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 Training LangChain - Orchestrating Advanced AI Agents training are: Advanced mastery of Python, LLM APIs (OpenAI GPT-4), expert prompting, and AI frameworks. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LangChain - Orchestrating Advanced AI A... | 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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10 spots per session maximum — 7 already taken

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Methods, approaches, and teaching resources

The Training LangChain - Orchestrating Advanced 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 Training LangChain - Orchestrating Advanced 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 Training LangChain - Orchestrating Advanced 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

Master LangChain chains for professional AI applications

Develop intelligent autonomous agents for enterprise use

Implement advanced RAG for certified augmented search

Design optimized and scalable LLM pipelines

Integrate LangChain with vector databases and external tools

Deploy secure LangChain solutions in production

Optimize AI performance for expert-level skills

Training program

Module 1Day 1 - Advanced Fundamentals and Complex LangChain Chains

Expert LangChain environment installation. Advanced LLM providers configuration. Creation of custom chains with LCEL. Hands-on dynamic prompts. Integration of memory tools and parsing. Professional exercises on basic RAG cases. Development of red thread project: conversational AI agent. Advanced error handling and debugging. Optimization of chains for low latency. Real enterprise cases: intelligent chatbots. Implementation of vector retrievers. Unit tests for LangChain chains. Real-time trainer support. Step-by-step progression on expert skills. Day 1 review with quick quiz.

Module 2Day 2 - LangChain Agents, RAG, and Production Deployment

Design of multi-tool autonomous LangChain agents. Integration of ReAct and Plan-and-Execute. Development of advanced RAG with embeddings. Hands-on vector databases (FAISS, Pinecone). Professional exercises: enterprise semantic search. Enhancement of red thread project with collaborative agents. Streaming responses and performance monitoring. Securing sensitive AI data. Deployment with Docker and cloud (AWS, Vercel). CI/CD for LangChain pipelines. End-to-end testing of expert solutions. Optimization of LLM API costs. Real cases: professional virtual assistants. Defense of red thread project. Evaluation of acquired skills. Access to post-training resources.