LangChain vs LangGraph vs AgentKit: A 2025 guide for devs

If you’re diving into building AI agents, you’ve likely stumbled across a few heavy-hitting frameworks: LangChain, LangGraph, and OpenAI's AgentKit. They all promise to help developers build powerful, autonomous agents, which sounds great.

But when you're a team just trying to solve a business problem, the differences can get a little blurry. When is a simple "chain" good enough? At what point do you need a more complex "graph"? And is a starter kit the right move, or will it box you in?

This guide cuts through the noise. We'll break down the core ideas behind LangChain vs LangGraph vs AgentKit, look at what they're actually good for, and talk about the real costs involved so you can pick the right tool for the job.

Breaking down LangChain vs LangGraph vs AgentKit

Before we get into the nitty-gritty, let's get a clear picture of what each of these frameworks brings to the table. They all help you build AI agents, but they come at it from pretty different angles.

What is LangChain?

Core Idea: LangChain is a popular open-source framework for building apps with large language models (LLMs). The main concept is "chaining" together different components, like an LLM, a prompt, and an output parser, to create a single application.

Analogy: Think of LangChain as a big box of Lego bricks for LLM development. It gives you all the individual pieces and connectors you need to build all sorts of things, from simple chatbots to more involved agents.

Key Features: It’s known for being model-agnostic, meaning it plays nice with models from OpenAI, Anthropic, and others. It also has a huge ecosystem of integrations for databases, APIs, and various tools.

What is LangGraph?

Core Idea: LangGraph is a library that builds on top of LangChain, designed for creating more robust and stateful agents. Instead of simple, one-way chains, it lets you map out agent workflows as a graph. Nodes in the graph are steps, and the edges are the paths between them.

Analogy: If LangChain gives you the bricks, LangGraph is the architectural blueprint. It’s built for workflows that aren't just a straight line. It can handle loops, branches, and even pauses for a human to step in, kind of like a flowchart.

Key Benefit: This graph structure gives you a ton more control and makes it easier to see what’s going on. That makes it a better fit for building reliable agents that can handle errors, reflect on their work, and tackle problems that take multiple steps to solve.

What is OpenAI's AgentKit (Agents SDK)?

Core Idea: AgentKit (which is officially called the Agents SDK) is OpenAI's own open-source toolkit for building agents. It’s meant to be a simpler, more opinionated "starter kit" that helps you get going faster, especially if you're already using OpenAI's models.

Analogy: This is more like a pre-fabricated model kit. It comes with the main pieces and clear instructions, helping you get a working agent off the ground quickly without getting bogged down in too many architectural choices.

Key Features: It’s designed for simplicity, with built-in ideas for things like guardrails and having agents "hand off" tasks to each other. The flip side is that it's naturally optimized for the OpenAI ecosystem, which might lead to vendor lock-in down the road.

LangChain vs LangGraph vs AgentKit: Architecture and control

The biggest difference between these frameworks isn't just what they can do, but how they let you do it. The right choice for you really comes down to a trade-off: how much control do you need over the agent's inner workings versus how fast do you need to get a prototype up and running?

LangChain: The flexible composer

LangChain gives you a ton of flexibility by letting you piece together modular components: Chains, Agents, Tools, and Memory. It’s fantastic for creating straightforward, sequential workflows where one step just follows the next. You can stitch together an LLM call, a database lookup, and an output parser with just a few lines of code.

Limitation: But that simplicity can become a problem when things get complicated. Managing an agent's state across a long, cyclical task is tough. If an agent’s thought process goes off the rails ten steps in, debugging feels like searching for a needle in a haystack, which link in the chain broke?

LangGraph: The structured orchestrator

With LangGraph, you get fine-grained control because you have to define the workflow as an explicit state machine. Each node in your graph is a clear step (like "call a tool" or "ask for human input"), and you define the logic for every single transition.

This is perfect for tasks that need loops (e.g., try something, check the result, try again) or have conditional logic ("if this tool fails, use that one instead"). It makes you think through the agent's entire decision process, which usually leads to a more reliable and easier-to-debug system.

Limitation: All that explicit control comes with a cost. The learning curve is steeper, and you’ll find yourself writing more boilerplate code. For a simple task, defining a full graph can feel a bit like using a sledgehammer to hang a picture.

AgentKit: The opinionated starter kit

AgentKit hides a lot of this architectural complexity. For the most part, you just define the agent's instructions and the tools it can use, and its "Runner" component handles the execution loop for you. Here, control is less about wiring up a state machine and more about writing clear prompts and simple Python functions.

Limitation: While it’s easy to get started, it’s not as transparent as LangGraph. The agent's reasoning happens inside a pre-built loop that can be hard to visualize and debug. It also nudges you heavily toward OpenAI's patterns and APIs, which can feel a bit restrictive if you want the freedom to switch models or avoid being tied to one vendor.

An alternative: Control for business outcomes, not code

These frameworks give developers deep control through code, but that doesn't always line up with what a business actually needs. A support manager, for example, needs to control things like ticket resolution rates and customer satisfaction, not Python objects. They need to tweak an AI's tone, decide which tickets it should handle, and add new knowledge without having to file a request with the engineering team.

This is where a dedicated platform offers a different kind of control that’s more accessible. Instead of coding graphs, platforms like eesel AI let non-technical users define an AI agent's behavior, set up automation rules, and create custom actions through a simple dashboard. It gives teams the power of a custom agent without the heavy engineering lift.

A screenshot of the eesel AI platform showing how non-technical users can set up custom automation rules and actions through a simple dashboard, illustrating a business-focused alternative to code-heavy frameworks in the LangChain vs LangGraph vs AgentKit debate.

When to use each framework

Picking the right framework is all about the job you're trying to do. You wouldn't use a power drill to stir your coffee, and you wouldn't use a simple chatbot builder for a complex, multi-agent system.

Best use cases for LangChain

• Rapid Prototyping: It's perfect for quickly spinning up a proof-of-concept to see if an idea is even worth pursuing.

• Simple RAG Systems: It's great for creating chatbots that can answer questions based on a set of documents, like a help center or an internal wiki.

• Basic Tool-Using Agents: A good fit for agents that need to perform a clear sequence of actions, like looking up the weather and then sending you an alert.

Best use cases for LangGraph

• Complex Multi-Agent Systems: This is the go-to for orchestrating a "team" of specialized agents that need to collaborate, like a researcher agent passing its findings to a writer agent.

• Self-Correcting Workflows: It lets you build agents that can review their own work, spot mistakes, and try again until the output is right.

• Production-Grade Agents: It’s a solid choice for building reliable agents for business processes where you absolutely need clear error handling and the option for human oversight.

Best use cases for AgentKit

• OpenAI-Centric Projects: If you're already all-in on the OpenAI stack and want the smoothest experience possible, this is your best bet.

• Fast Implementation: For teams that need to get a simple, functional agent running quickly without sweating the architectural details.

• Simple Tool Use: It works well when the agent's task is straightforward enough that the model can handle most of the reasoning on its own.

The challenge of building for a specific domain like customer support

These frameworks are powerful, but they're also general-purpose. Building a customer support agent that's actually helpful requires more than just a generic toolkit; you need deep, domain-specific functionality.

Frameworks don't come with out-of-the-box logic for ticket triage, sentiment analysis, or understanding the nuances of your brand's voice from past conversations. You have to build all of that from scratch, which can easily turn into a months-long development project.

This is where a specialized platform can give you a major head start. A tool like eesel AI is built specifically for customer service. It goes beyond a generic framework by instantly learning from your past tickets, understanding support actions like tagging and escalating, and giving you a simulation environment to test its performance on your real historical data before you go live. That can save a team months of custom development and fine-tuning.

The eesel AI simulation dashboard shows how the platform uses historical data to predict performance, a key advantage over general frameworks in the LangChain vs LangGraph vs AgentKit comparison for domain-specific tasks.

Framework	Best For	Good Example
LangChain	Flexible composition & rapid prototyping	A simple chatbot that answers questions from a PDF.
LangGraph	Complex, reliable, multi-step orchestration	A multi-agent system that researches a topic, writes a draft, and asks for human approval.
AgentKit	Fast implementation in the OpenAI ecosystem	A simple agent that can look up order statuses via an API.

Pricing and total cost of ownership

It’s easy to look at these open-source frameworks and think they’re "free." But as anyone who's managed a project knows, that’s rarely the full story. The real cost comes from everything else you need to get a reliable agent into production.

LangChain & LangGraph pricing

The libraries themselves won't cost you a dime. They're open-source. However, your main costs will be the LLM API calls, the infrastructure to host your application, and, most importantly, the developer time it takes to build and maintain it. For observability and debugging, you'll probably want a tool like LangSmith. While it has a free developer tier, its Plus plan runs $39 per seat per month, plus usage-based fees.

AgentKit (Agents SDK) pricing

The SDK is also open-source and free to use. But since it’s built around OpenAI's APIs, your costs are tied directly to your usage. The fees for tokens and tool use can be hard to predict and can scale up quickly, which makes budgeting a real challenge for a growing business.

The hidden costs: Development and maintenance

The "free" framework is just the tip of the iceberg. The total cost of ownership for a self-built agent includes engineering salaries, cloud hosting bills, API fees, and the ongoing work to maintain and update the system as frameworks and models change. An AI agent isn't a "set it and forget it" kind of project.

This is a huge factor when deciding whether to build from scratch or go with a dedicated platform. A platform like eesel AI offers predictable, transparent pricing with no per-resolution fees. The subscription covers the AI models, infrastructure, integrations, and maintenance, turning a large, unpredictable development cost into a simple, manageable operational expense.

A screenshot of the eesel AI pricing page, highlighting transparent and predictable costs as a key differentiator when evaluating the total cost of ownership of LangChain vs LangGraph vs AgentKit.

Frameworks vs. platforms

So, to wrap it all up: LangChain is your flexible toolkit for piecing together LLM apps, LangGraph is the heavy-duty orchestrator for complex and reliable agents, and AgentKit is the streamlined starter kit for building quickly in the OpenAI world.

All three are fantastic tools for developers. But they all require a serious investment in engineering time, infrastructure, and ongoing maintenance to turn a cool technical framework into a dependable business solution.

For specific business challenges, like automating customer support, a dedicated platform can often get you better results, faster, and with a lower total cost. It handles the technical complexity for you, so you can focus on the business outcome, not the code underneath.

Take the fast track to AI-powered support

Instead of spending months building an AI agent from the ground up, you could have one live this week. eesel AI integrates with your existing helpdesk and knowledge bases to deploy a powerful, context-aware AI agent in minutes, not months.

You can even simulate its performance on your own historical tickets to see the exact impact and ROI before you even get started.

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