Comparisons / LangGraph vs Semantic Kernel
LangGraph vs Semantic Kernel: Which Agent Framework to Use?
LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. Semantic Kernel is Microsoft's enterprise SDK for building AI agents. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
LangGraph
18.9k
3.4k
Python
MIT
2024-01-17
LangChain Inc (Harrison Chase)
Sequoia Capital, Benchmark
Part of LangChain Inc — $50M raised across A and B
8.2M
LangGraph Platform (hosted), LangSmith (observability)
Yes
Used by: Replit, Klarna, Elastic
github.com/langchain-ai/langgraph→Semantic Kernel
27.6k
4.5k
C#
MIT
2023-02-27
Microsoft
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LangGraph | Semantic Kernel |
|---|---|---|
| Agent | A `StateGraph` with nodes, edges, and a typed `State` channel | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | `ToolNode(tools)` paired with a conditional edge for routing | — |
| Loop | `add_conditional_edges` from a node back to itself until a `END` condition | — |
| State | Typed `State` channels with reducers (`Annotated[list, add_messages]`) | — |
| Checkpointing | `MemorySaver` / `PostgresSaver` persists state per `thread_id` | — |
| Human-in-loop | `interrupt_before` / `interrupt_after` pauses execution for review | — |
| Parallel fanout | Multiple edges from one node + reducers merge results | — |
| Tools / Plugins | — | `KernelPlugin` with `@kernel_function` decorators, typed parameters |
| Planning | — | `StepwisePlanner`, `HandlebarsPlanner` for multi-step decomposition |
| Memory | — | `SemanticTextMemory` with embeddings and vector stores |
| Orchestration | — | `Kernel.invoke()` with plugin resolution and filter pipeline |
| Multi-Language | — | C#, Python, Java SDKs with shared abstractions |
LangGraph vs Semantic Kernel, head to head
LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.
Semantic Kernel Semantic Kernel is Microsoft's enterprise SDK for building AI agents.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick LangGraph if
Pick LangGraph if langGraph earns its weight when your agent is a workflow — explicit branches, checkpoints, parallel branches, or a human approval gate. For a single-agent loop, the graph machinery is overkill and a plain while loop is faster to write, debug, and ship. LangGraph is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Semantic Kernel would force you to translate.
Pick Semantic Kernel if
Pick Semantic Kernel if semantic Kernel earns its complexity in enterprise environments with Azure OpenAI, .NET backends, and existing Microsoft infrastructure. But the core agent pattern — LLM call, tool dispatch, loop — is identical to what you can build in 60 lines of Python. Semantic Kernel is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LangGraph would force you to translate.
What both add
Both LangGraph and Semantic Kernel pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both LangGraph and Semantic Kernel implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →