LangGraph vs Semantic Kernel: Which Framework Fits Your Stack?

The most common question when evaluating LLM orchestration frameworks: LangGraph or Semantic Kernel? The answer depends on your stack and requirements. LangGraph offers low-level control over stateful workflows through graph-based execution. Semantic Kernel brings enterprise tooling to agent development with a plugin architecture designed for .NET and Azure environments. 

In this guide, we’ll compare LangGraph and Semantic Kernel so you can see how each handles workflows, state, and integrations.

What Are AI Orchestrator Libraries?

Orchestrator libraries handle the complexity between language models and application code. They manage state across conversations, coordinate multi-step workflows, integrate external tools, and control execution flow. Rather than building custom logic for each agent interaction, you define behaviors through framework abstractions.

These frameworks emerged as the missing layer in the AI stack. While LLM APIs provide raw intelligence, and application frameworks handle user interfaces, orchestrators bridge the gap by managing the intricate dance of agent behaviors, tool calls, and state management.

Defining a New Layer in the AI Stack

Early LLM applications used simple prompt templates and single API calls. Production systems need more: persistent memory across sessions, dynamic decision-making based on context, tool integration with databases and APIs, and multi-agent coordination for complex tasks. Orchestrators standardize these patterns and provide reusable infrastructure.

The shift from basic prompt engineering to sophisticated agent systems created demand for frameworks that could handle complex workflows reliably. Modern orchestrators manage not just individual LLM calls but entire agent lifecycles including error recovery, state persistence, and execution monitoring.

The First Breakout Star of LLM Tooling

LangChain established itself as the first widely adopted orchestration framework with abstractions for chains, agents, and memory. It democratized agent development by providing ready-made components for common patterns. However, developers building complex agent systems found its sequential chain model limiting for advanced control flow scenarios.

This limitation became apparent in production deployments where agents needed to loop through refinement cycles, branch based on runtime conditions, or pause for human approval. The gap created space for LangGraph, which introduced graph-based orchestration with explicit state management, and Semantic Kernel, which brought enterprise-focused tooling to .NET environments with Microsoft backing.

LangGraph: A Graph-Based Upgrade for Power Users

LangGraph models agent workflows as directed graphs where nodes represent operations (LLM calls, database queries, processing steps) and edges define transitions. You build conditional branching, loops, and parallel execution that respond to runtime conditions.

The framework comes from LangChain's creators, but addresses a specific need: stateful workflows that don't fit linear patterns. State flows through the graph, and nodes read, modify, and pass that state forward based on your logic.

What Makes LangGraph Stand Out

LangGraph executes nodes asynchronously when dependencies allow, reducing latency in multi-step workflows. It provides low-level control without abstracting your architecture or prompts. The framework includes durable execution with automatic checkpointing, allowing agents to persist through failures and resume from exact states.

Companies like Klarna, Replit, and Elastic use LangGraph for production agent systems. It's open source (MIT license) with both Python and JavaScript implementations. The ecosystem integrates with LangSmith for observability and debugging, plus LangGraph Studio for visual development.

Semantic Kernel: Built for Enterprise AI at Scale

Microsoft built Semantic Kernel as a model-agnostic SDK for .NET, Python, and Java developers. It provides native APIs for each language with async patterns developers expect. The architecture centers on plugins (reusable functions), planners (execution orchestrators), and memory abstractions.

Semantic Kernel runs on MIT license and supports multiple LLM providers including OpenAI, Azure OpenAI, Hugging Face, and Nvidia. The framework handles both single agents and multi-agent systems with built-in observability and enterprise security features.

Why Enterprises Choose It

Organizations working within Microsoft's ecosystem get native integration with Azure OpenAI, Azure AI Search, and Microsoft 365 APIs. The framework includes governance features like content filtering, audit logging, and access control that map to Azure policies.

.NET teams use existing deployment pipelines, monitoring tools, and security practices without adopting Python tooling. The multimodal support processes text, vision, and audio inputs through unified APIs.

Head-to-Head Comparison: LangGraph vs Semantic Kernel

Feature	LangGraph	Semantic Kernel
License	MIT (Open Source)	MIT (Open Source)
Primary Languages	Python, JavaScript	Python, .NET (C#/F#), Java
Architecture	Graph-based with nodes and edges	Plugin and planner-based
Control Flow	Explicit state management, custom graphs	Automatic planning with structured plugins
State Management	Checkpointing (memory, SQLite, Postgres)	Built-in memory abstractions
LLM Support	OpenAI, Anthropic, Cohere, HuggingFace, Ollama, LMStudio	OpenAI, Azure OpenAI, HuggingFace, Nvidia, Ollama, LMStudio, ONNX
Vector Databases	Pinecone, Weaviate, Qdrant, Chroma, FAISS	Azure AI Search, Elasticsearch, Chroma, Pinecone, Qdrant
Async Execution	Native Python async/await	Native async/await (all supported languages)
Deployment Options	LangGraph Cloud, Kubernetes, AWS ECS, self-hosted	Azure App Service, Container Apps, Functions, self-hosted
Security	Community-driven, manual implementation	Azure AD, managed identities, built-in content filtering
Compliance	Manual implementation required	SOC 2, HIPAA, GDPR (via Azure)
Observability	LangSmith integration	Application Insights integration
Multi-Agent Support	Yes, custom graph orchestration	Yes, agent-as-plugin pattern
Human-in-the-Loop	Built-in with execution pauses	Requires custom implementation
Streaming	Async generators	Async enumerables/generators
Learning Resources	Code examples, tutorials, LangGraph Academy	Microsoft Learn modules, 100+ samples, structured docs
Community	GitHub discussions, LangChain Forum	GitHub, Microsoft Q&A
Best For	Research, startups, Python teams, flexible workflows	Enterprises, .NET teams, Azure ecosystems, structured planning
Enterprise Support	Community support, LangGraph Cloud support	Microsoft support channels

Architecture & Philosophy

LangGraph: Flexible Innovation-Focused:

LangGraph provides low-level primitives without prescribing architecture. You construct graphs programmatically with explicit state management and control flow. This supports novel agent designs and research prototypes that need custom execution patterns.

The framework exposes checkpointing APIs directly, letting you implement exactly how state persists and restores. You control when to checkpoint, what to save, and how to handle failures.

Semantic Kernel: Structured Enterprise Approach:

Semantic Kernel organizes functionality around plugins and planners. Plugins encapsulate capabilities as semantic functions (prompt templates) or native functions (language code). Planners analyze goals, select plugins, and generate execution plans automatically.

This structure enforces boundaries between components. Multiple teams can contribute plugins independently, while planners handle orchestration logic separately from business logic.

Agent Development Workflow

LangGraph: Dynamic Approach

Building in LangGraph starts with defining state schemas using TypedDict or Pydantic models. You create node functions that transform state and specify edges with conditional logic. The prebuilt create_react_agent provides a starting point, but custom graphs give you full control.

Graphs support cycles for iterative refinement, conditional branching based on state, and human-in-the-loop patterns where execution pauses for approval. Changes to behavior mean rewiring graphs or adjusting conditions rather than rewriting individual nodes.

Semantic Kernel: Structured Approach:

Semantic Kernel development involves defining plugins with the @kernel_function decorator in Python or [KernelFunction] attribute in C#. You write functions that agents can call, and planners orchestrate them to achieve goals.

Agents can invoke other agents as plugins, creating multi-agent hierarchies. The triage pattern routes requests to specialized agents based on request content, as shown in their billing/refund example.

Memory Management

LangGraph: Flexible Options:

LangGraph uses checkpointing for short-term state persistence across graph execution. Each checkpoint captures the complete graph state at a specific point. You can store checkpoints in memory, SQLite, Postgres, or custom backends.

For long-term memory, LangGraph integrates with vector databases through LangChain's ecosystem. You implement retrieval logic explicitly in your nodes when context needs historical information.

Semantic Kernel: Simplified Abstraction:

Semantic Kernel provides memory abstractions with collections for organizing related memories and embedding-based semantic search. Azure Cognitive Search and Elasticsearch integrate with configuration rather than custom code.

The memory system connects to agents automatically. When agents need context, they query relevant memories through the framework's interfaces without explicit retrieval logic in your code.

Ecosystem & Integrations

LangGraph: Broad Connectivity:

LangGraph works with any LLM provider through LangChain integrations: OpenAI, Anthropic, Cohere, Hugging Face, and local models via Ollama or LMStudio. Vector database support includes Pinecone, Weaviate, Qdrant, and Chroma.

The framework connects to any Python-accessible API. Integration means writing node functions that call your services and return results in the state format.

Semantic Kernel: Microsoft-Centric with Broad Support:

Semantic Kernel now supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and local deployment with Ollama, LMStudio, or ONNX. Vector database integrations include Azure AI Search, Elasticsearch, and Chroma, with community support for Pinecone and Qdrant.

Microsoft services receive first-class support with dedicated documentation. Connecting

to other services requires writing custom plugins using provided interfaces.

Performance & Scalability

LangGraph: Flexible Performance Tuning:

Python's async capabilities enable concurrent node execution. Performance depends on graph structure and whether you leverage parallelism effectively. LangGraph adds minimal overhead beyond your node implementations and LLM API latency.

LangGraph Cloud provides purpose-built deployment for stateful workflows with automatic scaling, persistent storage, and built-in APIs. You can also deploy to standard platforms like Kubernetes or AWS ECS.

Semantic Kernel: Enterprise-Grade Performance:

.NET compiles to native code with efficient async/await handling. The framework includes telemetry integration with Application Insights for production monitoring.

Azure deployment options include App Service, Container Apps, and Azure Functions with autoscaling through Azure portal. The framework runs on Windows, macOS, and Linux.

Security & Compliance

LangGraph: Community-Driven Security:

LangGraph follows standard open source security practices. You handle credential management, implement authentication, and configure network policies yourself. The framework works within whatever security infrastructure you establish.

LangGraph Cloud includes authentication and access control for deployed agents. Compliance requirements around data handling, and audit logs need implementation based on your needs.

Semantic Kernel: Enterprise Security First:

Semantic Kernel integrates with Azure Active Directory, supports managed identities for credential-free authentication, and includes built-in content filtering. Azure's compliance certifications extend to applications built with the framework when deployed on Azure.

Audit logging and data residency controls configure through Azure policy. The framework's enterprise-ready features support SOC 2, HIPAA, and GDPR requirements.

Developer Experience & Learning Curve

LangGraph: Research & Experimentation-Focused:

LangGraph documentation emphasizes code examples, tutorials, and the LangGraph Academy course. Learning involves understanding graph concepts and studying reference implementations. The community uses GitHub discussions and the LangChain Forum.

Templates provide starting points for common patterns: ReAct agents, memory systems, and retrieval workflows. LangGraph Studio offers visual debugging and prototyping for deployed agents.

Semantic Kernel: Enterprise-Friendly:

Microsoft provides structured documentation, quickstart guides, and sample applications. The learning path follows standard .NET or Python conventions, depending on your language choice.

Official Microsoft Learn modules include Semantic Kernel content. The repository contains over 100 detailed samples showing plugin creation, multi-agent systems, and enterprise integrations.

When to Choose LangGraph

1. Flexibility & Experimentation

LangGraph suits projects exploring novel agent architectures. You get low-level control over state management, execution flow, and checkpointing behavior. Research teams and startups testing unconventional workflows benefit from minimal framework constraints.

2. Extensive Tool Integrations

Projects requiring diverse third-party integrations work well with LangGraph's broad ecosystem. The framework connects to most LLM providers, vector databases, and Python libraries without adapter code.

3. Python-Centric Development

Teams with Python expertise and existing Python infrastructure deploy LangGraph applications naturally. The JavaScript version serves TypeScript/Node.js teams with equivalent capabilities.

4. Startup & Research Environments

Early-stage companies prioritizing rapid iteration and technical teams comfortable with open source tooling work effectively with LangGraph. Case studies from companies like Replit show production deployments at scale.

When to Choose Semantic Kernel

1. Microsoft/Azure Ecosystem Fit

Organizations standardized on Azure services gain immediate value from native integrations. Authentication, monitoring, and deployment align with existing Azure practices without custom integration work.

2. Enterprise Stability & Reliability

Companies requiring vendor support and predictable releases choose Semantic Kernel. Microsoft's backing provides assurance around long-term maintenance and enterprise feature development with stable APIs.

3. Structured Orchestration & Planning

Projects where agent behavior needs to be explainable benefit from Semantic Kernel's planner approach. The plugin system makes components testable independently and debugging more systematic.

4. .NET Development Teams

Organizations with C#, F#, or VB.NET expertise integrate Semantic Kernel without expanding language footprint. Python and Java support also available for polyglot teams.

Practical Use Cases - LangGraph

You can use LangGraph when you need explicit control over agent workflows and state. It works well for loops, branching paths, and tasks that must resume from the exact point they stopped.

Typical cases:

• Multi-agent research pipelines.

• Iterative generate-and-review loops.

• Customer interactions that depend on persistent state.

Klarna’s support system is a solid example, since their flows change often and need reliable state recovery.

Semantic Kernel

Semantic Kernel fits setups that depend on structured integrations and predictable governance. Its plugin model and .NET focus make it a steady choice for enterprise systems.

Typical cases:

• Internal copilots connected to SharePoint, Teams, or internal APIs.

• Workflows built on audited plugins.

• Routing patterns that hand requests to specialized agents.

You can also reach out to us for guidance in choosing the best approach for your project.

Frequently Asked Questions

What are actual code examples for creating agents in each?

LangGraph's repository (langchain-ai/langgraph) includes examples of multi-agent systems, human-in-the-loop workflows, and tool-using agents. The quickstart shows the create_react_agent function for rapid prototyping.

Semantic Kernel's repository (microsoft/semantic-kernel) contains over 100 samples demonstrating plugin creation, planner usage, and multi-agent orchestration in Python, .NET, and Java.

How much does it cost to run LangGraph vs Semantic Kernel with GPT-4?

Both frameworks generate similar LLM API costs since they call the same models. Infrastructure differs: LangGraph runs on general-purpose compute or LangGraph Cloud. Semantic Kernel typically deploys to Azure services with managed infrastructure overhead but reduced operational complexity.

Do they support streaming responses for real-time applications?

LangGraph supports streaming through async generators. You stream tokens as models generate them, with state updates flowing through the graph incrementally.

Semantic Kernel implements streaming through async enumerables. The framework streams both token generation and intermediate planning steps in real-time.

Which vector databases work with each framework?

LangGraph connects to Pinecone, Weaviate, Qdrant, Chroma, and FAISS through LangChain integrations. Any Python-accessible vector database works with minimal code.

Semantic Kernel integrates with Azure AI Search, Elasticsearch, and Chroma natively. Community contributions add Pinecone, Qdrant, and Weaviate support.

What are the performance benchmarks (latency/throughput)?

Neither project publishes official benchmarks. Performance depends on workflow design, model selection, and infrastructure rather than framework overhead. Both add minimal latency beyond LLM API calls and custom logic. GitHub discussions include user-reported characteristics for specific use cases.