Best Semantic Kernel Alternatives & Competitors

LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.

LangGraph is LangChain's graph-based orchestration framework for building stateful, multi-step AI agents. It models agent workflows as directed graphs with nodes and edges, enabling complex control flow patterns like branching, looping, and human-in-the-loop interactions. LangGraph supports persistent state, streaming, and deployment via LangGraph Cloud.

The OpenAI Agents SDK is a lightweight Python framework for building multi-agent workflows with built-in tracing and guardrails. It provides primitives for defining agents with instructions and tools, orchestrating handoffs between agents, and implementing input/output guardrails for safety.

CrewAI is a framework for orchestrating multi-agent AI systems where specialized agents collaborate to complete complex tasks. It provides abstractions for defining agent roles, goals, tools, and workflows, enabling teams of AI agents to work together like a human crew. CrewAI supports sequential, parallel, and hierarchical task execution patterns and integrates with all major LLM providers.

Llama Stack is Meta's standardized API and SDK for building AI applications on top of Llama models. It provides a unified interface for inference, safety, memory, and agentic workflows — with swappable providers for local, cloud, and on-device deployment. As the official framework for the Llama ecosystem, it is becoming the default for teams building on open-source Llama models.

AutoGen is Microsoft's open-source framework for building multi-agent AI systems. It enables the creation of conversational agents that can work together, use tools, and interact with humans to solve complex tasks. AutoGen supports customizable agent behaviors, flexible conversation patterns, and integrations with various LLMs. The framework is popular for building research assistants, coding agents, and automated analysis pipelines.

Google's Agent Development Kit (ADK) is a modular framework for building AI agents that integrates natively with Gemini models and Vertex AI. It supports multi-agent architectures, tool use, memory, and deployment to Google Cloud, providing an end-to-end solution for building agents in the Google ecosystem.

Swarm is OpenAI's experimental multi-agent orchestration framework that introduced the "handoff" and "routine" patterns for agent coordination. While marked as educational, its lightweight design — agents as instructions + functions with explicit handoffs between them — has become the dominant architectural pattern adopted across the industry for building multi-agent systems.

The Vercel AI SDK is a TypeScript toolkit for building AI-powered web applications with React, Next.js, and other frameworks. It provides streaming UI components, structured generation, tool calling, and multi-step agent workflows. The SDK supports all major LLM providers through a unified interface and is the most popular choice for frontend developers building AI features into web applications.

Dify is an open-source platform for building LLM applications with both visual and code-based interfaces. It provides a workflow orchestration engine, RAG pipeline builder, agent framework, and model management—all accessible through a web UI. Dify supports 50+ LLM providers, offers enterprise features like SSO and access control, and can be self-hosted or used as a cloud service.

DSPy is a framework from Stanford for programming—not prompting—foundation models. It replaces manual prompt engineering with composable, optimizable modules. DSPy compilers automatically tune prompts and weights for your specific pipeline and dataset, enabling more reliable LLM applications.

Instructor is a popular open-source library for getting structured outputs from LLMs using Pydantic models.

Pydantic AI is an agent framework from the creators of Pydantic that leverages Python type hints for building type-safe AI agents. It provides structured output validation, dependency injection for tools, and a model-agnostic interface, making it popular with Python developers who value code quality and type safety.

Agno (formerly Phidata) is a framework for building multi-modal AI agents.

Smolagents is Hugging Face minimalist agent framework for building AI agents with code-based actions.

Mastra is a TypeScript-first agent framework for building production AI applications. It provides primitives for agents, workflows, RAG, integrations, and memory with a focus on developer experience and type safety. Mastra is designed for full-stack TypeScript developers who want to build AI features without leaving their existing tech stack.