Langchain In Action is live · paid plans (see vendor prices) · last verified 20 Oct 2026 · viability monitor
Langchain In Action
LangChain 实战课: a 29-lesson Mandarin course building a RAG chatbot around the 易速鲜花 case study
Buy it if you read Chinese and want the mental model of how a LangChain app is wired — RAG pipeline, memory, agents — rather than a tour of today's API signatures. The single 易速鲜花 case study and a runnable companion repo are what justify ¥59 over free docs. Skip it if you already ship chains and agents, or if you need code matching the current release; the lessons target earlier APIs and the author says so.
Verdict generatedVerified 25d ago · liveness 60/100 · cite: rightaichoice.com/tools/langchain-in-action
- Chinese-speaking developers new to LangChain who want structure before diving into docs
- Junior-to-mid-level engineers with a basic AI or deep-learning background
- Product and project managers scoping an LLM feature and its cost drivers
- Engineers specifically tasked with shipping a retrieval-augmented generation pipeline
- Non-Chinese speakers — instruction is entirely in Mandarin
- Developers already fluent in LangChain chains, agents, and memory
- Anyone who needs code matching the very latest LangChain API release
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Skip LangChain In Action if you need code that matches the current LangChain release, or if you cannot read Mandarin — the 29 lessons are in Chinese and were written against early APIs.
Coin and coupon discounts on the ¥59 list price depend on promotions and new-learner rebates, so the price on the order page can differ from the price shown on the course page.
At ¥59 for 29 lessons, this sits at the low end of paid technical education — below most multi-hundred-yuan Chinese platform columns and far below instructor-led bootcamps. The real comparison is free: LangChain's official documentation and free English-language courses cost nothing but give you no sequence. Pay here only if you want the ordered RAG walkthrough in Mandarin.
In short
Langchain In Action — LangChain 实战课: a 29-lesson Mandarin course building a RAG chatbot around the 易速鲜花 case study. Best for Chinese-speaking developers new to LangChain who want structure before diving into docs, Junior-to-mid-level engineers with a basic AI or deep-learning background, Product and project managers scoping an LLM feature and its cost drivers. Paid, in a currency we have not confirmed — see the pricing table for the vendor’s own figures.
What is Langchain In Action?
Langchain In Action — LangChain 实战课: a 29-lesson Mandarin course building a RAG chatbot around the 易速鲜花 case study. Best for Chinese-speaking developers new to LangChain who want structure before diving into docs, Junior-to-mid-level engineers with a basic AI or deep-learning background, Product and project managers scoping an LLM feature and its cost drivers. Paid, in a currency we have not confirmed — see the pricing table for the vendor’s own figures.
LangChain In Action (LangChain 实战课) is a finished 29-lecture column on Geek Time from 黄佳, a Lead Researcher at Singapore's Agency for Science, Technology and Research and a former Accenture Singapore consultant. Roughly 18,000 learners have taken it. The pitch is straightforward: build a working ChatGPT-style application on top of LangChain while learning how the framework's pieces fit together, delivered as illustrated text plus audio through the Geek Time App or web. Four modules carry the arc. 启程 covers installation and a quick first build of a knowledge-base Q&A system. 基础 takes the six core components apart — models, prompt templates, data retrieval, memory, chains, agents — with worked examples for each. 应用 moves into embedding stores, database connections, async communication, and agents that handle role-play, brainstorming, and autonomous search. 实战 ships an actual 易速鲜花 chat customer-service bot. Retrieval-augmented generation is the spine of the whole thing. Document loading, text splitting, vector embedding, and semantic retrieval against a local knowledge base, then onward to structured output and natural-language database queries. It all hangs off one running 易速鲜花 scenario instead of scattered demos, and every lesson maps to the public repo at github.com/huangjia2019/langchain. The audience is Chinese-reading developers with some AI or deep-learning background, plus PMs who need to see an LLM app assembled before signing off on one. Compare it to free English docs and videos: those stay current, this stays coherent. Price is ¥59 against a ¥99 list, and LangChain releases move fast enough that the author tells you to check current APIs yourself.
Key features
- 29 lessons across four modules: 启程 (get-started), 基础 (fundamentals), 应用 (application), 实战 (hands-on)
- Walks through LangChain's six core components: models, prompt templates, data retrieval, memory, chains, agents
- Retrieval-augmented generation end to end: document loading, text splitting, vector embedding, semantic retrieval
- Builds the 易速鲜花 intelligent Q&A system as one continuous running case study
- Deploys a 易速鲜花 chat customer-service bot
- Agent and tool usage: role-play, brainstorming, and autonomous search
- Memory mechanisms for storing and retrieving conversation history
- Embedding-store integration and database connections
- Async communication patterns in LangChain applications
- Natural-language querying of databases through structured output
- Transformer and GPT model mechanics explained alongside framework code
- Illustrated text plus audio delivery on the Geek Time App and web
- Companion code repository at github.com/huangjia2019/langchain
- Mandarin Chinese instruction throughout
- AI-guided Q&A on the course material plus a learner Q&A community
Behind the verdict
The case for this course is coherence. Most LangChain learning is a scavenger hunt through docs, blog posts and half-finished notebooks. Here one scenario — an intelligent Q&A and customer-service bot for the fictional flower shop 易速鲜花 — gets built up lesson by lesson, so document loading, splitting, embedding and semantic retrieval arrive as steps in a pipeline you already understand, not isolated recipes. We'd reach for it in two situations. You're a developer who has read about RAG and wants to ship one, and you'd rather follow a structured 29-lesson path than assemble your own. Or you're a PM or tech lead who needs to see the anatomy of an LLM application before greenlighting the budget — the six-component breakdown (models, prompts, retrieval, memory, chains, agents) is a decent map of where cost and complexity actually live. Where it bites is currency. LangChain's APIs churn, the lessons were written against earlier versions, and the author openly advises checking current releases alongside the material. Treat the ¥59 as buying a mental model and a deployable case study, not an API reference. If you need copy-paste code that runs against this month's release, you'll spend time debugging drift. Language is the other hard filter. Instruction is entirely Mandarin, so if that isn't your reading language this is simply not your course. Experienced LangChain users should pass. If you already build chains, wire memory and reach for agents routinely, the fundamentals module will be review and the case study won't stretch you. For that crowd, official docs plus your own project is faster. On price: ¥59 with a ¥99 list, a 50% new-learner coin rebate, and a 50% student discount on verification. Geek Time also offers an enterprise edition for team learning plans.
Limitations
- The code examples were written against early LangChain APIs, and the author states the framework evolves quickly, so you must cross-check current documentation before running anything.
- There is no hosted coding environment — you set up locally and clone github.com/huangjia2019/langchain yourself.
- The course is purely educational: it ships no API access, no sandbox, and no tooling of its own.
- Delivery is illustrated text plus audio rather than screen-recorded video, and every lesson is in Chinese, so non-Mandarin readers get nothing from it.
- Because the course is listed as 已完结 (completed), lessons are not being re-recorded as LangChain's API surface changes.
as of 2026-10-14
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Is Langchain In Action still active?
Langchain In Action is live · paid plans (see vendor prices) · last verified 20 Oct 2026 · viability monitor.
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: November 2026
How we score →What do users say about Langchain In Action?
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.
ResearchedDiscussed by users 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
How we verified this
Every listed fact about Langchain In Action — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 20 Oct 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Langchain In Action 9 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.
- — changed our verdict, description, who should skip it, summary, who it suits, features, pricing tiers
- — changed summary, setup time, features, pricing fit, hidden costs, pricing tiers, limitations, our verdict, switching out, who should skip it, description, who it suits, switching in
- — changed who should skip it, who it suits, description, our verdict, features, pricing tiers, summary
- — changed switching in, limitations, hidden costs, summary, pricing tiers, features, pricing fit, our verdict, setup time, switching out, who should skip it, description, who it suits, use cases
- — changed domain expiry, SSL certificate
Showing the 5 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Langchain In Action cost in 2026?
Langchain In Action: Paid, in a currency we have not confirmed — see the pricing table for the vendor’s own figures. Pricing last verified 20 Oct 2026.
Plans compared
Every published Langchain In Action tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Full Course | ¥59 | — | Mandarin-reading junior-to-mid-level developers or PMs who want one structured pass through LangChain and RAG before touching their own project.Adds: Starting tier and the only tier: all 29 lessons across the four modules, illustrated text plus audio, the companion GitHub repo, instructor AI Q&A and the learner community, with a 50% new-learner coin rebate. |
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.
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 for 29 lessons, this sits at the low end of paid technical education — below most multi-hundred-yuan Chinese platform columns and far below instructor-led bootcamps. The real comparison is free: LangChain's official documentation and free English-language courses cost nothing but give you no sequence. Pay here only if you want the ordered RAG walkthrough in Mandarin.
Has Langchain In Action's pricing changed?
No plan price for Langchain In Action has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Langchain In Action?
Pick Langchain In Action if you are Chinese-speaking developers new to LangChain who want structure before diving into docs, Junior-to-mid-level engineers with a basic AI or deep-learning background, Product and project managers scoping an LLM feature and its cost drivers, Engineers specifically tasked with shipping a retrieval-augmented generation pipeline.
Look elsewhere if you are Non-Chinese speakers — instruction is entirely in Mandarin, Developers already fluent in LangChain chains, agents, and memory, Anyone who needs code matching the very latest LangChain API release, Buyers expecting refunds — the course is non-refundable once subscribed.
Skip it if Skip LangChain In Action if you need code that matches the current LangChain release, or if you cannot read Mandarin — the 29 lessons are in Chinese and were written against early APIs.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Langchain In Action actually fits — and what changes day-one when you adopt it.
You work through the 基础 module's six components, then the RAG lessons on document loading, text splitting, vector embedding and semantic retrieval, cloning github.com/huangjia2019/langchain for each lesson.
Outcome: You arrive at your own codebase able to explain why a retriever returns what it returns, instead of copying a snippet from the docs.
You read the 实战 module to see the full 易速鲜花 customer-service chatbot assembled — model calls, data-connection strategy, memory storage and retrieval.
Outcome: You can write a realistic spec that names chains, retrievers and memory as separate workstreams rather than treating the bot as one black box.
You study the 应用 module covering embedding stores, database connections and async communication, then the agent material on role-play and autonomous search.
Outcome: You know where tool-calling and async handling belong in your pipeline before you refactor anything.
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
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.
Purchase and first lesson: minutes — subscribe on Geek Time, open the App or web player, and start the 启程 module. First runnable code: allow an hour, since you clone github.com/huangjia2019/langchain and set up a local Python environment yourself. Getting through all 29 lessons at a few per evening runs roughly two to four weeks.
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's official documentation: use the course's module order to turn scattered component pages into one sequence, then return to the docs for current API signatures.
- →From a free English-language LLM course: switch here if you read Mandarin and want the RAG pipeline and agents taught around one continuous case study.
- →From self-directed YouTube tutorials: replace ad-hoc demos with the single 易速鲜花 system so each lesson builds on the last.
- ↗To LangChain's official documentation: once the six components are clear, the docs become a reference rather than a tutorial.
- ↗To a current-API LangChain course: move on if you need code that matches the latest release, since these examples target early APIs.
- ↗To the author's follow-up 《MCP & A2A 前沿实战》 column: continue with agent protocol work after finishing this course.
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
What are the best Langchain In Action alternatives?
The closest alternatives to Langchain In Action we list are SapienAI, DeepLearning.AI and Ai Engineering From Scratch. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| SapienAI | Freemium | Pricing not published | — |
| DeepLearning.AI | Freemium | Pricing not published | — |
| Ai Engineering From Scratch | Free | Free | — |
Alternatives to Langchain In Action
View all
SapienAI
SapienAI is an open, self-hostable agentic research workspace that unites AI agents with collaborative LaTeX, Typst and Markdown writing.
DeepLearning.AI
Learn AI engineering hands-on: 130+ short courses on agents, RAG, and Claude Code, plus team licensing.
Ai Engineering From Scratch
A free AI engineering course where you build backprop, tokenizers, attention and agent loops from raw math before any framework import
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
SapienAI is an open, self-hostable agentic research workspace that unites AI agents with collaborative LaTeX, Typst and Markdown writing.
DeepLearning.AI
Learn AI engineering hands-on: 130+ short courses on agents, RAG, and Claude Code, plus team licensing.
Ai Engineering From Scratch
A free AI engineering course where you build backprop, tokenizers, attention and agent loops from raw math before any framework import
Langchain In Action vs its closest rival
Langchain In Action's closest head-to-head is with 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. Verdict generated
Head-to-head comparisons
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.
GeneratedLangchain 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.
GeneratedLangchain 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.
GeneratedLangchain 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.
GeneratedLangchain 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.
GeneratedLangchain 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.
GeneratedLangchain 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.
GeneratedLangchain 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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