I keep referencing this breakdown by Anshuman, so I figured it was worth putting together a concise, quick reference version. If you’re navigating the Lang* ecosystem — LangChain, LangGraph, LangFlow, LangSmith — here’s how to think about each tool and when to use what.
LangChain: Your Foundation for LLM Apps
LangChain gives you the primitives to build apps powered by large language models. It’s open-source, flexible, and gives you control over how prompts, memory, agents, and data sources are wired together.
Use it when you need to:
- Build workflows involving multiple steps (e.g. input → LLM → call API → return response)
- Manage context and memory across sessions
- Integrate RAG pipelines, vector DBs, file loaders, etc.
Example:
You’re building a support chatbot that pulls real-time order info from a DB, generates human-like answers, and remembers past conversations.
LangChain is your backend logic layer for structured LLM flows.
LangGraph: For Complex, Multi-Agent Logic
LangGraph is built on top of LangChain, but adds a graph-based execution model. It’s ideal when you’ve got branching logic, feedback loops, or agents that need to pass tasks between each other.
Use it when you need to:
- Build systems with multiple “thinking parts”
- Route decisions dynamically (e.g. A → B → loop back to A)
- Maintain and mutate shared state across steps
Example:
A research agent that pulls articles, another that summarizes, and a third that turns them into slides. LangGraph handles the orchestration between them.
LangGraph is LangChain, but with visual structure and state for complex workflows.
LangFlow: Visual LLM App Prototyping
LangFlow gives you a drag-and-drop interface to visually build and test workflows. Great for ideation, non-dev teams, or quickly mapping out app logic before writing code.
Use it when you need to:
- Rapidly prototype a workflow before building in code
- Collaborate with non-technical folks
- Show clients or stakeholders how the flow works
Example:
You want to mock up a resume parser and summarizer app before handing it off to a dev team. LangFlow lets you string together components visually and test outputs.
LangFlow is the Figma of LangChain — design it before you build it.
LangSmith: For Debugging and Scaling LLM Apps
LangSmith is what you use when your app hits production. It’s your observability layer — like Datadog for LLMs — and helps monitor, test, and fine-tune your workflows.
Use it when you need to:
- Track token usage, cost, and performance
- Debug prompt failures or hallucinations
- Evaluate outputs and improve agent reasoning
Example:
Your AI helpdesk agent is eating too many tokens or occasionally gives weird answers. LangSmith helps you catch and fix those issues.
LangSmith keeps your LLM app stable, cheap, and predictable in prod.
Quick Recommendations
Each tool plays a distinct role. You’ll often use 2–3 together — LangChain to build, LangGraph to orchestrate, LangSmith to test and scale.
