# Microsoft Open Sources Semantic Kernel, Aiming to Standardize the LLM Orchestration Layer > New SDK offers a standardized architecture for 'Copilot' applications, challenging existing frameworks like LangChain. **Published:** March 24, 2023 **Author:** Editorial Team **Category:** devtools **Tags:** Microsoft, Semantic Kernel, Generative AI, LLM Ops, Open Source, Azure **Canonical URL:** https://pseedr.com/devtools/microsoft-open-sources-semantic-kernel-aiming-to-standardize-the-llm-orchestrati --- Microsoft has released Semantic Kernel, an open-source software development kit (SDK) designed to bridge the gap between traditional programming languages and Large Language Models (LLMs), effectively offering a standardized architecture for building "Copilot-like" applications. Microsoft’s release of the Semantic Kernel represents a calculated move to reclaim the developer narrative from third-party orchestration frameworks like LangChain. By open-sourcing this technology, Microsoft is providing a reference architecture for what it terms "Copilot" applications, moving beyond simple API calls to complex, multi-step AI workflows. ### The Kernel Concept The Semantic Kernel acts as an orchestration layer—or "glue code"—that sits between an application's code and the AI model. The SDK is explicitly described as a "lightweight" solution, distinguishing it from heavier, more opinionated frameworks that may introduce unnecessary latency or complexity. Its primary function is to encapsulate complex AI design patterns that have emerged over the last year, allowing developers to integrate LLM capabilities without building the infrastructure from scratch. According to the release details, the kernel natively handles "prompt chaining, recursive reasoning, summarization, zero/few-shot learning, context memory, long-term memory, and embeddings". These features address the stateless nature of standard LLM APIs. While a raw API call to GPT-4 is isolated, Semantic Kernel provides the state management required to maintain context over a long conversation or a complex multi-stage task. ### Memory and Planning A critical component of the release is the SDK's approach to data integration. The documentation highlights support for "semantic indexing, planning, and access to external knowledge stores and internal data". This functionality is essential for implementing Retrieval-Augmented Generation (RAG), a pattern where the model is grounded in proprietary enterprise data to reduce hallucinations and improve relevance. By formalizing "planning"—the ability of the AI to break a user request into sub-tasks—Microsoft is attempting to standardize how agents operate. The kernel allows the model to mix and match "skills" (defined capabilities) to achieve a goal, a process that was previously manual and error-prone for developers to implement. ### Strategic Implications This release arrives at a moment when the "LLM Ops" landscape is fragmented. Tools like LangChain and LlamaIndex have gained significant traction among Python developers for their agility. Microsoft’s entry signals an intent to bring order to this chaos, likely targeting enterprise developers who require more robust, standardized tooling than what is currently available in the community-driven open-source space. While the SDK is open source, it serves as an on-ramp to the Azure ecosystem. By reducing the friction involved in building complex AI apps, Microsoft encourages higher consumption of Azure OpenAI services. The move also suggests a shift in focus from the models themselves to the application layer; the value is no longer just in the weights of the model, but in the orchestration layer that directs the model's attention and actions. ### Limitations and Outlook Despite the robust feature set, questions remain regarding the breadth of language support at launch. While the generative AI community is heavily Python-centric, Microsoft’s tooling often prioritizes C# and .NET initially, which could impact immediate adoption rates among data scientists and AI researchers. Additionally, the performance overhead of introducing an intermediate SDK layer compared to direct API calls remains a metric that engineering teams will need to evaluate closely. ### Key Takeaways * \*\*Standardization of Orchestration:\*\* Microsoft Semantic Kernel provides a structured SDK for managing prompt chaining, memory, and reasoning, attempting to standardize the "glue" between code and LLMs. * \*\*Enterprise-Grade RAG:\*\* The tool includes native support for semantic indexing and accessing external knowledge stores, facilitating Retrieval-Augmented Generation for enterprise data. * \*\*Strategic Ecosystem Lock-in:\*\* By open-sourcing the orchestration layer, Microsoft lowers the barrier to entry for building complex apps on Azure OpenAI, competing directly with frameworks like LangChain. * \*\*Focus on "Copilot" Architecture:\*\* The SDK is positioned specifically as a solution for building "Copilot" applications, codifying the design patterns Microsoft uses in its own products. --- ## Sources - https://github.com/microsoft/semantic-kernel