LangChain training, 1-on-1.You build LLM apps and agents.
This LangChain training in 1-on-1 puts an expert on your code to work through what matters: clean chains with LCEL, a RAG over your own data, agents with tools and LangGraph, and LangSmith observability all the way to prod. We start from your real app, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
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Claude CodeClaude CoworkClaude Design
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Gamma
GeminiWe ship LangChain apps for real clients, not just in theory.
Most LangChain trainings are videos recorded by people who followed the quickstart the night before, on a version that has already changed. At Hack'celeration it's the opposite: wiring LCEL chains, standing up RAG over knowledge bases, building agents with LangGraph and plugging them into LangSmith, that's our daily agency work. We ship LangChain agents and RAG for clients every week. Everything we teach you, we practice on shipped projects. We know the traps (the RAG that hallucinates from bad chunking, the agent that spins in an infinite loop) because we've already solved them.
- We ship LangChain agents and RAG for clients every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, LLM beginner or seasoned dev
- We tell you when LangChain is overkill (sometimes a plain API call or LlamaIndex is enough)
- We start from your real app and your actual data, not a dummy example
The LangChain training rests on four concrete pillars.
Badly handled, LangChain means tutorials that no longer compile, a RAG that hallucinates, and an agent that burns your token budget. Most of the trouble comes from method, not the framework. We pick up your real code and work through the four pillars together.
- Chains and LCEL
Clean chains, with LCEL and the fundamentals
We start with the foundations: prompts, models, output parsers, and the LangChain Expression Language (LCEL) to wire them together with the pipe operator. You understand what a Runnable is, how to chain, stream and run things in parallel, and why LangChain often shifts syntax between versions. We start from your real app idea, not a hello world. You leave with a first chain that runs and that you know how to grow.
See the chains - RAG over your data
Sourced answers, over your own documents
RAG is the number one use case for LangChain. We load your real documents (PDFs, knowledge base, Notion, website), split them cleanly, embed them and store them in a vector store (Chroma, pgvector, Pinecone). Then we wire the retrieval so your LLM answers grounded in your data, with sources. We handle chunking, metadata and re-ranking. You finish with a RAG that stops making things up.
See the RAG - Agents, tools and LangGraph
Agents that act, orchestrated with LangGraph
We move from text to action. You learn tool calling so your agent invokes your functions and APIs, and you build multi-step workflows with LangGraph: stateful graphs, loops, conditional branches, a human in the loop. It's far more reliable than a ReAct agent left to run free. We set the guardrails so it doesn't spin in an infinite loop or burn your token budget.
See the agents - LangSmith and production
Ship your app to prod, observed with LangSmith
An LLM app without observability is a black box you can't debug. We wire LangSmith to trace every call, see the real prompts, tokens, latency and cost, and set up evals to measure whether you're improving. Then we deploy: handling keys, streaming, errors and budget. We also tell you when LangChain is overkill for your case. You leave with an app you can watch, not a prototype that breaks silently.
Talk to a trainer
Meet our trainers, leave with a plan.
Drop your email. We get back to you to connect you with a Hack'celeration-certified trainer: we look at your app idea, spot what's blocking you on LangChain, and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your project and your LangChain architecture
- The first actions to take, in priority order
- The right 1-on-1 format for your level and your goals
- An honest take: LangChain, LlamaIndex or no-code for your case
Your LangChain program, step by step.
Five steps, no skipping. Each one on your real code, with a clear deliverable. From the first session we frame your app idea and wire your first chain. By the end, you build and ship your LangChain apps without us.
- Step 1 · App idea audit
We open your real code and frame what you want to build
First session, we share your screen and look at your project together. What do you want to build: a chatbot over your docs, an assistant that acts, an extraction pipeline? We clarify the need, pick the right model and split, and decide right away whether LangChain fits or a plain API call is enough. You leave with a clear architecture and the list of workstreams, in priority order. No theory, your real project.
- Step 2 · Your first chain
We wire a clean chain with LCEL
We get concrete. We lay down the base pieces (prompt, model, output parser) and wire them with LCEL and the pipe operator. You understand Runnables, streaming, parallel runs and error handling. We pick up what was blocking you in the docs, often a version that changed the syntax, and make your chain readable and testable. By the end, you have a first chain running on your real case that you know how to grow.
- Step 3 · RAG over your data
We stand up a RAG over your own documents
We tap LangChain's star use case: RAG. We load your real documents, split them cleanly (chunking changes everything), embed them and store them in a vector store sized for your volume (Chroma locally, pgvector, Pinecone). We wire the retrieval, add metadata and re-ranking so answers are sourced and accurate. You practice on your own files, not a dummy example. You finish with a RAG that genuinely answers over your base.
- Step 4 · Agents and LangGraph
We build an agent orchestrated with LangGraph
We move to action. We set up tool calling so your agent invokes your functions and APIs, then we structure the workflow with LangGraph: a stateful graph, conditional branches, loops, and a human in the loop when something needs approval. It's far more reliable than an agent left to run free. We put the guardrails in place (iteration limits, token budget) so it doesn't go off the rails. You leave with an agent that takes real actions on your case.
- Step 5 · Production and observability
We deploy your app, watched with LangSmith
The number one goal: you become autonomous all the way to prod. We wire LangSmith to trace every run, see the real prompts, tokens, latency and cost, and set up evals to measure your progress. Then we deploy: handling keys, streaming, errors and budget. By the end, you know how to build, debug and ship a LangChain app without us. And if you want to delegate later, we also run a LangChain agency, but that's not the point here.
Why train 1-on-1 with us.
- 300+Pros already trained on AI
More than 300 people have gone through our trainings across France and Europe. Devs, data folks, small-business founders. Not vanity numbers: people who shipped a real LLM app, RAG or agent, instead of staying stuck on a LangChain tutorial that no longer compiles.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't promise a LangChain agent works on the first try: you have to iterate on your real data. But the 1-on-1 format makes the difference between following a notebook and being able to debug your own RAG.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens your real code, looks at your chain and your RAG, and works through your actual cases. We schedule sessions around your availability, replays included.
A working agency, recognized by the French State.
Hack'celeration is certified Activateur France Numérique and holds the AI Ambassador title, both granted by France Num to organizations that genuinely drive the digital transformation of companies. On the ground, we ship LangChain agents and RAG for clients every week: more than 300 pros trained and a 4.7/5 rating across 334 verified reviews, left by the people who took our programs, not just by the buyer.
- Certified Activateur France Numérique
- AI Ambassador (France Num)
- 300+ pros trained across France and Europe
- 4.7/5 across 334 verified reviews
The questions we get the most.
What is a 1-on-1 LangChain training?
An individual program with a LangChain expert, not a class of 100 people. We open your real code, look at your chain and your data, and work through your actual cases: LCEL chains, RAG, agents, LangGraph, LangSmith observability. You ask your questions live, the trainer adapts the pace to your level. We schedule sessions around your availability, and you leave with working code every time. That's what makes the difference between following a notebook and being able to debug your own LLM app.How much does the LangChain training cost?
There is no single price. We connect you with a trainer certified by Hack'celeration, matched to your need and your level. It varies from one trainer to another, based on their profile and the format that fits your project.Do I need to know Python to take the training?
Yes, a decent Python level is needed, since LangChain is a code library. You don't have to be a senior dev, but you should be comfortable with functions, classes and installing packages. If you already know some Python and are new to LLMs, the 1-on-1 format starts from your exact level. There's also LangChain.js if you're more of a JavaScript or TypeScript person, and we adapt to your stack. If you don't want to code at all, we'll say so honestly and point you toward no-code.LangChain or LlamaIndex: which one to pick?
It depends on your case. LangChain is broader: chains, agents, tools, orchestration with LangGraph, a full ecosystem to build all kinds of LLM apps. LlamaIndex is more focused on RAG and data indexing, often simpler and more direct if your only need is querying your documents. Many projects combine both. If you want agents that act and complex workflows, LangChain. If you just want a solid RAG fast, LlamaIndex is worth a look. In training, we help you decide based on your real project, no dogma.LangChain or a no-code tool like n8n or Flowise?
Fair question. For a quick prototype or a simple workflow, a no-code tool like n8n, Make or Flowise saves you time and needs no code. As soon as you need fine logic, control over the RAG, reliable agents or a real versioned production setup, LangChain in Python gives you far more control. The right answer isn't always LangChain, and we'll tell you. In 1-on-1, we look at your real need and pick the tool that costs you the least effort for the result you want.How do you build a RAG with LangChain?
RAG always follows the same steps, and we run them on your real data: load your documents, split them into chunks (the splitting really drives quality), turn them into embeddings, and store them in a vector store like Chroma, pgvector or Pinecone. Then we wire the retrieval to pull the relevant passages and pass them to the LLM, with sources. We add metadata and sometimes re-ranking for precision. In training, we build this pipeline over your own document base, not a toy example.What is LangGraph and when should I use it?
LangGraph is LangChain's building block for orchestrating complex workflows as a stateful graph. Instead of a ReAct agent left to run free, you define nodes, conditional branches, loops and points where a human approves. It's what we use whenever an agent has to chain several steps reliably and reproducibly: extract, then verify, then act, for example. You need it when a plain call or a linear chain no longer cuts it. In 1-on-1, we build a LangGraph graph on your concrete case and set the guardrails.What does LangSmith do for my LangChain apps?
LangSmith is observability and evaluation for your LLM apps. Concretely, it traces every run: you see the real prompts sent, the responses, the tokens used, the latency and the cost of each step. Without it, a LangChain app is a black box you can't debug when an answer goes sideways. LangSmith also lets you set up evals to measure whether a change actually improves results, instead of judging by feel. In training, we wire it in as soon as we have a chain, so you debug with data, not guesses.How long to get a LangChain app into production?
It depends on complexity. A first chain or a simple RAG over a doc can run locally in a few sessions. A multi-step agent with LangGraph, LangSmith evals and a real deployment takes longer, especially to make it reliable on your real data. The classic trap: a prototype that works in a notebook but breaks in prod on edge cases. We prioritize from the first session so you get a working deliverable fast, then we harden it for production. There's no magic button, but a clear method speeds things up a lot.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen and your LangChain code live. Sessions are recorded if you want to revisit them. The individual format means real interaction: you're not a number in a webinar of 100, the trainer answers your questions about your chain, your RAG and your agent. That's what makes learning concrete on a framework you mostly learn by building.
LangChain can do a lot for you. Meet your trainer.
Drop your email. An expert who ships LangChain apps daily looks at your project and shows you how to build your RAG or your agent for real. No commitment, even if you don't take the training.