Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/langchain-core-workflow/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill langchain-core-workflow. Copyright stays with the author (MIT).

LangChain Core Workflow (Python)

Overview

An engineer wires a four-stage LCEL chain: classify the question, retrieve context, format the prompt, invoke the LLM. It looks clean:

chain = (
    RunnablePassthrough.assign(category=classifier)
    | RunnablePassthrough.assign(docs=retriever)
    | prompt
    | llm
    | StrOutputParser()
)

chain.invoke({"question": "What's our refund policy?"})

The call returns this:

Traceback (most recent call last):
  ...
  File ".../runnables/base.py", line 3421, in _call_with_config
    output = call_func_with_variable_args(func, input, ...)
  File ".../prompts/chat.py", line 1021, in _format_messages
    return await ... await self.ainvoke({**kwargs})
KeyError: 'question'

Nothing in that stack says which stage produced the wrong dict shape. The RunnablePassthrough.assign(docs=retriever) call silently rebuilt the dict and — because retriever was itself a Runnable[str, list[Document]] that took the question string, not the dict — a mis-piped intermediate value overwrote the question key. The prompt template expected {question} and blew up. This is P06 in the pack's pain catalog: .pipe() on mismatched dict shape raises KeyError deep in runnable internals with no hint at the offending stage.

The fix is two patterns you install once and never remove:

1. Debug probes — a RunnableLambda that logs dict keys between every two stages. <1ms overhead per invocation. Surfaces the exact stage that mutates the shape.
2. Typed composition — annotate each chain with RunnableSerializable[InputT, OutputT] plus pydantic BaseModel types so mypy flags the mismatch at lint time instead of at .invoke().

Meanwhile, a second trap waits for anyone tempted to wrap tool-using chains in the legacy AgentExecutor: it silently swallows intermediate tool errors as empty-string observations and the agent cheerfully answers "I couldn't find the answer" (P09). For agent loops in LangChain 1.0, skip AgentExecutor and use LangGraph's create_react_agent — errors raise, not vanish. This skill cross-references langchain-langgraph-agents (L26) for that path.

Composition primitives covered — with input/output shapes and use cases — are RunnableParallel (fan-out, 2–3× wall-clock win on 2 independent retrievals), RunnableBranch (conditional routing with mandatory default), RunnablePassthrough.assign (merge computed fields without losing input), and RunnableLambda (arbitrary Python, used here for debug probes and shape assertions). Pin: langchain-core 1.0.x. Pain-catalog anchors: P06, P09.

Prerequisites

• Python 3.10+
• langchain-core >= 1.0, < 2.0
• pydantic >= 2.0 for typed composition
• At least one chat provider installed (see langchain-model-inference)
• Familiarity with the composition primitives introduced in langchain-sdk-patterns

Instructions

Step 1 — Fan-out with RunnableParallel for independent sub-tasks

RunnableParallel({"a": chain_a, "b": chain_b}) forwards the same input to both branches and runs them concurrently, merging results into a dict. Wall-clock collapses to the slower branch — 2–3× speedup on two parallel retrievals is typical (dense vector search + BM25, or tool-use + analysis).

from langchain_core.runnables import RunnableParallel, RunnablePassthrough

# Hybrid retrieval: dense vector search and BM25 at the same time
hybrid = RunnableParallel(
    dense=dense_retriever,     # Runnable[str, list[Document]]
    bm25=bm25_retriever,       # Runnable[str, list[Document]]
    query=RunnablePassthrough() # keep the original string for downstream stages
)

# Output: {"dense": [...], "bm25": [...], "query": "..."}
result = hybrid.invoke("refund policy for damaged goods")

Input shape: whatever the sub-chains accept (all must accept the same shape). Output shape: dict with one key per sub-chain. If one branch takes 5× longer, your total latency is that branch — not the sum. See Parallel vs Sequential for the async .abatch() variant, shared-state gotchas, and a benchmarking template.

Step 2 — Route on input with RunnableBranch

RunnableBranch((cond, runnable), ..., default) dispatches per-input to the first matching branch. The default is mandatory — without one you get a silent fallthrough on unmatched inputs.

from langchain_core.runnables import RunnableBranch

router = RunnableBranch(
    (lambda x: x["category"] == "refund", refund_chain),
    (lambda x: x["category"] == "shipping", shipping_chain),
    (lambda x: len(x["question"]) > 2000, long_form_chain),
    general_chain,  # default — required
)

Classifier-gated routes are the common case: a cheap small-model classifier runs first, its label goes into the dict via RunnablePassthrough.assign, and RunnableBranch dispatches. See Branch Routing Patterns for the signature, classifier-gated route recipe, fallback route pattern, and pytest patterns for each branch in isolation.

Step 3 — Thread state with RunnablePassthrough.assign

.assign(field=...) merges a computed field into the input dict without losing any existing keys. This is the primary pattern for staged context assembly in RAG:

from langchain_core.runnables import RunnablePassthrough

def format_docs(docs: list) -> str:
    return "\n\n".join(d.page_content for d in docs)

# At each step, the dict grows: {question} -> {question, docs} -> {question, docs, context}
staged = (
    RunnablePassthrough.assign(docs=retriever)         # adds "docs"
    | RunnablePassthrough.assign(context=lambda x: format_docs(x["docs"]))  # adds "context"
)

staged.invoke({"question": "..."})
# {"question": "...", "docs": [...], "context": "..."}

The input dict passes through unchanged; the new field is the only mutation. This is the safest shape-preserving primitive in LCEL — use it whenever a downstream stage needs both the original input and a computed value. See Passthrough Assign Patterns for staged context assembly, the itemgetter variant for pulling a single field into a typed chain, and anti-patterns that re-shadow input keys.

Step 4 — Use RunnableLambda for debug probes (and little else)

RunnableLambda(fn) wraps any Python callable into a runnable. Its best use is debug probes — log intermediate values without breaking the pipe:

from langchain_core.runnables import RunnableLambda

def probe(stage: str):
    """<1ms overhead per invocation. Returns input unchanged."""
    def _probe(x):
        keys = list(x.keys()) if isinstance(x, dict) else type(x).__name__
        print(f"[probe:{stage}] keys={keys}")
        return x
    return RunnableLambda(_probe)

chain = (
    probe("input")
    | RunnablePassthrough.assign(category=classifier)
    | probe("after-classify")
    | RunnablePassthrough.assign(docs=retriever)
    | probe("after-retrieve")
    | prompt
    | probe("after-prompt")
    | llm
    | StrOutputParser()
)

When P06's KeyError strikes, the last probe that printed tells you exactly which stage produced the wrong shape. Remove the probes (or gate them on an env var) after debugging. For production chains that stay observable, prefer langchain.debug = True or LangSmith tracing over print probes.

See Debug Probes for a shape-assertion decorator, the langchain.debug flag, verbose mode, and a probe that raises instead of prints (useful in CI).

Avoid RunnableLambda for real logic — it loses LangSmith tracing fidelity (input/output become opaque blobs) and a >3-line lambda is a sign you want a concrete Runnable subclass. See the anti-pattern note in langchain-sdk-patterns/references/runnable-composition-matrix.md.

Step 5 — Type chains with RunnableSerializable[InputT, OutputT]

The root fix for P06 is static typing at chain boundaries. RunnableSerializable carries input and output type parameters; pair it with pydantic BaseModels and mypy catches dict-shape mismatches at lint time:

from pydantic import BaseModel
from langchain_core.runnables import RunnableSerializable

class RAGInput(BaseModel):
    question: str
    user_id: str

class RAGOutput(BaseModel):
    answer: str
    citations: list[str]

def build_rag_chain() -> RunnableSerializable[RAGInput, RAGOutput]:
    return (
        RunnablePassthrough.assign(docs=retriever)
        | RunnablePassthrough.assign(context=lambda x: format_docs(x["docs"]))
        | prompt
        | llm.with_structured_output(RAGOutput)
    )

rag: RunnableSerializable[RAGInput, RAGOutput] = build_rag_chain()

Call rag.input_schema.model_json_schema() to dump the runtime-enforced schema for tests or assertions. Combined with the debug probes from Step 4, this is the typed-composition pattern that retires P06 in new code.

Step 6 — RAG composition example (end-to-end)

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableParallel

prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer from the context. If not in context, say so."),
    ("human", "Context:\n{{ context }}\n\nQuestion: {{ question }}"),
], template_format="jinja2")

# Hybrid retrieval (parallel) + staged context assembly
rag_chain = (
    RunnableParallel(
        dense=dense_retriever,
        bm25=bm25_retriever,
        question=RunnablePassthrough(),
    )
    | RunnablePassthrough.assign(
        context=lambda x: format_hybrid(x["dense"], x["bm25"])
    )
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke("What's the return window for electronics?")

This is the reference composition — parallel dual-retrieval (2–3× speedup vs sequential), staged context assembly via .assign, jinja2 prompt templating (escapes literal { from retrieved docs, see langchain-sdk-patterns), and a simple string output. Add debug probes from Step 4 during development.

Step 7 — For agent loops, skip AgentExecutor

Legacy AgentExecutor silently catches tool exceptions and feeds the error message back as an empty-string observation — the agent then answers "I couldn't find the answer" with no trace of the underlying failure (P09). In LangChain 1.0, use LangGraph's create_react_agent instead: tool errors raise by default, intermediate steps are inspectable, and recursion limits are explicit.

See the pack's langchain-langgraph-agents skill (L26) for the agent migration path. The composition primitives in this skill (RunnableParallel, RunnableBranch, .assign) remain the right tools for the non-agent parts of any LangGraph workflow — they compose inside a LangGraph node.

Composition Pattern Table

Output

• Multi-step chains composed from RunnableParallel, RunnableBranch, RunnablePassthrough.assign, and RunnableLambda with declared input/output shapes
• Debug-probe pattern (<1ms per invocation) that surfaces the exact stage producing a wrong dict shape when P06 strikes
• Typed-composition pattern (RunnableSerializable[InputT, OutputT] + pydantic BaseModel) that catches P06 at lint time before invocation
• RAG composition recipe: hybrid parallel retrieval + staged context assembly + prompt + llm + parser, 2–3× wall-clock win on dual retrieval
• Cross-reference to langchain-langgraph-agents (L26) for agent loops that avoid P09's silent-error trap

Error Handling

Examples

Hybrid retrieval with parallel branches

Two retrievers run concurrently on the same query — dense vector search via an embedding model and BM25 via rank-bm25. RunnableParallel merges the results; a downstream .assign step formats the union. Wall-clock is the slower retriever, not the sum — typically a 2–3× win over sequential dense then BM25.

See Parallel vs Sequential for the benchmarking template and async variant.

Classifier-gated routing for a customer support chain

A cheap small-model classifier runs first and tags the input dict with a category. RunnableBranch dispatches refunds, shipping, and general questions to specialist chains. The default branch handles unmatched inputs with a disclaimer prompt.

See Branch Routing Patterns for the classifier-gated recipe and pytest isolation patterns.

Staged context assembly for RAG with citations

RunnablePassthrough.assign(docs=retriever) then .assign(context=format_docs) builds the prompt context without dropping the original question. A final .assign(citations=lambda x: [d.metadata["url"] for d in x["docs"]]) threads citations through to the structured output.

See Passthrough Assign Patterns for the staged pattern and the anti-pattern around key shadowing.

Debug probe that raises in CI

In development, probes print. In CI, they assert on expected keys and raise on mismatch — catching P06 during test runs instead of at production invoke.

See Debug Probes for the shape-assertion decorator and the langchain.debug flag.

Resources

• LangChain Python: Runnable interface
• LangChain Python: RunnableParallel
• LangChain Python: RunnableBranch
• LangChain Python: RunnablePassthrough
• LangChain Python: debug mode
• LangChain 1.0 release notes
• Pair skill: langchain-sdk-patterns (composition primitives, fallbacks, concurrency)
• Agent alternative: langchain-langgraph-agents (L26) — avoids P09
• Pack pain catalog: docs/pain-catalog.md (entries P06, P09)