Topic
LangChain Agents & Tool Calling
14 posts on langchain agents & tool calling — part of framework tutorials on the n4n AI blog.
Structured output with LangChain and Pydantic tools
Build reliable structured output pipelines using LangChain tool calling with Pydantic models — complete with validation, error handling, and production patterns.
Parallel tool calling in LangChain agents
Learn to enable and configure parallel tool calling in LangChain agents with runnable code, error handling, and production verification steps.
Multi-tool LangChain agents: search, calculator, and code
Build a LangChain multi-tool agent with search, calculator, and code execution tools. Step-by-step tutorial with runnable code and verification steps.
LangChain AgentExecutor verbose mode explained
Understand LangChain AgentExecutor verbose mode, what intermediate steps reveal, and how to debug agent reasoning without drowning in output.
Building a SQL agent with LangChain and tool calling
Build a production-ready SQL agent with LangChain using tool calling — step-by-step guide with runnable code, schema handling, and verification.
Tool calling in LangChain with GPT-4o via n4n.ai
A step-by-step guide to implementing tool calling with LangChain and GPT-4o using n4n.ai as the inference gateway, including runnable code and verification steps.
LangChain tool calling with Llama 3.3 70B on n4n.ai
Build a LangChain agent that calls tools with Llama 3.3 70B via n4n.ai — complete setup, code, and verification steps.
LangChain function calling on Claude 3.5 Sonnet
A step-by-step guide to implementing LangChain function calling with Claude 3.5 Sonnet, covering tool definition, agent setup, and production patterns.
LangChain create_tool_calling_agent explained
A practical breakdown of LangChain's create_tool_calling_agent — what it does, how it wires models to tools, and the pitfalls that trip up production teams.
LangChain agents vs function calling: which to use
A practical comparison of LangChain agents versus native function calling for engineers choosing the right tool orchestration approach.
Debugging LangChain agent tool selection errors
A step-by-step guide to diagnosing and fixing LangChain agent tool selection failures, from schema mismatches to prompt confusion.
LangChain ReAct agents: a hands-on tutorial
Hands-on langchain react agent tutorial: build a ReAct agent with tools, step-by-step code, and expected outputs using LangChain's current API.
Building a web search agent with LangChain and Tavily
Build a LangChain web search agent with Tavily and tool calling. Step-by-step setup, runnable code, and verification for production use.
Build a LangChain agent with custom tool calling
A step-by-step guide to langchain agent custom tool calling: define tools, wire a reasoning loop, verify with Python code and pytest for production use.
More topics in framework tutorials
- Building Chatbots with Vercel AI SDK & Next.js15
- LangChain RAG with Vector Databases15
- LangGraph Multi-Agent Workflows15
- LlamaIndex Query Engines for RAG15
- AutoGen Multi-Agent Conversations & Group Chat14
- Choosing an AI Framework by Use Case14
- CrewAI Agent Roles & Task Design14
- CrewAI Real-World Crew Examples14
- Haystack RAG Pipelines14
- LangChain Multi-Model Fallback & Routing14
- LangChain.js for Node & TypeScript14
- One Backend, Every Model: Swapping GPT-5, Claude, Gemini & Llama Across Frameworks14