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LangChain / LangGraph

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★★★★☆ 4.5 / 5 on G2 · 4.5 on Capterra (50+ G2 reviews)
4.5 / 5
G2 rating, 50+ reviews [source]
Framework
LLM apps, agents [source]
Open source
+ paid tiers [source]
LangSmith
Observability [source]

Quick facts

CategoryDeveloper agent framework
VendorLangChain
Starts atOpen source; paid tiers
Best forDeveloper agent frameworks
DeploymentLibrary / cloud
Free planYes (open source)
IntegrationsLLMs, tools, data
G2 rating4.5 / 5
Capterra rating4.5 / 5

What is LangChain and LangGraph?

LangChain and LangGraph are the most popular developer frameworks for building LLM apps and agents (LangChain) and stateful agent workflows (LangGraph), with LangSmith for observability, suited to developers building custom AI agents in code. LangChain is a widely used framework providing building blocks (models, prompts, tools, memory, retrieval, chains) to build LLM applications and agents, LangGraph adds a framework for building stateful, multi step and multi agent workflows as graphs, and LangSmith provides observability, debugging and evaluation. Together they are a leading ecosystem for developers building AI agents and applications, from simple to complex and multi agent, with code level control. For developer agent frameworks, LangChain and LangGraph are a leading choice.

LangChain's distinction is being the most popular, comprehensive developer framework ecosystem for LLM apps and agents, with observability. It gives developers extensive, flexible building blocks (LangChain), stateful and multi agent orchestration (LangGraph), and observability and evaluation (LangSmith), with a huge community and integrations, which narrower or less mature frameworks do not match for breadth, adoption and tooling. That comprehensive, popular, developer first ecosystem sets it apart.

LangChain at a glanceLangChain and LangGraph are the most popular developer frameworks for building LLM apps and agents [LangChain]. They hold about a 4.5 out of 5 rating on G2 and 4.5 on Capterra, a leading developer agent ecosystem.

Who LangChain and LangGraph are best for

LangChain and LangGraph are best for developers and engineering teams that want to build custom AI agents and LLM applications in code, with full control and flexibility, including complex, stateful and multi agent workflows, plus observability. They suit developer agent frameworks. They are a weaker fit for business teams wanting low code visual building, where Copilot Studio, Relevance AI or n8n fit, those wanting agents within a specific ecosystem out of the box, where Agentforce or Vertex AI fit, or non developers.

Key features

The core strengths are extensive LLM app building blocks (LangChain), stateful and multi agent workflow orchestration (LangGraph), observability, debugging and evaluation (LangSmith), a huge ecosystem of integrations, and code level flexibility. LangChain and LangGraph offer comprehensive, developer first agent building. Their comprehensive, popular, developer first ecosystem is their defining advantage.

LangChain pricing

LangChain and LangGraph are open source and free to use (you pay for LLM API usage and any hosting), while LangSmith and hosted or enterprise offerings have free tiers and paid plans (by usage and seats). So the frameworks are free, with paid observability and enterprise tiers, plus underlying LLM costs. Confirm details on the LangChain pricing page.

Integrations and ecosystem

LangChain integrates with a huge range of LLMs, vector databases, tools, data sources and services, with a large community and many extensions, plus LangGraph and LangSmith. Its vast integration ecosystem and community, which let developers build agents with almost any model and tool, are major strengths.

LangChain and the developer ecosystem

Being the most popular, comprehensive developer framework ecosystem with observability is the signature strength: LangChain and LangGraph give developers extensive building blocks, stateful and multi agent orchestration, and observability, with a huge community and integrations, which narrower frameworks do not match for breadth and tooling. For developer agent frameworks, this is the main reason to choose them.

Ease of use and support

LangChain and LangGraph are well rated by developers for flexibility, breadth, the ecosystem and observability (LangSmith), a leading choice for building agents in code, with strong community and docs, though reviewers note a learning curve, the fast moving nature of the field and APIs, and that they are for developers. Its reviews praise flexibility and ecosystem, with the learning curve the main consideration.

Are LangChain and LangGraph worth it?

For developers and teams building custom AI agents and LLM apps in code with full control, including complex and multi agent workflows and observability, LangChain and LangGraph are an excellent choice. The main caution is the learning curve and fast moving APIs, so low code building suits Copilot Studio, Relevance AI or n8n, and ecosystem agents Agentforce or Vertex AI, but for developer agent frameworks LangChain and LangGraph lead.

Pricing

Open source

$0
self use
  • LangChain and LangGraph
  • Build in code
  • Developers

LangSmith

Free / paid
by usage
  • Observability and eval
  • Debugging
  • Teams

Enterprise

Quote
by plan
  • Hosted and support
  • Scale and governance
  • Enterprises

Pros and cons

Pros

  • Most popular developer framework
  • Comprehensive building blocks
  • Stateful and multi agent (LangGraph)
  • Observability (LangSmith)
  • Huge integration ecosystem

Cons

  • Learning curve
  • For developers (not low code)
  • Fast moving APIs and field
  • LLM usage costs on top

LangChain / LangGraph alternatives

These ai agent platforms tools come up most often against LangChain / LangGraph.

CrewAI

Multi-agent orchestration framework and platform

Microsoft Copilot Studio

Low-code platform for building AI agents

Salesforce Agentforce

Enterprise AI agents on Salesforce platform

LangChain / LangGraph FAQ

What are LangChain and LangGraph used for?
LangChain and LangGraph are used by developers to build LLM applications and AI agents in code: LangChain provides building blocks (models, tools, memory, retrieval, chains), LangGraph adds stateful, multi step and multi agent workflows, and LangSmith adds observability, so developers build custom, flexible agents with full control.
Are LangChain and LangGraph free?
LangChain and LangGraph are open source and free to use (you pay for LLM API usage and any hosting), while LangSmith and hosted or enterprise offerings have free tiers and paid plans. See the LangChain pricing page.
What is the difference between LangChain and LangGraph?
LangChain is the broad framework of building blocks for LLM apps and agents, while LangGraph is a framework for building stateful, multi step and multi agent workflows as graphs, giving more control over complex agent behaviour and orchestration. They are used together for flexible, advanced agent building.
LangChain vs CrewAI, which is better?
LangChain and LangGraph are broad, comprehensive developer frameworks, while CrewAI focuses specifically on orchestrating multiple collaborating agents. LangChain and LangGraph suit general, flexible agent building; CrewAI suits multi agent, role based crews. Some developers use CrewAI or LangGraph for multi agent needs.
What is LangSmith?
LangSmith is LangChain's platform for observability, debugging and evaluation of LLM apps and agents, letting developers trace, monitor, test and evaluate their agents, which is important because agents can be complex and take actions, so observing and evaluating them is crucial for reliability.
Do I need to be a developer to use LangChain?
Yes, largely. LangChain and LangGraph are developer frameworks used in code (mainly Python and JavaScript), so they suit developers. Business teams wanting to build agents without heavy coding would use low code platforms like Copilot Studio, Relevance AI or n8n instead.

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