthcare with document retrieval, LLM answers, and OpenAI-compatible model routing.
- Building a multi-document summary index in LlamaIndexTutorialHands-on llamaindex document summary index tutorial: build a multi-document summary index from scratch, query across docs, and inspect per-doc summaries.
- Building a multi-hop RAG pipeline in Haystack 2.0How-toStep-by-step guide to building a multi-hop RAG pipeline in Haystack 2.0 that retrieves, rewrites queries, and synthesizes answers across two passes.
- Building a multi-tool agent with Vercel AI SDK and n4n.aiTutorialHands-on tutorial to build a Vercel AI SDK multi-tool agent on n4n.ai's OpenAI-compatible gateway, with runnable TypeScript, tool calling, and step logs.
- Building a multi-tool Haystack agent pipelineHow-toA hands-on multi-tool Haystack 2.0 agent pipeline tutorial: define tools, wire an Agent into a Pipeline, run it, and verify tool orchestration.
- Building a multi-turn chatbot with Vercel AI SDK's useChatTutorialHands-on tutorial to build a vercel ai sdk multi-turn chatbot usechat in Next.js: scaffold route, wire client, stream, persist, and verify context.
- Building a multi-turn support bot in LangGraph with memoryTutorialA hands-on tutorial for building a multi-turn LangGraph customer support bot with durable conversation memory via checkpointers, including runnable Python code and expected output.
- Building a portfolio analysis agent with LangGraphTutorialHands-on tutorial: build a portfolio analysis agent with LangGraph step by step: fetch prices, compute risk metrics, and generate LLM-backed reports.
- Building a RAG chain entirely in LCELHow-toStep-by-step lcel rag chain tutorial: load docs, embed with FAISS, and compose retrieval and generation into one LangChain Expression Language chain. Runnable code included.
- Building a RAG evaluation pipeline in LlamaIndexTutorialA hands-on llamaindex rag evaluation pipeline tutorial: build retrieval and response eval with LlamaIndex, pytest, and OpenAI-compatible APIs.
- Building a RAG pipeline with Haystack and n4n.aiHow-toStep-by-step guide to building a Haystack RAG pipeline with n4n.ai as the OpenAI-compatible LLM gateway, covering install, indexing, retrieval, and verified queries.
- Building a ReAct-style agent in Semantic KernelHow-toHands-on react-style agent semantic kernel tutorial: implement a ReAct reasoning loop with FunctionCallingStepwisePlanner, custom plugins, and step-by-step verification.
- Building a regression test suite for Haystack pipelinesHow-toPractical steps to build a regression test suite for Haystack pipelines that catches retrieval and generation drift using pytest, snapshots, and evaluators in CI.
- Building a Semantic Kernel plugin from scratchHow-toStep-by-step guide to build semantic kernel plugin from scratch in C#: define native functions, semantic functions, wire up LLM, and verify with tests.
- Building a shopping assistant agent with LangGraphTutorialBuild a shopping assistant agent langgraph in this hands-on tutorial: product search, recommendation reasoning, and tool-calling with LangGraph.
- Building a Slack support bot with LangChain and RAGTutorialStep-by-step tutorial to build a Slack support bot with LangChain and RAG over your docs, including runnable code and expected output checkpoints.
- Building a stock research agent with LangChainTutorialHands-on tutorial: build a stock research agent LangChain that fetches live prices, financials, and news with tool calling and conversation memory.
- Building a support agent in CrewAI that escalates to a humanTutorialA hands-on practical guide to building a CrewAI support agent with human escalation logic, runnable Python code, and resilient LLM routing.
- Building a tool-calling agent in Haystack 2.0How-toA hands-on haystack 2.0 tool-calling agent tutorial: define tools, configure a chat generator, build a pipeline, and verify function calls end to end.
- Building a trading signal agent with CrewAITutorialBuild a multi-agent trading signal agent with CrewAI that pulls market data, analyzes trends, and emits risk-aware buy/sell signals. Step-by-step tutorial.
- Building a web-research AutoGen team with tool useHow-toA practical step-by-step tutorial for building an autogen web research agent team tool use with live web tools, verifiable output, and OpenAI-compatible models.
- Building a web search agent with LangChain and TavilyHow-toBuild a LangChain web search agent with Tavily and tool calling. Step-by-step setup, runnable code, and verification for production use.
- Building an agent with Semantic Kernel's Agent FrameworkHow-toA hands-on semantic kernel agent framework tutorial: build a task-completing agent with planners, plugins, and OpenAI-compatible endpoints.
- Building an audit trail for legal and healthcare LLM answersHow-toStep-by-step guide to building an audit trail for legal and healthcare LLM answers: capture prompts, responses, sources, and prove compliance.
- Building an AutoGen agent that asks before it actsHow-toLearn how to build an autogen agent ask before acting pattern with human-in-the-loop approval using AutoGen's callable tools and a console input gate.
- Building an enterprise chatbot with Semantic Kernel and .NETHow-toStep-by-step enterprise chatbot semantic kernel .net tutorial: scaffold .NET 8 API, connect OpenAI-compatible LLM gateway, add plugins, memory, guardrails.
- Building an LLM-as-judge evaluator in Haystack 2.0How-toStep-by-step guide to building an llm-as-judge evaluator haystack 2.0 pipeline for scoring RAG answers with runnable code and verification.
- Building long-term memory for Semantic Kernel agentsHow-toA hands-on long-term memory semantic kernel agents tutorial: wire vector stores, embeddings, and retrieval into Semantic Kernel agents for persistent context.
- Building semantic product search with LlamaIndexTutorialStep-by-step tutorial to build semantic product search with LlamaIndex using local embeddings, metadata filters, and persistence for e-commerce catalogs.
- Cache embeddings in LlamaIndex to reduce OpenAI API spendTutorialLearn how to implement a LlamaIndex embedding cache to avoid duplicate OpenAI embedding calls, cut token spend, and speed up RAG pipelines.
- Call 240+ models from LangChain through n4n.aiGuideStep-by-step LangChain guide to routing 240+ models via one OpenAI-compatible gateway, with fallback, metering, and cache-control patterns.
- Chatbot system prompts: a Vercel AI SDK guideGuideA practical guide to designing and implementing Vercel AI SDK chatbot system prompts in Next.js, with code, pitfalls, and tradeoffs for production.
- Choosing a framework for multi-step research agentsGuideA practical guide to selecting the best framework for research agents that perform multi-step tasks, covering state, tools, and tradeoffs.
- Choosing an AI framework for document processing at scaleGuideA practitioner's path to picking the best ai framework for document processing at scale: workload profiling, extraction vs RAG, idempotent batches, and inference fallback.
- Choosing the right chunk size for LangChain RAG appsGuidePractical guide to selecting chunk size for LangChain RAG apps: tradeoffs, code samples, and an ordered path to tune retrieval and context windows.
- Chunk overlap in LangChain: how much is too muchGuidePractical guide to langchain chunk overlap tuning: how to set chunk_size and overlap to balance RAG retrieval quality, token cost, and latency.
- Chunking strategies for LangChain RAG pipelinesGuideA practical guide to langchain rag chunking strategies: fixed-size, semantic, and recursive splitting with code, tradeoffs, and common pitfalls.
- Chunking strategies for LlamaIndex document ingestionGuideStep-by-step llamaindex chunking strategies tutorial: token, sentence, markdown, and semantic splitters with code, pitfalls, and a production checklist.
- Chunking strategies in LangChain: fixed size vs semanticComparisonA practitioner's head-to-head comparison of langchain chunking fixed size vs semantic across cost, latency, ergonomics, and limits, with code and a clear verdict.
- Claude 4.5 vs GPT-5 vs Gemini 2.5 on one LangChain promptComparisonHead-to-head LangChain test of Claude 4.5 vs GPT-5 vs Gemini 2.5 on one prompt: capabilities, cost, latency, ergonomics, limits, and which to choose.
- Code generation and execution agents in AutoGenHow-toStep-by-step guide to building autogen code generation execution agents with AutoGen: configure LLM, run local code executor, verify output, and scale to group chat.
- Comparing CrewAI process types by cost and latencyComparisonHead-to-head CrewAI process type cost latency comparison: sequential vs hierarchical crews across cost, speed, ergonomics, and real-world limits.
- Comparing Haystack document stores for production RAGComparisonA head-to-head comparison of Haystack document stores for production RAG: Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Milvus, and Chroma.
- Comparing models with Haystack evaluation pipelinesComparisonPractical guide to using Haystack evaluation pipelines to compare models head-to-head on cost, latency, and quality with reproducible code.
- Comparing Pinecone and Chroma for LangChain RAG appsComparisonHead-to-head comparison of Pinecone vs Chroma for LangChain RAG apps across cost, latency, ergonomics, and limits, with a verdict per use case.
- Comparing vector stores for Semantic Kernel memoryComparisonA practitioner's head-to-head comparison of vector stores for Semantic Kernel memory across cost, latency, ergonomics, and limits, with a verdict.
- Composing prompt, model, and parser with LCELHow-toLearn how to lcel compose prompt model parser into a single runnable LangChain chain with code, deployment notes, and verification steps for engineers.
- Configure @ai-sdk/openai for n4n.ai in 5 minutesHow-toA practical walkthrough to configure ai-sdk openai provider for n4n.ai using the Vercel AI SDK, including env setup, code, and success checks.
- Configure ChatOpenAI for the n4n.ai unified API endpointHow-toPractical walkthrough to configure ChatOpenAI for the n4n.ai endpoint: set base URL, API key, model routing, and verify a live LangChain call.
- Configure LlamaIndex to use n4n.ai as the LLMHow-toStep-by-step llamaindex configure n4n.ai llm tutorial: point LlamaIndex at the OpenAI-compatible n4n.ai gateway and run a verified query in minutes.
- Configure OAI_CONFIG_LIST for AutoGen with n4n.aiHow-toStep-by-step guide to configuring AutoGen's OAI_CONFIG_LIST for n4n.ai, including runnable JSON and Python examples plus verification steps for engineers.
- Configuring CrewAI's LLM class for n4n.ai endpointsHow-toPractical walkthrough for crewai llm class n4n.ai endpoint config: install, authenticate, instantiate LLM, build a crew, and verify model routing works.
- Configuring Semantic Kernel's OpenAI connector for n4n.aiHow-toStep-by-step guide to point Microsoft Semantic Kernel's OpenAI connector at n4n.ai's OpenAI-compatible gateway, with runnable Python code and verification tips.
- Connect LangChain.js to n4n.ai with the OpenAI packageHow-toStep-by-step guide to connecting LangChain.js to n4n.ai using the OpenAI package, with runnable Node.js code for chat, streaming, and model routing.
- Connect LangChain to n4n.ai using the OpenAI SDK formatHow-toStep-by-step guide to connect LangChain to n4n.ai using the OpenAI SDK format. Configure ChatOpenAI, stream tokens, route models, and verify usage.
- Connect LlamaIndex agents to external APIsHow-toStep-by-step llamaindex agents external api tutorial: build LlamaIndex agents that call real HTTP APIs as tools, with runnable Python code and verification.
- Connect Vercel AI SDK to n4n.ai's OpenAI-compatible APIHow-toStep-by-step guide to connect Vercel AI SDK to n4n.ai API via the OpenAI-compatible endpoint, covering auth, model routing, streaming, and verification.
- Connecting Haystack to n4n.ai with a custom generatorHow-toLearn how to build a Haystack custom generator for n4n.ai to route pipelines through an OpenAI-compatible gateway with fallback and per-token metering.
- Connecting Haystack to Pinecone for vector retrievalHow-toStep-by-step haystack pinecone document store tutorial: install deps, configure Pinecone, embed docs, build a retriever, and verify vector search in Haystack 2.x.
- Connecting Semantic Kernel to Qdrant for vector memoryHow-toStep-by-step semantic kernel qdrant vector memory tutorial: wire SK memory to Qdrant, configure embeddings, store and query vectors with runnable code.
- Continuous evaluation for Haystack pipelines in CIGuideA practical guide to continuous evaluation of Haystack pipelines in CI: metrics, golden datasets, wiring, thresholds, and pitfalls for LLM quality gates.
- Contract review with LangChain: extracting clausesTutorialA hands-on tutorial for contract review LangChain clause extraction: build a pipeline to parse legal PDFs, classify clauses, and export structured JSON.
- Coordinating AutoGen agent teams across GPT-5.1 and Gemini 3How-toHands-on tutorial for engineers coordinating an AutoGen agent team across GPT-5.1 and Gemini 3, with runnable Python code and success checks.
- Cost-aware model routing in Vercel AI SDK appsGuideStep-by-step vercel ai sdk cost-aware model routing: route by task complexity, unify metering via gateway, and avoid common cost traps in production.
- Cost optimization: cheap models per agent with n4n.aiGuidePractical guide to autogen cost optimization cheap models n4n.ai: assign per-agent model tiers, configure gateway, measure token usage in multi-agent flows
- CrewAI agent roles: assigning the right LLM per roleTutorialLearn how to configure CrewAI llm per agent role assignment with runnable code, routing different models to planner, researcher, and writer agents in production.
- CrewAI agent roles: specialist vs generalist designComparisonCompare CrewAI specialist vs generalist agent roles across cost, latency, ergonomics, with a table and verdict for which to use when.
- CrewAI agent roles: tools, memory, and delegation settingsGuideHands-on guide to CrewAI agent roles: configure tools, memory, and delegation settings with Python examples, plus pitfalls and a sane default setup.
- CrewAI agent roles tutorial: goal, backstory, and toolsTutorialHands-on CrewAI tutorial: define agent roles, goals, backstories, and tools step by step to build a working multi-agent crew with runnable Python code.
- CrewAI agent roles tutorial: manager and worker patternsTutorialBuild a working CrewAI hierarchical crew with manager and worker agents. Step-by-step code, prerequisites, and expected output for the manager worker pattern.
- CrewAI and n4n.ai: your first agent in 10 minutesHow-toHands-on crewai n4n.ai first agent tutorial: install CrewAI, point it at the n4n.ai OpenAI-compatible gateway, and run a multi-agent crew in 10 minutes.
- CrewAI BaseTool class explained with a working exampleDefinitionLearn the CrewAI BaseTool class with a working example. Understand its structure, why it matters for agents, and avoid common custom tool mistakes.
- CrewAI custom tools tutorial: building your first toolTutorialHands-on CrewAI tutorial: step by step build, test, and integrate your first custom tool with a live agent, including runnable code and expected output.
- CrewAI custom tools tutorial: web search with SerperTutorialHands-on tutorial: build a crewai serper web search tool from scratch with the Serper API, wrap it for CrewAI agents, and run a live search crew.
- CrewAI example: a crew that drafts and reviews codeHow-toBuild a CrewAI code review crew example that drafts and critiques Python functions. Step-by-step setup, runnable code, and verification tips.
- CrewAI Process.sequential vs Process.hierarchical: a guideComparisonEngineering comparison of CrewAI Process.sequential vs Process.hierarchical across cost, latency, ergonomics, and limits, with a use-case verdict.
- CrewAI quickstart: agents, tasks, and n4n.ai routingTutorialHands-on CrewAI tutorial: build a multi-agent crew and route LLM calls through one OpenAI-compatible gateway with fallback and per-token metering.
- CrewAI real-world example: automated blog writing crewHow-toBuild a working CrewAI blog writing crew example with researcher, writer, and editor agents using OpenAI-compatible LLMs and verify the output locally.
- CrewAI sequential process tutorial: step-by-step tasksTutorialHands-on CrewAI sequential process tutorial: build a linear multi-agent pipeline with tasks that run in order, including runnable code and output.
- CrewAI sequential vs hierarchical process explainedComparisonA practitioner's head-to-head comparison of CrewAI sequential vs hierarchical process across cost, latency, ergonomics, and limits, with a verdict.
- CrewAI setup tutorial: connecting n4n.ai as your LLMHow-toStep-by-step guide to point CrewAI at an OpenAI-compatible inference gateway, configure the LLM client, run a multi-agent crew, and verify token usage.
- CrewAI task design tutorial: chaining task outputsTutorialHands-on CrewAI tutorial: chain task outputs to build multi-step agent workflows. Learn to use Task context, structured outputs, and avoid common pitfalls.
- CrewAI vs AutoGen vs LangGraph: frameworks comparedComparisonHead-to-head crewai vs autogen vs langgraph comparison on capabilities, cost, latency, ergonomics, ecosystem, and limits, with a use-case verdict.
- CrewAI vs AutoGen vs LangGraph: latency and costAnalysisA practitioner's analysis of crewai vs autogen vs langgraph latency cost, covering orchestration overhead, token growth, and decisive tradeoffs for production.
- CrewAI vs AutoGen vs LangGraph: which scales to productionAnalysisA practitioner's analysis of crewai vs autogen vs langgraph production scaling, weighing state management, fault tolerance, and operability to pick a framework.
- Cut LangChain costs with semantic caching via GPTCacheHow-toStep-by-step guide to langchain semantic caching gptcache cost reduction: deploy a semantic cache layer to slash LLM token spend and p95 latency.
- Define tools with Zod schemas in the Vercel AI SDKHow-toLearn how to define tools with Zod schemas in the Vercel AI SDK for type-safe function calling. Step-by-step guide with runnable TypeScript code.
- Deploy a Vercel AI SDK app to Cloudflare WorkersHow-toA practical walkthrough of vercel ai sdk cloudflare workers deployment: scaffold an edge AI app, configure the provider, ship to Workers, and verify.
- Deploy DeepSeek-V3 with vLLM behind a LangChain agentHow-toStep-by-step tutorial: deploy DeepSeek-V3 with vLLM and connect it to a LangChain agent on self-hosted local hardware via OpenAI-compatible API.
- Designing CrewAI tasks: context, output, and expected formatGuidePractical guide to CrewAI task design: structure context, define expected output formats, and avoid common pitfalls when building multi-agent crews.
- E-commerce chatbot: from search to checkout with LangChainTutorialHands-on ecommerce chatbot search to checkout langchain tutorial: build semantic search, tool-calling agent, cart and checkout with runnable Python code.
- Evaluate LlamaIndex retrieval with hit rate and MRRTutorialHands-on llamaindex retrieval evaluation hit rate mrr tutorial: build a vector index, define a dataset, and measure retriever quality with code.
- Evaluating RAG pipelines in Haystack with n4n.ai modelsGuideStep-by-step guide to evaluate RAG pipeline Haystack n4n.ai models with Haystack 2.x, covering setup, evaluation harness, and model swapping pitfalls.
- Force JSON mode in LangChain using n4n.ai modelsHow-toLearn to force JSON mode in LangChain via the n4n.ai OpenAI-compatible gateway, with runnable code for structured output and validation across 240+ models.
- Function calling with GPT-4o via Vercel AI SDK and n4n.aiTutorialStep-by-step tutorial for gpt-4o function calling with Vercel AI SDK via n4n.ai gateway, including runnable code and expected output.
- Generative UI with Vercel AI SDK and React Server ComponentsTutorialPractical vercel ai sdk generative ui rsc tutorial: build streaming React Server Components that render UI from LLM tool calls with runnable code.
- Getting started with AutoGen and n4n.aiTutorialHands-on autogen n4n.ai getting started tutorial: wire Microsoft AutoGen to an OpenAI-compatible gateway, run multi-agent tasks, and track token usage.
- Getting started with CrewAI and n4n.aiTutorialA hands-on tutorial to crewai n4n.ai getting started: install CrewAI, point it at the OpenAI-compatible gateway, and run a multi-agent workflow.
- Getting started with Haystack and n4n.aiTutorialLearn getting started with Haystack and n4n.ai: install the framework, configure the OpenAI-compatible endpoint, and build a chat pipeline.
- Getting started with LangChain.js and TypeScriptGuideA practical, opinionated guide to langchain.js typescript getting started: project setup, first chains, streaming, pitfalls, and provider fallback.
- Getting started with LlamaIndex and n4n.aiTutorialA hands-on llamaindex n4n.ai setup tutorial: configure LlamaIndex to use the n4n.ai OpenAI-compatible gateway for LLM and embeddings in minutes.
- Getting started with Semantic Kernel and n4n.aiGuideStep-by-step setup for getting started with Semantic Kernel and n4n.ai, including Python code, configuration pitfalls, token metering, and routing across 240+ models.
- Getting started with Vercel AI SDK and n4n.aiTutorialPractical step-by-step tutorial to build a streaming chat app with Vercel AI SDK and n4n.ai's OpenAI-compatible gateway, from scaffold to working UI.
- Give a LlamaIndex agent custom Python toolsHow-toA practical llamaindex custom tools tutorial: build Python functions, wrap them as tools, and wire them into a LlamaIndex agent that calls them reliably.
- Haystack agent pipeline tutorial: n4n.ai function callingTutorialBuild a Haystack 2.0 agent pipeline with function calling via the n4n.ai OpenAI-compatible gateway. Step-by-step code, tools, and expected output.
- Haystack setup tutorial: connecting n4n.ai generatorsHow-toStep-by-step guide to configure Haystack's OpenAIChatGenerator with n4n.ai's OpenAI-compatible endpoint, including code, verification, and production tips.
- HIPAA-aware document Q&A: a LangChain tutorialTutorialHands-on tutorial for building a HIPAA-aware document Q&A system with LangChain: vector search, PHI redaction, and compliant LLM routing for healthcare teams.
- How to avoid vendor lock-in with LangChain or LlamaIndexGuidePractical steps to avoid llm vendor lock-in langchain llamaindex by routing both frameworks through one OpenAI-compatible gateway with code.
- How to connect Haystack to n4n.ai's OpenAI-compatible APIHow-toA practical guide to connect Haystack to n4n.ai's OpenAI-compatible API, configure generators, and ship a RAG pipeline with built-in fallback.
- How to swap GPT-5 for Claude Opus 4.5 in LangChainHow-toLearn how to swap GPT-5 for Claude Opus 4.5 in LangChain using native Anthropic SDK or an OpenAI-compatible gateway, with code and verification steps.
- Ingest PDFs into LlamaIndex with n4n.ai embeddingsHow-toA step-by-step llamaindex pdf ingestion n4n.ai tutorial: wire LlamaIndex to an OpenAI-compatible embeddings endpoint and index PDFs into a vector store.
- Install AutoGen and connect it to n4n.ai's APIHow-toStep-by-step guide to install AutoGen and connect n4n.ai API: set up Python, configure OpenAI-compatible endpoint, run multi-agent chat, verify.
- Install CrewAI and run your first crew with n4n.aiHow-toPractical walkthrough to install CrewAI and run your first crew using the n4n.ai OpenAI-compatible endpoint, with runnable code and verification steps.
- Install Haystack 2.0 and run your first pipelineHow-toA practical walkthrough to install Haystack 2.0 and build a runnable RAG pipeline, from environment setup to verifying output with real code.
- Integrating CrewAI with n4n.ai's function calling modelsHow-toStep-by-step guide to wiring CrewAI agents to n4n.ai's OpenAI-compatible function calling models, with runnable code for custom tools and verification.
- LangChain document loaders: PDF, HTML, and MarkdownGuidePractical guide to langchain document loaders pdf html markdown: install, load each format with code, dodge pitfalls, and chunk for retrieval.
- LangChain Expression Language: a beginner's guideGuideA practical langchain expression language beginner guide: build composable LLM chains with LCEL, avoid common pitfalls, and ship faster.
- LangChain function calling for data extractionTutorialHands-on langchain function calling structured extraction tutorial: use Pydantic and with_structured_output to parse text into typed objects.
- LangChain.js quickstart: your first chat chain in NodeTutorialA hands-on langchain.js quickstart node chat chain tutorial: build a runnable Node.js chat chain with LangChain.js, from setup to streaming responses.
- LangChain.js tool calling with GPT-4o via n4n.aiHow-toHands-on tutorial for LangChain.js tool calling with GPT-4o via n4n.ai: wire OpenAI-compatible models to LangChain tools in Node with working TypeScript code.
- LangChain memory types explained: buffer vs summaryDefinitionA practical definition of LangChain memory types buffer vs summary: how buffer and summary memory work, code samples, and when to use each for engineers building LLM chat systems.
- LangChain plus n4n.ai: your first chat completion callTutorialThis langchain n4n.ai chat completion tutorial walks through a runnable LangChain setup against an OpenAI-compatible gateway with streaming and usage metering.
- LangChain quickstart: n4n.ai as your model providerGuideStep-by-step LangChain quickstart: use n4n.ai as your model provider via one OpenAI-compatible endpoint, route 240+ models, and manage fallback and caching.
- LangChain RAG tutorial: Chroma plus n4n.ai modelsTutorialBuild a retrieval-augmented generation pipeline with LangChain and Chroma using n4n.ai models via an OpenAI-compatible endpoint. Hands-on tutorial with runnable code.
- LangChain ReAct agents: a hands-on tutorialTutorialHands-on langchain react agent tutorial: build a ReAct agent with tools, step-by-step code, and expected outputs using LangChain's current API.
- LangChain RecursiveCharacterTextSplitter explainedDefinitionA precise technical breakdown of LangChain's RecursiveCharacterTextSplitter: how its hierarchical separator recursion works, key params, and common pitfalls.
- LangChain RunnableWithFallbacks explainedTutorialHands-on langchain runnablewithfallbacks tutorial: build resilient multi-model LLM chains with graceful degradation using LangChain's RunnableWithFallbacks and runnable code.
- LangChain RunnableWithMessageHistory tutorialTutorialHands-on langchain runnablewithmessagehistory tutorial: build stateful LCEL conversational chains with custom memory backends and OpenAI-compatible APIs.
- LangChain streaming with Claude 3.5 Sonnet via n4n.aiHow-toPractical steps to implement langchain streaming claude 3.5 sonnet n4n.ai: install deps, point ChatOpenAI at gateway, write callbacks, verify tokens.
- LangChain streaming with n4n.ai and Server-Sent EventsHow-toStep-by-step guide to langchain streaming n4n.ai sse with FastAPI and EventSource, covering config, SSE wiring, error handling, and verification.
- LangChain vs LlamaIndex: cost per query on GPT-5 and ClaudeAnalysisEngineering analysis of langchain vs llamaindex rag cost per query on GPT-5 and Claude: where token waste hides in retrieval and how to cut it.
- LangChain vs LlamaIndex for RAG: how to choose in 2026ComparisonA pragmatic 2026 engineering comparison of LangChain and LlamaIndex for RAG across capabilities, cost, latency, ergonomics, and ecosystem to help you choose.
- LangChain with_structured_output explained step by stepTutorialHands-on langchain with_structured_output tutorial: define Pydantic schemas, bind to chat models, parse LLM responses, and avoid common pitfalls.
- LangGraph state machines explained with a code exampleDefinitionA precise LangGraph state machine explained: how graph-based agent orchestration works, with a runnable Python example and common pitfalls to avoid.
- LangGraph supervisor pattern: coordinating multiple agentsGuidePractical guide to the LangGraph supervisor pattern for multi-agent systems: architecture, code, pitfalls, and tradeoffs for production orchestration.
- LCEL pipe syntax explained with real examplesDefinitionLCEL pipe syntax explained: how LangChain's | operator composes runnables into chains, with working Python examples, debugging tips, and fixes for common mistakes.
- LCEL with n4n.ai: routing between GPT-4o and Llama 3.3How-toStep-by-step guide to implementing lcel n4n.ai model routing gpt-4o llama with LangChain Expression Language for cost-efficient and resilient LLM inference.
- LlamaIndex chat engines explained: context vs condenseComparisonCompare LlamaIndex ContextChatEngine and CondenseChatEngine in this llamaindex chat engine tutorial: cost, latency, ergonomics, and verdicts.
- LlamaIndex data connectors: a complete overviewGuideA practical llamaindex data connectors overview tutorial: build ingestion pipelines from local files, APIs, and databases with real code and pitfalls.
- LlamaIndex FunctionAgent tool calling explainedGuideA practical llamaindex functionagent tool calling tutorial: build agents with function tools, handle schemas, streaming, and avoid common pitfalls.
- LlamaIndex ingestion pipeline explained step by stepGuideA practical llamaindex ingestion pipeline tutorial: build a robust data connector and transformation flow with code, pitfalls, and tradeoffs for engineers.
- LlamaIndex PropertyGraphIndex explainedGuidePractical llamaindex propertygraphindex tutorial: build, persist, and query knowledge graphs from docs with hybrid retrieval, plus pitfalls and tradeoffs.
- LlamaIndex quickstart: n4n.ai API key setupTutorialStep-by-step llamaindex n4n.ai api key setup tutorial: configure LlamaIndex with the n4n.ai OpenAI-compatible endpoint and run a query in minutes.
- LlamaIndex RelevancyEvaluator explainedGuideA hands-on llamaindex relevancyevaluator tutorial covering setup, minimal code, output schema, judge model tradeoffs, and common pitfalls when evaluating RAG retrieval quality.
- LlamaIndex SimpleDirectoryReader for local file ingestionTutorialHands-on llamaindex simpledirectoryreader tutorial: load local files into LlamaIndex, filter by type, add metadata, and split into nodes with runnable code.
- LlamaIndex tutorial: a support bot for your help docsTutorialBuild a RAG-powered llamaindex support bot help docs pipeline with Python, from indexing to query, using OpenAI-compatible LLMs and embeddings.
- LlamaIndex VectorStoreIndex query engine explainedGuideA practical llamaindex vectorstoreindex query engine tutorial: build the index, configure models, tune retrieval, run RAG queries, and avoid common pitfalls.
- Migrating a raw OpenAI SDK app to support multiple providersHow-toLearn how to migrate openai sdk app to multi-provider support with concrete steps, runnable code, and verification tips for backend engineers.
- Migrating the raw OpenAI SDK to LangChain step by stepHow-toStep-by-step guide to migrate OpenAI SDK code to LangChain without rewrites. Swap clients, adopt chains, and verify behavior with runnable examples.
- Mix GPT-4o and Claude 3.5 Sonnet per agent via n4n.aiHow-toLearn to autogen mix gpt-4o claude 3.5 sonnet agents n4n.ai: configure per-agent models in AutoGen via a single OpenAI-compatible gateway endpoint.
- Multi-agent research assistant using LangGraph and n4n.aiHow-toBuild a langgraph n4n.ai multi-agent research assistant with LangGraph: step-by-step setup, runnable code, and verification for engineers shipping LLM systems.
- Multi-model fallback in LangChain with n4n.aiGuideA practical langchain multi-model fallback tutorial: wire LangChain to an OpenAI-compatible gateway, configure ordered fallbacks, and handle real failure modes.
- Multi-model switching in Vercel AI SDK using n4n.ai routingTutorialLearn how to implement vercel ai sdk multi-model switching n4n.ai routing with fallback, per-token metering, and client routing directives in a runnable tutorial.
- Multi-provider apps: GPT-5, Claude, Gemini, and Llama 4ComparisonA head-to-head comparison of GPT-5, Claude, Gemini, and Llama 4 for multi-provider LLM framework integration across cost, latency, and ergonomics.
- Multimodal RAG in LlamaIndex with GPT-4o and imagesTutorialBuild a working multimodal RAG pipeline in LlamaIndex with GPT-4o and images. Step-by-step code for indexing and querying visual documents.
- Ollama vs vLLM for serving Llama 4 in production appsComparisonPractical comparison of Ollama vs vLLM for serving Llama 4 in production: capabilities, cost, latency, ergonomics, limits, and which to choose.
- One API, every model: routing GPT-5 and Gemini in LangGraphGuidePractical guide to route GPT-5 and Gemini in LangGraph: build a multi-provider agent with conditional routing, unified gateways, and avoid common pitfalls.
- One endpoint for GPT-5, Claude, Gemini, and LlamaGuidePractical guide to routing GPT-5, Claude, Gemini, and Llama through one OpenAI-compatible endpoint: client setup, model IDs, fallback, and cost control.
- One LangChain client for OpenAI, Anthropic, and n4n.aiGuideA practical langchain multi-provider setup tutorial for routing OpenAI, Anthropic, and n4n.ai through one client, with code and production pitfalls.
- Point LangChain at n4n.ai instead of the OpenAI APIHow-toLearn how to point LangChain at n4n.ai instead of the OpenAI API with a drop-in base URL change, keeping your code intact while accessing 240+ models.
- Prompt caching in LangChain: cut Claude costs by 90%TutorialHands-on LangChain tutorial: use Anthropic prompt caching with Claude to slash input token costs by 90%. Step-by-step code, usage metrics, and gotchas.
- RAG pipeline: LangChain vs LlamaIndex step by stepTutorialStep-by-step tutorial to build RAG pipeline LangChain vs LlamaIndex with runnable code, comparing retrieval and generation tradeoffs for engineers.
- Real-time voice apps with LiveKit Agents and WhisperHow-toStep-by-step guide to building a production-ready real-time voice app with LiveKit Agents and Whisper STT, including LLM wiring and TTS.
- Redacting PHI before it reaches your LLM in LangChainHow-toStep-by-step guide to implement a redact phi langchain llm pattern that removes protected health information from healthcare docs before LLM calls.
- Route LangChain requests by cost using n4n.aiHow-toLearn to build a cost-aware LangChain router that selects models per request via a unified OpenAI-compatible gateway in this hands-on tutorial.
- Run Llama 4 Maverick locally with Ollama and LangChainHow-toPractical steps to run Llama 4 Maverick with Ollama and LangChain locally, from Ollama install to verified streaming chat in Python.
- Running Vercel AI SDK on the Edge runtimeHow-toA practical vercel ai sdk edge runtime tutorial: deploy streaming LLM routes on Vercel Edge with fetch-based providers, real code, and verification steps.
- Semantic Kernel agent tutorial with n4n.ai function callingTutorialHands-on tutorial: build a Semantic Kernel ChatCompletionAgent that calls Python functions via the n4n.ai OpenAI-compatible gateway. Step-by-step code.
- Semantic Kernel enterprise tutorial: n4n.ai cost trackingGuidePractical guide to implementing semantic kernel enterprise cost tracking with per-token metering on an OpenAI-compatible gateway for .NET enterprise apps.
- Semantic Kernel memory tutorial: embeddings with n4n.aiTutorialHands-on Python tutorial for Semantic Kernel memory: configure embeddings via n4n.ai's OpenAI-compatible endpoint, build an in-memory vector store, query.
- Semantic Kernel plugins tutorial: n4n.ai function callingTutorialBuild semantic kernel plugins function calling n4n.ai: a hands-on Python tutorial to wire native functions to an OpenAI-compatible gateway with runnable code.
- Semantic Kernel plugins tutorial: writing native functionsTutorialHands-on semantic kernel plugins native functions tutorial: build Python native function plugins, register them, invoke from kernel or LLM, with runnable code.
- Semantic Kernel setup tutorial: connecting to n4n.aiTutorialHands-on Python guide to a semantic kernel n4n.ai setup tutorial: connect Semantic Kernel to the OpenAI-compatible gateway with runnable code.
- Set up LangChain with n4n.ai in under 10 minutesHow-toLearn how to set up LangChain with n4n.ai using its OpenAI-compatible endpoint, with runnable code, config, and verification steps.
- Speech-to-speech apps with the OpenAI Realtime APIGuideA practical openai realtime api speech-to-speech tutorial: build low-latency voice apps with WebSockets, handle audio, and avoid common pitfalls.
- Stream LangChain responses token by tokenHow-toA practical langchain stream tokens tutorial: wire up token-by-token streaming from LLMs to your app with callbacks, async generators, and verification steps.
- Streaming responses from Vercel Edge Functions with AI SDKHow-toLearn how to implement vercel edge functions ai sdk streaming end to end, from project setup to streaming completions on the edge runtime.
- Structured output in LangChain with Pydantic and n4n.aiGuideStep-by-step langchain structured output pydantic tutorial: bind typed schemas to LLM calls, validate responses, and handle provider failures in production.
- Swapping OpenAI for n4n.ai in a Haystack RAG pipelineHow-toA practical how-to for engineers to swap OpenAI for n4n.ai in a Haystack RAG pipeline using OpenAI-compatible endpoints, with runnable code and verification steps.
- Switch between GPT-4o and Claude 3.5 Sonnet in Vercel AI SDKHow-toPractical walkthrough to vercel ai sdk switch gpt-4o claude 3.5 sonnet dynamically, with install steps, Next.js route code, and verification.
- Switch LlamaIndex from OpenAI to n4n.aiHow-toHands-on steps to migrate LlamaIndex from OpenAI to any OpenAI-compatible API, with code for LLM, embeddings, and success checks.
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