LangGraph vs Semantic Kernel

Core agent capabilities: LangGraph vs Semantic Kernel

LangGraph is built around a graph-based state machine: nodes represent LLM calls, function executions, or tool invocations, and edges define transitions (including conditional branching). This makes multi-step workflows with checkpointing, parallel branches, and human-in-the-loop pause/resume natural. LangGraph persists state across steps, supports time-travel debugging via LangGraph Studio, and allows replaying from any historical checkpoint. Semantic Kernel takes a more conventional orchestration approach: a central Kernel manages plugins, planners break goals into steps, and the Agent Framework provides group chat and handoff patterns. The Process Framework in Semantic Kernel supports durable, long-running workflows, but it does not offer the same visual debugging or time-travel replay as LangGraph. LangGraph wins for production agents needing durable, inspectable execution.

AI/model approach: LangGraph vs Semantic Kernel

Both frameworks are model-agnostic, supporting OpenAI, Anthropic, Gemini, and Ollama. LangGraph integrates tightly with LangChain’s model abstractions and tool definitions. Semantic Kernel provides its own model abstraction with a focus on .NET, Python, and Java consistency. LangGraph’s streaming exposes every intermediate step, enabling fine-grained observability. Semantic Kernel’s filters act as middleware for LLM calls, allowing centralized logging, security checks, and policy enforcement. For purely Python-based AI teams, LangGraph’s alignment with LangChain is an advantage; for .NET or Java teams, Semantic Kernel’s first-class support is better. Tie for model flexibility; Semantic Kernel wins for multi-language teams.

Integrations & ecosystem: LangGraph vs Semantic Kernel

LangGraph integrates with OpenAI, Anthropic, Gemini, Ollama, Azure OpenAI, AWS Bedrock, LangChain, and LangSmith (for tracing). It is used by Replit and Klarna in production. Semantic Kernel integrates with Azure OpenAI, Azure AI Search, Microsoft 365, Entra ID, and vector stores (Pinecone, Qdrant). It is used inside Microsoft Copilot. LangGraph’s ecosystem is broader for multi-cloud and has stronger observability via LangSmith. Semantic Kernel’s ecosystem is deeper within Azure and Microsoft 365. LangGraph wins for multi-cloud production; Semantic Kernel wins for Azure-first enterprises.

Performance & scale: LangGraph vs Semantic Kernel

LangGraph’s durable state persistence is designed for long-running agent workflows that may pause and resume. The hosted LangGraph Platform provides a managed runtime with scheduled runs and cron. Semantic Kernel’s Process Framework supports durable workflows but is typically deployed on infrastructure the user manages. LangGraph has proven at scale with Replit and Klarna. Semantic Kernel’s scale is seen in Microsoft Copilot extensions. No public latency benchmarks exist for either. LangGraph likely wins for production scale due to its managed platform and proven deployments.

Developer experience: LangGraph vs Semantic Kernel

LangGraph requires Python knowledge and comfort with graph abstractions. LangGraph Studio provides a visual debugger showing state transitions. Semantic Kernel offers familiar dependency injection and middleware patterns for .NET/Java developers. Both have sample projects and tutorials. LangGraph’s documentation is robust but assumes LangChain familiarity. Semantic Kernel’s documentation integrates with Microsoft Learn. Semantic Kernel wins for .NET/Java developers; LangGraph wins for Python developers building complex agents.

LangGraph pricing (2026)

LangGraph is open source under MIT license – the full framework, self-hosting, and LangGraph Studio visual debugger are free. For those wanting a managed, durable hosted runtime, the LangGraph Platform offers:

• Plus plan: from $39/month, includes durable hosted runtime, scheduled runs, human-in-the-loop API, and cron agents. Additional features like fleet agents and prebuilt templates are available.
• Overage and higher tiers exist but are not detailed in the provided input.

The hosted platform eliminates the need to run your own queue for scheduled tasks.

Semantic Kernel pricing (2026)

Semantic Kernel is entirely free and open source under MIT license. There is no paid tier, hosted platform, or premium add-on. All SDKs (.NET, Python, Java), the Agent Framework, Process Framework, and integrations are included at no cost. Users must manage their own infrastructure for production workloads.

Value-per-dollar: LangGraph vs Semantic Kernel

For teams willing to self-host and manage infrastructure, Semantic Kernel offers indefinite zero-cost usage. LangGraph’s free open-source tier also provides robust capabilities for self-hosting, but the highest value features (durable hosted runtime, cron, human-in-the-loop API) require the $39/month Plus plan. For enterprise .NET/Java teams already on Azure, Semantic Kernel’s free SDK combined with Azure services can be cost-effective. For Python teams needing managed production durability, LangGraph Platform’s $39/month is a good value compared to building equivalent infrastructure. Semantic Kernel wins on raw cost; LangGraph wins on managed production features.