Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Advanced LangChain Training - Develop Complex AI Agents training are: Advanced mastery of Python, LangChain basics, LLM APIs (OpenAI, Hugging Face), Git and Docker.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Advanced LangChain Training - Develop Complex AI Agents training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Advanced LangChain Training - Develop Complex AI... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Advanced LangChain Training - Develop Complex AI Agents training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Advanced LangChain Training - Develop Complex AI Agents training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Advanced LangChain Training - Develop Complex AI Agents training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Design complex multi-step chains with LangChain

Develop autonomous and reactive intelligent agents

Implement advanced RAG for precise augmented search

Optimize performance and costs of LLM pipelines

Integrate external tools and persistent memories

Deploy scalable applications in production

Evaluate and debug AI workflows in real-time

Training program

Module 1Advanced Chains: Custom Tools, Parsing, Routing (LCEL)

Dive into LCEL to chain models, prompts, and dynamic parsers, build conditional chains with exercises on real datasets, test intelligent routings for multi-model flows, produce functional deliverables ready to integrate, gain fluency to scale your AI apps from day one.

Module 2Agents and Tools: ReAct, Planning, Custom Integrations

Create autonomous agents via ReAct and hierarchical planners, integrate custom Python tools and third-party APIs, simulate real cases like virtual assistants, debug interactions live, generate collaborative multi-agent systems, feel the power of proactive AI that boosts your productivity.

Module 3RAG and Memory: Embeddings, Retrieval, Advanced Vector Stores

Master RAG with Pinecone and FAISS for precise searches, implement persistent conversational memories, optimize hybrid embeddings on massive documents, practice semantic extractions via enterprise exercises, deliver robust RAG pipelines, transform your data into competitive AI assets.

Module 4Evaluation and Optimization: Tracing, Caching, Fine-Tuning

Use LangSmith to trace and evaluate chains in depth, apply caching and batching to reduce costs by 70%, test A/B on LLM metrics, optimize via advanced prompts and grounding, analyze real logs for rapid iterations, achieve pro-level performance that impresses in production.

Module 5Deployment and Real Cases: FastAPI, Docker, Monitoring

Deploy via FastAPI and Docker in the cloud, integrate monitoring with LangSmith, simulate end-to-end projects like enterprise chatbots, secure LLM APIs, finalize portfolio with deployed source code, leave with complete expertise to lead your innovative AI initiatives.