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
If there's one software provider out there that has really got it in for players' teeth, it's Pragmatic Play. This guide focuses on code-first frameworks — libraries and SDKs you integrate into your own application. TypeScript is the fastest-growing alternative and is the better choice if your application stack is already JavaScript/TypeScript or if you’re building agents that integrate deeply with web applications. Semantic Kernel (via Microsoft Agent Framework) is the most production-ready option for .NET/Azure teams, with GA 1.0 guarantees and long-term support commitments. LangGraph consistently ranks #1 in production-readiness across independent comparisons, with confirmed enterprise deployments at Klarna, Uber, Cisco, LinkedIn, JPMorgan, and Elastic. LangGraph, OpenAI Agents SDK, Mastra, and Vercel AI SDK all support 80+ LLM providers including local models via Ollama, vLLM, or similar.
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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What about LangChain itself?
Their Hermes model series has consistently ranked among the top open-source 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.
Yes, all eight frameworks are provider-agnostic or support multiple providers. For single-agent applications, the OpenAI Agents SDK is similarly approachable with minimal boilerplate. CrewAI has the gentlest learning curve for multi-agent systems — its role-based abstraction is intuitive and the CLI scaffolds working crews in minutes. The community fork AG2 continues development, but for new projects, MAF or alternative frameworks are recommended. Microsoft is no longer adding features to AutoGen and has merged its orchestration concepts into the Microsoft Agent Framework (MAF). For agent-specific work in 2026, LangGraph is the recommended starting point within the LangChain ecosystem.
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.
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