Microsoft Agent Framework

As of Sept. 1, 2026, the practical shortlist is:

Best AI agent frameworks people actually use

My blunt recommendations

• If you’re building a serious custom agent system: use LangGraph.
• If you want quick multi-agent demos/workflows: use CrewAI.
• If the core problem is documents/RAG/search: use LlamaIndex or Haystack.
• If you’re OpenAI-first and want minimal abstraction: use OpenAI Agents SDK.
• If you’re a Microsoft/.NET/Azure enterprise: use Microsoft Agent Framework, not new AutoGen.
• If you’re Google Cloud/Gemini-heavy: use Google ADK.
• If you’re Python and care about typed outputs: use Pydantic AI.
• If you’re TypeScript/Next.js: use Mastra for full agent/workflow structure or Vercel AI SDK for product/UI integration.

Important note on AutoGen

AutoGen is still widely known and has ~61k GitHub stars, but Microsoft now marks it as maintenance mode and tells new users to start with Microsoft Agent Framework. So I would not choose AutoGen for a new production project unless you have a specific legacy or research reason. (github.com)

Here's a rundown of the AI agent frameworks that developers are actually adopting in production right now, based on the latest 2026 comparisons and benchmarks:

Top General-Purpose Frameworks

LangChain / LangGraph — The incumbent leader. The veteran framework continues its dominance by evolving into a highly structured, enterprise-ready platform. LangGraph in particular is favored for complex, stateful multi-step agent workflows, while LangChain remains popular for custom pipelines.

CrewAI — Widely used for multi-agent orchestration, where you define a "crew" of agents with specific roles that collaborate on tasks.

Microsoft Agent Framework — The successor to AutoGen and Semantic Kernel, now Microsoft's official recommendation. Microsoft Agent Framework is the unified successor to AutoGen and Semantic Kernel, with graph-based workflows, responsible AI guardrails available through Azure AI Foundry, and Python +.NET runtimes. Microsoft consolidated AutoGen and Semantic Kernel into the unified Microsoft Agent Framework in October 2025. If you're already invested in AutoGen, existing AutoGen or Semantic Kernel applications will continue to receive bug fixes and security patches during the support window.

AutoGen — Still relevant for its core concept. If you want to build something complex with large language models, the best way to do it isn't with a single, monolithic agent — it's with multiple agents talking to each other. That's the core idea behind AutoGen, an open-source framework for building LLM applications using multi-agent conversations.

Google ADK (Agent Development Kit) — Strong choice if you're in the Google ecosystem. Released in April 2025, Google's ADK provides a hierarchical agent tree where a root agent delegates to sub-agents, which can in turn have their own sub-agents. The framework integrates tightly with Vertex AI, Gemini models, and Google Cloud services. Notably, the standout feature is native support for the A2A (Agent-to-Agent) protocol, which enables communication between agents from different frameworks — an ADK agent can discover and invoke an agent built with LangGraph or CrewAI. It also has an edge in multimodality: ADK incorporates multimodal capabilities that other frameworks lack, processing images, audio, and video natively through Gemini's multimodal API.

OpenAI Agents SDK / Responses API — OpenAI's own toolkit, commonly paired with GPT-4o for orchestration. GPT-4o delivers fast, high-quality responses with strong reasoning capabilities, and frameworks like CrewAI, LangGraph, and FastAgency use GPT-4o to power their orchestration layers, allowing agents to coordinate tasks with minimal delay.

Rising / Specialized Picks

Mastra — Gaining serious traction for TypeScript/JS teams building agentic apps.

PydanticAI — Popular with Python teams that want strict schema enforcement. It's built for Python teams who believe schemas are how you make LLMs behave, and who want the framework to enforce that belief — Pydantic validation is already the schema layer inside the OpenAI SDK, Google ADK, LangChain, and CrewAI. Its structured output handling stands out: the structured-output story is the best in Python — define a Pydantic model as your output_type and the framework validates, retries, and hands you a real object.

Vercel AI SDK — The go-to for JS/TS developers adding agent capabilities to existing apps. It's described as the dominant TypeScript AI library (unified provider API

The AI agent landscape has matured significantly. Building an AI agent is no longer just about sending a prompt to an LLM; it is about managing state persistence, tool routing, human-in-the-loop (HITL) handoffs, and preventing "token-burning" infinite loops.

The top AI agent frameworks that developers and enterprises are actually using in production are divided by design philosophy, programming language, and level of control.

1. The Heavyweight Standards (Most Widely Adopted)

LangGraph (by LangChain)

• The Vibe: The ultimate standard for highly customized, production-grade agent architectures.
• Best For: Complex branching state machines, cyclic loops, and setups requiring precise execution control.
• Why people use it: LangGraph moves away from linear "chains" and models agents as stateful graphs. It features built-in persistence (allowing you to pause an agent, request human approval, and resume), "time-travel" (rewinding an agent's execution history to correct a mistake), and direct access to LangChain’s massive ecosystem of integrations.
• The Catch: A steep learning curve. It requires you to write a lot of boilerplate code and manage your own graph state.

CrewAI

• The Vibe: The favorite for role-based multi-agent collaboration.
• Best For: Fast prototyping and automations where tasks map onto "human-like" team structures (e.g., one agent researches, one writes, and one edits).
• Why people use it: CrewAI is incredibly intuitive. It allows you to define "Crews" consisting of individual "Agents" with distinct roles, backstories, and private tools. It handles delegation, cooperative execution, and memory sharing out of the box.
• The Catch: High abstraction. In complex production environments, debugging can be difficult because the framework hides what prompts are actually being sent to the LLM.

Microsoft Agent Framework

• The Vibe: The enterprise powerhouse, especially for Microsoft-centric environments.
• Best For: Large organizations requiring rigid governance, enterprise integrations, and support for both.NET and Python.
• Why people use it: As the successor directing new projects away from Microsoft’s pioneering AutoGen framework, Agent Framework is built for robust routing and enterprise-grade security. It integrates seamlessly with Azure, Microsoft 365, and enterprise identity management.
• The Catch: Less appealing if you prefer lightweight, cloud-agnostic, open-source-first tech stacks.

2. The Python Developer Favorites (Code-First & Ergonomic)

PydanticAI

• The Vibe: "FastAPI ergonomics" brought to AI agents.
• Best For: Python developers who prioritize strict type safety, data validation, and clean codebases.
• Why people use it: Built by the creators of Pydantic (the validation library used by almost all major LLM SDKs), PydanticAI treats every agent input and output as a strictly typed and validated model. It features clean dependency injection (perfect for testing), built-in streaming of validated JSON, and native tracing via Pydantic Logfire.
• The Catch: It is a younger framework compared to LangChain, meaning its ecosystem of pre-built tool integrations is smaller.

Agno (formerly Phidata)

• The Vibe: Minimalist, highly readable, multi-modal by default.
• Best For: Developers who want to build agents with built-in memory, vector databases, and tools without dealing with heavy framework "bloat."
• Why people use it: Agno is extremely straightforward. You can spin up an agent with a web-search tool or a PgVector database connection in under 20 lines of clean code. It also ships with an "Agent Playground" UI, allowing you to visually chat with and debug your agents locally.

3. The Best for JavaScript / TypeScript

Mastra

• The Vibe: The go-to native TS/JS framework.
• Best For: Full-stack and frontend developers building on Next.js, Vercel, or Node.js.
• Why people use it: Historically, AI agent frameworks were overwhelmingly Python-centric. Mastra is built from the ground up for TypeScript, offering built-in developer tools, suspend/resume approval workflows, and tight web-app integrations without needing to spin up a separate Python microservice.

Quick Decision Matrix

💡 The Big "Production" Trend: The Roll-Your-Own Approach

If you ask senior AI engineers what they actually ship to production, a surprising number will answer: "None, we rolled our own."

Heavy framework abstractions often hide prompt logic, make debugging LLM reasoning failures difficult, and add execution latency. If your use case is highly specific, it is very common to prototype using CrewAI or LangGraph, but ultimately write a lightweight, bespoke Python/TypeScript state loop directly utilizing raw model SDKs (like OpenAI's or Anthropic's) paired with Pydantic for structured output validation.

The "best" AI agent framework depends on what you're building: simple RAG, multi-agent orchestration, enterprise automation, or coding assistants. But these are the frameworks that are actually used in production and have the largest mindshare in 2025.

Here’s a practical breakdown:

1. LangChain + LangGraph (Most popular general-purpose)

• What it is: The most widely adopted agent ecosystem. LangGraph is the newer agent-orchestration layer built on LangChain.
• Why people use it: Huge community, tons of integrations, supports workflows/state machines rather than just “agents.”
• Best for: Custom agents, RAG pipelines, complex tool-use, production apps that need control and persistence.
• Watch out for: Steep learning curve and API churn. If you just want a quick agent, it can feel heavyweight.

2. CrewAI (Best for role-based multi-agent teams)

• What it is: A framework for defining “crews” of AI agents with specific roles, goals, and tools.
• Why people use it: Simple, human-readable abstraction. Makes multi-agent collaboration feel intuitive.
• Best for: Marketing, research, content generation, and any workflow where you want agents to “hand off” work to each other.
• Watch out for: Can be too high-level for complex state management or low-latency production systems.

3. AutoGen (now part of Microsoft’s Agent Framework) — Strong for conversational agents

• What it is: Microsoft’s open-source framework for multi-agent conversations and code generation.
• Why people use it: Very flexible, supports complex dialog between multiple agents, strong for problem-solving and coding tasks.
• Best for: Research prototypes, coding assistants, data analysis agents.
• Watch out for: More research-y feel; production tooling is less polished than LangGraph.

4. Microsoft Semantic Kernel

• What it is: Enterprise-friendly orchestration framework from Microsoft, designed with C#/Python support.
• Why people use it: Native integration with Azure OpenAI, good for enterprises heavily invested in Microsoft stack.
• Best for: Enterprise bots, internal automation, and apps that need structured planning.
• Watch out for: Smaller ecosystem compared to LangChain, but very stable for.NET shops.

5. LlamaIndex (Best for RAG-heavy agents)

• What it is: A data framework built for connecting LLMs to private data (retrieval-augmented generation).
• Why people use it: Deep indexing/retrieval features are unmatched. Agents can query across many data sources.
• Best for: Knowledge assistants, document Q&A, data-augmented agents.
• Watch out for: Less focused on pure agent orchestration; you often combine it with LangGraph or CrewAI.

6. Google ADK (Agent Development Kit) — Rising star

• What it is: Google’s official framework for building agents on Gemini/Gemma models, with built-in support for tools and multi-agent configs.
• Why people use it: Native Gemini access, simple API, backed by Google Cloud integrations.
• Best for: Developers already using Vertex AI or Gemini APIs.
• Watch out for: Newer than LangChain, so community/plugin ecosystem is still growing.

7. OpenAI Agents SDK (Successor to the deprecated tools)

• What it is: OpenAI’s lightweight, production-focused SDK for building agents with the OpenAI API.
• Why people use it: Minimal boilerplate, explicit tool calling, built-in handoffs between agents.
• Best for: Simple, reliable agents that rely on GPT models.
• Watch out for: Locks you into OpenAI models (though you can use compatible APIs).

8. Claude Agent SDK (Best for Anthropic-ecosystem users)

• What it is: Anthropic’s agent toolkit for building with Claude, featuring computer-use and long-context tools.
• Why people use it: Strong for coding and web-automation tasks with Claude’s 200k context.
• Best for: AI assistants that need deep reasoning and tool use through Anthropic models.

9. Haystack (Best for production NLP pipelines)

• What it is: An older, battle-tested framework for building LLM pipelines and agents.
• Why people use it: Ideal if you need reliability, evaluation, and deployment stability rather than experimental flexibility.
• Best for: Enterprise search / QA systems.

10. Pydantic AI (Fast-growing favorite)

• What it is: A framework that uses Pydantic’s schema validation to force agents to return structured, type-safe outputs.
• Why people use it: Very clean Pythonic API, great for anyone who loves type safety and structured data.
• Best for: Agents that need reliable JSON outputs, financial/analytical use cases.

So what should you actually use?

My default recommendation: Start with LangGraph or CrewAI. If you’re already in a cloud ecosystem (Azure, AWS, GCP), go with the framework that has first-party support there. For enterprise stability, Semantic Kernel or Haystack are the safest bets.

There is no single "best" AI agent framework in 2026—the right choice depends on your language/stack, workflow complexity (e.g., stateful/long-running vs. simple tool-calling or role-based teams), need for production durability/observability, and any vendor/cloud preferences.

The top frameworks that developers and teams actually use in production (based on GitHub stars, adoption mentions like enterprise deployments at Uber/LinkedIn/Klarna, PyPI downloads, and recent comparisons) cluster around a few leaders. Older hype projects like the original AutoGPT or BabyAGI have faded in favor of more mature, controllable options. AutoGen (Microsoft) has largely moved into maintenance mode, with its capabilities folded into the Microsoft Agent Framework.

Top Recommended Frameworks

Here are the standout ones based on consensus across 2026 rankings and usage data:

• LangGraph (LangChain ecosystem) — Often ranked #1 for production. It models agents as directed graphs with nodes (actions) and edges (transitions), offering excellent control over stateful, long-running, branching workflows. Key strengths include durable execution/checkpointing, time-travel debugging, human-in-the-loop, retries, and auditability. It supports any model and is used in real enterprise deployments. Languages: Primarily Python, with JS/TS support. License: MIT. GitHub stars: ~35k–40k for LangGraph (LangChain core is much higher at ~130k+). Best for: Complex production orchestration where reliability and inspectability matter.

• CrewAI — Extremely popular for role-based multi-agent systems ("crews" of specialized agents with defined roles, goals, and tasks that collaborate sequentially, hierarchically, or in parallel). It has one of the highest star counts and is praised for its low barrier to entry and fast prototyping of team-like workflows. Supports any model. Language: Python. License: MIT. GitHub stars: ~54k–56k (one of the highest among pure agent frameworks). Best for: Quick multi-agent prototypes and role-based collaboration; also used in early production.

• OpenAI Agents SDK (or AgentKit) — A clean, lightweight, code-first option with strong support for tool use, agent handoffs, tracing, and sessions. It's model-flexible (though optimized for OpenAI) and billed only at standard API rates. Good built-in guardrails and simplicity. Languages: Python and TypeScript. License: MIT. Growing adoption for straightforward agents. Best for: Lean single- or simple multi-agent apps, especially in OpenAI-centric environments.

Other Notable Mentions (Vendor or Niche)

• Microsoft Agent Framework — Successor to AutoGen + Semantic Kernel; strong for.NET/Azure/enterprise teams with sessions, compliance features, and multi-agent patterns.
• Google ADK (Agent Development Kit) 2.0 — Polyglot (Python, TS, Go, Java, etc.), Gemini-first but model-agnostic, with good workflow and multi-agent support for Google Cloud users.
• LlamaIndex Workflows — Excellent for RAG/data-heavy agents.
• Others gaining traction: Pydantic AI (type-safe Python), Mastra (TypeScript-native), AWS Strands (AWS/Bedrock), smolagents (Hugging Face, minimalist/code-focused), and n8n (low-code workflows).

Quick Comparison and Selection Advice

Stars and adoption are rough proxies—CrewAI and LangChain/LangGraph lead in visibility and downloads, while LangGraph stands out for verified production use cases. Many teams combine frameworks (e.g., LangGraph for orchestration + CrewAI patterns or LlamaIndex for retrieval).

Choose based on your needs:

• Complex/stateful/production workflows with control and durability → LangGraph.
• Fast role-based multi-agent teams/prototypes → CrewAI.
• Simple, clean tool-using agents (especially OpenAI) → OpenAI Agents SDK.
• Locked to a cloud/vendor (.NET/Azure, Google/Gemini, AWS) → Respective first-party SDK.
• RAG-heavy → LlamaIndex.
• TypeScript focus → Mastra or Vercel AI SDK.
• Low-code → n8n.

Start simple (many agents don't need a heavy framework at all) and scale to graphs or crews only when orchestration complexity demands it. Evaluate against your requirements for state management, observability, error handling, and multi-agent coordination rather than chasing the highest stars.

For the absolute latest stars, releases, or specific benchmarks, check the projects' GitHub repos directly, as the space evolves quickly.

The best AI agent frameworks have split into clear categories based on engineering control, speed, and programming language. Choosing the right one depends entirely on whether you are prioritizing production stability, fast role-based prototyping, or native ecosystem integration.

🏆 S-Tier: The Production Standards

These open-source frameworks are the most widely adopted by developers for building scalable, reliable multi-agent workflows.

LangGraph: Best For: Complex, stateful workflows with looping, branching, and human-in-the-loop approvals. Key Features: Built by the LangChain team, it models agents as state machines using directed graphs, providing full control and built-in "time travel" state checkpointing. CrewAI: Best For: Rapid prototyping and task-oriented "teams" of agents collaborating via defined roles. Key Features: Highly popular among Fortune 500 companies; it abstracts the complexity of agent loops into clean, role-based "Crews" and deterministic "Flows".

💻 Developer-First & Language-Specific Favorites

These frameworks excel at giving developers an intuitive developer experience without learning a complex new software philosophy.

Pydantic AI: Best For: Python developers demanding type safety and structured data. Key Features: Built by the trusted Pydantic team; it provides robust validation for LLM outputs, preventing malformed JSON from crashing long-running agent loops. Mastra: Best For: Full-stack JavaScript and TypeScript teams. Key Features: Designed specifically for the Node ecosystem; it offers built-in developer tooling, suspend/resume approval workflows, and clean integration with modern frameworks like Next.js.

☁️ Provider & Enterprise SDKs

If your infrastructure is already tied to a specific cloud provider or LLM vendor, these native toolkits minimize setup friction.

Microsoft Agent Framework: Best For: Enterprise teams heavily embedded in Azure or the.NET ecosystem. Key Features: The unified, production-ready successor to AutoGen and Semantic Kernel. It shines at asynchronous inter-agent messaging and robust enterprise routing. OpenAI Agents SDK: Best For: Lightweight, fast setups exclusively utilizing OpenAI models. Key Features: Replaced OpenAI Swarm; provides an incredibly simple, low-boilerplate way to establish tool-calling, agent handoffs, and basic guardrails. Google Agent Development Kit (ADK): Best For: Teams native to Google Cloud Platform (GCP) and Vertex AI. Key Features: An open-source, code-first Python toolkit heavily optimized for Gemini models and Model Context Protocol (MCP) server integrations.

📊 Direct Comparison: Finding Your Fit

To help narrow down the best choice for your project, what programming language is your current stack written in, and what specific task or workflow are you trying to automate with these agents?