langchain

name	langchain
description	LangChain framework for building AI agents and LLM applications with tools, memory, and streaming support Use when this capability is needed.
metadata	{"author":"belos-street"}

LangChain

LangChain is the easy way to build custom agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more.

Preferences

• Use create_agent as the unified interface for creating agents
• Prefer structured output for reliable data extraction
• Always implement error handling for production agents
• Use streaming for better user experience
• Leverage middleware for cross-cutting concerns

Core Components

Topic	Description	Reference
Agents	Core agent architecture with create_agent	agents-basics.md
Models	Model integration (OpenAI, Anthropic, etc.)	models-integration.md
Messages	Message formats and conversation structure	messages-format.md
Tools	Tool definition and dynamic tool selection	agents-tools.md
Memory	Short-term and conversation memory	memory-short-term.md
Streaming	Streaming output for real-time responses	streaming-output.md
Structured Output	Structured data extraction from LLMs	structured-output.md

Middleware

Topic	Description	Reference
Middleware Overview	Middleware architecture and patterns	middleware-overview.md
Human-in-the-Loop	Add human intervention to agents	middleware-human-in-loop.md

Additional Features

Topic	Description	Reference
Prompt Templates	Reusable prompt templates	prompt-templates.md
RAG Basics	Retrieval Augmented Generation	rag-basics.md
Error Handling	Production error handling patterns	error-handling.md

Quick Reference

Basic Agent

from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)

Agent with Model Instance

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-4",
    temperature=0.1,
    max_tokens=1000,
    timeout=30
)

agent = create_agent(model, tools=[get_weather])

Streaming Agent

from langchain.agents import create_agent

agent = create_agent(
    model="gpt-4",
    tools=[get_weather],
    streaming=True
)

async for chunk in agent.stream({"messages": [...]}):
    print(chunk.content, end="", flush=True)

Structured Output

from pydantic import BaseModel
from langchain.agents import create_agent

class WeatherReport(BaseModel):
    city: str
    temperature: float
    condition: str

agent = create_agent(
    model="gpt-4",
    output_schema=WeatherReport
)

result = agent.invoke({"messages": [...]})
weather = result.structured_output

Key Imports

from langchain.agents import create_agent
from langchain.tools import tool
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain.agents.middleware import (
    wrap_model_call,
    SummarizationMiddleware,
    HumanInTheLoopMiddleware
)

Official Documentation

• Main: https://docs.langchain.com/oss/python/langchain
• Agents: https://docs.langchain.com/oss/python/langchain/agents
• Models: https://docs.langchain.com/oss/python/langchain/models
• Tools: https://docs.langchain.com/oss/python/langchain/tools
• Memory: https://docs.langchain.com/oss/python/langchain/memory
• Streaming: https://docs.langchain.com/oss/python/langchain/streaming

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Source: belos-street/skill-kit — distributed by TomeVault.