AG2 is the community option for conversation-centered AutoGen-style experimentation; it is not the replacement Microsoft recommends for new AutoGen projects. Microsoft Agent Framework is the supported enterprise direction for Azure and .NET. New Microsoft-platform projects should evaluate Agent Framework; teams specifically invested in the community AutoGen lineage can evaluate AG2 on its own merits. It remains useful for research workflows, group chat, human participation and systems where agent-to-agent conversation is the main abstraction. Its agent capabilities are most compelling when combined with Haystack's established retrieval and pipeline strengths. Haystack uses explicit component pipelines for retrieval, routing, generation and agent behavior. You need a general durable orchestration engine and RAG is only one minor tool.
Their Hermes model series has consistently ranked among the top open-source Common Wealth Casino models on benchmarks like LMSYS Chatbot Arena and Open LLM Leaderboard. If you tell Hermes Agent about your product architecture on Monday, it remembers on Friday. It supports structured output (JSON mode), function calling, and long-context understanding up to 128K tokens. Hermes 3 was specifically trained for agentic behavior — tool use, multi-step reasoning, and instruction following. The architecture of Hermes Agent is what sets it apart from simpler AI agent frameworks. It's built on top of the Hermes 3 model (based on Meta's Llama 3.1) and is fully MIT-licensed, meaning you can use it commercially, modify it, and deploy it anywhere without restrictions. It's no longer the right primary abstraction for agents in 2026; that role has moved to LangGraph (by the same team). This guide ranks each on what it's actually good at, who it's right for, and the trade-offs that matter when your agent stack outgrows a hackathon prototype.
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Hermes Agent represents a broader shift in how we think about AI tools. The Docker image includes the Hermes 3 model, all 40+ tools, and the memory system pre-configured. Hermes Agent by Nous Research is the hottest open-source AI agent framework of 2026 — 32K+ GitHub stars, persistent memory, 40+ tools, self-improving skills, and runs on a $5 VPS. LangChain remains useful as a library of integrations — document loaders, retrievers, model providers, tools. A single agent with clear tools and deterministic workflow steps is easier to test and operate. For this guide, a framework must provide an installable open-source core for coordinating model calls, tools, state, workflows or multiple agents.
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Agents, workflows, memory, RAG, model routing, evaluation and observability share one ecosystem. Mastra is the most complete TypeScript-first choice for teams building agents inside Node, Next.js or modern web applications. You need a TypeScript runtime or a visual graph mental model across every workflow. The trade-off is that teams must design state and transitions deliberately, which is unnecessary for a small tool-calling assistant. Rank11FrameworkHaystackStackPythonCore LicenseApache 2.0Best ForRAG pipelines and retrievalMain Trade-offPipeline-first and Python-centric Rank10FrameworkLlamaIndex WorkflowsStackPython / TypeScriptCore LicenseMITBest ForData and RAG agentsMain Trade-offData-centric rather than general runtime-first Rank9FrameworkAgnoStackPythonCore LicenseApache 2.0Best ForAgent teams and runtime platformsMain Trade-offMore platform than small apps need Rank8FrameworkStrands AgentsStackPython / TypeScriptCore LicenseApache 2.0Best ForModel-driven portable agentsMain Trade-offNewer unified SDK ecosystem
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