LangChain is an open-source framework for building AI agents and LLM applications with tools, model integrations, prompts, and workflows.

The LangChain framework is open source. Paid costs usually come from model providers and optional LangSmith platform usage.

How is LangChain different from LangGraph?

LangChain is the broader agent framework, while LangGraph is a lower-level runtime for controllable, stateful, long-running agents.

How does LangSmith fit in?

LangSmith adds observability, evaluation, deployment, and monitoring around LangChain, LangGraph, or other agent stacks.

Who should use LangChain?

LangChain is best for developers building agentic apps that need model integrations, tools, traces, and production workflows.

What should teams watch for?

Teams should avoid unnecessary abstraction, keep docs current, and add evaluation, tracing, and cost monitoring before production.

LangChain

Name: LangChain
Brand: LangChain

Open-source framework for building AI agents, LLM apps, and tool workflows.

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What is LangChain?

TL;DR

LangChain is an open-source framework for building LLM applications and agents, while LangSmith adds observability, evaluation, deployment, monitoring, and agent engineering infrastructure for production AI systems.

Build LLM applications and agents with LangChain and connect models, tools, retrieval, memory, and application logic.
Use LangSmith tracing, monitoring, datasets, offline and online evaluations, and human feedback to improve agent quality.
Deploy and operate long-running agents with LangSmith infrastructure, sandboxes, authorization, scaling, and enterprise security options.

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About

LangChain is an open-source framework for building applications and agents powered by language models, while LangSmith provides the production platform around those applications. Together they cover model and tool integration, agent development, tracing, evaluation, monitoring, deployment, and continuous improvement for AI systems. Developers can use LangChain to connect models with tools, retrieval systems, structured application logic, and agent workflows. LangSmith is framework-agnostic and can trace applications built with LangChain, LangGraph, or custom code, giving teams visibility into prompts, model calls, tool usage, latency, costs, failures, and outputs. LangSmith also provides offline and online evaluations, datasets, human feedback, monitoring, alerts, deployment infrastructure, sandboxes, and agent debugging. Production traces can become evaluation datasets, while Engine can analyze failure patterns and help teams identify and address recurring agent problems. Enterprise deployments can use self-hosted or hybrid infrastructure and stronger security controls. LangChain is best for developers and founders building AI applications who need control over agent architecture, while LangSmith becomes especially valuable once reliability, evaluation, observability, and deployment matter. The ecosystem is powerful but can require more engineering knowledge than a fully managed no-code AI builder.

Use Cases

Build AI Agents

Compose models, tools, retrieval, memory, and application logic into custom agent workflows with LangChain and related components.

Evaluate Agent Quality

Run offline and online evaluations against datasets and production traces to catch regressions and measure quality.

Debug Production Agents

Trace model calls and tool steps, monitor latency and costs, identify failures, and investigate problematic agent runs.

Deploy AI Systems

Use LangSmith deployment, sandboxes, authorization, and scaling infrastructure to operate long-running agents in production.

Key Features

Open-source framework for LLM applications and agents
LangSmith tracing and production observability
Offline and online AI evaluations
Datasets, human feedback, and regression testing

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