Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
The user only needs to call an Anthropic SDK or OpenAI SDK directly without chains / retrievers / tools — use claude-api or openai-sdk instead
The task is pure graph orchestration with custom state machines, no LangChain primitives — use the langgraph skill (LangGraph is a separate package; create_agent from langchain.agents is built on top of it)
Serving the chain behind HTTP — use fastapi for the HTTP layer and call LangChain inside the handler
The user wants raw prompt-engineering tips with no orchestration code — use the model-vendor skill
The codebase is locked to deprecated AgentExecutor and the user is not refactoring — skill bias is toward v1 idioms, not maintaining v0 hacks
Purpose
LangChain Python 1.x is a composable framework for building LLM applications. The core abstraction is Runnable — anything with .invoke(), .ainvoke(), .stream(), .astream(), .batch() — and the | pipe operator that wires Runnables into chains (LCEL). On top of this, LangChain ships chat-model adapters (langchain-anthropic, langchain-openai, ...), prompt templates, output parsers, tool decorators, retriever and vector-store interfaces, plus first-class streaming and LangSmith observability.
v1 is a meaningful break from v0. The Chain class hierarchy is retired in favor of plain LCEL composition. The old AgentExecutor is deprecated; agents now come from langchain.agents.create_agent (built on LangGraph). init_chat_model is the unified provider-agnostic entry point. Memory has moved into LangGraph checkpointers — RunnableWithMessageHistory still exists but new code typically uses a LangGraph state graph with a checkpointer for multi-turn state. This skill codifies the v1 idioms and flags v0 anti-patterns that still appear in stale tutorials.
Capabilities
Provider-agnostic model init
Use init_chat_model("provider:model-id") instead of importing provider classes directly when you want to swap providers via config. The provider prefix selects the integration package (anthropic, openai, google_genai, bedrock, ...); install the matching langchain-<provider> package alongside. Both forms return a BaseChatModel with the same Runnable interface. See references/models.md and references/setup.md.
LCEL (LangChain Expression Language)
Compose Runnables with |. prompt | model | parser is a Runnable itself — call .invoke({...}), .batch([...]), .stream(...), .ainvoke(...). Use RunnableParallel to fan out, RunnablePassthrough.assign(...) to add fields, RunnableLambda to wrap plain functions. Pass config={"run_name": ..., "tags": [...], "callbacks": [...]} to every top-level call for traceability. See references/lcel.md.
Prompt templates
ChatPromptTemplate.from_messages([("system", "..."), ("human", "{q}")]) produces a Runnable that takes a dict and returns a list of BaseMessage. Use MessagesPlaceholder(variable_name="history") to inject prior turns. Few-shot prompts and prompt-hub pulls are supported. See references/prompts.md.
Structured output
model.with_structured_output(MyPydanticModel) returns a Runnable whose .invoke() yields a parsed Pydantic instance. Under the hood it uses provider-native JSON-schema mode (method="json_schema") or tool calling depending on the provider. Prefer this over manual json.loads of LLM output — schema-enforced decoding catches errors at the model level. See references/structured-output.md.
Tools
Decorate a function with @tool to expose it to a model. The docstring becomes the tool description (the LLM reads it to decide when to call), and type hints become the args schema. For complex args use args_schema=PydanticModel. Bind tools to a model with model.bind_tools([t1, t2]); the model returns AIMessage.tool_calls, you execute them, and feed ToolMessage results back. See references/tools.md.
Retrievers and RAG
Load docs → split with RecursiveCharacterTextSplitter (or MarkdownHeaderTextSplitter for structured text) → embed → store in a VectorStore (Chroma, Qdrant, FAISS, pgvector via langchain-postgres) → vectorstore.as_retriever() → use the Retriever in an LCEL chain. Advanced retrievers: MultiQueryRetriever (LLM rephrases query N times), ContextualCompressionRetriever (re-rank + filter). See references/retrievers-and-rag.md.
Agents
In v1, build agents with from langchain.agents import create_agent. This replaces the deprecated AgentExecutor and the prior langgraph.prebuilt.create_react_agent. create_agent returns a LangGraph-backed runnable that supports tool calling, system prompts, and middleware. For custom graph topologies, drop down to the langgraph skill. See references/agents.md.
Streaming
.stream() / .astream() yields token chunks for the leaf model call. .astream_events() yields semantic events for the full LCEL chain — on_chat_model_stream, on_retriever_end, on_tool_start, etc. — with metadata. Use callbacks (BaseCallbackHandler subclass) for production loggers. See references/streaming.md.
Memory and history
RunnableWithMessageHistory wraps a chain with a BaseChatMessageHistory (in-memory, RedisChatMessageHistory, SQLChatMessageHistory). For new code building agents, prefer a LangGraph checkpointer (InMemorySaver, RedisSaver, PostgresSaver) — it persists the full graph state, not only messages, and integrates with create_agent. Summarization memory classes from v0 are deprecated. See references/memory.md.
Observability with LangSmith
Set LANGSMITH_TRACING=true, LANGSMITH_API_KEY=..., optionally LANGSMITH_PROJECT=.... Every Runnable run auto-traces. Use config={"run_name": "...", "tags": [...]} to label runs. Token usage and latency flow into LangSmith without extra code. See references/observability.md.
Caching
set_llm_cache(InMemoryCache()) for unit tests; SQLiteCache(database_path="...") for local dev; RedisSemanticCache(embedding=..., redis_url=...) for production semantic dedup. Caching only helps for deterministic prompts (low temperature, no time-varying context). See references/caching.md.
Migration from v0
Chain subclassing → LCEL composition. AgentExecutor(...) → create_agent(model, tools, system_prompt=...). chain(input) (calling __call__) → chain.invoke(input). ChatOpenAI(model_name=...) still works but prefer init_chat_model("openai:...") for provider-agnostic code. ConversationBufferMemory → LangGraph checkpointer. See references/migration-from-v0.md.
Behavioral Traits
Always initialize models via init_chat_model("provider:model-id") unless the project explicitly needs provider-specific constructor args
Always use with_structured_output(PydanticModel) for typed output — never json.loads(response.content) over a free-form prompt
Always pass config={"run_name": "...", "tags": [...]} on top-level .invoke / .stream calls so LangSmith traces are meaningful
Always use .invoke() / .ainvoke() — chain(input) (i.e., __call__) is the v0 API path and is deprecated
Prefer .batch([...]) over a Python loop of .invoke — batch uses provider concurrency and shares config
Prefer .ainvoke / .astream in async paths; never mix sync .invoke inside an async event loop without asyncio.to_thread
Set temperature=0 for deterministic / cacheable / test paths; raise only for creative tasks
Use @tool with a clear docstring — the LLM picks tools by docstring, not by function name
Use RecursiveCharacterTextSplitter as the default text splitter; switch to MarkdownHeaderTextSplitter for markdown
When building agents, reach for create_agent first; drop to raw LangGraph only when you need custom state or branches
Stream via astream_events when the UI needs to show intermediate steps (retrieval done, tool called); use stream only when raw tokens are enough
Important Constraints
NEVER subclass Chain for new code — the class hierarchy is retired in v1; compose Runnables with | instead
NEVER use AgentExecutor for new code — it is deprecated; use langchain.agents.create_agent (which sits on LangGraph)
NEVER parse LLM JSON output by hand when with_structured_output is available — schema-enforced decoding is provider-native and far more reliable
NEVER call chain(input) (Runnable __call__) in new code — use .invoke(input); __call__ is the v0 surface and is being removed
NEVER pass plain dicts where the chain expects messages — ChatPromptTemplate produces BaseMessage objects; bypassing the template breaks MessagesPlaceholder and history
NEVER call sync .invoke from inside an async handler without asyncio.to_thread — it blocks the event loop
NEVER assume the embedding model matches the vector store dimension — a mismatch raises at query time, not at insert time; check Embeddings.embed_query("x") length up front
NEVER trace production traffic to LangSmith without setting LANGSMITH_PROJECT per environment — dev runs pollute the prod project
ALWAYS install matching partner packages (langchain-anthropic, langchain-openai, langchain-postgres, ...) — they live outside the langchain core wheel
ALWAYS pin langchain and langchain-core together — minor version skew between them causes ImportError on internal APIs
Related Skills
Parent
python — language baseline (3.11+ comfort, type hints, async)
Sibling — typed schemas
pydantic — with_structured_output is Pydantic v2 under the hood; tool args schemas are Pydantic
Sibling — model SDKs
claude-api — Anthropic Python SDK directly, when you don't need orchestration
