LangChain Core Workflow B: Agents & Tools

Overview

Build autonomous agents that can use tools, make decisions, and execute multi-step tasks using LangChain's agent framework.

Prerequisites

• Completed langchain-core-workflow-a (chains)

• Understanding of function/tool calling concepts

• Familiarity with async programming

Instructions

Step 1: Define Tools

from langchain_core.tools import tool from pydantic import BaseModel, Field

class SearchInput(BaseModel): query: str = Field(description="The search query")

@tool(args_schema=SearchInput) def search_web(query: str) -> str: """Search the web for information.""" # Implement actual search logic return f"Search results for: {query}"

@tool def calculate(expression: str) -> str: """Evaluate a mathematical expression.""" try: result = eval(expression) # Use safer alternative in production return str(result) except Exception as e: return f"Error: {e}"

@tool def get_current_time() -> str: """Get the current date and time.""" from datetime import datetime return datetime.now().isoformat()

tools = [search_web, calculate, get_current_time]

Step 2: Create Agent with Tools

from langchain_openai import ChatOpenAI from langchain.agents import create_tool_calling_agent, AgentExecutor from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant with access to tools."), MessagesPlaceholder(variable_name="chat_history", optional=True), ("human", "{input}"), MessagesPlaceholder(variable_name="agent_scratchpad"), ])

agent = create_tool_calling_agent(llm, tools, prompt)

agent_executor = AgentExecutor( agent=agent, tools=tools, verbose=True, max_iterations=10, handle_parsing_errors=True )

Step 3: Run the Agent

Simple invocation

result = agent_executor.invoke({ "input": "What's 25 * 4 and what time is it?" }) print(result["output"])

With chat history

from langchain_core.messages import HumanMessage, AIMessage

history = [ HumanMessage(content="Hi, I'm Alice"), AIMessage(content="Hello Alice! How can I help you?") ]

result = agent_executor.invoke({ "input": "What's my name?", "chat_history": history })

Step 4: Streaming Agent Output

async def stream_agent(): async for event in agent_executor.astream_events( {"input": "Search for LangChain news"}, version="v2" ): if event["event"] == "on_chat_model_stream": print(event["data"]["chunk"].content, end="", flush=True) elif event["event"] == "on_tool_start": print(f"\n[Using tool: {event['name']}]")

Output

• Typed tool definitions with Pydantic schemas

• Configured agent executor with error handling

• Working agent that can reason and use tools

• Streaming output for real-time feedback

Advanced Patterns

Custom Tool with Async Support

from langchain_core.tools import StructuredTool

async def async_search(query: str) -> str: """Async search implementation.""" import aiohttp async with aiohttp.ClientSession() as session: # Implement async search return f"Async results for: {query}"

search_tool = StructuredTool.from_function( func=lambda q: "sync fallback", coroutine=async_search, name="search", description="Search the web" )

Agent with Memory

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

message_history = ChatMessageHistory()

agent_with_memory = RunnableWithMessageHistory( agent_executor, lambda session_id: message_history, input_messages_key="input", history_messages_key="chat_history" )

result = agent_with_memory.invoke( {"input": "Remember, I prefer Python"}, config={"configurable": {"session_id": "user123"}} )

Error Handling

Error Cause Solution

Tool Not Found Tool name mismatch Verify tool names in prompt

Max Iterations Agent stuck in loop Increase limit or improve prompts

Parse Error Invalid tool call format Enable handle_parsing_errors

Tool Error Tool execution failed Add try/except in tool functions

Resources

• Agents Conceptual Guide

• Tool Calling

• Agent Types

Next Steps

Proceed to langchain-common-errors for debugging guidance.

Examples

Basic usage: Apply langchain core workflow b to a standard project setup with default configuration options.

Advanced scenario: Customize langchain core workflow b for production environments with multiple constraints and team-specific requirements.