What is LangChain?

LangChain is an open agent engineering platform built around two open-source frameworks — LangChain and LangGraph — plus LangSmith, a hosted layer for tracing, evaluating, deploying and governing the agents you build with them. The company's own positioning has shifted from "a library for chaining LLM calls" toward what it now calls an open agent platform for companies to "own their intelligence": you write the agent logic in code using the open-source frameworks, then LangSmith gives you observability into what your agents actually did in production, evaluation tooling to catch regressions, and deployment infrastructure so agents don't just live in a notebook.

LangChain (the framework) handles the building blocks — prompts, retrieval, tool calling, memory — while LangGraph adds explicit state machines for agents that need to loop, branch or run multiple steps reliably rather than a single prompt-response call.

Who it is for

LangChain is for developers and engineering teams building LLM-powered applications and agents in Python or JavaScript who want a mature, widely adopted toolkit rather than writing every integration from scratch. It fits teams that need to move from prototype to production and want visibility into failures, latency and cost once real users are hitting the agent — less so hobbyists who just want a single API call to a model, or teams allergic to picking up a framework's own abstractions and conventions.

Key features

LangChain framework — open-source building blocks for prompts, retrieval-augmented generation, tool/function calling and memory, in Python and JavaScript
LangGraph — a lower-level framework for building stateful, multi-step agents as explicit graphs, useful when a single prompt-response loop isn't enough
LangSmith tracing — see the exact sequence of calls, tool invocations and intermediate outputs behind any agent run, which is often the fastest way to debug why an agent did something unexpected
Evaluation tooling — run datasets of test cases against your agent and track quality over time as you change prompts or models
Deployment/Engine — host and scale agents built on the frameworks without standing up your own infrastructure
The largest integration ecosystem among agent frameworks, with connectors for most popular vector stores, model providers and tools

Pricing

LangSmith has a free Developer plan at $0 per seat per month, capped at one seat and 5,000 base traces monthly, with pay-as-you-go charges for usage beyond that. The first paid tier, Plus, is $39 per seat per month, raises the trace allowance to 10,000 and removes the seat cap, and adds deployment and Engine features. Usage above included volumes is billed per LangChain Compute Unit ($1.50) and Storage Unit ($1.00). Enterprise pricing, with self-hosting, SSO and support SLAs, is custom and requires talking to sales. The open-source LangChain and LangGraph frameworks themselves are free to use regardless of whether you pay for LangSmith.

Strengths

The integration ecosystem is genuinely the largest in the space, so whichever vector database, model provider or tool you're already using, there's a good chance LangChain already has a connector for it. LangSmith's tracing is consistently cited as one of its best features — being able to click into a single agent run and see every intermediate step is a real time-saver when something goes wrong in production. LangGraph fills a gap that plain LangChain didn't handle well: agents that need explicit control flow rather than a single linear chain.

Weaknesses

The framework's abstractions have a real learning curve and can get in the way when you just want direct control over a prompt or API call — a common enough complaint that LangChain has iterated on its APIs multiple times in response. Frequent breaking changes between versions are a known pain point for teams that adopted early and now maintain older chains. And the useful parts of the platform — evaluation and production tracing — sit behind LangSmith's paid tiers once you outgrow the free seat and trace limits.

Getting started

Install the langchain or langgraph package for Python or JavaScript from langchain.com and start with their quickstart docs; both frameworks are free and open source. Sign up for LangSmith separately if you want tracing and evaluation, starting on the free Developer plan before deciding whether you need the $39/seat Plus tier.

Verdict

LangChain remains the default starting point for teams building LLM agents in Python or JavaScript, mainly because of its integration breadth and LangSmith's genuinely useful tracing — just expect to spend time learning its conventions and budget for LangSmith once you need production-grade observability. If you want a lighter-weight, more code-first alternative to the LangChain framework itself, compare it with CrewAI for role-based multi-agent setups, and see our broader LLM APIs & developer platforms roundup for model-hosting alternatives.