LangChain

Agent frameworks

The most widely-used framework for building LLM apps and agents, with LangGraph for stateful multi-agent control: open-source and free to self-host.

Verified 2026-08-23 · theagentsindex.com

From: Free
Pricing model: freemium
Free tier: Yes
Deployment: Self-host
Interface: Library
Open source: Yes
Public API: Yes
Model / LLM: Model-agnostic (any LLM)

Our verdict

LangChain is the default starting point for building a custom, production-grade agent rather than adopting an off-the-shelf tool, mostly because nothing else matches its integration ecosystem.

LangGraph adds real stateful control, branching, loops, human-in-the-loop, multiple coordinated agents, that a simple chain-based library cannot offer, and LangSmith adds production tracing and evals once you are ready to pay for them. The cost is a real learning curve: the core abstractions have changed across major versions, and older tutorials can actively mislead.

It is the right tool when you are building something bespoke, and overkill for a single prompt-and-response wrapper.

Worth knowing: LangChain raised a $125 million Series B in October 2025 at a $1.25 billion valuation, becoming a unicorn, a real signal of staying power, and because the LangChain and LangGraph libraries themselves are open-source and self-hostable, your own deployment is not at the mercy of the company’s fate the way a closed-source tool’s would be.

What is LangChain?

LangChain is an open-source framework for building applications and agents on top of LLMs. It provides the building blocks: model and tool abstractions, retrieval, memory. Via LangGraph it adds a way to build stateful, controllable multi-step and multi-agent systems, and a newer Deep Agents package targets autonomous, long-running, open-ended tasks specifically.

LangSmith is its paid observability and eval layer, and by LangChain's own count more than 7,000 active customers (including 5 of the Fortune 10) pay for it, while the open-source libraries see more than 350 million monthly downloads. In October 2025 the company raised a $125 million Series B at a $1.25 billion valuation, reaching unicorn status.

What does LangChain do?

Developers use LangChain’s components to wire models to tools, data and memory, then use LangGraph to define the agent’s control flow as a graph, enabling loops, branching, human-in-the-loop and multiple coordinated agents.

LangSmith adds tracing, evaluation and monitoring for production, plus an “Engine” LangChain says autonomously surfaces and diagnoses issues in a running agent and a no-code “Fleet” layer for routine enterprise agents. Vendor-published case studies attribute concrete results to it: Klarna reports an 80% improvement in case resolution and Monday.com an 8.7x faster evaluation-feedback loop after adopting LangSmith.

How LangChain works

Wire models, tools, data and memory together using LangChain's component abstractions.
Define the agent's control flow as a graph with LangGraph — enabling loops, branching and human-in-the-loop.
Coordinate multiple agents within that same stateful graph for multi-agent systems.
Add LangSmith (paid) for tracing, evaluation and monitoring once the agent moves to production.

Key features

Rich integrations: A large ecosystem of model, vector-store and tool integrations.
LangGraph: Graph-based control for stateful, multi-step and multi-agent workflows.
LangSmith observability: Tracing, evals and monitoring for agents in production (paid).

What are LangChain's use cases?

Build a custom agent: A team composes retrieval, tools and control flow into a bespoke production agent.
Multi-agent systems: Coordinate several agents with LangGraph’s stateful graph.

Who is LangChain for?

Engineering teams building a bespoke, production-grade agent who want the largest integration ecosystem
Teams that need real stateful control — branching, loops, human-in-the-loop, multi-agent coordination — via LangGraph
Developers who want to self-host an open-source framework rather than depend on a single vendor's continued existence

Not forTeams that want a tiny, zero-abstraction wrapper for a single prompt-and-response.

What does LangChain integrate with?

100s of model & vector-store integrations
LangGraph — stateful, multi-agent control
LangSmith — tracing, evals & monitoring
Python and JavaScript / TypeScript SDKs
Model Context Protocol (MCP) — the official langchain-ai/langchain-mcp-adapters package connects agents to external MCP servers as a client

Why use LangChain?

The largest ecosystem and integration surface in the space.
LangGraph gives real control over agent state and flow.
Open-source and self-hostable; pay only for LangSmith if you want it.

What are LangChain's pros and cons?

What's great

Unmatched integration ecosystem and community.
LangGraph provides genuine stateful control, not just chains.
Well-capitalized and independent (a $125M Series B in October 2025 at a $1.25B unicorn valuation), and because the core libraries are open-source and self-hostable, that funding trajectory does not create the vendor-lock risk a closed-source tool’s ownership change would.

Watch-outs

The abstractions have churned across versions; a learning curve and some API instability.
Powerful but heavier than minimal frameworks for simple agents.
Fast-moving: core abstractions have changed across major versions, so older tutorials can mislead.
The breadth is overkill for a single, one-shot LLM call, a thin wrapper is simpler there.

LangChain pricing

LangChain and LangGraph themselves are free and open-source. LangSmith (observability) is free up to 5,000 traces/month on 1 seat. Plus is $39/seat/month with unlimited seats and 10,000 traces/mo. Enterprise is custom.

Open source
Free
- LangChain + LangGraph libraries
LangSmith Developer
Free
- Up to 5k traces/month, then pay-as-you-go
- 1 seat
LangSmith Plus
$39 / seat/month
- Up to 10k traces/month, then pay-as-you-go
- Unlimited seats, 1 free dev deployment

See current pricing on langchain.com ↗Compare LangChain alternatives →

Frequently asked questions

What is the difference between LangChain and LangGraph?

LangChain provides the building blocks, model, tool and retrieval abstractions, while LangGraph is its lower-level library for stateful, graph-based control flow (loops, branching, human-in-the-loop and multi-agent coordination). Both are open-source; LangSmith is the separate paid observability layer.

Is LangChain free and open-source?

Yes, the LangChain and LangGraph libraries are open-source and free to self-host in Python or JavaScript/TypeScript. You pay only if you adopt LangSmith (tracing and evals): the Developer tier is free for up to 5k traces/month on 1 seat, and Plus is $39/seat/month for up to 10k traces/month with unlimited seats, both then bill pay-as-you-go beyond those trace limits.

Which language models does LangChain support?

It is model-agnostic: LangChain integrates with essentially any LLM provider (OpenAI, Anthropic, Google and many more) plus a large catalogue of vector stores and tools, so you are not locked to one model.

Is LangChain a financially stable company?

Yes, in October 2025 LangChain raised a $125 million Series B at a $1.25 billion valuation, reaching unicorn status, led by IVP with existing investors Sequoia and Benchmark returning. Because the core LangChain and LangGraph libraries are open-source (MIT-licensed) and free to self-host, your own deployment is not dependent on the company's continued existence the way a closed-source SaaS tool's would be.

Which is better, CrewAI or LangChain?

Different shapes, not a clear winner. Both are free open-source Python frameworks. CrewAI's role-based crew model gets a working multi-agent system to production in under an hour, while LangChain with LangGraph trades that speed for fine-grained graph control, checkpointing, branching and human-in-the-loop, backed by the larger integration ecosystem.

LangChain alternatives

LlamaIndex
The retrieval-first RAG specialist — 300+ data connectors and hybrid/recursive retrieval — for document Q&A and enterprise-search workloads; in production the two are commonly paired (LlamaIndex for retrieval, LangGraph for orchestration) rather than chosen instead of each other.
CrewAI
A faster, more readable path to a working multi-agent "crew" for teams that want role-based coordination shipped in under an hour, trading LangGraph's fine-grained branching and checkpointing control for speed.
LangGraph
The lower-level orchestration runtime underneath LangChain itself, for teams that need explicit graph control, checkpointed state and replay rather than LangChain's own quick-start abstractions.
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Anything to add?

This page is researched and re-verified by our editors — but the people who actually use LangChain know things we cannot see from the outside.

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The evidence

Facts re-verified 4 days ago

How we checked this

The default starting point for custom agents, unmatched integrations and LangGraph’s stateful control, offset by a real learning curve and cross-version churn.