Key takeaways
- Harrison Chase released LangChain in October 2022, and the team launched LangGraph in early 2024.
- Use books for concepts and current documentation for syntax, since APIs changed quickly.
- LangChain Academy, GitHub repositories and DeepLearning.AI courses are strong free starting points.
- Certificates help a little, while deployed RAG and agent projects matter far more.
- Null Qdrant vectors usually mean with_vectors was not requested, not that data is missing.
LangChain began in October 2022 as an open-source project by Harrison Chase and grew into LangChain, Inc., which also builds LangGraph and LangSmith. Learning it well means combining current documentation with a good book or course, since APIs changed quickly before the 1.0 releases. I build RAG systems in Python, so this page covers LangChain's history and creators, books, PDFs, GitHub, Coursera and Udemy courses, certificates, Krish Naik's tutorials and null vectors in Qdrant.
When was LangChain created?
LangChain was created in October 2022, when Harrison Chase released it as an open-source Python library, weeks before ChatGPT's launch. The JavaScript version followed in early 2023, and LangChain, Inc. was incorporated in 2023.
Also asked as: when langchain was created
The project grew rapidly as developers needed tools to connect language models with documents, APIs and memory, becoming one of the fastest-growing open-source AI projects on GitHub. Its API changed often in early versions, which frustrated some users, until the LangChain Expression Language standardised composition. LangChain and LangGraph reached version 1.0 in October 2025, signalling greater stability. Tutorials written before then may use deprecated patterns, so check publication dates when learning.
Who created LangChain and LangGraph?
Harrison Chase created LangChain and co-founded LangChain, Inc. with Ankush Gola. The LangChain team released LangGraph in early 2024 as a library for building stateful, controllable agents as graphs.
Before LangChain, Chase worked in machine learning at Robust Intelligence and Kensho. The company now maintains LangChain, LangGraph and LangSmith, its commercial platform for tracing, evaluation and deployment, with many open-source contributors adding integrations. LangGraph emerged because developers needed more control than early agent abstractions gave: explicit state, loops, branching, persistence and human approval steps. Today, LangChain's agent features are built on LangGraph, so learning both together makes sense.
What are the best LangChain books?
Useful books include Learning LangChain by Mayo Oshin and Nuno Campos, which covers LangChain and LangGraph, and Generative AI with LangChain by Ben Auffarth, now in an updated edition. Check that any book covers current versions.
Because the library evolves quickly, books are best for concepts: chains, retrieval, agents, memory, evaluation and deployment patterns. Use the official documentation for exact syntax. Broader books such as AI Engineering by Chip Huyen explain when to use frameworks at all, and Hands-On Large Language Models covers the underlying models. Read sample chapters first, and prefer books with companion GitHub repositories updated after the 1.0 releases. Pair each chapter with a small project.
Is there a LangChain tutorial PDF?
There is no official LangChain PDF course. The official documentation, LangChain Academy and the GitHub repositories are the authoritative, current resources. PDFs circulating online are usually outdated exports or unofficial notes.
If you prefer offline reading, save documentation pages as PDFs from your browser, or use a book from a reputable publisher, many of which offer PDF editions. Avoid pirated copies, which are often incomplete and outdated. Unofficial PDFs written before the 1.0 releases may teach deprecated imports and classes, causing confusing errors. For printable references, the LangChain documentation includes conceptual guides and how-to pages that work well when saved. Always run examples against your installed version.
Where can you find a LangChain course on GitHub?
Useful repositories include langchain-ai/langchain-academy, with notebooks for LangChain Academy courses; langchain-ai/rag-from-scratch, covering retrieval techniques step by step; and the examples folders in the LangChain and LangGraph repositories.
Also asked as: langchain course github
Community repositories also collect tutorials, cookbooks and projects, such as multi-agent systems and RAG chatbots. Treat GitHub repositories as hands-on courses: clone them, run notebooks in order, change parameters and build your own variant. Check the last commit date and dependency versions, since older repositories may break with current releases. Contributing fixes or examples back to these repositories is a strong way to learn and to build a visible track record.
Is there a LangChain course on Coursera?
Coursera hosts LangChain content inside generative AI programmes, such as IBM's courses on building RAG applications, plus guided projects. DeepLearning.AI's short courses on LangChain and LangGraph, co-taught with Harrison Chase, run on its own platform.
Also asked as: langchain course coursera
Coursera courses usually let you audit lectures for free, with certificates available through paid enrolment or financial aid. They suit learners who want structured programmes with assessments. DeepLearning.AI's short courses, such as LangChain for LLM Application Development and AI Agents in LangGraph, take a few hours each and offer practical notebooks. Whichever you choose, check the recording date, since older courses may use deprecated APIs, and follow up with the current documentation.
What is the best LangChain course on Udemy?
Udemy has many LangChain courses; the best are recently updated for LangChain 1.0 and LangGraph, include RAG and agent projects, and come from instructors who respond to questions. Check update dates, previews and recent reviews before enrolling.
Prefer courses that build complete applications, such as a document assistant with citations or a multi-tool agent, and that cover evaluation and deployment rather than only basic chains. Courses frequently go on sale, so compare before enrolling. Read negative reviews for complaints about outdated code. Since the free LangChain Academy courses come from the framework's creators, start there and use Udemy for additional projects or a teaching style you prefer.
Can you get a LangChain course certificate?
Yes. Coursera programmes and Udemy courses provide certificates of completion, and some generative AI programmes from IBM and others include LangChain modules. Check LangChain Academy's current course pages for its own completion options.
Certificates show commitment, but employers value demonstrated projects far more. Pair any certificate with a GitHub repository and live demo: a RAG application with hybrid retrieval and citations, or a LangGraph agent with tools and human approval. Explain your design choices in a README, including evaluation results. On LinkedIn and resumes, list the certificate alongside the project it produced. That combination shows both structured learning and the ability to ship working software.
What is LangChain by Krish Naik?
Krish Naik is an AI educator whose YouTube channel and courses include popular LangChain tutorials and playlists, covering chains, RAG, agents and projects with open-source and commercial models, often aimed at learners in India.
Also asked as: what is langchain krish naik
His videos are a common starting point for beginners because they walk through complete projects step by step. As with any tutorial, check the recording date and library versions, since LangChain's APIs changed significantly before the 1.0 releases. Combine video tutorials with the official documentation and LangChain Academy to stay current. After following along, rebuild each project with your own data and features, which turns passive watching into real skill.
Why are vectors null in Qdrant collections created with LangChain?
Usually the vectors exist but are hidden: Qdrant's scroll and search APIs omit vectors unless you request them with with_vectors set to true. The dashboard or client therefore shows vector as null by default.
Also asked as: why vector is null in qdrant which is created with langchain
If vectors are truly missing, check that you passed a working embeddings object, that documents were actually embedded, and that the vector name matches the collection's configuration, since named vectors require the same name in LangChain's Qdrant integration. Recreate the collection if its vector size differs from your embedding model's output. Test by embedding a sample query and searching; if results return with scores, the vectors are stored correctly. Logging upsert responses also helps diagnose silent failures.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
