Building Production AI Applications with LangChain and LangGraph Training

This training is designed for senior software engineers, AI engineers, ML engineers, and platform developers who want to build production-grade agentic AI applications using the LangChain ecosystem with multi-provider abstraction and a framework-driven approach. At the heart of the program is the following approach: learning LangChain is not simply copying 'chain' or 'agent' examples. Real engineering value comes from mastering composable runnable architecture with LCEL, using multi-provider abstraction as an anti-vendor-lock-in strategy, deeply grasping LangGraph's stateful-execution paradigm with cycle, channel, and reducer logic, designing multi-agent patterns like supervisor / hierarchical / network on real production scenarios, building an observability + evaluation framework with LangSmith, performing FastAPI-based deployment with LangServe, and ensuring cost-aware production performance with caching + async patterns.

The LangChain ecosystem has matured rapidly over the past three years and as of 2026 is structured around five main products: LangChain Core (chain primitives and the Runnable interface), LangChain Integrations (model, embedding, vector store, and tool integrations), LangGraph (stateful multi-actor applications), LangSmith (observability + evaluation), LangServe (FastAPI deployment), and LangChain Hub (prompt + agent sharing). This training addresses these five products not as separate silos but as an integrated AI application engineering framework. LangChain courses exist in Turkey, but the majority remain at the level of LangChain Core fundamentals; training that addresses critical components like LangGraph multi-agent architecture, the LangSmith evaluation framework, and LangServe production deployment end to end is virtually nonexistent. This program is designed to fill that gap as Turkey's most comprehensive production-grade LangChain reference training.

A strategic dimension of the program is positioning LangChain's place in the agentic AI ecosystem by comparing it with other frameworks and SDKs. Head-to-head comparisons are made with alternatives like Anthropic Claude Agent SDK, OpenAI Agents SDK, AutoGen, and CrewAI; the strengths and weaknesses of each approach and which project type calls for which are analyzed in detail. LangChain's main differentiator is multi-provider abstraction: working with a uniform interface across OpenAI, Anthropic, Google, Mistral, AWS Bedrock, Vertex AI, Azure OpenAI, and self-hosted models (Ollama, vLLM, LM Studio); building anti-vendor-lock-in multi-provider routing via modifiers like with_fallbacks and init_chat_model. This approach offers a strategic advantage for enterprise AI engineering teams.

The LCEL (LangChain Expression Language) and Runnable architecture module, which forms the technical backbone of the training, enables a deep understanding of the framework's composability discipline. Standard methods like invoke, batch, stream, ainvoke, abatch, astream; composition patterns like RunnablePassthrough.assign(), RunnableParallel, RunnableBranch; and reliability modifiers like with_fallbacks, with_retry, with_config are addressed hands-on. With this architecture, complex AI applications turn into type-safe, test-friendly codebases built from small composable parts. It is shown in detail that the pipe operator is not just syntactic sugar; it is the foundation of making chains introspectable, observable, and runtime-configurable.

The embeddings, vector stores, and retriever module addresses LangChain's strongest layer — RAG patterns. Comparisons of OpenAIEmbeddings, VoyageAIEmbeddings, and Turkish-optimized multilingual-e5, jina-embeddings-v3, and bge-m3 models; the right selection criteria for Qdrant, Pinecone, Weaviate, pgvector, and Chroma vector-store integrations; and retriever patterns like MultiQueryRetriever, EnsembleRetriever, ContextualCompressionRetriever, ParentDocumentRetriever are addressed in detail. In the RAG module, document loaders (PDF, Notion, Confluence), text-splitter strategies (recursive, semantic, markdown header), advanced RAG patterns (HyDE, Self-RAG, Corrective RAG, Adaptive RAG), and RAG evaluation with the RAGAS framework are covered end to end.

The backbone of the program is the three modules dedicated to LangGraph. LangGraph is a framework designed for stateful, cyclic, and multi-actor applications beyond classic LangChain chain architecture. The training addresses end to end the StateGraph structure from TypedDict state schema to add_node / add_edge / add_conditional_edges APIs; message accumulation via Annotated[list, add_messages] through custom reducers; cycle detection and infinite-loop prevention. As multi-agent patterns, Supervisor (including the create_supervisor helper), Hierarchical team-of-teams, Network peer-to-peer, Plan-and-Execute, and ReAct agent are addressed hands-on. Rapid prototyping with prebuilt create_react_agent and modular agent design with subgraph composition are also part of this module.

The checkpointing, persistence, and human-in-the-loop module matures LangGraph's production-grade stateful-execution architecture. Checkpointer implementations like SqliteSaver, PostgresSaver, and RedisSaver; conversation persistence via threads and thread_id; time-travel debugging and state branching with get_state_history; retroactive correction with update_state; human-in-the-loop approval flows via the interrupt mechanism; integration of human approvals through Slack or a web UI; resumable execution and state restoration are addressed hands-on. This discipline transforms agent applications from merely working prototypes into resilient, auditable, and operationalizable systems.

The observability and evaluation module with LangSmith represents the production-discipline dimension of the training. LANGSMITH_TRACING configuration, the anatomy of runs and traces (inputs, outputs, metadata, feedback), production debugging with custom metadata and tags, dataset creation and gold-answer-set management, built-in evaluators (LLM-as-judge, exact match, regex, embedding similarity), regression testing and A/B experiment setup, and online evaluation and continuous monitoring are addressed end to end. LangSmith is the observability backbone of the LangChain ecosystem and the core tool for abandoning the habit of 'blindly deploying' in production AI applications.

The production deployment module with LangServe teaches the discipline of turning LangChain runnables and LangGraph agents into FastAPI-based production endpoints. Attaching a runnable to a FastAPI app with add_routes; Pydantic input/output schemas and type safety; configuration of stream, batch, and playground endpoints; JWT, API key, OAuth2 authentication; rate limiting, CORS, gzip compression; OpenTelemetry distributed tracing; multi-tenant deployment and thread-aware routing; Vercel, AWS, GCP, Kubernetes topologies are addressed in detail. In the cost-optimization module, production performance optimization with LLM caching (InMemoryCache, RedisCache, SQLiteCache), embedding caching, semantic cache, async batching, parallel execution, and model routing is taught end to end.

In the capstone project, each participant designs an end-to-end production-grade LangGraph multi-agent application tailored to their own use case: end-to-end stack (Core + Graph + Smith + Serve), RAG and tool-calling integration, memory and human-in-the-loop, LangSmith eval framework, LangServe deployment topology, and a 90-day operational roadmap. By the end of the training, participants reach a level of technical and architectural competence to manage the LangChain ecosystem as an integrated framework, design composable runnable architecture with LCEL, build stateful multi-agent systems with LangGraph, apply the observability + evaluation discipline with LangSmith, perform production deployment with LangServe, and manage provider-agnostic AI applications at enterprise scale. The training consists of 3 days, 12 modules, and over 80 hands-on lessons.