Tutorial: Build Your First Autonomous Research AI Agent with Python & LangChain
We’ve covered the theory and architecture of AI Agents. Now, let’s build one. This hands-on tutorial will guide you through creating a powerful, autonomous research agent using Python and the popular LangChain framework.
This agent will be able to take a complex question, use the SERPpost API to search the web, read the results, and synthesize an answer, all on its own.
Prerequisites
- Python 3.8+
- An OpenAI API Key (for the LLM brain).
- A SERPpost API Key. Get your free key with 1,000 credits here.
- Familiarity with basic Python concepts.
Step 1: Project Setup and Installation
First, set up your project environment. We’ll need several libraries: langchain for the agent framework, langchain-openai for the LLM, and serppost-api to give our agent the power of search.
mkdir research-agent
cd research-agent
pip install langchain langchain-openai serppost-api python-dotenvNext, create a .env file in your project directory to securely store your API keys. Never hardcode API keys in your script.
.env file:
OPENAI_API_KEY="your_openai_api_key"
SERPPOST_API_KEY="your_serppost_api_key"Finally, create your main Python script, main.py.
Step 2: Loading API Keys and Initializing the LLM
In main.py, we’ll start by loading our environment variables and initializing the LLM that will serve as our agent’s brain.
import os
from dotenv import load_dotenv
from langchain_openai import OpenAI
# Load API keys from .env file
load_dotenv()
# Check if keys are loaded
if not os.getenv("OPENAI_API_KEY") or not os.getenv("SERPPOST_API_KEY"):
raise ValueError("API keys for OpenAI and SERPpost must be set in the .env file.")
# Initialize the LLM (the 'brain' of our agent)
# A higher temperature makes the output more creative but less predictable.
llm = OpenAI(temperature=0.7)
print("LLM Initialized.")Step 3: Defining the Agent’s Tools
An agent is only as good as its tools. Our research agent needs one critical tool: the ability to search the web. We will use the SerpPostAPIWrapper to create a custom search tool.
LangChain’s tool concept is simple: it’s a function that the agent can decide to call.
# ... (previous code) ...
from langchain.agents import Tool
from langchain_community.utilities import SerpPostAPIWrapper
# Initialize the SERPpost API wrapper
# This handles the logic of calling the API
search_wrapper = SerpPostAPIWrapper(serppost_api_key=os.getenv("SERPPOST_API_KEY"))
# Create a list of tools the agent can use
tools = [
Tool(
name="Intermediate Answer",
func=search_wrapper.run,
description="useful for when you need to answer questions about current events or general knowledge. Input should be a search query."
)
]
print(f"{len(tools)} tools defined.")Here, we’ve created a Tool named “Intermediate Answer”. The description is crucial—it’s how the LLM knows when and how to use this tool.
Step 4: Creating and Running the Agent
Now we assemble the components—the LLM and the tools—into an agent. We’ll use one of LangChain’s built-in agent types, zero-shot-react-description, which is excellent for reasoning and tool use.
# ... (previous code) ...
from langchain.agents import initialize_agent
from langchain.agents import AgentType
# Initialize the agent
# - tools: The list of tools the agent can use.
# - llm: The language model that powers the agent's reasoning.
# - agent: The type of agent logic to use.
# - verbose: True, so we can see the agent's thought process.
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
print("Agent initialized. Ready to run.")
# Now, let's run the agent with a complex question
question = "What were the key highlights of the last G7 summit and what were the main outcomes for climate policy?"
# The agent.run() method kicks off the agentic loop
result = agent.run(question)
print("\n--- Final Answer ---")
print(result)Step 5: Observing the Agent’s Thought Process
When you run the script, the verbose=True flag allows you to see the magic happen. The output will show the agent’s entire reasoning loop:
> Entering new AgentExecutor chain...
Thought: I need to find information about the last G7 summit and its outcomes on climate policy. The best way to do this is to perform a web search.
Action: Intermediate Answer
Action Input: "key highlights of last G7 summit climate policy outcomes"
Observation: [List of search results from SERPpost API... e.g., "The G7 summit concluded with a renewed commitment to the 1.5°C goal... Major outcomes included the launch of a new 'Climate Club'..."]
Thought: The search results provide a good summary. I have enough information to answer the user's question. I will synthesize the key points from the observation.
Final Answer: The last G7 summit featured several key highlights regarding climate policy. A major outcome was the launch of the 'Climate Club,' an initiative aimed at coordinating climate action among member nations. Additionally, leaders reaffirmed their commitment to limiting global warming to 1.5°C and discussed accelerating the phase-out of fossil fuels...
> Finished chain.This output clearly shows the Reason -> Act -> Observe cycle in action. The agent made a plan, used its tool, analyzed the result, and formulated a final answer.
Conclusion
Congratulations! You’ve successfully built a basic but functional autonomous research agent. This simple script is the foundation for a wide range of powerful applications, from automated report generation to proactive market monitoring.
The key takeaway is the synergy between the LLM’s reasoning and the real-time data provided by tools like the SERPpost API. This combination is what elevates a simple chatbot into a powerful, goal-driven agent.
What will you build next?
Explore more advanced agent architectures → (Coming Soon)