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The Haystack 2.x rewrite modernized the framework significantly, adding agentic pipelines, multimodal support (text + images), and a growing component ecosystem. Key features include built-in guardrails (parallel input validation that halts on failure), tracing via OpenAI’s observability platform, and — as of April 2026 — sandboxed code execution with providers like Modal, E2B, Cloudflare, and Vercel. It ships for both Python (v0.17.3 as of May 2026) and TypeScript (v0.8.3 as of April 2026), making it one of the few frameworks with first-class support in both ecosystems. LangChain is the granddaddy of the open-source LLM application ecosystem — 137,000 GitHub stars, 3,900+ contributors, and 281,000 dependent repositories as of mid-2026. We compare eight frameworks across eight criteria — language support, agent types, key features, learning curve, production readiness, best use case, GitHub stars, and 2026 momentum — with deep dives into each. Customer support, content research, data analysis, and routine development tasks are all within its capabilities.
The goal is not to crown a winner but to help you choose the right tool for your use case, team, and stack. Get daily insights, curated opportunities, and peer support. Whether you're building a Micro SaaS, running a freelance business, or bootstrapping your next startup, Hermes Agent deserves a spot commonwealth casino in your toolkit. Hermes Agent is a general-purpose autonomous agent that can code but also handles research, communication, data analysis, and multi-step workflows. This is a massive advantage over cloud-based AI agents for founders handling sensitive customer data, proprietary code, or confidential business information. Because Hermes Agent runs entirely on your infrastructure, your data never leaves your server. Hermes Agent supports CPU-only mode and can also connect to external model APIs (including local models via Ollama or LM Studio). The second wave was RAG (retrieval-augmented generation) — AI with access to your documents.
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.
The most comprehensive list of AI agents, frameworks, and tools in 2026.
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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.
LlamaIndex is the strongest option here when data access is the product's central challenge. You only need one or two agents and want the smallest possible abstraction surface. You are building a Python agent platform with teams, workflows, memory and operational runtime concerns. Its concepts span individual agents, multi-agent teams, workflows, memory, knowledge and runtime operations. Agno is designed for teams building an agent platform rather than a single prompt-and-tool loop. You want Python and TypeScript support, provider flexibility and observable agent-loop controls. It is a credible current option for teams that want more runtime control than a tiny SDK without adopting a graph model. You want a compact SDK for OpenAI-centered applications with handoffs, guardrails and tracing.
Hermes Agent vs alternatives: How does it compare?
AG2 remains an active community project based on the AutoGen lineage, but it should be evaluated separately from Microsoft's supported successor. Microsoft's original AutoGen repository is in maintenance mode and directs new projects to Microsoft Agent Framework. LangGraph JS is stronger for graph-based orchestration, while OpenAI Agents SDK TS is better for a smaller OpenAI-centered abstraction. LangGraph is the best overall choice for stateful production workflows because durable execution, persistence, streaming and human review are central to its runtime. The license shown below refers to the main repository checked on July 23, 2026; teams should still inspect package-level and enterprise-directory terms before shipping a commercial product. Tool or SDKSillyTavernCategoryEnd-user frontendBest FitCharacter chat and roleplayWhy It Is SeparateIt is a user-facing application layer, not a general agent-development framework. Tool or SDKOpenHandsCategoryCoding-agent applicationBest FitRepository and software tasksWhy It Is SeparateIt is a working coding agent, not a framework for arbitrary agent products. Haystack is especially strong when you want explicit, composable Python pipelines for retrieval and generation.
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Rank4FrameworkCrewAIStackPythonCore LicenseMITBest ForRole-based multi-agent teamsMain Trade-offCrew abstractions can become rigid Rank3FrameworkMastraStackTypeScriptCore LicenseApache 2.0 coreBest ForNext.js and Node productsMain Trade-offYounger ecosystem Rank1FrameworkLangGraphStackPython / TypeScriptCore LicenseMITBest ForDurable stateful orchestrationMain Trade-offMore architecture than a simple agent loop Agents, workflows, memory, RAG, evaluation and observability live in one TypeScript-first ecosystem, so a Next.js product gets an agent without assembling four separate libraries. Pydantic validation, structured outputs and a familiar Python application model make correctness easier to enforce. This is exactly where most open-source Agent projects fall short for SMEs and mid-sized companies.
How to install Hermes Agent: Step-by-step tutorial
Tool or SDKsmolagentsCategoryMinimal agent libraryBest FitSmall experiments and code agentsWhy It Is SeparateIts intentionally small abstraction targets a different level of complexity. LlamaIndex offers a broad data framework and connector ecosystem for knowledge agents. Choose Pydantic AI when application correctness, typed dependencies and validated model outputs matter more than a graph-first runtime. You need Microsoft's supported enterprise path or a typed production-service architecture. You need TypeScript support or a general multi-agent runtime more than a RAG pipeline. You want a transparent Python pipeline for production retrieval, routing and agent tool use. It is a mature fit for teams that want the data path to remain visible and testable rather than hidden inside an autonomous-agent abstraction. Your agent is primarily a knowledge assistant, document workflow or retrieval-heavy application.
