Langchain In Action
Chinese-language Geek Time course teaching LangChain's core modules through the 易速鲜花 customer-service case study.
If you read Chinese and you want to internalize why LangChain is structured the way it is, this is one of the better-priced on-ramps available — ¥59 gets you 29 lessons, the 易速鲜花 case study, and the companion GitHub repo at huangjia2019/langchain. The six-component breakdown (models, prompts, retrieval, memory, chains, agents) is the clearest part; the RAG walkthrough — loaders, splitters, embeddings, semantic search — is the reason to buy. The honest caveat is version drift: the code was written against early LangChain APIs and the author says so himself. Treat it as a mental-model purchase, not a current-API reference. English speakers and anyone already fluent in LangChain internals
Verified 2d ago · liveness 60/100 · cite: rightaichoice.com/tools/langchain-in-action
- Chinese-speaking developers new to LangChain
- Junior-to-mid-level engineers building LLM applications
- Product and project managers scoping AI features
- Tech leads evaluating LangChain for a project
- Non-Chinese speakers — instruction is entirely in Mandarin
- Advanced users already fluent in LangChain chains, agents, and memory
- Developers who need code matching the very latest LangChain API release
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Skip LangChain In Action if you don't read Chinese, or if you already understand LangChain chains, agents, and retrieval well enough to read the current docs directly — the value here is the conceptual grounding, not fresh API coverage.
The listed ¥59 is a promotional 到手价 against a ¥99 strikethrough reference price, and Geek Time states the reference price is not the original price — the actual charge depends on which coupons or rewards you hold at
At ¥59 promotional / ¥99 reference for 29 lessons and lifetime access, this sits at the low end of Geek Time's catalogue and far below English-language bootcamps or paid LangChain cohorts. The same instructor's Agent 设计模式之美 is priced at ¥68 / ¥199 for 43 lessons, so this course is the cheaper entry point into his LLM series. Money is rarely the deciding factor here — language and API recency are.
In short
Langchain In Action — Chinese-language Geek Time course teaching LangChain's core modules through the 易速鲜花 customer-service case study. Best for Chinese-speaking developers new to LangChain, Junior-to-mid-level engineers building LLM applications, Product and project managers scoping AI features. Paid, in a currency we have not confirmed — see the pricing table for the vendor’s own figures.
What people actually say about Langchain In Action — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 3 sources (YouTube, GitHub, Lemmy) · researched Sep 29, 2026.
Weighted by the 43 posts each of 3 sources contributed.
- +Structured 29-lesson arc takes you from LangChain basics through RAG, memory, and agents
- +Single running 易速鲜花 case study ties every module to one coherent application
- +Taught by a working AI researcher (A*STAR Singapore) with real consulting background
- +18,000+ enrolled learners and 766 GitHub stars signal strong peer validation
- +Complete RAG pipeline covered end-to-end: loading, splitting, embedding, semantic retrieval
- −Examples use deprecated LLMChain; LangChain 0.3.0 removes it entirely
- −Model names like text-davinci-003 already retired, breaking example code
- −Open GitHub issue: Qdrant.from_documents fails with connection errors in RAG lesson
- −No pinned requirements.txt — latest libraries often break the demos
- −Course authored against early LangChain APIs; author himself warns it ages fast
- • Time cost of migrating deprecated LLMChain examples and stale model names to modern LangChain
- • Potential OpenAI API spend while experimenting with RAG and agent examples
- • Possible need to separately source pinned dependency versions the course doesn't provide
Viability Score
How well maintained and how widely used is Langchain In Action? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- 29 lessons across four modules: 启程 (get-started), 基础 (fundamentals), 应用 (application), 实战 (hands-on)
- Deep dive into LangChain's six core components: models, prompt templates, data retrieval, memory, chains, agents
- Retrieval-augmented generation (RAG) walkthrough: document loading, text splitting, vector embedding, semantic retrieval
- Full development of the 易速鲜花 intelligent Q&A system as a running case study
- Agent and tool-usage examples including role-play, brainstorming, and autonomous search
- Memory mechanism coverage: storing and retrieving conversation history for context-aware apps
- Async communication and embedding-store integration with database connections
- Deployment of a 易速鲜花 customer-service chatbot
- Transformer and GPT model operation explained alongside the framework code
- Companion code repository at github.com/huangjia2019/langchain
- Illustrated text plus audio delivery, accessible via Geek Time App and web
- Q&A community for problem-solving during the course
- Chinese-language instruction throughout (Mandarin)
- Certificate of completion
About Langchain In Action
LangChain In Action (LangChain 实战课) is a 29-lesson Chinese-language video-and-text course on Geek Time, taught by 黄佳, a Lead Researcher at Singapore's Agency for Science, Technology and Research and a former Accenture Singapore consultant. The course walks you through LangChain's six core components — models, prompt templates, data retrieval, memory, chains, and agents — and anchors every one of them inside a single running case study: an intelligent Q&A system for the fictional flower-delivery business 易速鲜花. Lessons cover retrieval-augmented generation (RAG) end-to-end: document loading, text splitting, vector embedding, and semantic retrieval over a local knowledge base, plus how memory and agent frameworks combine conversation history, external tool calls, and dynamic reasoning. The final module moves from principle to practice — deploying the 易速鲜花 customer-service chatbot and a networking tool for the flower e-commerce site, covering model invocation details, data connection strategy, and memory storage and retrieval. All course code lives in a public GitHub repository (huangjia2019/langchain), so you can run every example yourself. It is aimed at junior-to-mid-level developers with basic AI or deep-learning familiarity, plus product and project managers who need to understand how LLM applications are actually assembled before committing budget. The course was written against early LangChain APIs; the author explicitly notes the framework evolves quickly and recommends learners track the latest LangChain releases alongside the lessons. Counted at 1.8w (18,000+) enrolled learners and ranked 7th on Geek Time's rising chart at the time of scraping.
Behind the Verdict
The strongest argument for LangChain In Action is that it teaches architecture, not trivia. Most LangChain material online is a sprawl of tutorials that each solve one narrow problem; this course instead takes six named components — 模型 (models), 提示模版 (prompt templates), 数据检索 (data retrieval), 记忆 (memory), 链 (chains), 代理 (agents) — and shows how they cooperate inside one non-trivial system. That systems view is what survives framework churn, and the course leans into it deliberately: the author states that the concepts remain valuable even as the code ages. Where it earns its ¥59 is the RAG sequence. The course walks document loading, text splitting, vector embedding, and semantic retrieval in order, then shows you how to bolt the result onto a Q&A system so an LLM can answer questions about enterprise data it was never trained on. That is the highest-value pattern in commercial LLM work right now, and having it demonstrated in narrative order — rather than reverse-engineered from scattered docs — is genuinely useful. The case-study structure is the second strength. Everything converges on 易速鲜花: the local knowledge base Q&A system in the 启程 (get-started) module, the component deep-dives in the 基础 (fundamentals) module, the async communication, embedding stores, database connections, and role-play agents in the 应用 (application) module, then the final deployment of the flower-shop chatbot. Because the same business context recurs, you see how a memory choice made in lesson eight affects the deployment in lesson twenty-five. The weaknesses are real and the author is upfront about them. First, version drift: LangChain's APIs have moved substantially since the course was recorded, so copying code verbatim may fail and you will need to consult current documentation. The course is a foundation, not a reference. Second, there is no hosted coding environment — you set up locally and pull the GitHub repo yourself, which is fine for developers but friction for product managers. Third, the course offers no API or tool access of its own; it teaches you to build, it does not give you a sandbox. Fourth, it is entirely in Chinese, delivered as image-and-audio (图文 + 音频) rather than video, which matters if you prefer watching someone code. On fit: this lands best for a developer who has touched Python and knows roughly what an LLM is, but has not yet built a multi-step system with retrieval and memory. It is also defensible for a tech lead or PM who needs the vocabulary to scope an LLM project — the 适合人群 list explicitly includes product and project managers. It is a poor fit for advanced practitioners who already understand chains and agents, and useless for non-Chinese readers. For alternatives: if you prefer English, the official LangChain documentation and DeepLearning.AI's short courses cover similar ground with fresher APIs, at no cost, though without the single-case-study narrative thread. Within Geek Time, the same instructor's 大模型应用开发实战 and Agent
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Real-world workflow fit
Concrete scenarios for the personas Langchain In Action actually fits — and what changes day-one when you adopt it.
You want to add a document Q&A feature to an internal tool but have never used retrieval. You work through the 基础 module for retrieval and memory, then open the GitHub repo at huangjia2019/langchain and run the 易速鲜花 knowledge-base example locally, swapping the flower documents for your own PDFs.
Outcome: You end up with a working RAG pipeline — loaders, splitters, embeddings, semantic search — and a mental model of which component to change when answers get worse.
You need to write a credible spec for a customer-service chatbot. You take the 启程 and 应用 modules and follow how the 易速鲜花 chatbot is designed end to end — model invocation, data connection, memory storage and retrieval — without writing code yourself.
Outcome: You can name the components and trade-offs in engineering meetings instead of asking developers to explain what RAG is.
You want a tool-calling agent that can search, reason, and hold context. You study the agent and memory lessons together, then extend the course code to connect a different data source and a different LLM provider.
Outcome: A working agent prototype that reuses the course's memory and tool patterns, adapted to your own stack.
Use Cases
- Build a question-answering system over your own documents using LangChain chains and retrievers
- Create a conversational agent with memory and tool-calling for customer support
- Implement a custom chain for multi-step LLM workflows with branching logic
- Develop a summarization pipeline using LangChain text splitters and document loaders
- Design a reusable prompt template library for consistent LLM interactions
- Orchestrate multiple LLM calls with error handling and asynchronous communication
- Learn RAG architecture before applying it to an internal enterprise knowledge base
Limitations
- The code examples were written against early LangChain APIs and the author openly says the framework evolves fast, so you must cross-check current documentation before running anything.
- There is no hosted coding environment — you set up locally and pull the GitHub repo at huangjia2019/langchain yourself.
- The course is purely educational: it provides no API access, no sandbox, and no tooling of its own.
- Delivery is illustrated text plus audio rather than screen-recorded video, and everything is in Chinese, so non-Mandarin readers get nothing from it.
as of 2026-09-26
Verification history
We have re-verified Langchain In Action 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
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- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Langchain In Action tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Full Course
¥59 promotional (¥99 reference price, as listed on Geek Time
Ideal for
Chinese-speaking developers or PMs buying a single self-paced course — no subscription, no team licence needed.
What this tier adds
Starting tier and the only published option: one purchase, lifetime access to all 29 lessons, the GitHub code repo, and Q&A community access.
Where the pricing makes sense
The company stage and team size where Langchain In Action's pricing actually pencils out — and where peers do it cheaper.
At ¥59 promotional / ¥99 reference for 29 lessons and lifetime access, this sits at the low end of Geek Time's catalogue and far below English-language bootcamps or paid LangChain cohorts. The same instructor's Agent 设计模式之美 is priced at ¥68 / ¥199 for 43 lessons, so this course is the cheaper entry point into his LLM series. Money is rarely the deciding factor here — language and API recency are.
Setup time & first value
How long it actually takes to get something useful out of Langchain In Action — broken out by persona, not the marketing-page minute.
Developers: budget about 30 minutes to install Python, clone github.com/huangjia2019/langchain, and get the first example running, plus time to obtain LLM API keys and a vector store. Non-coders (PMs, tech leads): no setup at all — install the Geek Time App or open the web player and start with the free preview lessons. Full completion of 29 lessons realistically spans several weeks of evenings.
Switching to or from Langchain In Action
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangChain official docs alone: use the 基础 module's six-component walkthrough to get the conceptual ordering that the docs assume you already have.
- →From scattered YouTube/Bilibili tutorials: replace piecemeal, one-problem-each videos with the single 易速鲜花 case study that connects prompts, retrieval, memory, chains, and agents.
- →From an English-language LangChain course: keep it for current API syntax, and use this course for the architecture-level explanation in Mandarin.
- ↗To the instructor's 大模型应用开发实战: continue into broader LLM application development with the same teaching style.
- ↗To Agent 设计模式之美 (43 lessons, ¥68 / ¥199): go deeper on agent design patterns once the six components are solid.
- ↗To MCP & A2A 前沿实战: move on to agent communication protocols after finishing the 实战 module.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Langchain In Action”, and we withheld 5: 5 did not mention Langchain In Action. Showing the 1 we can prove is about Langchain In Action.
Official links
Tools that pair well with Langchain In Action
Common stack mates teams adopt alongside Langchain In Action, with the specific reason each pairing earns its keep.
SapienAI
Agentic AI research workspace with real-time collaborative LaTeX, Typst, and Markdown writing.
Metaso AI
Chinese-language search engine that returns cited, synthesized answers instead of link lists.
Heptabase
A visual knowledge base with an AI Tutor that builds structured courses from your own sources.
Featured Head-to-Head Comparisons
Langchain In Action vs Surge Ai
These are not alternatives to each other. LangChain In Action is a $ one-time Chinese-language course that teaches you the framework's early core modules with hands-on examples; you buy it if you are a Chinese-speaking developer who wants concepts before diving into current docs. Surge AI sells the opposite kind of thing: credentialed human labor — doctors, lawyers, engineers — producing RLHF preference data, adversarial red teaming, and benchmarks like GDP.pdf and ComplexConstraints that frontier labs cite in release materials and system cards. If you are an individual learning LangChain, Surge is irrelevant. If you are a post-training team that needs expert-graded feedback a gig annotator cannot provide, a LangChain course teaches you nothing about it.
Langchain In Action vs Praktika
These two do not compete for the same budget. Praktika is a ~$8/month consumer app for people whose bottleneck is speaking a foreign language out loud, and it sells unlimited low-stakes reps with named AI tutors. LangChain In Action is a paid, Chinese-language Geek Time course for developers who want a conceptual foundation in prompts, chains, agents, and memory before reading current LangChain docs. If you are a developer, buy the course; if you are a learner who freezes when speaking, buy Praktika. Nobody is shortlisting both.
Langchain In Action vs Genspark
These aren't competitors; don't treat them as alternatives. If you want to research a topic and produce cited summaries, decks, or internal tools without code, Genspark is the buy — its freemium entry and Google Workspace/Microsoft 365 hooks make it easy to trial. If you're a Chinese-speaking developer who wants to understand LangChain's core concepts (prompts, chains, agents, memory, loaders) before reading current docs, buy the course. The only overlap is the word 'AI'; budgets, workflows, and success metrics are unrelated.
Langchain In Action vs Goodfire
These are not competitors and you should not be choosing between them. LangChain In Action is a one-time-purchase Chinese-language course for developers who need a conceptual on-ramp to LangChain's prompt, chain, agent, and memory patterns; Goodfire is a freemium interpretability platform for research teams who already have models in production and need to inspect, debug, and steer their internals. Buy the course if you are learning to build LLM apps and read Chinese; buy Goodfire if you have a research org and an interpretability problem.
Langchain In Action vs Coursera
These aren't competitors — they're different species. LangChain In Action is a single Chinese-language course that teaches one Python framework's fundamentals, purchased once for lifetime access. Coursera is a subscription marketplace where you buy access to credentials and degrees from 350+ institutions. If you're a Chinese-speaking developer trying to understand LangChain concepts before wrestling with the latest docs, buy the course. If you need an employer-recognized credential, accredited degree, or a curriculum across many AI topics, Coursera is the only one of the two that can deliver that — and it's not a close call.
Langchain In Action vs Anara
These aren't competitors, and nobody should be picking one over the other. Buy Anara if you're a researcher, clinician or R&D team that must trace every claim to an exact page across thousands of files — and note that since the 2026-08-17 change every plan now runs on one AI usage meter, so heavy users should plan for credit top-ups or a Max 5x/20x tier. Buy Langchain In Action only if you're a Chinese-speaking developer who wants a structured conceptual foundation in LangChain's prompts, chains, agents and memory modules, accepting that its examples will lag the framework. A realistic buyer could conceivably do both: learn with the course, research with Anara.
Langchain In Action vs Sakana Ai
These are not competitors; they don't belong on the same shortlist. If you are a Chinese-speaking developer who wants a structured, example-driven grounding in LangChain's core modules before reading the docs, the Geek Time course is the relevant purchase. If you are a regulated Japanese enterprise that cannot move data offshore and needs Japanese-specialised LLMs (Namazu), multi-agent orchestration (Fugu), on-demand analyst reports (Marlin) or translation (Sakana Translate), you talk to Sakana AI's sales team instead. The only overlap is the word 'AI' — budgets, buyers and procurement paths are entirely different.
Langchain In Action vs Undermind
These two are not competitors — picking one over the other doesn't make sense. Undermind is a research tool for scientists who need exhaustive, citation-traceable literature reviews; you'd buy it if your problem is 'find every relevant paper.' LangChain In Action is a Chinese-language course from Geek Time that teaches the fundamentals of the LangChain framework; you'd buy it if your problem is 'learn how to build LLM apps and I read Chinese.' If you're a researcher, take Undermind. If you're a Chinese-speaking developer learning LangChain, take the course. Nobody should be choosing between them — and if you're not a Chinese speaker, the course is a non-starter.
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