𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻

LangChain agents use LangGraph to work. The model calls tools in a loop. It continues until it finds a final answer.

You can build a support triage agent using createAgent. You need three things:

• A model
• A list of tools
• A system prompt

How the loop works: The model takes a turn. In one turn, it does one of two things:

• It returns text to end the run.
• It calls a tool. LangChain runs the tool and starts a new turn with the result.

You should set a recursionLimit. This prevents the agent from looping forever.

How to define tools: Use the tool function with a Zod schema. Give each tool a name and a description. This helps the model decide when to use it.

Example flow for a support agent:

• User asks a question.
• Model calls get_customer.
• Model calls get_invoice.
• Model calls search_knowledge_base.
• Model creates a ticket or gives an answer.

Comparing SDKs:

LangChain:

• Best for RAG and complex agent stacks.
• Uses tool() with Zod schemas.
• Uses agent.invoke or agent.stream.
• Uses LangGraph for memory.

Vercel AI SDK:

• Best for TypeScript apps.
• Uses tool() with inputSchema.
• Uses generateText.

OpenAI Agents SDK:

• Best for OpenAI-first workflows.
• Uses tool() with Zod parameters.
• Uses run().

Choose LangChain if you need document loaders and retrievers in one place. Choose Vercel or OpenAI if you want a simple agent layer.

Source: https://dev.to/zsevic/building-ai-agents-with-langchain-5e69

Optional learning community: https://t.me/GyaanSetuAi