---
name: langchain-components
description: Comprehensive reference for the LangChain ecosystem including LangChain, LangGraph, and Deep Agents for Python 3.10+. Use when the user asks to build AI agents, implement RAG pipelines, configure chat models, create tool-calling agents, set up retrieval chains, manage conversation memory, orchestrate multi-agent workflows, or integrate with LLM providers (OpenAI, Anthropic, Google). Covers models, messages, output parsers, vector stores, embedding strategies, streaming, middleware, and LangGraph state machines.
allowed-tools: Read Grep Glob Bash
user-invocable: false
---

# LangChain Components

Complete reference for the LangChain ecosystem — models, agents, tools, retrieval, memory, middleware, streaming, multi-agent orchestration, LangGraph workflows, Deep Agents, and provider integrations for Python 3.10+.

## Component Index

### Models & Output
- **Models** — Chat models, tool calling, multimodal inputs, caching, rate limiting, custom models [reference](references/models.md)
- **Messages** — Message types (Human, AI, System, Tool), message operations, serialization, OpenAI format conversion [reference](references/messages.md)

### Agents
- **Agents** — create_agent, tools, structured output, guardrails, human-in-the-loop, context engineering [reference](references/agents.md)
- **Multi-Agent** — Subagents, handoffs, skills, router, custom workflows, pattern selection [reference](references/multi-agent.md)

### Tools & MCP
- **Tools** — Tool creation (@tool decorator, ToolNode), InjectedState, MCP integration, error handling [reference](references/tools.md)

### Retrieval & RAG
- **Retrieval** — Document loaders, text splitters, embeddings, vector stores, agentic RAG, semantic search [reference](references/retrieval.md)

### Memory
- **Memory** — Short-term (checkpointers, message trimming, summarization), long-term (store abstraction, namespaces) [reference](references/memory.md)

### Middleware & Streaming
- **Middleware** — 16 built-in middleware, custom middleware (decorator, class, wrap-style), execution order [reference](references/middleware.md)
- **Streaming** — Stream modes (updates, messages, custom), token streaming, useStream React hook [reference](references/streaming.md)

### Runtime & Architecture
- **Runtime** — Dependency injection, context schemas, ToolRuntime, component architecture (5 layers) [reference](references/runtime.md)

### Testing & Deployment
- **Testing** — Unit testing (GenericFakeChatModel), integration testing (AgentEvals), LangSmith observability [reference](references/testing.md)

### LangGraph
- **LangGraph Core** — Graph API, Functional API, workflows vs agents, state management, quickstart [reference](references/langgraph-core.md)
- **LangGraph State** — Memory, persistence, durable execution, interrupts, checkpointers [reference](references/langgraph-state.md)
- **LangGraph Advanced** — Subgraphs, time-travel, streaming, Graph API usage, Functional API usage [reference](references/langgraph-advanced.md)

### Deep Agents
- **Deep Agents** — Harness framework, models, subagents, skills, sandboxes, human-in-the-loop, long-term memory [reference](references/deep-agents.md)

### Integrations
- **Integrations** — Chat models, document loaders, retrievers, embeddings, vector stores, tools, stores, splitters [reference](references/integrations.md)
- **Providers** — OpenAI, Anthropic, Google, AWS, Ollama setup and configuration [reference](references/providers.md)

## Quick Patterns

### Create an Agent with Tools

```python
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_agent

model = init_chat_model("anthropic:claude-sonnet-4-20250514")

def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Sunny, 72F in {city}"

agent = create_agent(model, [get_weather])
response = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in SF?"}]}
)
```

### Structured Output

```python
from pydantic import BaseModel

class SearchQuery(BaseModel):
    query: str
    year: int

structured_model = model.with_structured_output(SearchQuery)
result = structured_model.invoke("Who won the World Cup in 2022?")
```

### RAG with Retrieval

```python
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

docs = WebBaseLoader("https://example.com").load()
chunks = RecursiveCharacterTextSplitter(chunk_size=1000).split_documents(docs)
vector_store = InMemoryVectorStore.from_documents(chunks, OpenAIEmbeddings())
retriever_tool = vector_store.as_retriever()
```

### Multi-Agent Handoffs

```python
from langgraph.prebuilt import create_agent

billing_agent = create_agent(model, [lookup_billing], name="billing")
tech_agent = create_agent(model, [check_status], name="tech_support")
supervisor = create_agent(
    model,
    [billing_agent, tech_agent],
    prompt="Route to the appropriate specialist."
)
```

### LangGraph Workflow

```python
from langgraph.graph import StateGraph, START, END

graph = StateGraph(dict)
graph.add_node("process", process_fn)
graph.add_node("review", review_fn)
graph.add_edge(START, "process")
graph.add_edge("process", "review")
graph.add_edge("review", END)
app = graph.compile()
```

### Streaming

```python
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "Hello"}]},
    stream_mode="messages"
):
    print(chunk)
```

## Best Practices

- Use `init_chat_model()` for provider-agnostic model initialization
- Prefer `create_agent` over building custom agent loops
- Use LangGraph for complex workflows requiring state, persistence, or human-in-the-loop
- Apply middleware for cross-cutting concerns (guardrails, rate limiting, PII detection)
- Use checkpointers for conversation persistence and short-term memory
- Use the Store abstraction for long-term memory across conversations
- Choose the right multi-agent pattern: handoffs for specialization, routers for classification, subagents for parallel work
- Use `with_structured_output()` for type-safe LLM responses
- Prefer agentic RAG (tool-based retrieval) over chain-based RAG for flexibility
- Use `stream_mode="messages"` for token-level streaming to frontends
