Open-Source AI Agent Frameworks: A Mid-2026 Comparison

Think of building an AI agent system like opening a restaurant. You wouldn't forge your own pots or grow your own wheat. Instead, you buy kitchen equipment, source ingredients, hire chefs and servers, and define a workflow that connects them: order → prep → cook → serve → bill.

An AI Agent Framework provides the "kitchen equipment" (tool calling), the "ingredients" (model integrations), the "chefs" (agent reasoning), and the "servers" (task orchestration). You define the menu—the business logic—and you're in business.

But there are a dozen-plus "kitchen-equipment suppliers" on the market, each claiming to be the best. This guide cuts through the noise by comparing 16 major open-source frameworks across four dimensions—architecture, capabilities, ecosystem, and adoption threshold—and concludes with a selection guide.

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At-a-Glance Table

A panoramic view of 16 frameworks (data as of early July 2026):

| Framework | GitHub Stars | Maintainer | Core Positioning | Language | |------|-------------|--------|---------|------| | Dify | ~139K | langgenius | Visual LLM application platform | Python/TS | | AutoGPT | ~185K | Significant-Gravitas | Autonomous agent platform | Python | | MetaGPT | ~64K | FoundationAgents | Multi-agent collaboration simulating a software company | Python | | DeerFlow 2.0 | ~57K | ByteDance | Super-agent deep-research framework | Python | | CrewAI | ~46K | crewAIInc | Role-playing multi-agent orchestration | Python | | Agno | ~40K | agno-agi | Lightweight high-speed multimodal agent | Python | | LangGraph | ~36K | LangChain AI | Stateful agent graph orchestration runtime | Python/JS | | smolagents | ~26K+ | HuggingFace | Minimalist code-agent library | Python | | AgentScope 2.0 | ~26K | Alibaba Tongyi Lab | Production-grade event-driven agent framework | Python/Java | | OpenAI Agents SDK | ~23K+ | OpenAI | Lightweight multi-agent orchestration (Swarm successor) | Python | | Microsoft MAF | ~12K | Microsoft | Production-grade multi-agent framework (AutoGen successor) | Python/.NET | | PocketFlow | ~10K | The-Pocket | 100-line minimalist LLM framework | Python + 6 languages | | Google ADK | ~10K | Google | Code-first multi-agent toolkit | Python/TS/Go/Java | | CAMEL / OWL | ~10K+ | CAMEL-AI | Multi-agent collaboration research framework | Python | | VEADK | Emerging | Volcengine | Enterprise agent development-and-deployment suite | Python | | AWS MAO | Emerging | AWS | Multi-agent intelligent routing and orchestration | Python/TS |

> Note: Star counts are approximate. Some projects split or merge repositories, so numbers fluctuate. Always verify against the current GitHub page before making a selection.

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In-Depth Reviews

I. Veterans: First-Mover Advantage and Historical Baggage

#### 1. LangGraph — A Turing-Machine-Style Agent Engine

If an agent framework is a car, LangGraph hands you a precision engine + transmission + chassis. You assemble the body, install the seats, and tune the suspension yourself. It's effortful, but the freedom is unmatched.

The core abstraction is a single concept: StateGraph. Each step of the agent is a Node; transitions between steps are Edges; the entire behavior is a directed graph with state. A Checkpoint mechanism saves, rolls back, and restores state at every step.

This delivers a killer feature: best-in-class human-in-the-loop (HITL). If the agent stalls, it pauses—humans intervene, edit state, and resume. Essential for enterprise scenarios.

The cost is a steep learning curve: State, Node, Edge, Conditional Edge, Checkpointer, Store, Interrupt. A simple agent takes noticeably more code than with CrewAI or Agno.

LangGraph is also tightly bound to the LangChain ecosystem. You can use LangGraph without LangChain, but documentation, examples, and community best practices assume you use both. Note this soft lock-in.

One-liner: For engineers who demand extreme control. Not for those who want quick wins.

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#### 2. AutoGPT — From Viral Sensation to Platform

AutoGPT's story is a legend of open-source AI. In spring 2023, a demo of "an AI that assigns itself tasks" exploded onto the scene, and 185K stars keep it the absolute top of the agent category.

The path has been turbulent. The original "classic" version was a prompt-driven loop—AI reads goal → decomposes tasks → executes → reflects → continues. Conceptually elegant, practically fragile: it often looped indefinitely, burning tens of dollars in API fees for nonsense output.

In 2025–2026, AutoGPT underwent a major architectural overhaul and introduced the Platform architecture:

• Agent Builder: low-code, drag-and-drop agent construction
• Marketplace: a trading hub for agents and reusable Blocks
• Monitor: runtime monitoring and observability

It transformed from "an AI that runs itself" into "a platform that lets ordinary people build and run AI." Complexity is hidden in the platform layer; users get a friendlier interface.

One-liner: Largest community, earliest concept, but engineering maturity still catching up. Great for learning and prototyping.

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#### 3. MetaGPT — Encapsulating a Software Company in Code

MetaGPT's premise is striking: Code = SOP(Team). Rather than treating agents as generic "intelligences," it simulates a real software company's org chart—Product Manager writes a PRD, Architect designs, Engineers code, QA writes test cases.

The beauty is explicit role boundaries and output formats. Each agent plays its part and passes standardized documents (SOPs) to the next. This acts as strong regularization against hallucination.

Recognition as an ICLR 2024 Oral (Top 1.2%) is a bonus.

Limitations are equally clear: the scope is vertical. MetaGPT excels at end-to-end software development automation (requirements → code → docs). For customer service, data analysis, or content creation, the SOP templates miss the mark.

One-liner: Want to auto-generate a software project? This is your pick. Need a general-purpose agent? Look elsewhere.

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II. Big-Tech Regulars: Ecosystem Lock-In and Engineering Depth

#### 4. Microsoft Agent Framework (MAF) — AutoGen's Phoenix

In October 2025, Microsoft announced that AutoGen would stop independent iteration and that its concepts would merge into the Microsoft Agent Framework. MAF 1.0 GA shipped in April 2026.

This was not a rename—it was a thorough engineering rewrite.

The "production-grade" mantra runs throughout:

• First-class multi-language support: Python and .NET share the same concepts and API. .NET enterprises can write agents in C# without forcing a switch to Python.
• Middleware pipeline: every reasoning step and tool call can host middleware for logging, auth, rate-limiting, or rewriting—mirroring ASP.NET middleware.
• Workflow engine: built-in DAG workflow with sequential, parallel, conditional, and loop branches.
• Telemetry & observability: native OpenTelemetry integration—logs, metrics, traces out of the box.

A unique advantage in China: MAF integrates deeply with Semantic Kernel, which has solid support for non-Azure / non-OpenAI domestic models (including those with OpenAI-compatible APIs).

One-liner: First choice for .NET stacks or Windows enterprise environments. Stars are low at 12K, but Microsoft's internal momentum will accelerate the ecosystem.

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#### 5. Google ADK — Multi-Language, Multi-Protocol, Multi-Agent Weaver

Google's Agent Development Kit shipped version 2.0 in April 2026. The headline feature: first-class support for Python + TypeScript + Go + Java.

ADK embraces a "code-first" philosophy. There is no drag-and-drop Web UI—you control agent behavior precisely through code. This aligns with Google's engineering culture.

ADK also embeds the A2A (Agent-to-Agent) protocol, Google's standard for inter-agent communication. In theory, an ADK-Python agent can collaborate seamlessly with an ADK-Java agent over A2A. Forward-looking, but the ecosystem is still nascent.

One-liner: Top choice for multi-language teams; A2A is worth watching, but 10K stars show the community has not fully bought in.

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#### 6. OpenAI Agents SDK — Minimal but Not Simple

The OpenAI Agents SDK is the production-grade successor to the experimental Swarm project. Its philosophy: fewer abstractions.

Core components are few: Agent, Tool, Handoff, Guardrail, Runner. No graphs, no workflow engine, no role-playing. An Agent carries tools, can hand off tasks to other Agents (Handoff), and can be wrapped in safety Guardrails.

This minimalism makes it extremely fast to adopt—a multi-agent collaboration system in under 100 lines. It supports 100+ third-party LLMs, so you aren't locked to OpenAI.

The trade-off: complex multi-step reasoning, state persistence, and HITL must be implemented or integrated externally.

One-liner: The lightest-weight multi-agent framework. Anyone who dislikes framework lock-in will love it.

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#### 7. HuggingFace smolagents — Code as Action

smolagents' core insight: let agents write code rather than text to perform actions.

Traditional agents output natural language, which the framework parses to decide which tools to call. smolagents flips this—the agent generates Python code directly. This is a profound design choice: code is more precise and less ambiguous than text.

The entire core logic is about 1,000 lines. Thanks to the HuggingFace ecosystem, you can directly leverage Hub models, datasets, and Spaces—model switching is effortless. 26K+ stars validate community enthusiasm for the minimalist route.

One-liner: The best code-agent implementation and the first choice for HuggingFace users. If you believe "code is more reliable than text," use this.

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III. Rising Challengers: New Approaches

#### 8. CrewAI — Industrialized Role-Playing

CrewAI has turned "role-playing agents" into production. Four core abstractions—Agent (role) → Task (task) → Crew (team) → Process (workflow)—are highly intuitive.

Defining an Agent is like writing a character card: Role, Goal, Backstory. Defining a Task is like writing a work order. Then the Crew ties them together. The design is so intuitive that non-technical stakeholders can grasp it quickly.

The latest version adds a Flows architecture—a more flexible workflow orchestration system that frees agent collaboration from simple sequential or hierarchical patterns.

46K stars and active commercialization (CrewAI Enterprise) confirm market acceptance.

One-liner: The first choice for multi-agent collaboration—intuitive concepts, strong docs, active community. Less fine-grained control than LangGraph.

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#### 9. Agno — Fast, Absurdly Fast

Agno's most eye-catching claim: agent creation is 6,000× faster than LangGraph.

Sounds like marketing, but there's technical substance. The design is "zero-overhead abstraction"—agent initialization does no heavyweight work; all components are lazy-loaded. When you create 100 agent instances, LangGraph may take seconds or tens of seconds; Agno finishes almost instantly.

It also supports multimodal I/O (text, image, audio, video), built-in Memory and Knowledge (RAG), and is model-agnostic.

One-liner: The performance pick for large-scale agent deployment. If you need dozens or hundreds of agents running simultaneously, take a look.

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#### 10. CAMEL / OWL — Research-Driven, Benchmark King

CAMEL is one of the earliest academic frameworks to explore multi-agent collaboration. OWL (Optimized Workforce Learning), released in 2026, scored first among open-source projects (69.09%) on the GAIA Benchmark.

OWL pushes tool-use to the limit: web browsing, code execution, file operations, multimodal understanding—the agent works like a real human researcher. It natively supports the MCP protocol, so it can integrate an ever-growing catalog of third-party tools.

One-liner: Want a deep-research agent? OWL is the current open-source ceiling. Engineering maturity lags behind commercial frameworks.

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#### 11. PocketFlow — The 100-Line Rebel

PocketFlow's core claim is its name: the core abstraction is 100 lines. Zero dependencies, zero lock-in.

Design inspiration comes from "minimum viable abstraction": everything is modeled as Node + Flow; nodes share state through a common Store. That's it. On top of these 100 lines, you can implement multi-agent, RAG, workflow, and task decomposition.

The most surprising aspect is its multilingual ambition: beyond Python, TypeScript, Java, C++, Go, Rust, and PHP versions are already released. You can embed the same agent engine in nearly any tech stack.

One-liner: The ultimate in minimalism. Ideal for developers who want full control of the agent engine or need cross-language embedding.

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IV. Chinese Forces: A Trio from Alibaba, ByteDance, and Volcengine

#### 12. AgentScope 2.0 — Alibaba's "Agent Operating System"

Alibaba Tongyi Lab released AgentScope 2.0 in May 2026, positioned as "from running a demo to stable production deployment."

Three core innovations:

• Event-driven architecture: a unified event bus connects the agent, frontend, and HITL. Every reasoning step and tool call is an event that can be monitored, intercepted, or rewritten.
• Middleware mechanism: insert custom logic at key execution points—pre/post model-call logging, pre-tool-call security checks, business logic injection.
• Agent Service: built-in multi-tenancy, session persistence, scheduled tasks, and other production-grade capabilities—nothing for you to build from scratch.

AgentScope Java 2.0 followed in June 2026 for the JVM ecosystem. This "Python for innovation + Java for deployment" dual-track strategy mirrors Alibaba's tech stack.

One-liner: One of the most complete solutions for enterprise-grade agent deployment in China. If you're inside the Alibaba Cloud ecosystem, this is the obvious choice.

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#### 13. DeerFlow 2.0 — ByteDance's "Super-Agent Factory"

DeerFlow hit GitHub Trending on the day of its February 2026 release and has accumulated 57K stars—remarkable momentum.

It is positioned as a Super Agent—not a framework for executing simple tasks, but a "mothership" that decomposes complex goals, dispatches sub-agents, and uses memory and sandboxes. The core flow:

1. Deep Exploration — deeply understand the task goal 2. Task Decomposition — automatically break it into subtasks 3. Sub-agent Orchestration — dispatch specialized sub-agents 4. Memory & Sandbox — context memory, sandboxed execution 5. Synthesis — aggregate results

DeerFlow is built on LangGraph but adds a substantial layer of its own abstractions. It inherits LangGraph's graph orchestration power while hiding complexity behind a "super-agent" interface.

One-liner: Want an agent that can decompose and execute complex long-horizon tasks? DeerFlow is the best open-source option today. 57K stars is not luck.

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#### 14. VEADK — Volcengine's Agent "All-in-One"

VEADK (Volcengine Agent Development Kit) is the "two sides of one coin" of ByteDance's agent strategy: DeerFlow drives open-source community influence, while VEADK drives commercial cloud deployment.

VEADK is deeply tied to the Volcengine ecosystem:

• Defaults to Doubao / Skylark models
• Native Feishu (Lark) bot channel (FeishuChannelExtension)
• Integrated A2UI (agents return declarative UI instead of plain text)
• One-click deployment to VeFaaS
• Built-in PromptPilot for prompt optimization

One-liner: If your infrastructure already lives on Volcengine, VEADK is the smoothest choice. Otherwise, prefer the more general-purpose DeerFlow.

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#### 15. Dify — Not a Framework, a Platform

Dify is strictly speaking not an "agent framework" but an LLM application development platform. Its influence in the agent ecosystem is too large to ignore—139K stars, second only to AutoGPT and far above any pure agent framework.

The core differentiator: visualization-first. Drag-and-drop workflows, form-based RAG pipelines, Web UI management of models and agents. Code is optional.

This brings an extremely low threshold and very high adoption. The trade-off: less flexibility than code frameworks; customization often requires workarounds.

One-liner: Don't want to code? Dify. Want full control? LangGraph or CrewAI. Dify is also strengthening agent capabilities (agent nodes, tool calling), blurring the line between platform and framework.

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#### 16. AWS Multi-Agent Orchestrator — The Intelligent Switchboard

AWS MAO acts as an intelligent switchboard: user requests arrive, it auto-detects intent, and routes to the most suitable agent. It supports streaming responses, Python + TypeScript, and deep integration with Amazon Lex / Bedrock.

Its focus is narrow: it is not for building agents from scratch, but for orchestrating dispatch across existing agents.

One-liner: The agent orchestration layer inside the AWS ecosystem. If your agents already run on AWS, this is the natural pick.

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Cross-Framework Comparison Matrix

A six-dimension comparison. More ⭐ = better.

| Framework | Learning Curve | Multi-Agent | Production Ready | Model Compatibility | Visualization | Community Activity | |------|:------:|:------:|:------:|:------:|:------:|:------:| | LangGraph | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | | AutoGPT | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | | MetaGPT | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | | MAF | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐ | | Google ADK | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐ | | OpenAI Agents SDK | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | | smolagents | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | | CrewAI | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | | Agno | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | | Dify | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | | DeerFlow 2.0 | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | | VEADK | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | ⭐ | | AgentScope 2.0 | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | | PocketFlow | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐ | ⭐⭐ | | CAMEL / OWL | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐ | ⭐⭐⭐ | | AWS MAO | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐ |

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Selection Guide

If thousands of words haven't clarified your choice, here is a decision flowchart.

By Scenario

I want to quickly build an agent app with a Web UI and minimal code → Dify. Drag-and-drop workflow, ready out of the box.

I need a multi-agent collaboration system with clear roles and processes → CrewAI (intuitive), MetaGPT (software-development specific), or DeerFlow 2.0 (deep research).

I need maximum flexibility and control, and don't mind writing more code → LangGraph. Graph orchestration + state management + HITL—enterprise-grade control.

I'm in the OpenAI ecosystem and want the lightest multi-agent solution → OpenAI Agents SDK. Handoff + Guardrail, simple yet powerful.

I need to deploy agents in .NET or Windows enterprise environments → Microsoft MAF. Python + .NET dual language, mature middleware pipeline.

My team uses multiple languages (Python/Java/Go/TS) → Google ADK (four languages, first-class) or PocketFlow (six languages, minimalist core).

I'm in Alibaba Cloud / Chinese enterprise environments → AgentScope 2.0 (production-grade event-driven + Java support).

I'm in the Volcengine / ByteDance ecosystem → VEADK (commercial deployment) or DeerFlow 2.0 (open-source deep research).

I'm a HuggingFace user wanting the most concise code agent → smolagents. 1,000-line core, seamless Hub integration.

I want to challenge the GAIA Benchmark or do deep academic research → CAMEL / OWL. Open-source SOTA.

I run dozens or hundreds of agents simultaneously; performance first → Agno. The 6,000× creation speed claim is real.

I want an ultra-minimal core and I'll build the rest myself → PocketFlow. 100 lines, zero dependencies.

By Tech Stack

| Tech Stack | Recommended Frameworks | |--------|---------| | Python-primary | CrewAI / LangGraph / Agno / DeerFlow | | .NET / C# | MAF | | Java / JVM | AgentScope Java 2.0 / Google ADK (Java) | | TypeScript / Node | Google ADK (TS) / PocketFlow (TS) | | Multi-language | Google ADK / PocketFlow | | Low-code / No-code | Dify / AutoGPT Platform | | Alibaba Cloud ecosystem | AgentScope 2.0 | | Volcengine / Feishu | VEADK | | AWS ecosystem | AWS MAO | | Azure / OpenAI | MAF / OpenAI Agents SDK | | Google Cloud | Google ADK |

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Observations That Don't Fit in the Table

First, star count does not equal engineering maturity. AutoGPT's 185K stars didn't prevent its Platform architecture from maturing only in 2026. PocketFlow's 10K stars belie excellent code quality. Don't choose by hype alone.

Second, "multi-agent" has shifted from concept to default. In 2024, people still argued whether single-agent was enough. By 2026, nearly every new framework natively supports multi-agent orchestration. This is not a trend; it's a fait accompli.

Third, the MCP protocol is the next battleground. Anthropic's Model Context Protocol is being adopted by nearly every major framework—CrewAI, CAMEL/OWL, OpenAI Agents SDK all support it. MCP support should be a serious consideration when choosing a framework today.

Fourth, Chinese open-source agent frameworks are rising. Alibaba AgentScope 2.0 + ByteDance DeerFlow 2.0 + Dify (Chinese team) together exceed 200K stars. That would have been unthinkable in 2024.

Fifth, there is no silver bullet. Choosing a framework means choosing trade-offs: control vs convenience, generality vs specialization, community heat vs engineering stability. There is no "best framework"—only the framework that best fits your scenario.

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Conclusion

If agent frameworks are vehicles:

• LangGraph is a manual-transmission sports car—maximum control, demands skill.
• CrewAI is an automatic SUV—easy to drive, roomy, fits most people.
• Dify is a self-driving taxi—you only tell it the destination.
• PocketFlow is a self-assembled bicycle—minimal, zero-dependency, fully yours.
• DeerFlow is an aircraft carrier—sails out with a fleet.
• OpenAI Agents SDK is a light motorcycle—agile, fast, goes anywhere.

Which one do you drive? It's not about which is "best"—it's about which road you're on.

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> *Compiled from publicly available information as of July 6, 2026. Framework versions and star counts change continuously—verify the latest data before making a selection.*

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