AI Agent Frameworks Compared: CrewAI, LangGraph and More (2026)

Short answer: For new business projects in late 2026, choose between CrewAI, LangGraph, Microsoft Agent Framework, the OpenAI Agents SDK and Google’s Agent Development Kit (ADK), based on your team’s language and how much control you need. Avoid starting on SuperAGI (no code changes since January 2025) or the archived AutoGen. AutoGPT is now an agent platform, not a framework.

If your team picked an agent framework from a 2023 comparison, you may be building on something nobody maintains any more. Of the three tools our original “AutoGPT vs CrewAI vs SuperAGI” post compared, two are no longer what they were, and Microsoft, OpenAI and Google have since released frameworks of their own.

This replacement post is for CTOs, product owners and founders deciding what their team or agency should build on. It gives the current status of each option (checked against its GitHub repository on 2 November 2026), a comparison table of language, best fit, maturity and licence, short profiles and a decision guide. If you are new to agents, start with our practical guide to AI agents; if you want budgets, see AI agent development cost in India.

What is an AI agent framework?

An AI agent framework is a code library that handles the plumbing of building agents: calling a large language model (LLM), giving it tools, keeping track of state and memory, coordinating several agents and letting a person approve or correct steps. You still write the business logic; the framework saves you from rebuilding the loop, error handling and tracing each time.

Frameworks differ in how much control they give you. Some, like CrewAI, offer high-level building blocks such as “agents with roles working as a crew”. Others, like LangGraph, are lower level: you describe the workflow as a graph of steps and decide exactly what happens at each one. Higher-level frameworks are faster to start with; lower-level ones are easier to control when a process must behave predictably.

A framework is not always necessary. Anthropic, in its widely cited engineering post, recommends “finding the simplest solution possible, and only increasing complexity when needed”, and suggests many developers start by calling LLM APIs directly (Anthropic, Building effective agents). A single well-designed workflow with one or two tools can beat a multi-agent system on cost, speed and reliability.

Are AutoGPT, CrewAI and SuperAGI still maintained?

Two of the three are active, but only CrewAI is still a developer framework in the sense our original post meant. Here is what each repository showed on 2 November 2026.

• CrewAI: actively developed. Its GitHub repository had a new release (1.15.34) on 1 November 2026, and the project describes itself as an open-source Python framework for building multi-agent workflows. Version 1.0 was published on PyPI in October 2025.
• AutoGPT: active, but now a platform. The AutoGPT repository is actively updated, with a platform beta release (v0.10.0) on 27 October 2026. It now presents itself as a team of AI “Experts” coordinated by an assistant, offered as a paid hosted platform or as software you self-host, with a visual builder for workflows. The original standalone AutoGPT survives as “AutoGPT Classic”.
• SuperAGI: inactive. The SuperAGI repository has not had a commit since 22 January 2025, and its latest release (v0.0.14) dates from January 2024. It is not formally archived, but we would not start a new business project on it.

Microsoft’s AutoGen, often compared with these three, has also changed status. Its README now states that the project “has been archived and is no longer actively maintained”, with no bug or security fixes, and points new users to Microsoft Agent Framework. OpenAI’s experimental Swarm library has similarly been replaced: its repository recommends migrating to the OpenAI Agents SDK.

Which AI agent frameworks should you consider in 2026?

For most business projects, the shortlist is CrewAI, LangGraph, Microsoft Agent Framework, the OpenAI Agents SDK and Google ADK. All five are open source, actively released and able to work with more than one model provider. The table summarises them alongside the three older tools.

A note on licences: MIT and Apache 2.0 are permissive and allow commercial use. AutoGPT’s Polyform Shield licence, according to its README, is free for personal and internal business use but does not allow selling it as a competing hosted service, so check the terms if you plan to build a product on top of it.

What is each framework best at?

Each of the five active frameworks takes a different approach. The profiles below draw on each project’s own documentation.

CrewAI

CrewAI is the quickest way to model a team of specialised agents. Its README describes two building blocks: Crews, which organise role-based agents that collaborate autonomously, and Flows, which give “precise, event-driven control” and can combine single LLM calls with crews. You define each agent’s role, goal, tools and model, and CrewAI handles delegation between them.

It defaults to OpenAI models but supports other providers and local models through tools such as Ollama and LM Studio. It requires Python 3.10 to 3.13. CrewAI also sells a commercial control plane (CrewAI AMP) for managed deployment, observability and governance; the open-source framework works without it. Choose CrewAI when the work splits naturally into roles, such as researcher, writer and reviewer, and you want a working prototype quickly.

LangGraph

LangGraph, from the LangChain team, is a “low-level orchestration framework for building, managing, and deploying long-running, stateful agents”. You model the process as a graph of nodes and edges, which makes it suitable for business processes that must follow defined paths with AI at specific steps.

Its documented strengths are durable execution (agents resume where they left off after a failure), human-in-the-loop interrupts that let a person inspect and change state at any point, and short- and long-term memory. It pairs with LangSmith for tracing and evaluation, though LangGraph can be used on its own. A JavaScript/TypeScript version exists for teams working in Node.js. Choose LangGraph when control, auditability and reliability matter more than speed of prototyping.

Microsoft Agent Framework

Microsoft Agent Framework is the successor to both AutoGen and Semantic Kernel. Microsoft announced version 1.0 for .NET and Python on 3 April 2026, describing it as the production-ready release with “stable APIs, and a commitment to long-term support” (Microsoft Agent Framework blog).

The announcement lists first-party connectors for Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini and Ollama, support for the Model Context Protocol (MCP), graph-based workflows and multi-agent orchestration patterns, with migration assistants for teams moving from AutoGen or Semantic Kernel. The README adds OpenTelemetry-based observability and declarative agents defined in YAML. Choose it if your organisation runs on .NET or Azure, or if you need an enterprise support commitment from a large vendor.

OpenAI Agents SDK

The OpenAI Agents SDK is a “lightweight yet powerful framework for building multi-agent workflows” and, despite the name, is provider-agnostic: its README says it supports OpenAI’s APIs and more than 100 other LLMs. Its core concepts are agents with instructions and tools, handoffs between agents, guardrails for input and output checks, human-in-the-loop, sessions and built-in tracing, plus realtime and voice agents.

It replaced OpenAI’s experimental Swarm library and is available for Python and JavaScript/TypeScript. It is still versioned below 1.0, so expect some API changes between releases. Choose it when you want a small, readable codebase with few abstractions, or when you are building voice agents.

Google Agent Development Kit (ADK)

Google’s ADK is “an open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents”, with sister projects in Java, Kotlin, Go and TypeScript. The README says it is optimised for Gemini but model-agnostic and deployment-agnostic.

Version 2 adds a graph-based workflow runtime with routing, loops, retries, state management and human-in-the-loop, alongside multi-agent hierarchies and a tool ecosystem that includes OpenAPI specs and MCP tools. Choose ADK if you are on Google Cloud, want first-class Gemini support, or need a language other than Python or JavaScript.

How do you choose the right AI agent framework?

Choose based on your team’s language, how much control the process needs, where you host, and whether you need vendor support. Fit matters more than popularity: all five active options are capable, and a poor fit shows up later as maintenance cost.

Before you commit, run through these checks:

1. Write down the process first. List the steps, the systems involved, the decisions that need judgement and where a person must approve. If most steps are fixed, you may need intelligent process automation with AI at a few points rather than autonomous agents.
2. Match your team’s skills. A framework in the language your developers already use will be cheaper to maintain than a better-reviewed one in a language they do not.
3. Check model flexibility. Model prices and quality change quickly. Make sure the framework lets you switch providers or run smaller models for simple steps without a rewrite.
4. Test observability early. You need to see every model call, tool call and decision to debug problems and control cost. Check what tracing the framework offers and whether it ties you to a paid service.
5. Plan guardrails and permissions. Give agents only the tools and access the task needs and require approval for high-impact actions. OWASP lists “excessive agency” among the top risks for LLM applications (OWASP LLM06:2025).
6. Look at project health. Check the date of the last release, how quickly issues get answered and whether the project has a stable (1.0 or later) version. The SuperAGI and AutoGen examples show how quickly a popular project can stop being a safe base.
7. Build a small proof of concept. Implement one real task end to end with your shortlisted framework, measure accuracy, cost per task and latency, and only then scale.

What are the most common mistakes when choosing an agent framework?

The most common mistake is starting with a multi-agent design when one agent, or a simple workflow, would do. Each extra agent adds model calls, cost, latency and more ways to fail. Anthropic notes that agentic systems “often trade latency and cost for better task performance”, so that trade should be deliberate.

Other common mistakes:

• Choosing by GitHub stars. AutoGPT and AutoGen were among the most-starred agent projects; neither is now a straightforward framework choice for new code.
• Skipping evaluation. Without a test set of real cases and expected outcomes, you cannot tell whether a prompt or model change made things better or worse.
• Locking business logic into the framework. Keep your tools, prompts and business rules in your own modules so you can change frameworks if you need to.
• Ignoring the business case. Gartner predicted that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs and unclear business value (Reuters, June 2025). Define the metric the agent must move before you choose any tool.

Frequently asked questions

Is SuperAGI still a good choice in 2026?

We would not choose SuperAGI for a new project. Its GitHub repository shows no commits since 22 January 2025 and no release since v0.0.14 in January 2024. The code is still available under the MIT licence, but without active maintenance you would be responsible for fixing bugs, security issues and compatibility with newer models yourself. CrewAI, LangGraph or one of the vendor frameworks is a safer base.

Is AutoGPT still a framework for developers?

Not in the original sense. AutoGPT has become an agent platform: a team of ready-made AI “Experts” with a visual workflow builder, offered as a paid hosted service or as software you self-host. Developers can still use its API and the original AutoGPT Classic code, but if you want a library to build agents into your own product, the frameworks in our shortlist are a better fit.

What happened to Microsoft AutoGen?

Microsoft has archived AutoGen. Its README says the project is no longer actively maintained and will not receive bug fixes, security fixes or new features, and it advises against production use. New users are directed to Microsoft Agent Framework, which reached version 1.0 in April 2026 and combines ideas from AutoGen and Semantic Kernel. Microsoft provides a migration guide for existing AutoGen users.

CrewAI or LangGraph: which is better?

Neither is better in general; they suit different jobs. CrewAI is faster for modelling a team of role-based agents and getting a prototype running. LangGraph gives finer control over each step, state and failure recovery, which suits processes that must be predictable and auditable. Some teams prototype the idea quickly, then build the production version in whichever framework fits the process.

Do I need an agent framework at all?

Not always. If your task is a single step, such as classifying an email or extracting fields from a document, a direct call to an LLM API inside your existing code or automation tool is simpler and cheaper. Frameworks earn their place when you need multiple steps, tool use, memory, several agents, human approvals and tracing, which become tedious to build and maintain by hand.

Can these frameworks use models other than OpenAI’s?

Yes. All five active frameworks in our shortlist support multiple model providers. The OpenAI Agents SDK says it works with more than 100 LLMs; Google ADK is optimised for Gemini but model-agnostic; Microsoft Agent Framework ships connectors for several providers including Anthropic, Google and Ollama; and CrewAI supports local models. Check the exact connector you need before committing.

If you want help choosing a framework, scoping a proof of concept or building a production agent with guardrails and monitoring, see digiPanda’s AI agent development services and tell us about the workflow you want to automate.