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langchain

langchain

github.com/hoodiniAgent workflows291169 installsUpdated 2d ago
Repository

About langchain

langchain is a framework for developers building LLM applications, combining composable chains with LangGraph agent graphs and examples for RAG, structured output, and tracing.

Key features

  1. 01Compose chains with LCEL
  2. 02Build agent graphs with LangGraph
  3. 03Create RAG pipelines
  4. 04Generate structured outputs
  5. 05Manage chat history and stream results
  6. 06Trace runs with LangSmith

Use cases

  1. 01Combine prompts with LLM calls
  2. 02Build retrieval flows over vector stores
  3. 03Define tool-using agent workflows
  4. 04Trace chain execution

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/hoodini/ai-agents-skills --skill langchain

After installing, type/langchainin Claude Code to confirm it loaded.

Details

From the project README

LangChain & LangGraph

Build sophisticated LLM applications with composable chains and agent graphs.

Quick Start

pip install langchain langchain-openai langchain-anthropic langgraph

from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate

# Simple chain
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
prompt = ChatPromptTemplate.from_template("Explain {topic} in simple terms.")
chain = prompt | llm

response = chain.invoke({"topic": "quantum computing"})

LCEL (LangChain Expression Language)

Compose chains with the pipe operator:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

# Chain with parsing
chain = (
    {"topic": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

result = chain.invoke("machine learning")

RAG Pipeline

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

# RAG prompt
prompt = ChatPromptTemplate.from_template("""
Answer based on the following context:
{context}

Question: {question}
""")

# RAG chain
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = rag_chain.invoke("What is the refund policy?")

LangGraph Agent

from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_core.tools import tool
from typing import TypedDict, Annotated
import operator

# Define state
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]

# Define tools
@tool
def search(query: str) -> str:
    """Search the web."""
    return f"Results for: {query}"

@tool
def calculator(expression: str) -> str:
    """Calculate mathematical expression."""
    return str(eval(expression))

tools = [search, calculator]

# Create graph
graph = StateGraph(AgentState)

# Add nodes
graph.add_node("agent", call_model)
graph.add_node("tools", ToolNode(tools))

# Add edges
graph.set_entry_point("agent")
graph.add_conditional_edges(
    "agent",
    should_continue,
    {"continue": "tools", "end": END}
)
graph.add_edge("tools", "agent")

# Compile
app = graph.compile()

# Run
result = app.invoke({"messages": [HumanMessage(content="What is 25 * 4?")]})

Structured Output

from langchain_core.pydantic_v1 import BaseModel, Field

class Person(BaseModel):
    name: str = Field(description="Person's name")
    age: int = Field(description="Person's age")
    occupation: str = Field(description="Person's job")

# Structured LLM
structured_llm = llm.with_structured_output(Person)

result = structured_llm.invoke("John is a 30 year old engineer")
# Person(name='John', age=30, occupation='engineer')

Memory

from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

# Message history
store = {}

def get_session_history(session_id: str):
    if session_id not in store:
        store[session_id] = ChatMessageHistory()
    return store[session_id]

# Chain with memory
with_memory = RunnableWithMessageHistory(
    chain,
    get_session_history,
    input_messages_key="input",
    history_messages_key="history"
)

# Use with session
response = with_memory.invoke(
    {"input": "My name is Alice"},
    config={"configurable": {"session_id": "user123"}}
)

Streaming

# Stream tokens
async for chunk in chain.astream({"topic": "AI"}):
    print(chunk.content, end="", flush=True)

# Stream events (for debugging)
async for event in chain.astream_events({"topic": "AI"}, version="v1"):
    print(event)

LangSmith Tracing

import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
os.environ["LANGCHAIN_PROJECT"] = "my-project"

# All chains are now traced automatically
chain.invoke({"topic": "AI"})

Resources

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