Eleven frameworks are worth evaluating for a new 2026 agent build. Two more — AutoGen and Semantic Kernel — are worth inheriting, not starting fresh with. This guide tells you which is which, and why.
Princeton’s Holistic Agent Leaderboard ran the same Claude Opus 4 model through two different orchestration scaffolds on the GAIA benchmark. One scored 64.9%. The other scored 57.6% — a seven-point swing from the framework layer alone, on identical model weights.
That gap rivals the jump between entire model generations. A framework isn’t a thin wrapper around an API call. It decides what the model actually gets to accomplish.
This guide walks through 11 frameworks worth evaluating in 2026, two you’ll inherit rather than choose, a decision matrix with real values in every cell, and the protocol layer — MCP and A2A — that increasingly shapes which framework fits your stack.
How We Evaluated These 11 Frameworks
Three filters decided what made this list. Each framework needed active development in 2026, not just a maintained changelog. Each needed documented production use, not a demo repo with a thousand stars. And each needed a distinct orchestration model — a real answer to “how does control move between steps” — rather than a thin skin over another framework’s API.
That ruled out prompt-optimization libraries like DSPy and no-code platforms like Copilot Studio or Bedrock AgentCore. Those solve a different problem and deserve their own comparison, not a slot on a code-first list.
Frameworks Microsoft has moved into maintenance mode get their own section further down. A 2026 buyer’s guide shouldn’t hand you a framework whose maker is quietly retiring.
What Actually Counts as an Agentic AI Framework?
An agentic AI framework lets a model plan, act, and revise its own steps instead of answering one prompt and stopping. It owns the loop — deciding when to keep going, when to wait on a tool’s output, and when to hand off or stop entirely.
LangGraph and Microsoft Agent Framework express that loop as an explicit graph you design node by node. CrewAI expresses it as a team of role-based agents working a shared task list. The OpenAI Agents SDK expresses it as a chain of explicit handoffs between specialists.
Different mental models, same underlying job.
The 30-Second Shortlist
| If your priority is… | Start with |
|---|---|
| Auditable, checkpointed production workflows | LangGraph |
| Fast multi-agent prototypes | CrewAI |
| Azure/.NET enterprise builds | Microsoft Agent Framework |
| OpenAI-first agents with built-in tracing | OpenAI Agents SDK |
| Google Cloud / Gemini ecosystem | Google ADK |
| Claude-native, safety-first agentic tasks | Claude Agent SDK |
| TypeScript-first production agents | Mastra |
| Typed, validated agent outputs | Pydantic AI |
| RAG-heavy, data-centric agents | LlamaIndex |
| Search and document-QA pipelines | Haystack |
| Lightweight, open-model experimentation | Hugging Face smolagents |
The rest of this guide explains why, and when each pick is wrong for you.
The Decision Matrix: Language, MCP Support, and Deployment Model
Last verified: August 2026. Framework APIs and protocol support move fast — check current documentation before you commit a stack to production.
General-Purpose and Vendor Frameworks: Core Profile
| Framework | Best for | Language(s) | Execution pattern |
|---|---|---|---|
| LangGraph | Auditable, stateful production workflows | Python, JS/TS | Directed graph, cyclic state |
| CrewAI | Fast multi-agent prototyping | Python | Role-based crews, sequential/hierarchical |
| Microsoft Agent Framework | Azure/.NET-native enterprise builds | .NET, Python | Unified agent core + graph workflows |
| OpenAI Agents SDK | OpenAI-first production agents | Python, JS/TS | Explicit handoffs + tool loop |
| Google ADK | Teams on Google Cloud/Gemini, multi-language shops | Python, Java, Go, TypeScript | Session-based, A2A-native |
| Claude Agent SDK | Safety-sensitive, extended-reasoning tasks | Python, TypeScript | Tool-use loop, native streaming |
| Mastra | TypeScript-first production agents | TypeScript | Agent + typed workflow graph |
General-Purpose and Vendor Frameworks: Protocol, Deployment, and State
| Framework | MCP support | Deployment | State / HITL |
|---|---|---|---|
| LangGraph | Native client | Self-hosted or LangGraph Platform | Native checkpointers, built-in interrupt/resume |
| CrewAI | Native client | Self-hosted or CrewAI AMP | SQLite/vector memory; checkpointing is newer, less mature |
| Microsoft Agent Framework | Native client | Self-hosted or Azure AI Foundry (Agent Harness) | Native thread-based state, built-in approval steps |
| OpenAI Agents SDK | Native client (MCPServerStdio/SSE) | Self-hosted or OpenAI-hosted tracing dashboard | Session objects only; no native long-running checkpoints |
| Google ADK | Native client | Self-hosted or Vertex AI Agent Engine | Session service + built-in HITL confirmation flow |
| Claude Agent SDK | Native client and server | Self-hosted or AWS AgentCore | Permission-callback approvals; no durable checkpoint store |
| Mastra | Native client | Self-hosted or Mastra Cloud | Built-in workflow suspend/resume for HITL |
Specialized Frameworks: Core Profile
| Framework | Best for | Language(s) | Execution pattern |
|---|---|---|---|
| Pydantic AI | Typed, structured agent outputs | Python | Validated I/O agent loop |
| LlamaIndex (agents) | Data/retrieval-heavy, RAG-first agents | Python, TypeScript | Data-centric agent loop |
| Haystack (agents) | Search and document-QA pipelines | Python | Pipeline-based |
| Hugging Face smolagents | Lightweight, open-model experimentation | Python | Minimal code-first loop |
Specialized Frameworks: Protocol, Deployment, and State
| Framework | MCP support | Deployment | State / HITL |
|---|---|---|---|
| Pydantic AI | Native client | Self-hosted | Basic; not built for long-running checkpointed state |
| LlamaIndex (agents) | Native client | Self-hosted or LlamaCloud | Workflow-based state; checkpointing via LlamaCloud |
| Haystack (agents) | Adapter/community integration | Self-hosted or Deepset Cloud | Pipeline state only; no dedicated HITL primitive |
| Hugging Face smolagents | Adapter/community integration | Self-hosted | None built in |
Reading the MCP Column
A framework listed as “native client” connects to MCP servers directly, without a third-party bridge. That’s still not one checkbox — auth handling, tool discovery, and permission scoping vary between these native integrations, so read the framework’s own MCP docs before you assume feature parity.
Choose LangGraph When You Need an Audit Trail
Pick LangGraph when you need retries, checkpoints, and precise control over branching — regulated workflows, human-approval steps, anything where you’ll need to reconstruct exactly what the agent did after the fact. It’s the framework most DAG-based orchestration setups converge on once a workflow outgrows a single linear chain.
Skip it for a single agent with one or two tools and no durable state. The graph model is overhead you don’t need yet, and CrewAI or Pydantic AI will get you there faster.
Choose CrewAI When You Need a Working Demo in Hours
CrewAI earns its place when your workflow maps cleanly onto named roles — researcher, writer, reviewer — and you want something running before lunch.
Don’t reach for it when you need fine-grained error recovery or long-running checkpointed state. CrewAI’s own documentation points teams toward LangGraph once they hit that wall.
Choose the OpenAI Agents SDK for OpenAI-First Production Agents
This SDK fits teams standardized on GPT models who want built-in guardrails, session management, tracing, and sandboxed code execution without much setup work.
Skip it when framework neutrality matters more than speed. The SDK genuinely runs other providers through Responses-API-compatible adapters or LiteLLM, but its native tooling — the tracing dashboard, the sandbox polish — is built around OpenAI’s own models first.
Choose Microsoft Agent Framework for Azure-Native Builds
Pick this if you’re building inside Azure or .NET and want one supported SDK instead of guessing between two frameworks Microsoft has since folded together.
Skip it if you’re not on Microsoft’s stack. The features that make it strong there — Azure AI Foundry integration, unified telemetry, the new Agent Harness for governed runtime deployment announced at Build 2026 — aren’t the point anywhere else.
Choose Google ADK for Multi-Language Google Cloud Teams
ADK now ships in Python, Java, Go, and TypeScript, so pick it if your team already spans languages and wants one consistent agent model across all of them, especially on Gemini and Vertex AI.
Skip it if you need broad non-Gemini model support as a first-class citizen rather than an add-on. Python ADK remains the most mature of the four language SDKs; the others are catching up.
Choose Claude Agent SDK for Safety-First, Tool-Heavy Agentic Coding
This fits tasks that need extended reasoning and conservative default behavior more than raw orchestration flexibility — think a Claude-native coding or research agent that calls tools directly rather than juggling a crew of role-based sub-agents. Budget for it the way you’d budget for Claude Code itself, since the SDK draws from the same usage limits and can burn through them fast on long agentic sessions.
Skip it when you need heavy multi-agent orchestration across many roles. It’s built around a strong single-agent loop with permission callbacks, not a crew model, and it doesn’t ship a durable checkpoint store of its own.
Choose Mastra for TypeScript-Native Agent Teams
Mastra fits teams that ship in TypeScript and don’t want to stand up a separate Python service just for agent logic. It routes across a broad set of model providers and gives you typed workflows with built-in suspend/resume for human-in-the-loop steps.
The Vercel AI SDK covers similar ground but leans toward UI streaming and simple tool loops rather than durable, typed workflow graphs — treat it as the lighter-weight option and Mastra as the one built for backend orchestration.
Skip Mastra if your stack and hiring pool are Python-first. LangGraph and CrewAI carry a bigger example base and a deeper talent pool.
Choose Pydantic AI When Output Validation Is Your Real Risk
Pick this when malformed or loosely structured model output is your biggest operational risk, and you want every input and output validated against a typed model.
Skip it when you need visual graph orchestration or heavy multi-agent choreography. That’s not what it optimizes for.
Three Orchestration Patterns, Side by Side
These are minimal sketches of each framework’s core pattern, not full working examples. Library APIs shift often enough that current docs beat anything printed here.
LangGraph makes nodes and the edges between them explicit:
python
graph = StateGraph(AgentState) graph.add_node("research", research_node) graph.add_node("write", write_node) graph.add_edge("research", "write") graph.set_entry_point("research") app = graph.compile(checkpointer=checkpointer)
CrewAI defines roles and lets the crew handle the handoffs:
python
researcher = Agent(role="Researcher", goal="Find accurate sources", tools=[search_tool]) writer = Agent(role="Writer", goal="Draft the article") crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential) crew.kickoff()
The OpenAI Agents SDK defines agents and lets a runner drive the loop:
python
triage_agent = Agent(name="Triage", instructions="Route to the right specialist", handoffs=[billing_agent, tech_agent]) result = Runner.run(triage_agent, input="My invoice looks wrong")
The graph makes state and transitions inspectable. The crew makes roles explicit and leaves the mechanics to the framework. The handoff model keeps everything inside native control flow while the runner drives underneath.
MCP vs. A2A: Two Protocols, Two Different Jobs
MCP connects an agent to tools and data. A2A connects one agent to another agent. Confusing the two is the fastest way to pick the wrong protocol for the problem you actually have.
Anthropic open-sourced the Model Context Protocol in November 2024, then donated it to the Agentic AI Foundation — a directed fund under the Linux Foundation — on December 9, 2025. The move brought Anthropic, Block, OpenAI, Google, Microsoft, AWS, Cloudflare, and Bloomberg under one governance structure for the standard, without changing how MCP itself works day to day.
Agent2Agent works differently. Google launched A2A in April 2025 with backing from more than 50 partners, then transferred the protocol to the Linux Foundation that June for the same vendor-neutral reasons.
An ADK agent can discover and call a LangGraph or CrewAI agent through A2A without a custom bridge between them. In practice, that matters most for larger organizations running several frameworks across teams — less for one team standardized on a single stack.
The stakes get sharper once you remove the human from the loop entirely. Fully autonomous MCP-bound agents running in persistent environments show what happens when a bad handoff has no approval gate to catch it — the same orchestration choices covered here, with no safety net.
Frameworks You’ll Inherit, Not Start With
Two names still dominate search volume. Neither is where a new 2026 project should start.
What Happened to AutoGen
Microsoft moved AutoGen into maintenance mode in October 2025 — bug and security fixes only, no new features. The framework’s ideas live on in two places now.
The original creators, who left Microsoft in late 2024, maintain a community fork called AG2 under open governance. It keeps AutoGen’s conversation-based API alive and actively developed.
Microsoft’s own investment went into Microsoft Agent Framework, which absorbed AutoGen’s orchestration concepts alongside Semantic Kernel’s enterprise plumbing and reached 1.0 general availability on April 2, 2026.
If you’re maintaining AutoGen today: decide deliberately between AG2’s API continuity and Microsoft Agent Framework’s enterprise backing. That’s a case-by-case call based on how deep your Azure investment already runs, not something with one right answer for every team.
What to Do With Semantic Kernel
Semantic Kernel still works fine for existing deployments and its plugin/connector ecosystem. Microsoft’s own new-feature investment now goes to Agent Framework, though.
Evaluate Microsoft Agent Framework first for new Microsoft-centric projects. Keep Semantic Kernel where your plugin, connector, or application architecture already depends on it.
What Happened to OpenAI’s Swarm
Swarm was an experimental, educational framework rather than a production recommendation. OpenAI now positions the Agents SDK as its successor for building production agent applications.
If you’re maintaining a Swarm codebase, evaluate migration rather than starting new work on it.
Already Building on Another Framework?
| Current stack | Consider |
|---|---|
| AutoGen | Microsoft Agent Framework, or AG2 for API continuity |
| Semantic Kernel | Microsoft Agent Framework |
| OpenAI Swarm | OpenAI Agents SDK |
| LangChain agents | LangGraph |
| A custom tool-calling loop | Pydantic AI, LangGraph, or your model vendor’s SDK |
Vendor Optimization Isn’t the Same as Lock-In
It’s tempting to lump every model-vendor SDK together as “locked in.” That overstates it.
The OpenAI Agents SDK is built around the Responses API, and its native tracing dashboard lights up fully only for OpenAI models. That’s real optimization, not a wall — you can route other providers through Responses-API-compatible adapters or LiteLLM; you just lose some first-party polish.
The more useful question per framework isn’t “is this vendor-tied?” It’s “what specifically breaks or degrades if I switch model providers later” — tracing, sandboxing, and prompt caching are usually the first casualties, even when basic model calls keep working.
A Quick Decision Tree
Need durable state and checkpoints for a long-running or regulated workflow? Go LangGraph, or Microsoft Agent Framework if you’re Azure-native.
No durable-state requirement, and you’re TypeScript-first? Go Mastra.
Python-first, and typed output validation is the priority? Go Pydantic AI.
Python-first, and the work is RAG or data-retrieval heavy? Go LlamaIndex.
Need role-based multi-agent simulation fast? Go CrewAI.
Standardized on one model vendor and want their native tooling? Go the vendor SDK — OpenAI Agents SDK, Google ADK, or Claude Agent SDK.
What to Check Before You Commit
MCP integration depth matters more than the presence of a checkbox — native transport versus adapter, and whether tool discovery, auth, and permissions are handled for you or left as homework.
Checkpointing and human-in-the-loop support matter if a workflow runs for hours or needs an approval step midway. Not every framework in this list persists state well yet, and the matrix above flags which ones do.
What breaks if you switch model providers deserves a direct answer, not a lock-in label. And migration exposure — what a framework’s maintainer would do to your codebase if their own roadmap changed — is worth asking before you build, given what just happened to AutoGen.
None of this produces one universally “best” framework. A regulated fintech workflow and a weekend prototype have different correct answers from the same list.
Frequently Asked Questions
Q. What is the best agentic AI framework in 2026?
There is no single best agentic AI framework in 2026. LangGraph is a strong choice for durable state and complex workflows, CrewAI for fast multi-agent prototyping, Microsoft Agent Framework for Microsoft-centric enterprise projects, and vendor SDKs for applications closely tied to OpenAI, Google, or Anthropic.
Q. Which agentic AI framework is best for Python?
LangGraph, CrewAI, and Pydantic AI are among the strongest Python options, but the right choice depends on the workload. LangGraph fits stateful orchestration, CrewAI suits role-based multi-agent systems, and Pydantic AI is particularly useful when typed and validated outputs are a priority.
Q. Which agentic AI framework is best for TypeScript?
Mastra is one of the strongest TypeScript-first agentic AI frameworks in 2026. It is designed around TypeScript workflows and tooling rather than treating TypeScript as a secondary language. The Vercel AI SDK is a lighter alternative for applications focused on streaming, UI integration, and straightforward tool-calling loops.
Q. Is AutoGen still a good choice for new projects in 2026?
No, Microsoft AutoGen is not the recommended starting point for new projects in 2026. Microsoft now lists AutoGen as being in maintenance mode and directs new users toward Microsoft Agent Framework. Existing AutoGen projects can follow Microsoft’s migration path.
Q. What should I use instead of AutoGen in 2026?
Microsoft Agent Framework is Microsoft’s successor to AutoGen, while AG2 is the community-led continuation of the original AutoGen approach. Choose Microsoft Agent Framework when you want Microsoft’s supported path; choose AG2 when maintaining compatibility with the original AutoGen-style ecosystem is the priority.
Q. Is AG2 the same as AutoGen?
No. AG2 is a community-governed continuation of the original AutoGen project, while Microsoft AutoGen is now maintained separately. AG2 was created by original AutoGen contributors and continues its own development under the AG2 organization.
Q. What is MCP in agentic AI?
Model Context Protocol (MCP) is an open standard for connecting AI applications and agents with external tools, data, and services. It helps standardize how an agent discovers and interacts with capabilities outside the model itself. MCP is now hosted by the Linux Foundation’s Agentic AI Foundation.
Q. What is the difference between MCP and A2A?
MCP connects an agent to tools and data, while A2A connects one AI agent to another. MCP is useful when an agent needs external capabilities such as databases, APIs, or applications; A2A becomes important when independent agents or agent frameworks need to communicate and collaborate.
Q. Does MCP support matter when choosing an AI agent framework?
Yes. Native MCP support can be an important framework-selection factor in 2026. A framework with built-in MCP support can reduce the need for custom adapters when connecting agents to external tools and data. It is also worth checking whether support is native or provided through a third-party integration.
Q. Can Microsoft Agent Framework use Ollama and other non-Microsoft models?
Yes. Microsoft Agent Framework supports third-party model providers, including Ollama, OpenAI, Anthropic, and Google Gemini. Ollama can run models locally, making it useful for development, testing, and some on-premises scenarios. Microsoft notes that third-party systems are subject to their own terms and support considerations, so production deployments should be evaluated individually.
Q. Which AI agent framework is best for RAG?
For RAG-heavy applications, LlamaIndex is a strong choice because it is specifically focused on data retrieval, indexing, and connecting LLM applications to external data. LangGraph is a better fit when the RAG system also requires complex, stateful agent workflows or long-running orchestration.
Q. Which AI agent framework is best for multi-agent systems?
CrewAI, LangGraph, and Microsoft Agent Framework are strong choices for multi-agent systems, but they target different needs. CrewAI emphasizes role-based agent collaboration, LangGraph provides fine-grained workflow and state control, while Microsoft Agent Framework targets enterprise orchestration and interoperability.
Q. Which agentic AI framework should I choose for a new project?
Choose the framework based on your architecture rather than popularity. Use LangGraph for durable state, Mastra for TypeScript-first development, Pydantic AI for typed Python outputs, LlamaIndex for RAG-heavy applications, CrewAI for rapid multi-agent workflows, and a vendor SDK when you are committed to one model provider.
Related: Training AI Models with Prompts: Best Practices That Actually Work (2026
| Disclaimer: AI frameworks and their features change quickly, so details in this guide may evolve after publication. We’ve made every effort to keep the information accurate and current, but always double-check official documentation before making development or production decisions. This guide is intended for general informational purposes and should not replace your own technical evaluation. |
