How to choose the right agentic AI framework
TLDR: Whether you’re creating one agent or a multi agent system, use the guide below to map your use case to the right framework.
Use LangGraph when the agent needs branching, loops, state, retries, human approval, or recovery after failure. It’s one of the better choices for agents that need to run through several possible paths rather than follow a straight line.
Use Microsoft Agent Framework when your team already works in Azure, .NET, Microsoft 365, or Azure AI Foundry. It’s best suited to enterprise teams that want Microsoft’s agent tools, deployment path, and governance model in one place.
Use OpenAI Agents SDK when the build is centered on OpenAI models and you want code-first agents with tools, handoffs, tracing, and guardrails inside the OpenAI ecosystem.
Use Claude Agent SDK when the build is centered on Claude, especially coding agents, internal automation, and workflows where Claude is already the main model.
Use Strands Agents when you want an AWS-backed agent framework that can work across model providers. It fits teams building production agents inside AWS while keeping some model flexibility.
Use Google ADK when the agent will use Gemini, Google Cloud, multimodal inputs, or Google’s agent evaluation and deployment tooling.
Use LlamaIndex agents when the agent depends heavily on documents, retrieval, knowledge bases, search, or RAG. It’s usually strongest when the hard part is getting the right context into the model.
Use Haystack when the core system is search, retrieval, question answering, or a production RAG pipeline. It fits teams that think in pipelines and want more control over retrieval behavior.
Use CrewAI when the work can be split into roles, tasks, and handoffs between specialized agents. It works well for research, content, marketing ops, and repeatable internal processes.
Use AutoGen / AG2 when you’re testing multi agent collaboration patterns or building research prototypes. It’s useful when the work is exploratory and conversation between agents is the main design pattern.
Use smolagents when you want a lightweight Python agent loop without a large framework around it. It’s a good fit for demos, internal scripts, and small single-agent builds.
Use Pydantic AI when typed outputs, validation, and Python reliability are more important than a visual workflow or a large orchestration layer.
Use Mastra when your team builds in TypeScript and wants agents, workflows, memory, and tool use inside a product engineering stack.
Use LangChain when you need broad integrations, quick prototypes, or access to the wider LangChain ecosystem. For complex stateful agents, use LangGraph rather than plain LangChain.
Use Dify when non-engineer teams need to build and run an internal AI application through a visual interface, especially in self-hosted settings.
Use n8n when the agent logic is part of a wider automation workflow involving forms, APIs, Google Drive, Slack, email, CRMs, databases, or scheduled jobs.
What does an agentic AI framework do?
Most agentic frameworks handle a mix of five jobs: planning, tool calling, memory and state, orchestration, and observability.
Agent orchestration is where frameworks differ most. Each one has a different model for how an agent decides its next step, calls tools, and routes work across multiple AI agents.
Three common orchestration patterns show up across agent frameworks:
- Graph-based frameworks like LangGraph map each action as a node and each transition as an edge, which suits cyclical or branching agent workflows.
- Role-based frameworks like CrewAI treat a job as a crew of specialized agents, each with a defined role and task.
- Event-driven frameworks like LlamaIndex trigger steps through events, so the paths between steps don’t need fixing in advance; agents can loop back or branch as the work demands.
Orchestration design drives agent behavior: the order of tool calls, fallback logic, and guardrails that stop an agent looping indefinitely.
For multi agent orchestration, the framework routes messages between agents and tracks a shared task list. This is how intelligent agents divide a complex AI workflow into parts they can run at once.
Why MCP changed tool integration
Before late 2024, connecting an agent to each new tool meant a custom integration. This was a bit of a pain.
Anthropic introduced the Model Context Protocol (MCP) in November 2024 as an open standard for that connection, and the company now calls it the de-facto standard for linking agents to AI tools and data.
Major agent frameworks now support MCP directly or through connectors, but maturity varies by framework. For teams building AI agents on many data sources, native MCP support cuts the integration work down to one protocol.
That convenience carries a cost. MCP can widen the attack surface because agents gain access to more tools and data.
That risk falls within a broader Gen AI security trend, as Gartner expects 25% of enterprise generative AI applications to experience at least five minor security incidents per year by 2028.
The agentic AI frameworks compared
‘Production fit’ is based on four practical questions:
- Can the framework manage state?
- Can the team observe what the agent is doing (observability)?
- Can it be deployed without building too much custom infrastructure?
- Does it give the team enough control over tools, governance, permissions, and failure handling?
Each of these factors should feature into your decision of one agentic framework over another.
| Factor | Rating signal |
| State management | Built-in, external, or DIY |
| Observability | Native tracing, OpenTelemetry, logs only |
| Deployment | Cloud product, self-hosted, library only |
| Ecosystem maturity | Docs, examples, field use, release cadence |
| Governance | Permissions, approvals, evals, audit trails |
Note that production fit isn’t a fixed quality score. A “high” fit means the framework is already a strong choice for production teams in its main use case.
A “medium” fit means it can work well, but teams may need more custom engineering, monitoring, or operational processes around it.
Below are the frameworks, their orchestration patterns, what they’re best for, and production fit given various constraints.
| Framework | Maker | Orchestration pattern | Best for | Production fit |
| LangGraph | LangChain | Graph-based | Stateful agents with branching, loops, approvals, and recovery paths | High |
| Microsoft Agent Framework | Microsoft | Workflow and multi-agent | Azure, .NET, Microsoft 365, and enterprise agent systems | High |
| OpenAI Agents SDK | OpenAI | Code-first | OpenAI-native agents with tool use, handoffs, tracing, and guardrails | High |
| Claude Agent SDK | Anthropic | Code-first | Anthropic-native agents, coding agents, and Claude-centered automation | High |
| Strands Agents | AWS | Model-driven | AWS teams building production agents across multiple model providers | High |
| LlamaIndex | LlamaIndex | Event-driven workflows | RAG-heavy agents, document systems, knowledge workflows, and search | High |
| Haystack | deepset | Pipeline-based | Search, retrieval, question answering, and enterprise RAG pipelines | High |
| Google ADK | Multi-agent workflow | Gemini, Google Cloud, multimodal agents, and agent evaluation | Medium | |
| CrewAI | CrewAI | Role-based | Research, content, operations, and task delegation across agent teams | Medium |
| AutoGen / AG2 | Microsoft Research, community | Conversation-based | Multi-agent experiments, research prototypes, and agent collaboration patterns | Medium |
| smolagents | Hugging Face | Code-first | Lightweight agents, small projects, demos, and fast prototyping | Medium |
| Pydantic AI | Pydantic | Type-safe, code-first | Python agents where typed outputs, validation, and reliability are central | Medium |
| Mastra | Mastra | Workflow-based | TypeScript teams building agents, workflows, and product features | Medium |
| LangChain | LangChain | Chain-based | Prototypes, tool integrations, and teams already using the LangChain ecosystem | Medium |
| Dify | Dify | Low-code visual | Internal tools, self-hosted AI apps, and teams that prefer visual builders | Medium |
| n8n | n8n | Low-code workflow automation | Self-hosted automation, API workflows, and agent steps inside broader business processes | Medium |
What changed in 2026
Model labs have entered AI agent development directly. OpenAI, Anthropic, Google with its Agent Development Kit (ADK), and AWS each shipped a native open source framework, so a framework choice can now follow your AI model choice.
Microsoft folded its two earlier projects, AutoGen and Semantic Kernel, into one Microsoft Agent Framework for building multi agent systems inside Azure AI Foundry. It reached v1.0 in April 2026.
AWS open-sourced Strands Agents in May 2025; it had passed 14 million downloads by early 2026.
Both AutoGen and Semantic Kernel now move to maintenance, with new work flowing into the merged framework.
Alongside Anthropic’s MCP, Google’s A2A is one of the main emerging protocols for agent-to-agent interoperability across AI systems.
Source: Google
The wider outlook on agentic frameworks
An AI agent framework is the control layer around an AI model. It sets the order of operations, decides when to call a tool, holds state between steps, and gives developers a way to observe the agent.
You can build AI agents from scratch in Python or JavaScript, though an AI framework removes most of the plumbing and gets you to production faster.
Agentic frameworks extend AI capability beyond a single prompt. They can turn a model or several into autonomous agents that plan, act, and coordinate with each other.
The category has grown fast. Gartner expects 33% of enterprise software applications to include agentic AI by 2028, up from under 1% in 2024.
By the end of 2026, the firm projects 40% of enterprise applications will run task-specific AI agents, up from under 5% a year earlier.
But the same analysts warn that over 40% of agentic AI projects will be cancelled by the end of 2027, due to rising costs and weak production controls.
And so framework choice today affects how much state, observability, permissioning, and failure handling you have to build yourself.
Getting started with agentic AI frameworks
- Define agent complexity: single or multi agent? Stateful or stateless?
- Assess your team‘s coding level and primary development language.
- Map your stack: cloud provider, preferred AI model, and existing tools.
- Check MCP support against the AI tools your agent needs to reach.
- Run a one-week proof of concept on your top two options before committing.
There’s no single best AI agent framework that fits every team. The right one depends on your model, your stack, the kind of agent you’re building, and whether you’re working with one or multiple agents.
Low-code AI agent builder tools like Dify and n8n suit non-engineer teams, while code-first SDKs suit teams shipping production agents at scale.
For a framework selection mapped to your own workflows, I run AI audits and agent-readiness reviews for teams moving agents into production. → Get in touch.