Best AI Agent Frameworks in 2026: Complete Comparison
TL;DR
There is no single "best" agent framework -- the right pick depends on how much orchestration control, multi-agent depth, and production tooling your project actually needs. LangGraph, CrewAI, the OpenAI Agents SDK, Google's ADK, Microsoft Agent Framework, LlamaIndex, and Pydantic AI all solve agent orchestration with different tradeoffs.
LangGraph leads this comparison for production-grade agents because its graph/state-machine model, durable execution, and human-in-the-loop support are built for long-running workflows that need to survive failures and resume, not just single-shot chat responses.
CrewAI has the largest community (56.9k GitHub stars) and the simplest onboarding for role-based multi-agent teams, combining autonomous "Crews" with deterministic "Flows" in one MIT-licensed framework.
The OpenAI Agents SDK is the lightest-weight option for provider-agnostic builds, with handoffs, guardrails, and tracing built in and support for 100+ LLMs beyond OpenAI's own.
Google's Agent Development Kit (ADK) and Microsoft Agent Framework are the two vendor-backed options built for their own cloud ecosystems -- ADK for Google-native, code-first Python development, Microsoft Agent Framework for teams standardizing on Python and .NET together.
LlamaIndex remains the strongest choice when the agent's core job is retrieval over your own data, since its agent capabilities sit on top of the same data-connector and indexing layer that made it a RAG standard.
Pydantic AI is the newest and smallest of the seven by community size, but its type-safe, FastAPI-style design is a real differentiator for teams that want compile-time confidence in agent inputs and outputs.
None of these frameworks fetch live web data on their own -- every one of them expects you to wire in a tool for that, which is a separate decision covered in the use-cases section below.
Introduction: What an Agent Framework Actually Does
An agent framework is a code library that handles the orchestration layer around a large language model -- deciding what tool to call next, managing multi-step state, coordinating multiple agents, and recovering from failures -- so a developer doesn't have to hand-write that control loop from scratch. The LLM still does the reasoning; the framework decides how that reasoning turns into actions, retries, and handoffs between agents or tools.
This guide compares seven agent frameworks developers evaluate most often in 2026 -- LangGraph, CrewAI, the OpenAI Agents SDK, Google's Agent Development Kit (ADK), Microsoft Agent Framework, LlamaIndex, and Pydantic AI -- verified against each project's own GitHub repository and documentation rather than recycled feature lists. It ranks them by overall production fit, then walks through a side-by-side spec table and a decision guide organized by what actually matters for your project: orchestration model, multi-agent depth, language support, and ecosystem backing.
Take a Quick Look
Every agent framework on this list can call a web-data tool but none of them ship one -- Nstproxy Crawl gives any agent a single API call that turns a URL into clean, LLM-ready content.
Rank reflects overall fit for building and shipping an agent system, weighted across four factors: how well the framework handles long-running, stateful execution versus single-shot calls; how deep its multi-agent orchestration goes (routing, handoffs, parallel and nested workflows); ecosystem signal (community size, backing organization, integration breadth); and language/platform flexibility. Community size (GitHub stars) is reported per entry but is not the sole ranking factor -- CrewAI and LlamaIndex both have larger communities than the top-ranked LangGraph, reflecting broader current adoption, but LangGraph's durable-execution model is what specifically qualifies it as the strongest production pick.
1. LangGraph: Best for Production-Grade Stateful Agents
LangGraph is a low-level orchestration framework, maintained by the LangChain team, for building long-running, stateful agents as a graph of nodes and transitions -- an architecture explicitly inspired by Google's Pregel and Apache Beam. Per its GitHub repository, it's MIT-licensed with 38.1k stars, and its core differentiator is durable execution: agents persist through failures and resume from the exact point they left off, rather than restarting a run from scratch. It also supports human-in-the-loop state inspection and modification mid-run, short- and long-term memory across sessions, and integrates with LangSmith for tracing and deployment.
Because LangGraph is a low-level primitive rather than an opinionated multi-agent framework, it takes more setup than a role-based tool like CrewAI to get a multi-agent system running -- the graph model gives precise control at the cost of a steeper initial build.
Best for: teams building agents that need to survive real-world failures mid-task and resume cleanly, not just answer a single prompt.
2. CrewAI: Best for Role-Based Multi-Agent Teams
CrewAI is a Python framework built specifically for orchestrating autonomous multi-agent systems, and per its GitHub repository, it has the largest community of any framework in this comparison at 56.9k stars under the MIT license. It centers on two complementary models: "Crews," teams of specialized agents with distinct roles and goals that collaborate autonomously through dynamic delegation, and "Flows," event-driven workflows with precise, deterministic control over execution paths and state -- letting a team combine autonomous reasoning with explicit business logic in the same system. An optional CrewAI AMP suite adds a control plane, observability, and enterprise support on top of the open-source core.
Best for: teams that want the fastest path to a working multi-agent system with named roles (researcher, writer, reviewer) without hand-building a graph.
3. OpenAI Agents SDK: Best for Lightweight, Provider-Agnostic Builds
The OpenAI Agents SDK describes itself, per its GitHub repository, as "a lightweight yet powerful framework for building multi-agent workflows" -- MIT-licensed, 26.3k stars, and explicitly provider-agnostic, supporting OpenAI's own models alongside 100+ other LLMs through integrations like LiteLLM. Its core primitives are agents (an LLM plus instructions, tools, and guardrails), handoffs (agents delegating work to other agents), built-in guardrails for validating input and output, automatic session/conversation history, and integrated tracing -- plus sandboxed agents for longer-running tasks and realtime, voice-enabled agents.
Best for: teams that want production primitives (guardrails, tracing, handoffs) without committing to a single model provider or a heavyweight graph model.
4. Google ADK: Best for Code-First, Google-Native Agent Development
Google's Agent Development Kit (ADK) is an open-source, code-first Python toolkit, licensed under Apache 2.0, for building, evaluating, and deploying agents. Per its GitHub repository, it has 21.1k stars and centers on two concepts: an Agent (instructions, tools, behavior) and a Workflow, orchestrated through a graph-based execution engine that supports routing, parallel processing, loops, and retry logic. It ships an interactive CLI and web UI for local development and a Task API for agent-to-agent delegation, and is Python-only (3.10 through 3.14).
Best for: teams already building on Google Cloud who want a code-first (not purely declarative) agent toolkit with deterministic workflow control.
5. Microsoft Agent Framework: Best for Unified Python and .NET Shops
Microsoft Agent Framework (MAF) is an open-source, multi-language platform, MIT-licensed, for building and operating production agent systems across both Python and .NET with a consistent API in each. Per its GitHub repository, it has 12.6k stars and includes multi-provider LLM support (Azure OpenAI, OpenAI, GitHub Copilot SDK, Microsoft Foundry), sequential/concurrent/collaborative orchestration patterns, a middleware system, OpenTelemetry-based observability, declarative YAML-defined agents, and Foundry-hosted deployment. It ships migration guides from both AutoGen and Semantic Kernel, but per its own documentation these remain separate projects rather than MAF being a formal successor or merger of either.
Best for: teams standardizing agent development across both Python and .NET codebases, especially inside the Azure/Microsoft Foundry ecosystem.
6. LlamaIndex: Best for Data- and RAG-Heavy Agents
LlamaIndex is an open-source data framework, MIT-licensed with 51.5k GitHub stars per its repository, built around ingesting and structuring external data (APIs, PDFs, SQL, and more through 300+ LlamaHub integration packages) so LLMs and agents can query it. Its agent capabilities -- Workflows and an Agent Builder for constructing end-to-end document agents, plus deployed document agents and structured extraction through its companion LlamaParse platform -- sit on top of that same indexing layer, which is what distinguishes it from frameworks that treat data retrieval as just another tool call.
Best for: agents whose primary job is answering questions over a large, structured, or frequently updated private dataset rather than general-purpose task automation.
7. Pydantic AI: Best for Type-Safe, FastAPI-Style Development
Pydantic AI is a Python agent framework, MIT-licensed, built by the Pydantic team on top of Pydantic's own validation layer -- describing itself, per its GitHub repository, as bringing FastAPI's ergonomic developer experience to agent development. At 17.5k stars it has the smallest community of the seven, but its differentiators are concrete: type-safe agent inputs and outputs validated at the Pydantic-model level, model-agnostic support (OpenAI, Anthropic, Gemini, DeepSeek, and more), dependency injection for tools, durable execution for long-running workflows, and native integration with Pydantic Logfire for observability.
Best for: teams that already lean on Pydantic and FastAPI and want the same type-safety guarantees extended into agent code, rather than adopting a separate orchestration paradigm.
Side-by-Side Specifications
Framework
Language(s)
Orchestration model
Multi-agent support
Managed/enterprise option
LangGraph
Python, JS/TS
Graph/state machine
Yes (via graph composition)
LangSmith Deployment/Studio
CrewAI
Python
Role-based Crews + event-driven Flows
Yes (native, role-based)
CrewAI AMP control plane
OpenAI Agents SDK
Python
Linear with handoffs
Yes (agent handoffs)
OpenAI platform tracing
Google ADK
Python
Graph-based workflow engine
Yes (Task API delegation)
Google Cloud / Vertex deployment
Microsoft Agent Framework
Python, .NET
Sequential/concurrent orchestration
Yes (native patterns)
Microsoft Foundry hosting
LlamaIndex
Python, TS
Workflows + Agent Builder
Yes (via Workflows)
LlamaParse / LlamaCloud
Pydantic AI
Python
Type-safe agent + dependency injection
Limited (single/simple multi-agent)
Pydantic Logfire
Selection Guide
You need agents that survive failures and resume mid-task: choose LangGraph.
You want named, role-based agents collaborating with the least setup: choose CrewAI.
You want a lightweight, provider-agnostic SDK with guardrails built in: choose the OpenAI Agents SDK.
You're building on Google Cloud and want code-first control: choose Google ADK.
You need one consistent framework across Python and .NET: choose Microsoft Agent Framework.
Your agent's real job is answering questions over your own data: choose LlamaIndex.
You want type-safe agent code in a Pydantic/FastAPI-style codebase: choose Pydantic AI.
Bonus Tip. Turn Any Url into Clean Content via Nstproxy
Agent frameworks solve the orchestration problem -- routing, state, retries, multi-agent handoffs -- but every one of the seven above expects the developer to supply the actual tools an agent calls: a calculator, a code interpreter, a database query function, or a way to read the live web. None of these frameworks ship their own web-data collection layer; LangGraph, CrewAI, the OpenAI Agents SDK, Google ADK, and the rest all define a "tool" as a function you register, not a built-in capability.
That's the gap Nstproxy Crawl is built to fill as a tool inside any of these frameworks: given a URL, it returns clean Markdown, cleaned HTML, or structured data (full output schema in the Crawl API docs) with JavaScript rendering and Nstproxy's own proxy network handled automatically, so an agent gets ready-to-use page content in one function call instead of the developer wiring up a headless browser, a proxy pool, and an HTML cleaner separately. It's a data layer, not a competing orchestration framework -- it slots in as a registered tool regardless of which of the seven frameworks above is doing the routing.
One function call instead of a scraping stack -- Markdown, cleaned HTML, raw page data, links, screenshots, or PDF from a single URL, so a framework's tool-calling agent gets structured content without a separate rendering or cleaning step.
JavaScript rendering for pages an agent's default HTTP client can't read -- renders dynamic content in a real browser before extracting it, relevant for any agent whose tool needs to read a client-side-rendered site.
Site-level crawling for multi-page research tasks -- when an agent's task needs more than one page from a site (documentation, a changelog, a product catalog), Crawl can walk the site directly with configurable depth and scope rules instead of one tool call per page.
Pricing runs pay-as-you-go from $1.20 per 1,000 successfully crawled requests on the free plan, dropping to $1.00 on the $79/month Starter tier, $0.80 on the $249/month Growth tier, and $0.60 on the $699/month Scale tier as included monthly credits increase -- billed only on pages that were actually fetched. Teams orchestrating this kind of ingestion across multiple agents or projects can also read how Nstproxy's Proxy Manager supports AI-agent and RAG data pipelines at scale.
Take a Quick Look
Whichever framework from this list you build with, Nstproxy Crawl plugs in as a single tool call that turns any URL into clean, agent-ready content.
LangGraph leads this comparison because durable, stateful execution is the feature that most separates a production agent from a demo, and CrewAI, the OpenAI Agents SDK, Google ADK, and Microsoft Agent Framework each answer a more specific need -- fastest multi-agent onboarding, lightweight provider-agnostic builds, Google-native code-first control, and unified Python/.NET development, respectively. LlamaIndex remains the strongest pick when an agent's job is fundamentally about your own data, and Pydantic AI is the one to watch if type safety matters more to your team than community size. None of that changes the separate fact that every framework here treats web-data access as a tool you have to supply yourself -- which tool you plug in for that job is a decision made independently of which framework is doing the orchestration.
An agent framework is a code library that handles the orchestration layer around an LLM -- routing between tools, managing multi-step state, coordinating multiple agents, and recovering from failures -- so a developer doesn't have to build that control loop from scratch for every project.
Q: Which agent framework should I choose for a production system?
LangGraph is the strongest default for production because of its durable-execution model, but the right choice depends on your priorities -- CrewAI for the fastest role-based multi-agent setup, the OpenAI Agents SDK for a lightweight provider-agnostic build, or Microsoft Agent Framework if you need one framework across Python and .NET.
Q: Do I need LangChain to use LangGraph?
No -- LangGraph is a standalone, low-level orchestration library; LangChain integration and LangSmith tracing/deployment are available but not required to build and run a LangGraph agent.
Q: Can I combine an agent framework with a RAG or vector database setup?
Yes -- all seven frameworks in this comparison support registering custom tools or data connectors, so pairing one with a vector database for retrieval, or with LlamaIndex specifically for its native data-indexing layer, is a standard pattern rather than an exception.
Q: Are these agent frameworks free to use?
Yes -- LangGraph, CrewAI, the OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, LlamaIndex, and Pydantic AI are all open-source (MIT or Apache 2.0) and free to use; several also offer optional paid managed platforms (LangSmith, CrewAI AMP, Microsoft Foundry hosting) for deployment, observability, or enterprise support.
Q: How do agents built with these frameworks get live data from the web?
None of the seven frameworks ship a built-in web-browsing or scraping capability -- each expects the developer to register a tool for it, which is typically a search API for finding pages and a separate extraction tool like Nstproxy Crawl for turning a found URL into clean, structured content the agent can reason over.
Marcus Chen
Aug. 12th 2026
110M+ real IPs with 99.9% access success
Blazing-fast average response ~0.5s for high-concurrency tasks
From only $0.1/GB
Get immediate access to premium residential, datacenter, IPv6 and ISP proxy pools.
