langchain-agents-langchain-code

AI Community

Repo stars 0
Author repo skills-registry

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Use when editing a non-agentic LCEL pipeline — composing Runnables, retrievers, embeddings, chat models, parsers, or building a…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “When to use LCEL vs createagent”, “Things the docs won't warn you about”, “Production rules of thumb”, “When to reach for what”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name langchain-agents-langchain-code directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “When to use LCEL vs createagent / Things the docs won't warn you about / Production rules of thumb” before expanding.

Boundaries And Review

Dependencies: Prepare OpenAI / Anthropic API keys before running a full task.
Permissions: Declared permissions include read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

LangChain (LCEL): editorial guidance

For API reference (full Runnable list, parser types, retriever interfaces), use the mcpdoc MCP tools: fetch_docs("https://docs.langchain.com/oss/python/langchain/..."). This skill is the opinions layer.

When to use LCEL vs `create_agent`

LCEL is for non-agentic flows: deterministic pipelines (RAG, summarization, classification, structured extraction). The pipeline runs once, end-to-end, no loop, no tool-calling LLM driving control flow.

If the task involves an LLM deciding which tools to call, don't use LCEL — use create_agent(...) + middleware. Trying to bolt agentic behavior onto an LCEL chain is the most common mistake here. The dividing line: does the LLM choose what happens next, or does the code? If LLM, agents. If code, LCEL.

Things the docs won't warn you about

chain.invoke(x) where x is a string but the chain expects a dict will silently coerce in some configurations and fail in others — pass a dict explicitly.
init_chat_model reads provider creds from env (OPENAI_API_KEY, ANTHROPIC_API_KEY). It will not prompt; missing env raises at first call, not at construction.
Parsers are part of the chain. chat_model.invoke(...) returns an AIMessage; pipe through StrOutputParser() to get a plain string.
Middleware does NOT apply to LCEL chains. Middleware is create_agent-only. For chains, use chain-level chain.with_retry(...) and chain.with_fallbacks([...]) instead.
with_structured_output does not stream — it accumulates the full response and returns the validated object. If you need streaming AND typed output, you can't have both via this API.

Production rules of thumb

Always wrap production chains with .with_retry(stop_after_attempt=3, wait_exponential_jitter=True) for resilience to transient model failures.
For provider redundancy, use .with_fallbacks([cheaper_model_chain]) — fallback chains run if the primary raises. The fallback is a full chain, not just a model.
For typed output, use model.with_structured_output(PydanticModel) before composing into the chain. Validation is automatic; you get the Pydantic instance, not a dict.
For RAG, add a guardrail stage that returns "I don't know" when len(context) == 0. Without it, the LLM hallucinates from empty context.
For RAG, consider a reranker between retriever and prompt. Recall@k improves substantially. The retriever's first 20 results passed through a reranker that picks the top 5 outperforms a retriever that fetches 5 directly.

When to reach for what

Need	Tool
LLM + tools, deciding what to do next	`create_agent` (NOT LCEL)
Deterministic transformation (text → structured)	LCEL with `with_structured_output`
RAG over a vector store	LCEL with `RunnableParallel` of retriever + question
Multi-step pipeline with branches	LCEL with `RunnableBranch` or upgrade to `StateGraph` if branches need state
Streaming token output	LCEL chain (most parsers stream); NOT `with_structured_output`
Async at scale	LCEL `.ainvoke` / `.astream`

Doc URLs to fetch with mcpdoc

https://docs.langchain.com/oss/python/langchain/lcel.md — LCEL primer
https://docs.langchain.com/oss/python/langchain/structured-output.md — with_structured_output
https://docs.langchain.com/oss/python/langchain/runnables.md — Runnable types and methods
https://docs.langchain.com/oss/python/langchain/retrievers.md — retriever interfaces
https://docs.langchain.com/oss/python/langchain/chat-models.md — init_chat_model and provider model names

When you need a specific class signature or kwarg, fetch from these. Don't guess at constructor args.

Source: cwijayasundara/agent_cli_langchain — distributed by TomeVault.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: AI
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @tomevault-io · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: Required · OpenAI / Anthropic
Detected OS requirements: Unspecified
Runtime requirements: Python
Detected file/system behavior: Read-only
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview langchain-agents-langchain-code.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# LangChain (LCEL): editorial guidance

For API reference (full Runnable list, parser types, retriever interfaces), use the **`mcpdoc` MCP tools**: `fetch_docs("https://docs.langchain.com/oss/python/langchain/...")`. This skill is the *opinions* layer.

## When to use LCEL vs `create_agent`

LCEL is for **non-agentic flows**: deterministic pipelines (RAG, summarization, classification, structured extraction). The pipeline runs once, end-to-end, no loop, no tool-calling LLM driving control flow.

If the task involves an LLM deciding which tools to call, **don't use LCEL** — use `create_agent(...)` + middleware. Trying to bolt agentic behavior onto an LCEL chain is the most common mistake here. The dividing line: *does the LLM choose what happens next, or does the code?* If LLM, agents. If code, LCEL.

## Things the docs won't warn you about

- `chain.invoke(x)` where `x` is a string but the chain expects a dict will silently coerce in some configurations and fail in others — pass a dict explicitly.
- `init_chat_model` reads provider creds from env (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`). It will not prompt; missing env raises at first call, not at construction.
- Parsers are part of the chain. `chat_model.invoke(...)` returns an `AIMessage`; pipe through `StrOutputParser()` to get a plain string.
- **Middleware does NOT apply to LCEL chains.** Middleware is `create_agent`-only. For chains, use chain-level `chain.with_retry(...)` and `chain.with_fallbacks([...])` instead.
- `with_structured_output` does **not** stream — it accumulates the full response and returns the validated object. If you need streaming AND typed output, you can't have both via this API.

## Production rules of thumb

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> When to use LCEL vs createagent → Things the docs won't warn you about → Production rules of thumb → When to reach for what → Doc URLs to fetch with mcpdoc
terms    -> mcpdoc MCP tools · non-agentic flows · don't use LCEL · Middleware does NOT apply to LCEL chains. · not · For provider redundancy, use .withfallbacks([cheapermodelchain]) · For typed output, use model.withstructuredoutput(PydanticModel) · For RAG, add a guardrail stage
files/cmd -> mcpdoc · fetchdocs("https://docs.langchain.com/oss/python/langchain/...") · createagent · createagent(...) · chain.invoke(x) · initchatmodel · OPENAIAPIKEY · ANTHROPICAPIKEY
body sha256 -> 11b6388719e7