Alternatives to Semantic Kernel
Microsoft SDK to build AI agents in C#, Python, Java
Semantic Kernel ranks #7 of 10 in AI agent frameworks, with an Alt Score of 86. It is licensed under MIT, free from Free and available on Linux, macOS and Windows. 13 of 13 checklist rows are verified against a public source.
Semantic Kernel is a Microsoft open-source SDK for integrating LLMs and building AI agents in C#, Python and Java.
Enterprise developers working in Python, .NET, or Java who want a model-agnostic SDK for building AI agents and multi-agent systems on Microsoft's stack, particularly teams already invested in Azure; Microsoft now directs new projects to the successor Microsoft Agent Framework.
An open-source (MIT-licensed) SDK for building agents with plugins/tools, memory, and streaming, a Process Framework for modeling stateful business workflows, and experimental multi-agent orchestration patterns (sequential, concurrent, handoff, group chat, Magentic) plus RAG and memory providers, mainly documented for C# with partial Python and Java support.
You build a Kernel with one or more AI service connectors (OpenAI, Azure OpenAI, and others), register native functions or plugins that the model can call, and compose ChatCompletionAgents that use those tools, retain conversational memory, and stream responses; multiple agents can be coordinated through built-in orchestration patterns, with OpenTelemetry-compatible logs, metrics, and traces throughout.
Why people leave Semantic Kernel
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Ranked alternatives
Ordered by Alt Score. Click any score to see the breakdown.
LangGraph is a low-level orchestration framework (by LangChain) for building stateful, multi-agent applications as graphs.
LlamaIndex is a data framework for building LLM applications and agent workflows over your own data (RAG + agents).
CrewAI is a Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a crew.
Haystack (by deepset) is an open-source framework for building production LLM applications, RAG pipelines and agents.
Mastra is an open-source TypeScript framework for building AI agents, workflows and RAG, from the team behind Gatsby.
Agno (formerly Phidata) is a full-stack Python framework for building multi-agent systems with memory, knowledge and tools.
AutoGen is an open-source Microsoft framework for building multi-agent applications with conversable, collaborating agents.
The OpenAI Agents SDK is a lightweight, provider-agnostic framework for building multi-agent workflows with handoffs and guardrails.
Pydantic AI is a Python agent framework that brings type-safe, structured outputs and dependency injection to LLM apps.
Feature comparison
Rows come from the AI agent frameworks checklist (17 rows). Human-verified cells only. ? means the value has not been verified.
| AI agent frameworks checklist | Semantic Kernel | LangGraph | LlamaIndex | CrewAI | Haystack | Mastra |
|---|---|---|---|---|---|---|
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| License | ||||||
| Platforms | ||||||
| Open source | ||||||
| Language (Python) | ||||||
| Language (JS/TS) | ||||||
| Multi-agent orchestration | ||||||
| Graph / stateful workflows | ||||||
| Human-in-the-loop | ||||||
| Streaming support | ||||||
| Built-in memory / state persistence | ||||||
| Tool / function calling | ||||||
| Model-agnostic (multi-provider) | ||||||
| Observability / tracing integration | ||||||
| Managed / hosted deployment | ||||||
| RAG support |
Every fact and feature listed for Semantic Kernel is verified against its own pages. Each alternative is sourced on its own page.
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License MIT verified 2026-07-16
MIT License
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/LICENSE -
Pricing model Free verified 2026-07-16
Semantic Kernel is a free, MIT-licensed open-source SDK with no paid tiers or first-party hosted offering.
Permission is hereby granted, free of charge, to any person obtaining a copy
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/LICENSE -
Starts at Free verified 2026-07-16
Same basis as pricing_model: MIT-licensed OSS SDK, no cost to use.
Permission is hereby granted, free of charge, to any person obtaining a copy
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/LICENSE -
Platforms Linux, macOS, Windows verified 2026-07-16
- **OS Support**: Windows, macOS, Linux
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Status maintenance verified 2026-07-16
README banner directs users to the Semantic Kernel migration guide for Microsoft Agent Framework (MAF), described as the 'enterprise-ready successor to Semantic Kernel.' Semantic Kernel itself still r
Semantic Kernel is now Microsoft Agent Framework! Microsoft Agent Framework (MAF) is the enterprise‑ready successor to Semantic Kernel.
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Open source Yes verified 2026-07-16
Licensed under the [MIT](LICENSE) license.
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Language (Python) Yes verified 2026-07-16
- **Python**: 3.10+
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Language (JS/TS) No verified 2026-07-16
README System Requirements list Python, .NET, and Java as the supported languages/runtimes; no JavaScript/TypeScript SDK is mentioned in the README or docs.
- **Python**: 3.10+ - **.NET**: .NET 10.0+ - **Java**: JDK 17+
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Multi-agent orchestration Yes verified 2026-07-16
Docs list supported patterns: Concurrent, Sequential, Handoff, Group Chat, and Magentic. Page notes these orchestration features are in the experimental stage.
Semantic Kernel’s Agent Orchestration framework enables developers to build, manage, and scale complex agent workflows with ease.
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-orchestration/ -
Graph / stateful workflows Yes verified 2026-07-16
Page notes the Process Framework package is currently experimental and subject to change until it reaches preview/GA.
The Process Framework provides a robust solution for automating complex workflows. Each step within the framework performs tasks by invoking user-defined Kernel Functions, utilizing an event-driven mo
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/process/process-framework -
Human-in-the-loop Yes verified 2026-07-16
Demonstrated via the Process Framework; the C# sample is documented in full, while the same page states Python support for this pattern is 'coming soon.'
In this section we will improve on that process again by requiring a human to approve or reject the documentation before it's published.
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/process/examples/example-human-in-loop -
Streaming support Yes verified 2026-07-16
A streamed response delivers the message content in small, incremental chunks.
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-streaming -
Built-in memory / state persistence Yes verified 2026-07-16
Covers Mem0Provider (long-term, cross-thread) and WhiteboardProvider (short-term context); page marks this functionality experimental and C#-only at time of writing (Python/Java sections say 'Coming S
To capture and retain memories, we support components that can be used with an `AgentThread` to extract memories from any messages that are added to the thread, and provide those memories to the agent
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-memory -
Tool / function calling Yes verified 2026-07-16
The most powerful feature of chat completion is the ability to call functions from the model.
https://learn.microsoft.com/en-us/semantic-kernel/concepts/ai-services/chat-completion/function-calling/ -
Model-agnostic (multi-provider) Yes verified 2026-07-16
README also lists built-in connector support for OpenAI, Azure OpenAI, Hugging Face, NVIDIA, and local runtimes like Ollama/LM Studio/ONNX.
Semantic Kernel is a model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems.
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
Observability / tracing integration Yes verified 2026-07-16
Same page states this observability tooling is 'not yet available for Java' at time of writing.
Semantic Kernel is designed to be observable. It emits logs, metrics, and traces that are compatible to the OpenTelemetry standard.
https://learn.microsoft.com/en-us/semantic-kernel/concepts/enterprise-readiness/observability/ -
Managed / hosted deployment No verified 2026-07-16
Semantic Kernel is a self-hosted SDK/library that developers install and run themselves; the README and docs describe no first-party managed or hosted runtime for Semantic Kernel itself.
Semantic Kernel is a model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems.
https://raw.githubusercontent.com/microsoft/semantic-kernel/main/README.md -
RAG support Yes verified 2026-07-16
Page marks this RAG functionality experimental and C#-only at time of writing (Python/Java sections say 'Coming Soon').
The `Microsoft.SemanticKernel.Data.TextSearchProvider` allows agents to retrieve relevant documents based on user input and inject them into the agent's context for more informed responses.
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-rag
FAQ
Yes. AutoGen, OpenAI Agents SDK and Pydantic AI have a free tier or are fully free. Free-tier limits in the comparison table are verified and dated.
LangGraph, LlamaIndex and CrewAI — every license claim links its source.