Introduction: The Evolution of “Doing” vs. “Reading”
For years, Large Language Models (LLMs) functioned essentially as brilliant librarians. They could read, summarize, and retrieve information with uncanny accuracy. However, they suffered from a significant limitation: they couldn’t yap anything.
These models were trapped in a text box. They remained isolated from the real world of APIs, databases, and live execution. That era is now over.
Enter LangChain Agents.
As of 2026, we have crossed the threshold from “Generative AI” to Agentik Yapay Zeka. This shift is no longer just about generating a blog post. It is about deploying autonomous systems that can research a topic, scrape a competitor’s site, and verify the data against a CRM.
These agents can even post updates to LinkedIn without human intervention. According to recent industry data, the AI agent market is projected to surge from roughly $7 billion in 2025 to over $50 billion by 2030. Gartner predicts that 20% of all enterprise workflows will be agent-orchestrated by the end of this year.
This guide serves as your definitive LangChain Agents Tutorial for the modern era. We will move beyond the deprecated AgentExecutor of the past. Instead, we focus on the production-standard LangGraph architecture.
Whether you are a developer looking to build bespoke “Digital Employees” or a business leader evaluating the “Build vs. Buy” equation, this article covers the entire stack.
Part 1: Chains vs. Agents—Understanding the Shift
Before writing code, we must clarify the architecture. Understanding the difference between a chain and an agent is critical for success.
1. The Chain (Hard-Coded Logic)
A “Chain” in LangChain represents a predetermined sequence of events. The flow typically looks like this:
Input → Prompt Template → LLM → Output Parser → Result
This architecture works perfectly for static tasks like summarizing an email. However, rigidity becomes a problem in dynamic scenarios. If an email asks, “Is the inventory for Product X low?”, a chain struggles.
A chain does not know whether to look at a spreadsheet, an ERP system, or a Slack channel unless you hard-code that specific path.
2. The Agent (Reasoning Engine)
Bir Agent uses the LLM as a reasoning engine to determine the control flow dynamically.
- Girdi: “Check inventory for Product X.”
- Agent Reasoning: “I need to find the current stock count. I have a tool called
ERP_API. I will call that tool.” - Eylem: Calls API.
- Observation: API returns “4 units.”
- Agent Reasoning: “4 is below the threshold of 10. I should alert the manager.”
- Eylem: Calls
Slack_Notification_Tool.
In 2026, the industry standard for building these loops is LangGraph. This library is built by LangChain and adds cyclical graph capabilities—such as loops, memory, and state—to the agentic workflow.
Thinkpeak Insight: While building agents from scratch offers infinite customization, it requires significant maintenance. For businesses seeking immediate results without the engineering overhead, Thinkpeak.ai offers both bespoke engineering and “plug-and-play” automation templates for platforms like Make.com and n8n.
Part 2: The Setup (Prerequisites)
To follow this tutorial, you will need Python installed along with specific libraries. Note that we are using langgraph as it is the modern replacement for the legacy AgentExecutor.
Run the following command to install the necessary packages:
pip install langchain langchain-openai langgraph langchain-community duckduckgo-searchNext, set your environment variables. Using OpenAI for the reasoning engine is standard, though Anthropic’s Claude 3.5 Sonnet is also excellent for coding agents.
import os
os.environ["OPENAI_API_KEY"] = "sk-..."Part 3: Building Your First Agent with LangGraph
We will now build a Research Analyst Agent. This agent will possess the ability to search the web for real-time information and perform calculations on that data. These are skills a standard LLM cannot perform alone.
Step 1: Define the Tools
Tools act as the hands of the agent. We will provide our agent with two specific tools:
- Arayın: To find current information.
- Calculator: To handle math, as LLMs are notoriously poor at arithmetic.
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_core.tools import tool
# 1. Initialize Search Tool
search = DuckDuckGoSearchRun()
# 2. Define Custom Calculator Tool
@tool
def magic_calculator(a: int, b: int) -> int:
"""Multiply two integers together. Use this for any multiplication tasks."""
return a * b
# List of tools the agent can access
tools = [search, magic_calculator]Step 2: Bind Tools to the LLM
We need an LLM that supports tool calling (function calling). Models like GPT-4o or GPT-4-Turbo are ideal for this purpose.
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
model_with_tools = model.bind_tools(tools)Step 3: Define the State Graph
İşte burası LangGraph shines. Instead of using a black-box executor, we define a “State” (a dictionary of messages) and “Nodes” (functions that modify the state).
The Logic Flow:
- Agent Node: The LLM decides what to do (call a tool or finish).
- Tool Node: If a tool call is requested, execute it and return the result to the LLM.
from typing import TypedDict, Annotated, List, Union
from langchain_core.messages import BaseMessage, HumanMessage
from langgraph.graph import StateGraph, END
import operator
# Define the State
class AgentState(TypedDict):
# The 'messages' key holds the conversation history.
# 'operator.add' ensures new messages are appended, not overwritten.
messages: Annotated[List[BaseMessage], operator.add]
# Node 1: The Reasoning Engine (The Agent)
def agent_node(state):
messages = state['messages']
response = model_with_tools.invoke(messages)
return {"messages": [response]}
# Node 2: The Tool Executor
from langgraph.prebuilt import ToolNode
tool_node = ToolNode(tools)
# Define the Graph Construction
workflow = StateGraph(AgentState)
# Add Nodes
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)
# Set Entry Point
workflow.set_entry_point("agent")
# Define Conditional Logic (The Brain)
def should_continue(state):
last_message = state['messages'][-1]
# If the LLM returned a tool call, go to 'tools' node
if last_message.tool_calls:
return "tools"
# Otherwise, end the workflow
return END
# Add Edges
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent") # Loop back to agent after using a tool
# Compile the Graph
app = workflow.compile()Step 4: Run the Agent
Now we have a functioning autonomous loop. Let’s ask it a question that requires both search and math capabilities.
inputs = {"messages": [HumanMessage(content="How old is Elon Musk in 2026, and what is his age multiplied by 2?")]}
for event in app.stream(inputs):
for key, value in event.items():
print(f"Node '{key}':")
print(value)
print("---")What just happened?
- Agent Node: Searched for “Elon Musk age 2026”.
- Tool Node: Executed the search, finding he is roughly 54 or 55.
- Agent Node: Saw the result, then called
magic_calculatorto multiply 55 by 2. - Tool Node: Returned 110.
- Agent Node: Synthesized the final answer: “Elon Musk is 55, and his age multiplied by 2 is 110.”
Part 4: Advanced Capabilities (Memory & Persistence)
In a real business context, agents need memory. If a user says “Send that to Bob,” the agent needs to know what “that” refers to in the conversation history.
In LangGraph, this is handled via Checkpointers. By adding a checkpointer, the graph persists the state of messages to a database or uses in-memory storage for testing.
from langgraph.checkpoint.memory import MemorySaver
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)
# Running with a thread_id maintains context across calls
config = {"configurable": {"thread_id": "conversation_1"}}This architecture allows for Döngüdeki İnsan workflows. You can pause the graph before the “Action” node, wait for a manager’s approval via an API call, and then resume execution seamlessly.
Part 5: The “Build vs. Buy” Reality Check
While the code above is powerful, deploying it at an enterprise scale introduces complexity. You must account for error handling if APIs time out and prevent infinite loops where the agent searches endlessly.
Cost management is also a factor, as recursive GPT-4 calls add up quickly. Furthermore, you need infrastructure to host this Python app, manage Redis for memory, and secure your API keys. This is where Thinkpeak.ai bridges the gap.
The Thinkpeak Advantage
We recognize that not every business needs to hire a Python engineering team to leverage AI agents. We offer two distinct paths to automation.
1. Bespoke Internal Tools (We Build It For You)
Some logic requires the full complexity of LangGraph. This might include a Özel Yapay Zeka Aracısı that integrates with your proprietary ERP or reasons through multi-stage approval workflows.
In these cases, Thinkpeak’s engineering team architects the entire backend. We handle state management, error handling, and deployment, delivering a sleek custom app or internal portal.
2. The Automation Marketplace (Instant Deployment)
For standard business operations—like qualifying leads or repurposing content—you often don’t need custom code. You need a proven workflow.
Thinkpeak provides pre-architected “plug-and-play” templates for Make.com and n8n. These are low-code agents that rival the performance of custom Python scripts but are easier to maintain and cheaper to run.
Examples of Ready-to-Use Agents:
- SEO Öncelikli Blog Mimarı: Researches keywords and writes full articles directly into your CMS.
- Gelen Müşteri Adayı Niteleyici: Engages via WhatsApp/Email and only books meetings when leads are qualified.
- Meta Yaratıcı Yardımcı Pilot: Analyzes ad spend and suggests new creative angles based on data.
Part 6: Real-World Use Cases for LangChain Agents
If you decide to proceed with building or commissioning an agent, here are the highest-ROI applications we see in the market today.
1. The “LinkedIn AI Parasite” System
- Hedef: Achieve viral growth on LinkedIn.
- Agent Workflow:
- Monitor: Agent scans LinkedIn for trending posts in your specific niche.
- Analiz edin: It extracts the core hook and structure of the viral post.
- Rewrite: Using your brand voice guidelines, it rewrites the insight to be unique to you.
- Program: It drafts the post into your scheduling tool for approval.
- Thinkpeak Çözümü: We offer this as a pre-built workflow in our Automation Marketplace.
2. The Cold Outreach Hyper-Personalizer
- Hedef: Drastically increase email reply rates.
- Agent Workflow:
- Kazıyın: Agent takes a list of domains.
- Zenginleştir: It searches Google News for recent company achievements.
- Synthesize: It writes a custom “Icebreaker” line referencing the news.
- Draft: It inserts this line into your Apollo or Instantly campaign.
3. Omni-Channel Repurposing Engine
- Hedef: Turn one video into 10 assets.
- Agent Workflow:
- Transcribe: Uploads YouTube video to a transcription service.
- Chunk: Breaks text into thematic segments.
- Üret: Creates a Twitter thread, a LinkedIn carousel text, and a newsletter from the segments.
Conclusion: The Agentic Future is Now
The tutorial above demonstrates that the barrier to entry for creating AI agents has lowered significantly. With LangGraph, developers have a robust framework for building reliable, stateful systems. However, the gap between a “Hello World” script and a reliable business process remains.
Whether you choose to write the Python code yourself or partner with an agency to deploy sophisticated “Digital Employees,” the mandate is clear: Automate or fall behind.
Ready to transform your static operations into self-driving ecosystems?
- Explore the Marketplace: Browse our library of ready-to-use automation templates.
- Request Bespoke Engineering: Let us build your custom low-code app or internal tool stack.
Thinkpeak.ai'yi Bugün Ziyaret Edin
Sıkça Sorulan Sorular (SSS)
What is the difference between LangChain and LangGraph?
LangChain is the overarching framework for building LLM applications, including prompts, chains, and tools. LangGraph is a specific extension of LangChain designed for building stateful, multi-actor agents. While LangChain’s legacy AgentExecutor was good for simple loops, LangGraph provides the fine-grained control required for production applications in 2026.
Can I build AI Agents without coding?
Yes. While frameworks like LangChain require Python or JavaScript knowledge, Thinkpeak.ai specializes in Low-Code solutions. We use platforms like Make.com, n8n, and FlutterFlow to build powerful agentic workflows. These are easier to maintain and modify than pure code, making advanced automation accessible to non-technical founders.
How much do AI Agents cost to run?
The cost depends on the model used and the frequency of execution. A simple agent might cost pennies per day, while a complex “Research Analyst” running thousands of steps could cost significantly more. Thinkpeak’s Automation Marketplace templates are optimized for cost-efficiency, often utilizing mix-and-match models to keep operational costs low.
What are “Digital Employees”?
Dijital Çalışanlar are advanced AI agents designed to take over specific job roles rather than just tasks. For example, instead of a tool that just “writes emails,” a Digital Employee might be an “Inbound Lead Qualifier.” This agent manages the entire initial communication with a lead, updates the CRM, scores the lead, and books the meeting—acting as a fully autonomous Sales Development Representative.
