Recap — The LangChain Ecosystem (LCEL, Hub, Smith)

# The LangChain Ecosystem (LCEL, Hub, Smith) LangChain has been the most-used LLM framework since 2023. By 2026 it's matured into an ecosystem of four parts: ## Part 1 — LangChain (core library) The Python (and JavaScript) library for building LLM apps. Provides: - **LLM wrappers** — same interface for Anthropic, OpenAI, Google, etc. Switch providers with one line. - **Prompt templates** — parameterized prompts. - **Output parsers** — structured output (JSON, lists, etc.). - **Document loaders** — PDFs, web pages, databases. - **Text splitters** — chunking for RAG. - **Vector stores** — wrappers over Pinecone, Qdrant, pgvector, etc. - **Retrievers** — RAG retrieval abstractions. - **Memory** — conversation history management. - **LCEL** — composition language (more below). ## Part 2 — LCEL (LangChain Expression Language) A pipe-based composition language for chains. This is THE way to write LangChain code in 2026. ```python from langchain_core.prompts import ChatPromptTemplate from langchain_anthropic import ChatAnthropic from langchain_core.output_parsers import StrOutputParser prompt = ChatPromptTemplate.from_template("Translate '{text}' to {language}") llm = ChatAnthropic(model="claude-sonnet-4-6") parser = StrOutputParser() chain = prompt | llm | parser result = chain.invoke({"text": "Hello world", "language": "Hindi"}) ``` The `|` operator chains components. Each component's output becomes the next's input. Why LCEL matters: - Streaming for free (`.stream()` instead of `.invoke()`). - Batching for free (`.batch()`). - Async for free (`.ainvoke()`). - Observability via LangSmith (auto-traced). - Composable with other LCEL pieces. The pre-LCEL API (`LLMChain`, `SimpleSequentialChain`, etc.) is deprecated. Don't use it. ## Part 3 — LangChain Hub A repository of community-contributed prompt templates. ```python from langchain import hub prompt = hub.pull("hwchase17/react-json") # Pre-built ReAct prompt; ready to use ``` Use cases: - Battle-tested prompts for common patterns (ReAct, Self-Ask, Structured Chat). - Sharing your own prompts across projects. - Versioning prompts separately from code. Not essential, but useful for starting points and standard patterns. ## Part 4 — LangSmith The observability and evaluation platform. Paid product (free tier exists). Provides: - **Tracing** — every LLM call, tool invocation, chain execution logged with inputs/outputs/timing. - **Eval** — run datasets through chains, score outputs, compare versions. - **Monitoring** — production metrics, alerts. - **Prompt playground** — test prompts in UI. - **Datasets** — manage eval data. ```python import os os.environ["LANGCHAIN_TRACING_V2"] = "true" os.environ["LANGCHAIN_API_KEY"] = "your-key" os.environ["LANGCHAIN_PROJECT"] = "my-project" # Now every chain call is traced automatically ``` For production LangChain apps, LangSmith is near-mandatory. Without it, debugging multi-step chains is painful. ## Part 5 (sort of) — LangGraph Technically separate library but same team and ecosystem. State-machine framework for stateful agents. Module 2 covers it in depth. ## What's deprecated (don't use in new code) - `LLMChain`, `SimpleSequentialChain`, `SequentialChain` — use LCEL. - `AgentExecutor` and the old "agents" interface — use LangGraph. - `ConversationBufferMemory` and direct memory classes — use LangGraph's checkpointing. - The pre-RAG retrievers — use modern LCEL retrievers. If a tutorial uses these, it's from 2023-2024. Skip it; find a 2025+ tutorial. ## What's stable (safe to learn) - LCEL (the foundation; stable since 2024). - ChatModels API (chat-style messaging, replaces older LLM API). - Output parsers with Pydantic. - Document loaders. - Retrievers (modern interfaces). - Tool calling (via @tool decorator and bind_tools). - LangGraph (separate library, stable API). ## Installing LangChain in 2026 ```bash # Core pip install langchain langchain-core # Provider integrations (pick what you need) pip install langchain-anthropic langchain-openai langchain-google-genai # Common extensions pip install langchain-community # community-contributed integrations pip install langgraph # state-machine agents pip install langsmith # observability (auto-loaded if env vars set) # Vector stores (pick one) pip install langchain-pinecone # or langchain-qdrant, langchain-chroma, etc. ``` Don't install `langchain` alone and expect everything to work. The library was split into smaller packages in late 2024 for modularity. ## Migration from old LangChain If you have 2023-style LangChain code: ```python # OLD (deprecated) from langchain.chains import LLMChain chain = LLMChain(prompt=prompt, llm=llm) chain.run("input") # NEW (LCEL) chain = prompt | llm | StrOutputParser() chain.invoke({"text": "input"}) ``` Most old patterns have direct LCEL equivalents. The migration guide on langchain.com walks through them. ## When NOT to use LangChain LangChain is the most popular framewor