Creating a Custom AI Agent
You can design your own agent to match a specific use case—like a financial advisor, medical assistant, or travel planner.
🧱 Steps to Build Your Custom Agent:
1. Choose an LLM
Example: GPT-4, Claude, or a local model like LLaMA.
2. Define Tools
Examples:
o Web search
o Calculator
o SQL query engine
o Weather API
o Custom Python functions
3. Initialize the Agent
Use initialize_agent() or create a new agent class.
4. Run Tasks
Send user input and let the agent act accordingly.
Adding tools like search, calculator, and database queries
🧩 Common Tools You Can Add:
🔎 Search Tool
• Uses SerpAPI or Google API.
• Real-time info fetching.
🧮 Calculator Tool
• Uses SerpAPI or Google API.
• Real-time info fetching.
🗃️ Database Tool
• Queries relational databases (SQL) or NoSQL like MongoDB.
• Useful for building internal knowledge bots.
⚙️ Example with Tools:
Wrap-Up
By completing this module, you’ll be able to:
• ✅ Understand how LangChain Agents add intelligence to AI apps.
• ✅ Use built-in agents like ConversationalAgent or ZeroShotAgent.
• ✅ Build custom agents for your own use cases.
• ✅ Equip agents with real tools to search, calculate, and query data dynamically.
Final Thought:
LangChain agents represent a powerful leap from static AI (just responding) to active AI (reasoning and acting). Whether you’re building a chatbot that books appointments or an AI assistant that searches your internal documents, agents are your go-to architecture for smart automation.
