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 Autonomous AI Agents training are: Mastery of Python, experience with LLM APIs (OpenAI, Hugging Face), advanced prompting.. A preliminary interview validates your eligibility.

Is the training available in person?

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

Advanced LangChain Training - Develop Autonomous… | 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 Autonomous 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 Autonomous 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 Autonomous 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 program

Module 1Day 1: Advanced LangChain Chains and LCEL (dynamic prompts, parsers)

Dive into LCEL to compose ultra-efficient modular pipelines, configure custom chains with retrieval and output parsers, perform hands-on exercises on adaptive dynamic prompts, test real integrations like intelligent chatbots, produce scalable functional deliverables, while mastering callbacks for live monitoring.

Module 2Day 2: LangChain Agents and External Tools (ReAct, custom tools)

Create autonomous agents with ReAct and custom tools, integrate external APIs like search engines or databases, simulate concrete cases of complex task automation, debug behaviors via detailed traces, develop collaborative exercises on multi-tool agents, generate powerful decision-making AI assistant prototypes.

Module 3Day 3: Advanced RAG and LangChain Memory (vector stores, embeddings)

Implement hybrid RAG with vector stores (FAISS, Pinecone), generate custom embeddings for massive documents, optimize multi-query retrieval, integrate persistent conversational memory, practice on real company datasets, build infallible RAG chatbots, test precision metrics for production-ready deliverables.

Module 4Day 4: LangChain Deployment and Optimization (streaming, scaling)

Deploy LangChain apps on cloud (LangServe, Docker), enable streaming and async for smooth UX, profile performance with LangSmith, secure sensitive flows, finalize full-stack agent capstone project, export deployable code, benefit from expert feedback for immediate excellence.