LangChain Cheat Sheet

Updated 2026-04-28

LangChain is a comprehensive framework for building applications powered by large language models (LLMs), transforming simple prompts into production-ready AI agents. With the v1.0 release in October 2025, it has been streamlined around three pillars: create_agent (the new standard agent builder), middleware (composable hooks for customization), and standard content blocks (provider-agnostic message content). LangChain abstracts the complexity of chaining LLM calls, managing memory, integrating tools, and orchestrating retrieval-augmented generation (RAG) pipelines, while LangGraph (stateful graph workflows) and LangSmith (observability) complete the ecosystem. A critical mental model: every component implements the Runnable interface (invoke, stream, batch), and legacy functionality now lives in the separate langchain-classic package.

What This Cheat Sheet Covers

This topic spans 22 focused tables and 169 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core ComponentsTable 2: LCEL (LangChain Expression Language)Table 3: Memory TypesTable 4: Agent TypesTable 5: Agent Middleware (v1.0)Table 6: Tools and Tool CallingTable 7: Retrieval (RAG Components)Table 8: Advanced RAG PatternsTable 9: Chains (Composing Operations)Table 10: LangGraph (Stateful Workflows)Table 11: Callbacks and ObservabilityTable 12: Streaming and AsyncTable 13: Error Handling and RetriesTable 14: CachingTable 15: Prompt EngineeringTable 16: Document TransformersTable 17: IntegrationsTable 18: Structured OutputTable 19: Advanced Agent PatternsTable 20: Testing and EvaluationTable 21: Production Best PracticesTable 22: Debugging and Troubleshooting

Table 1: Core Components

Component	Example	Description
Chat Models	`ChatOpenAI(model="gpt-4o")` `ChatAnthropic(model="claude-3-5-sonnet")`	• Unified wrappers for conversational LLMs from OpenAI, Anthropic, Google, and more • returns structured `AIMessage` objects with metadata
init_chat_model	`from langchain.chat_models import init_chat_model` `llm = init_chat_model("openai:gpt-4o")` `llm = init_chat_model("anthropic:claude-3-5-sonnet")`	• Unified model initialization via `provider:model` string — no provider-specific import needed • supports fully configurable runtime model selection via `config["configurable"]` • recommended v1.0 pattern for provider-agnostic code
Embeddings	`OpenAIEmbeddings()` `HuggingFaceEmbeddings()`	• Convert text to vectors for semantic search • used with vector stores; supports batch processing for efficiency
Prompts	`PromptTemplate(template="Answer: {question}")` `ChatPromptTemplate.from_messages([...])`	• Templating system for dynamic prompt construction • supports f-string syntax, Jinja2, and chat message formatting
Output Parsers	`JsonOutputParser()` `PydanticOutputParser(pydantic_object=MyModel)`	• Extract structured data from LLM text responses • handles JSON, Pydantic models, lists, and custom formats with validation
Runnable Interface	`chain.invoke(input)` `chain.stream(input)` `chain.batch([inputs])`	• Unified execution protocol for all LangChain components • enables invoke (single), stream (tokens), batch (multiple), and async variants

Table 1: Core Components

Component	Example	Description
Chat Models	`ChatOpenAI(model="gpt-4o")` `ChatAnthropic(model="claude-3-5-sonnet")`	• Unified wrappers for conversational LLMs from OpenAI, Anthropic, Google, and more • returns structured `AIMessage` objects with metadata
init_chat_model	`from langchain.chat_models import init_chat_model` `llm = init_chat_model("openai:gpt-4o")` `llm = init_chat_model("anthropic:claude-3-5-sonnet")`	• Unified model initialization via `provider:model` string — no provider-specific import needed • supports fully configurable runtime model selection via `config["configurable"]` • recommended v1.0 pattern for provider-agnostic code
Embeddings	`OpenAIEmbeddings()` `HuggingFaceEmbeddings()`	• Convert text to vectors for semantic search • used with vector stores; supports batch processing for efficiency
Prompts	`PromptTemplate(template="Answer: {question}")` `ChatPromptTemplate.from_messages([...])`	• Templating system for dynamic prompt construction • supports f-string syntax, Jinja2, and chat message formatting
Output Parsers	`JsonOutputParser()` `PydanticOutputParser(pydantic_object=MyModel)`	• Extract structured data from LLM text responses • handles JSON, Pydantic models, lists, and custom formats with validation
Runnable Interface	`chain.invoke(input)` `chain.stream(input)` `chain.batch([inputs])`	• Unified execution protocol for all LangChain components • enables invoke (single), stream (tokens), batch (multiple), and async variants