AI agent orchestration / LLM application framework
Model-agnostic SDK for building, orchestrating, and deploying AI agents and multi-agent systems with enterprise-readiness.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
choose for the official successor with stable APIs and long-term support.
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
choose for a production-focused multi-agent harness in Python with Apache-2.0.
Multi-Agent Harness for Production AI
choose for a lightweight SDK with end-to-end control of agent harnesses in Python/TypeScript.
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
choose for building agents in Java with a distributed, long-running design.
Build distributed, production-grade, long-running agents.
choose for a Rust-core agent framework with Python bindings when performance is critical.
GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.
choose for an event-driven agent framework in Go with LLM-agnostic support.
Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade observability.
choose for a lightweight, first-party OpenAI multi-agent framework in Python.
A lightweight, powerful framework for multi-agent workflows
choose for a code-first Python toolkit from Google with evaluation/deployment tooling.
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
choose for a batteries-included agent harness with tools and multi-agent support out of the box.
The batteries-included agent harness.
choose for building agents in Go with a code-first toolkit from Google.
An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
choose for building LLM applications in Rust with a modular, scalable framework.
⚙️🦀 Build modular and scalable LLM Applications in Rust
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
choose for a TypeScript agent engineering platform instead of a code-only SDK.
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
choose when you need to orchestrate existing CLI/harness agents rather than build them from scratch.
Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.
choose if you standardize on Model Context Protocol and want an MCP-native agent framework.
Build effective agents using Model Context Protocol and simple workflow patterns
choose if you need just the runtime layer for an agent harness, not a full orchestration SDK.
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
choose for stateful, graph-based agent orchestration with fine-grained control.
Build resilient agents.
choose for an event-driven multi-agent architecture that integrates with Solace messaging.
An event-driven framework designed to build and orchestrate multi-agent AI systems. It enables seamless integration of AI agents with real-world data sources and systems, facilitating complex, multi-step workflows.
choose for a pipeline-centric LLM framework with strong retrieval and modular components.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
choose for graph-orchestrated agent loops with Dify DSL if you already use LangGraph.
Graph-Orchestrated Agent Loop — a production-grade framework on LangGraph. Combine workflow graphs and agent loops, transpile Dify DSL to runnable code, swap wire protocols (Dify/OpenAI).
choose for a memory-first, self-improving agent harness with persistent memory as core.
The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
choose if you want a ready-to-deploy enterprise agent workflow platform with UI.
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
choose if you want a platform to create/manage agents visually rather than code your own orchestration.
Evo AI is an open-source platform for creating and managing AI agents, enabling integration with different AI models and services.
choose if you want a ready-to-deploy knowledge platform with RAG/agent features rather than an SDK.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
These projects were analysed and named semantic-kernel among their alternatives. The relationship is not symmetric — how semantic-kernel rates them is a separate judgement, made when semantic-kernel is analysed in its own right.
calls semantic-kernel “choose it if your app is on .NET/C# and you want a Microsoft-maintained agent/LLM SDK.”
Build resilient agents.
calls semantic-kernel “Broader LLM SDK for embedding AI into applications; choose it when you need tight app integration rather than a standalone agent.”
The batteries-included agent harness.
calls semantic-kernel “Choose when you want a C# SDK for integrating LLMs into apps with plugins and planners, not specifically multi-agent workflows.”
A lightweight, powerful framework for multi-agent workflows
calls semantic-kernel “cross-language SDK for integrating LLMs into apps; choose if you need C#/.NET support.”
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
calls semantic-kernel “C#-centric LLM orchestration SDK with a broader app-integration scope.”
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
calls semantic-kernel “choose if you prefer a planner/plugin-based SDK for integrating LLMs into existing apps.”
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
calls semantic-kernel “choose it when you need a .NET-first SDK for integrating LLM agents into apps.”
Multi-Agent Harness for Production AI
calls semantic-kernel “C# SDK for integrating LLMs and agents into .NET apps; choose if you are in .NET and need a broad AI integration layer.”
Agentic AI Framework for Java Developers
calls semantic-kernel “LLM integration SDK for .NET; choose when your app is .NET and you need agent capabilities.”
AI Agent Engineering Platform built on an Open Source TypeScript AI Agent Framework
calls semantic-kernel “when you want to integrate agent capabilities directly into .NET/Python apps via an SDK.”
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
calls semantic-kernel “if you want a C#/.NET SDK for integrating LLMs and agents.”
An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
calls semantic-kernel “Microsoft's LLM integration SDK; choose for C# or enterprise Microsoft stack.”
Build effective agents using Model Context Protocol and simple workflow patterns
calls semantic-kernel “choose when you need a cross-language SDK with strong .NET/C# support.”
⚙️🦀 Build modular and scalable LLM Applications in Rust
calls semantic-kernel “when your application is in C#/.NET and you need Microsoft's AI integration layer that can orchestrate agents”
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
calls semantic-kernel “Use when your app is built on C#/.NET or Microsoft kernel/plugin abstractions; seed does not target .NET.”
Build an agent harness and control it end-to-end. Open-source SDK for production AI agents in Python & TypeScript - any model, any cloud.
calls semantic-kernel “Choose when you need a Microsoft-supported SDK to embed agents/skills into existing .NET apps instead of deploying a Java agent platform.”
Build distributed, production-grade, long-running agents.
calls semantic-kernel “Choose when adding agent capabilities to existing .NET/Python apps with a broader plugin and planner ecosystem.”
AG2 (formerly AutoGen): The Open-Source AgentOS.Join us at: https://discord.gg/sNGSwQME3x
calls semantic-kernel “LLM integration SDK for embedding AI in applications; choose when you need app-level AI, not a standalone agent runner.”
The Harness of Harnesses: a trusted, persistent, self-evolving multi-agent ecosystem for all-domain collaboration.
calls semantic-kernel “C#/.NET SDK; choose it when integrating LLM agents into .NET applications rather than Node.js.”
An open-source, code-first Typescript toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
calls semantic-kernel “Choose when your application is primarily .NET/C# and you want an LLM orchestration SDK rather than a Python agent framework.”
Extensible AGI Framework