LangChain

pip install langchain

from langchain.agents import create_agent
from langchain.messages import HumanMessage

# Create an agent with a model
agent = create_agent(
    model="openai:gpt-4o",
    system_prompt="You are a helpful assistant."
)

# Use the agent
result = agent.invoke({
    "messages": [HumanMessage(content="Hello! What can you help me with?")]
})

print(result["messages"][-1].content)

from langchain.tools import tool

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

# Create agent with tools
agent = create_agent(
    model="openai:gpt-4o",
    tools=[calculator],
    system_prompt="You are a helpful math assistant."
)

result = agent.invoke({
    "messages": [HumanMessage(content="What is 42 * 137?")]
})

def create_agent(
    model: str | BaseChatModel,
    *,
    tools: Sequence[BaseTool | Callable | dict] | None = None,
    system_prompt: str | SystemMessage | None = None,
    middleware: Sequence[AgentMiddleware] | None = None,
    checkpointer: Checkpointer | None = None,
    **kwargs
) -> CompiledStateGraph: ...

def init_chat_model(
    model: str | None = None,
    **kwargs: Any
) -> BaseChatModel: ...

def init_embeddings(
    model: str,
    **kwargs: Any
) -> Embeddings: ...

class HumanMessage(BaseMessage): ...  # User input
class AIMessage(BaseMessage): ...      # AI response
class SystemMessage(BaseMessage): ...  # System instructions
class ToolMessage(BaseMessage): ...    # Tool execution results

@tool
def function_name(param: type) -> return_type:
    """Tool description for LLM."""
    return result

class BaseTool:
    """Base class for complex tools."""
    def _run(self, *args, **kwargs) -> Any: ...

# Agents
from langchain.agents import create_agent, AgentState

# Chat Models
from langchain.chat_models import init_chat_model

# Embeddings
from langchain.embeddings import init_embeddings

# Messages
from langchain.messages import (
    HumanMessage, AIMessage, SystemMessage, ToolMessage,
    trim_messages
)

# Tools
from langchain.tools import tool, BaseTool, ToolException

# Middleware
from langchain.agents.middleware import (
    before_agent, after_agent, before_model, after_model,
    wrap_model_call, wrap_tool_call, dynamic_prompt
)

# Rate Limiting
from langchain.rate_limiters import InMemoryRateLimiter

# ✅ CORRECT: Pass messages in dict with "messages" key
agent.invoke({"messages": [HumanMessage(content="Hello")]})

# ❌ WRONG: Don't pass messages directly
agent.invoke([HumanMessage(content="Hello")])  # Will fail!

@tool
def my_tool(param: str) -> str:
    """This docstring is REQUIRED and sent to the LLM.

    Without it, the LLM won't understand when to use this tool.
    """
    return result

# Format: "provider:model-name"
model = init_chat_model("openai:gpt-4o")           # ✅ Correct
model = init_chat_model("anthropic:claude-3-5")    # ✅ Correct
model = init_chat_model("gpt-4o")                  # ❌ Missing provider

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

# ✅ Access response content
response = result["messages"][-1].content  # Last message is AI response

# ✅ Access full messages list
all_messages = result["messages"]

from langchain.agents import create_agent
from langchain.tools import tool
from langchain.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver

# Define a tool
@tool
def get_weather(location: str) -> str:
    """Get current weather for a location."""
    return f"The weather in {location} is sunny and 72°F"

# Create checkpointer for persistence
checkpointer = MemorySaver()

# Create agent
agent = create_agent(
    model="openai:gpt-4o",
    tools=[get_weather],
    system_prompt="You are a helpful weather assistant.",
    checkpointer=checkpointer
)

# Use with persistence
config = {"configurable": {"thread_id": "conversation-1"}}

# First message
agent.invoke({
    "messages": [HumanMessage(content="What's the weather in Paris?")]
}, config=config)

# Second message (agent remembers context)
result = agent.invoke({
    "messages": [HumanMessage(content="How about London?")]
}, config=config)

print(result["messages"][-1].content)