Semantic Kernel vs LangChain: The Real Choice

Semantic Kernel and LangChain both let you build an AI agent that plans, calls tools, and holds a conversation, but they start from different places. LangChain is the bigger, Python-first open source project, with a JavaScript port and its own graph-based agent runtime, LangGraph, plus LangSmith as the default way to trace and evaluate what an agent actually did. Semantic Kernel is Microsoft’s own kit, built to sit inside a C#, Python, or Java codebase you already run, wired for OpenTelemetry from the start, and aimed at teams shipping agents on the Microsoft stack. A feature table makes the two look almost interchangeable. The part that costs you real time later is how much work it takes to trace a live agent, test it before a customer finds the bug, and run it safely for more than one paying customer at once, and that part is exactly what the existing comparisons on this pairing skip. This piece covers the standard ground, then all three of those.

What is Semantic Kernel and what is LangChain, at a glance?

Semantic Kernel is Microsoft’s open source SDK for building AI agents inside an existing C#, Python, or Java application, using a “kernel” object that wires your prompts, your own functions, and a chosen AI model together through one orchestration layer. LangChain is the older and considerably larger open source project built for the same job, an orchestration framework that chains prompts, tools, and memory into an agent, and now ships LangGraph, its graph-based agent runtime, as the recommended way to build anything with branching logic or a loop rather than a straight line of steps.

Both projects move fast enough that a real comparison needs numbers attached to it instead of a vague sense that one is bigger. As of this writing, the core LangChain repository carries 145,504 stars on GitHub, and LangGraph, its dedicated agent runtime, adds another 40,921 stars on top of that, with both shipping on the current 1.3.x release line. Semantic Kernel sits at 28,522 stars, backed by Microsoft directly, with Microsoft’s own documentation naming several Fortune 500 companies among its users, and its Python package is currently on version 1.44.1. Neither number decides which framework fits your stack, but it does tell you which one you’re more likely to find a fresh Stack Overflow answer for at eleven at night.

How do the two frameworks actually differ in what they’re built for?

The real split isn’t features, since most individual capabilities show up in both eventually, it’s which stack each one assumes you’re already standing on. Semantic Kernel’s plugin model wraps your existing code, including OpenAPI-described APIs, as callable functions the AI model can invoke, and it ships hooks and filters aimed at the kind of access control and audit logging an enterprise security team asks for before anything touches production.

LangChain’s ecosystem runs the other direction: a much longer list of supported model providers, vector databases, and community integrations, plus LangGraph’s state graph model for an agent that branches, loops, or hands off between steps based on what just happened. The language support splits cleanly too. Semantic Kernel ships official packages for C#, Python, and Java, which matters if your existing codebase is a .NET or Java shop and you’d rather not bolt a Python service onto it just to add an agent. LangChain’s own organization ships Python and JavaScript, through LangChain and LangChain.js, with no Java package of its own, so a Java-first team adopting LangChain is adopting a foreign runtime alongside it, not extending the one it already has.

Which one is actually easier to trace once your agent is live?

LangChain’s documentation states plainly that if you’re building with LangChain or LangGraph, you can turn on LangSmith tracing with a single environment variable, and the setup genuinely is that small: set the variable, add your API keys, and traces start flowing into LangSmith’s dashboard without touching your application code.

Semantic Kernel takes a different path that looks similar on paper and works differently in practice. It emits logs, metrics, and traces built to the OpenTelemetry standard rather than pointing you at one first-party dashboard, which means you plug it into whatever observability backend you already run, Application Insights, Grafana, or any other OpenTelemetry-compatible tool, instead of being handed a viewer by default. The depth of what you get also depends on which language you’re in. Microsoft’s own documentation lists token usage as a captured metric only under the C# implementation, while the Python implementation currently exposes just function invocation and streaming duration as histograms, with no built-in token usage metric of its own. Java trails further behind: Microsoft’s docs state outright that observability in Semantic Kernel is not yet available for Java at all. So the honest read is that LangChain gets you a usable trace faster with less setup, while Semantic Kernel gets you a more open, vendor-neutral signal that takes more work to turn into something you can actually look at, and that work isn’t evenly distributed across the three languages it supports.

Which one actually helps you test an agent before your customers do?

Neither framework ships a real built-in evaluation framework for your agent’s actual behavior, but they hand you off to different places once you go looking for one. LangChain’s own documentation points you toward LangSmith to find failure modes, evaluate quality, and improve agent behavior, so a serious LangChain testing setup usually means adopting that same commercial platform you’re already using for tracing.

Semantic Kernel doesn’t have an equivalent baked into the open source package either, and Microsoft’s own published guidance for scoring a Semantic Kernel agent’s output runs through Azure Machine Learning’s prompt flow evaluation tooling, a separate Azure product you wire up rather than a kernel.evaluate() call sitting in the SDK. That’s a materially different lift depending on your stack: a team already living in Azure barely notices the extra step, while a team running Semantic Kernel outside Azure is adopting a second cloud product just to get a scored evaluation loop going. Neither path replaces the actual test-set work covered in AI agent testing, hand-written adversarial cases, a golden dataset that grows from real production failures, and a decision on code-based checks versus a model as the judge. What the framework gives you is a head start on the mechanics, and right now neither Semantic Kernel nor LangChain gives you much of that head start for free in the open source library itself.

What happens once one agent has to serve more than one paying customer?

Neither framework has an opinion on this at all, and that’s worth knowing before you pick one expecting it to be solved for you. Semantic Kernel’s plugin and kernel objects are scoped to whatever process constructs them, and LangGraph’s state graph tracks a single conversation’s state cleanly, but keeping one customer’s conversation history, tool credentials, and evaluation results from ever touching another customer’s is work you build on top of either framework, not a setting you flip inside it.

That gap matters more than it looks like on day one. It’s invisible right up until your agent stops being an internal tool and becomes the product your own customers, each running their own account, rely on for the deflection rate and cost-per-resolution numbers you report back to them. Tenant isolation for AI agents covers the database and memory boundaries that catch this before it becomes an incident, and it applies the same way whether the agent underneath is built on Semantic Kernel, LangChain, or something else entirely, because the isolation problem lives one layer above the orchestration framework, not inside it.

So which one should you actually pick?

Pick LangChain if your team is Python or JavaScript first, you want the widest bench of model providers and integrations, and you’re fine adopting LangSmith as the paid layer that turns raw traces into a debuggable, evaluable pipeline. Pick Semantic Kernel if you’re already a C#, Python, or Java shop with enterprise security requirements, you want OpenTelemetry-native output that plugs into observability tooling you already run, and you’re comfortable that the polished, one-click experience LangSmith offers isn’t quite there yet on the Semantic Kernel side.

Neither choice costs you anything up front. Both projects sit under the same permissive MIT license on GitHub, so there’s no licensing fee gating either one, and what you actually pay for later is the commercial layer sitting on top, LangSmith for LangChain, or an Azure subscription once you lean on Application Insights and prompt flow evaluation for Semantic Kernel. That’s worth knowing before a team assumes “open source” means the whole pipeline is free forever, because the base orchestration layer is free either way, and the tracing and testing layer around it is where the real cost, in either time or money, actually lands.

One more thing worth knowing before you commit engineering time to either: Microsoft’s own documentation now describes a newer project, Microsoft Agent Framework, as combining AutoGen’s simpler agent abstractions with Semantic Kernel’s enterprise features, session state, type safety, filters, and telemetry, and calls it directly “the next generation of both Semantic Kernel and AutoGen.” Semantic Kernel isn’t abandoned. Microsoft shipped a .NET release as recently as mid-August this year, and migrating an existing production app isn’t urgent. But if you’re starting a brand-new C#, Python, or Go project today and want to be on Microsoft’s forward path rather than the one it’s already begun steering people away from, Microsoft Agent Framework is worth reading about before you write your first Semantic Kernel plugin.

What’s a practical way to start this week?

If you’re still deciding, build the same small agent twice: one tool call, one conditional branch, in both frameworks, and time how long each takes to get a trace you can actually read end to end. That single exercise tells you more about your team’s real velocity with each framework than any comparison article, this one included, because it surfaces the parts of your own stack, your logging setup, your existing Azure or AWS footprint, your team’s language comfort, that a generic comparison can’t see.

Once you’ve picked one, treat tracing and testing as day-one work rather than something you’ll add once the agent is stable, since an agent that’s already handling real conversations is a much harder place to retrofit either than a fresh project is. And if the agent you’re building is going to end up in front of your own customers under their own accounts, sketch the tenant boundary before you write the first plugin or tool node, not after the first cross-tenant bug report. If you’re building the kind of white-label AI product where your own customers eventually see the deflection rate and resolution numbers behind that agent, AiAgRe ties the trace data from either framework into a per-tenant dashboard, so the number your customer sees and the trace your team debugged come from the same evidence.

Frequently asked questions

Is Semantic Kernel better than LangChain for AI agents?

Neither one is better across the board, since the right pick depends on your existing stack rather than a raw feature count. Semantic Kernel fits a C#, Python, or Java codebase with enterprise security needs and OpenTelemetry-native output, while LangChain fits a Python or JavaScript team that wants the widest ecosystem of integrations and is willing to adopt LangSmith for tracing and evaluation.

Does Semantic Kernel support Python?

Yes, Semantic Kernel ships official packages for C#, Python, and Java, currently at version 1.44.1 on PyPI for the Python package. Observability support differs by language though: the Python package currently exposes function duration metrics but not the token usage metric that the C# package captures, and Java doesn’t have observability support yet at all according to Microsoft’s own documentation.

Can you use LangChain and Semantic Kernel together?

There’s no official integration between the two, and most teams pick one as their primary orchestration layer rather than running both inside the same agent. It’s more common to standardize on one framework per agent, then pick different frameworks for different agents across a product, for example a retrieval-heavy agent on one framework and an enterprise workflow agent on the other, all reporting into the same tracing and analytics layer underneath.

Is LangChain still relevant with LangGraph out?

Yes, LangGraph is LangChain’s dedicated agent runtime for branching and looping logic, not a replacement for the broader LangChain ecosystem. LangChain’s core package, its model provider integrations, and its retrieval tooling all still ship and update actively, and LangGraph is built to sit on top of that ecosystem rather than instead of it.

What is Microsoft Agent Framework and does it replace Semantic Kernel?

Microsoft Agent Framework is a newer Microsoft project that Microsoft’s own documentation describes as the next generation of both Semantic Kernel and AutoGen, combining AutoGen’s simpler agent abstractions with Semantic Kernel’s enterprise features. Semantic Kernel is still actively released, with a .NET update as recently as mid-August this year, so an existing production app doesn’t need to migrate urgently, but a brand-new project today is worth evaluating against Agent Framework first.

How do you keep one tenant’s data separate when using either framework for a multi-tenant product?

Neither framework has a built-in multi-tenancy setting. You need to scope conversation state, tool credentials, and stored evaluation results to a tenant identifier at the application layer, on top of whichever framework you choose, and verify that isolation holds under at least two separate test tenant identities before real customer data ever touches it.

Related reading: AI agent testing covers the test-set and eval work neither framework hands you for free, tenant isolation for AI agents walks through the multi-tenant boundary this piece only introduces, and LangChain vs LlamaIndex covers the other framework pairing most teams weigh before landing on LangChain. See pricing for how AiAgRe’s tracing and per-tenant dashboards fit into that loop.