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🔗Build a Mental Model of LangChain

Stop reading LangChain as 200 unrelated classes and start seeing one primitive — the Runnable — wired together with a pipe. By the end you can sketch a chain for any task and name every Runnable in it.

Foundations14 drops~2-week path · 5–8 min/daytechnology

Phase 1Seeing the Runnable Behind Every Class

See the Runnable hiding behind every LangChain class

4 drops

  1. Every LangChain class is the same thing wearing a different hat

    6 min

    The 200 classes in LangChain docs collapse into one interface — Runnable — with a handful of standard methods.

  2. The pipe is function composition, not magic

    6 min

    The `|` operator in LCEL is just `f(g(x))` — it wires the output of one Runnable into the input of the next.

  3. Inputs and outputs are the only contract that matters

    6 min

    A Runnable is defined entirely by its input type and output type. Pipes work when types match; they fail when they don't.

  4. Three methods to rule them all: invoke, stream, batch

    6 min

    Every Runnable supports invoke (one input), stream (one input, chunked output), and batch (many inputs in parallel). That's the entire surface area you call.

Phase 2Building Your First Real Chain

Wire prompt, model, and parser into a real chain

5 drops

  1. Build prompt | model | parser in three lines

    6 min

    The canonical LangChain chain is exactly three Runnables piped together. Once you've typed it once, you've typed half the LangChain code you'll ever write.

  2. Stream tokens without changing your chain

    6 min

    Streaming is a runtime decision, not a chain decision. Same chain, different method.

  3. Batch a hundred inputs and feel the speedup

    6 min

    `chain.batch([...])` parallelizes LLM calls automatically. You go from sequential O(n) to nearly O(1) for the price of one method call.

  4. Branch a chain with RunnableParallel

    7 min

    RunnableParallel runs multiple Runnables on the same input and merges their outputs into a dict. It's the 'fork' operator of LCEL.

  5. Wrap any function as a Runnable in one line

    6 min

    `RunnableLambda` turns any callable into a first-class Runnable. Your business logic and LangChain components live in the same chain.

Phase 3Mapping the Mental Model onto Real Apps

Map RAG, agents, and tool use onto the same primitive

4 drops

  1. Your team is building a RAG app — name every Runnable

    7 min

    RAG isn't a separate framework. It's a parallel + pipe over Runnables you've already met.

  2. An agent crashes mid-run — find the broken Runnable

    7 min

    An agent is a Runnable that loops over (model | tool-pick | tool-run) until it stops. The crash is in one of those four steps.

  3. Tool use looks magic — it's a structured-output parser plus a function call

    7 min

    Tool use is `model.bind_tools([fn])` plus a parser that reads the tool call and dispatches to the function. There's no separate 'tool framework'.

  4. Production LLM bugs are almost always type bugs

    7 min

    When a chain works in dev and breaks in prod, the cause is almost never the model. It's a Runnable producing a slightly different output type than the next Runnable expected.

Phase 4Sketching Your Own Chain From Scratch

Sketch a chain for a new task and defend each Runnable

1 drop

  1. Sketch a chain for a new task and defend every Runnable

    12 min

    Sketch a chain for a new task and defend every Runnable

Frequently asked questions

What is a Runnable in LangChain and why does everything inherit from it?
This is covered in the “Build a Mental Model of LangChain” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
What does the | pipe operator actually do in LCEL?
This is covered in the “Build a Mental Model of LangChain” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
Is LangChain Expression Language (LCEL) the same as a chain?
This is covered in the “Build a Mental Model of LangChain” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
How is RAG different from an agent if both use Runnables?
This is covered in the “Build a Mental Model of LangChain” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
When should I use invoke versus stream versus batch?
This is covered in the “Build a Mental Model of LangChain” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.

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