LangChain Trainings

LangChain, a widely used tool Python framework for LLM applications

LangChain is the open source Python framework (with a JavaScript/TypeScript variant) that has established itself as a widely used tool for developing AI applications based on large language models (LLM, Large Language Model) such as GPT-4, Claude, Gemini, Llama, Mistral, or self-hosted models. Created in 2022 by Harrison Chase, LangChain is now maintained by LangChain Inc. and its open source ecosystem. It provides Python developers with a unified toolbox to orchestrate LLMs: chains of treatments, autonomous agents, external tools, conversational memory, RAG (Retrieval Augmented Generation), evaluations, and observability.

The LangChain ecosystem now goes beyond the framework alone. LangSmith is the monitoring, debugging and evaluation platform for LLM applications in production (traces, prompts, latency, cost, response quality). LangGraph is the state graph orchestration extension for more advanced multi-agent architectures (workflows, conditional branching, parallelism, supervisions). LangServe allows exposing LangChain chains as HTTP APIs. Together they form a coherent stack to go from proof of concept to production. LangChain is compatible with the main LLM providers (OpenAI, Anthropic, Azure OpenAI Service, Google Vertex AI, AWS Bedrock, Hugging Face, Ollama, vLLM) and with the main vector databases (Pinecone, Weaviate, Qdrant, Chroma, pgvector, Azure AI Search).

In French-speaking Switzerland, the LangChain ecosystem is highly demanded in nascent AI teams within Geneva private banks, Lake Geneva international organisations, fiduciary firms, cantonal administrations, university hospitals, insurance, specialised AI agencies and industrial companies. AI engineer, LLM developer, AI engineer, MLOps engineer, AI architect and applied data scientist profiles are highly sought-after. Typical use cases include business chatbots, internal documentary assistants (RAG on SharePoint, Confluence, intranet base), specialised copilots, automation agents, and internal content generation. ITTA offers a dedicated LangChain catalogue, delivered by active expert AI trainers.

Following recognised LangChain training opens access to highly demanded skills: LLM application development in Python, RAG pipeline design, autonomous agent setup, multi-agent orchestration with LangGraph, observability with LangSmith, deploying LangChain chains in production, integration with Azure OpenAI, OpenAI or Anthropic, and prompt security and governance best practices. Whether you are starting in LLM development or structuring a complex multi-agent architecture, our LangChain training in Geneva and Lausanne covers the full path.

The ITTA LangChain catalogue

Developing LLM and RAG Applications with LangChain

The Developing LLM and RAG Applications with LangChain training is dedicated to LLM application development in Python with LangChain. The programme covers Python environment installation and configuration, framework introduction, chains (LCEL, Runnable, prompts templates, output parsers), chat models and embedding models, vector databases, the RAG pattern (chunking, indexing, retrieval, generation), memory stores, tools, simple agents, LangSmith observability, prompt security and integration with OpenAI, Anthropic and Azure OpenAI. The training targets Python developers upskilling on generative AI, data scientists, beginner AI engineers and AI architects.

Designing a Multi-Agent AI Architecture

The Designing a Multi-Agent AI Architecture training deepens the design and industrialisation of multi-agent architectures. The programme addresses agent patterns (ReAct, Plan-and-Execute, Routing, Supervisor), LangGraph for state graphs, shared memory and contexts, agent specialisation (writing, analysis, code, research), supervision and safety patterns, error handling and recovery, cross-LLM orchestration (OpenAI, Anthropic, Azure OpenAI), fine observability with LangSmith and production deployment. The training targets confirmed AI engineers, AI architects and MLOps profiles industrialising complex AI assistants.

RAG and vector databases

The RAG (Retrieval Augmented Generation) pattern is one of widely used architectures to anchor an LLM on internal business data (documents, knowledge bases, code, archives). LangChain offers connectors with the main vector databases (Pinecone, Weaviate, Qdrant, Chroma, pgvector, Azure AI Search) and with the main embedding providers. RAG best practices (adapted chunking, hybrid retrieval, re-ranking, evaluation) are at the heart of the Developing LLM and RAG Applications with LangChain training.

Agents, tools and orchestration

Agents are the building blocks that allow an LLM to act in an environment (launching a web search, querying a database, executing Python code, calling a business API). LangChain and LangGraph offer a rich palette of agent patterns (ReAct, function calling, tool calling, plan and execute, supervisor) and memory mechanics. These topics are deepened in the Designing a Multi-Agent AI Architecture training.

Building your LangChain path

For Python developers starting in LLM, the Developing LLM and RAG Applications with LangChain training is a frequent entry point. For confirmed AI engineers wanting to move toward multi-agent and advanced architecture, the Designing a Multi-Agent AI Architecture training is the target. For Python profiles who do not yet have the basics, the Python sub-publisher of the ITTA catalogue is the preliminary step. For organisations wanting to industrialise on Azure, the Azure OpenAI Service sub-publisher perfectly complements the path. Our pedagogical team guides you according to your role.

Featured courses in this catalogue

Here is a selection of reference training courses in this catalogue, accessible directly:

• Developing LLM and RAG Applications with LangChain
• Designing a Multi-Agent AI Architecture

LangChain and the publisher ecosystem at ITTA

LangChain fits into the full ITTA AI and Python ecosystem. The Python publisher covers the fundamentals of the language used by LangChain. The ITTA Artificial Intelligence publisher regroups the full ITTA AI catalogue (paths, introduction courses, certifications). The OpenAI publisher covers GPT models, OpenAI API and associated best practices, and Anthropic covers Claude, its API and best practices. The Azure OpenAI Service publisher covers the Azure deployment of GPT-4 models and the Foundry ecosystem.

LangChain trends in 2026

LangChain evolves with several structuring axes in 2026. LangGraph ramps up as a standard for multi-agent architectures and AI workflow supervision. LangSmith becomes essential for observability and evaluation of LLMs in production (tracing, evals, dataset management). RAG patterns become more sophisticated (hybrid search, re-ranking, agentic RAG, GraphRAG). Multi-LLM coexistence (OpenAI, Anthropic, Azure OpenAI, local open source models Ollama/vLLM) becomes a standard to optimise costs and quality. Security challenges (prompt injection, data exfiltration, governance) and compliance (GDPR, nFADP, AI Act) are at the heart of architectures. Our pedagogical content regularly integrates these evolutions to remain aligned with current practices.

LangChain training in Geneva, Lausanne and online

All our LangChain courses are available on-site in our Geneva and Lausanne centres, as well as in interactive virtual classroom with an expert AI trainer live. Our sessions are organised in 5-week cycles. Each session includes hands-on Python labs with LangChain, chains to build, RAG pipelines to index and query, simple agents to deploy, multi-agent architectures with LangGraph to orchestrate, integrations with OpenAI, Anthropic and Azure OpenAI, LangSmith observability to analyse, and concrete cases inspired by projects in banking, administration, health and industry. Customised corporate training is also possible at your premises, in Geneva, Lausanne, Vaud and across French-speaking Switzerland, with a programme adapted to your stack (LLMs used, vector databases, cloud infrastructure) and your constraints (GDPR, nFADP, banking secrecy, medical secrecy).

Why choose ITTA

ITTA offers a training catalogue and structures a dedicated LangChain AI dev catalogue. Our LangChain trainers are AI engineers, AI architects and data scientists active with Swiss and international clients, covering LangChain (chains, agents, RAG, memory), LangGraph, LangSmith, and more broadly LLM orchestration with OpenAI, Anthropic and Azure OpenAI. The LangChain catalogue regroups Developing LLM and RAG Applications with LangChain and Designing a Multi-Agent AI Architecture. Our pedagogical team supports you in choosing the path, identifying prerequisites (intermediate Python, LLM notions, Python dev environment) and identifying funding solutions adapted to your professional situation.

Our pedagogical approach favours learning by doing, with Python code to write from the first hours, chains to build, RAG pipelines to index, agents to deploy, multi-agent architectures to orchestrate, LangSmith traces to analyse, and concrete cases inspired by real AI projects in French-speaking Switzerland. Each session combines training time, applied exercises and exchanges with the trainer, allowing each participant to progress at their own pace and leave with skills directly usable in their professional context.

Our training is aimed at varied audiences: Python developers upskilling on generative AI, data scientists, beginner and confirmed AI engineers, AI architects, MLOps profiles, AI engineers, nascent AI teams in banking, fiduciary, hospital and industry. Our pedagogical team adapts the content to the participants’ context.

FAQ

Do I need to already program in Python to follow LangChain?

Yes, an intermediate Python level (variables, types, functions, classes, pip package management, file manipulation and HTTP requests) is expected to comfortably follow LangChain training. If you are starting in Python, the Python sub-publisher of the ITTA catalogue offers the preliminary fundamentals. Our pedagogical team checks your prerequisites and guides you toward a Python refresher if needed.

What is the difference between LangChain and LangGraph?

LangChain is the historical framework to orchestrate LLM treatment chains (LCEL, prompts, retrievers, simple agents). LangGraph is the state graph extension for more advanced multi-agent architectures (workflows, supervision, branching, parallelism). LangGraph relies on LangChain and is typically used for cases where simple agents are no longer enough. The Designing a Multi-Agent AI Architecture training covers LangGraph in depth.

Is LangChain compatible with Azure OpenAI and Anthropic?

Yes, LangChain offers native connectors for OpenAI, Anthropic, Azure OpenAI Service, Google Vertex AI, AWS Bedrock, Hugging Face, Ollama, vLLM and many other LLM providers. Training covers multi-LLM coexistence best practices and provider switching according to use cases (cost, latency, quality).

Are your LangChain courses available for companies?

Yes, the full LangChain catalogue is available in-house, in Geneva, Lausanne and in virtual classroom, with a programme adapted to your stack (LLMs used, vector databases, Azure/AWS/GCP/on-prem cloud infrastructure) and your compliance constraints (GDPR, nFADP, banking secrecy, medical secrecy). Our team builds the specifications with you and organises sessions according to your calendar.