Microsoft Build kicks off today in San Francisco, June 2 and 3. If you cannot make it in person, the sessions are streaming online for free, and I want to walk you through what we are announcing for Visual Studio this week.
One idea tie most of it together. Code is an asset, not just an artifact. The tools around it should help you keep it healthy, correct, and easy to evolve as your codebase grows. Every announcement below is a step toward that.
Agents that participate in the work, not next to it
GitHub Copilot in Visual Studio is moving beyond chat and completions. The direction is agents that can participate more actively in the development lifecycle, helping with debugging, profiling, and testing alongside you.
This is not about replacing the tools you already rely on. It is about connecting them more effectively. The debugger, profiler, and test tools already provide deep insight. Agents help turn that insight into action:
Identify issues faster
Explain what is going on
Suggest concrete fixes
Help validate the results
This matters most if you work in large C# or C++ codebases where the hard problems are not “write this function” but “figure out why this thing is slow under load.” That is the work Visual Studio has always been built for. Agents extend it.
Catching errors before the build starts
This one is small and I think you will notice it daily.
Today, a build can still run even when there are obvious errors already sitting in the Error List. The build runs, you wait, the build fails on something you could have seen up front.
We are changing that flow so Visual Studio checks errors and warnings before the build starts. Simple change. Real time saved. The kind of thing that adds up across a week.
Merge conflicts with less manual work
Merge conflicts are something every developer runs into, and they are rarely a good use of anyone’s time.
We are working on AI-assisted conflict resolution to reduce the manual effort these situations require. The goal is not to auto-merge everything. The goal is to help you understand the conflict, make a sensible decision, and get back to the work you were actually doing.
Modernization that moves your apps forward
This summer, we are bringing new capabilities to GitHub Copilot modernization, the integrated agent experience built into Visual Studio that helps you upgrade your applications to the latest .NET stack.
You can migrate Web Forms applications to Blazor for a modern, component-based web stack. You can add Aspire to existing apps for cloud-ready observability and orchestration. The modernization agent assesses your project, builds a plan, and executes upgrades step by step, helping you improve performance and security without starting from scratch.
If you have been carrying a Web Forms app for years because the rewrite math never penciled out, this is worth a fresh look.
Skills that show up when you need them
One of the harder problems with AI tooling is that the right capability often exists, but it shows up at the wrong moment, or you have to know to ask for it.
We are introducing Microsoft-authored skills that apply automatically based on your project type and the task at hand. Less prompting. Less guesswork. A more helpful experience overall. The right capabilities show up when you need them, without requiring you to already know they exist.
Bring your own key, bring your own model
This is the one I have been waiting to talk about.
Historically, AI integration in Visual Studio has been limited to a small set of sanctioned endpoints. That works for a lot of developers, but it has left real customers behind, including teams whose environments call for different choices.
We are moving toward a BYOK approach, bring your own key or model, so you can use different AI models whether they run locally or in the cloud. That gives you more flexibility around performance, cost, and compliance based on the needs of your environment.
If you have been waiting for Visual Studio to meet your environment instead of asking your environment to bend, this is the announcement to watch.
Built on the GitHub Copilot SDK
Underneath all of this is a more unified foundation. Visual Studio is moving to the GitHub Copilot SDK as the foundation for its AI integration going forward.
This one sits below the surface. You will not see it in a menu. What it means in practice is that we can move faster, stay aligned with the broader ecosystem, and bring new capabilities into Visual Studio sooner. Worth knowing about, even though you will mostly feel it through everything else getting better.
Where this is heading
If there is one way to sum up this roadmap, it is this. We are focused on a set of meaningful improvements that remove friction from the inner loop and make day-to-day development feel better.
Code that compiles by default. Faster feedback before you build. Smarter handling of real-world pain points like merge conflicts. AI that works with your tools, not next to them. Flexibility in how you bring AI into your environment.
All of it is designed to fit how you already use Visual Studio, not force you into a different workflow.
Watch it live at Build this week
If you want to see this work in action, here are the sessions I would put on your schedule. All times in Pacific.
Microsoft Build opening keynote (KEY01) Tuesday, June 2, 9:30 AM to 12:00 PM PT Satya Nadella and Microsoft leaders open the week with how Microsoft is creating new opportunities for developers across our platforms in this era of AI. This is the one that sets the frame for everything else.
GitHub, Copilot, VS Code, and More: Live from San Francisco (LIVE104) Wednesday, June 3, 9:00 AM to 11:00 AM PT The closest thing Build has to a hallway conversation with the engineers shipping the work. Live demos, surprise guests, live coding, straight from the teams. Watch this one live if you can.
GitHub Copilot in Visual Studio: Agents That Debug, Profile, and Test (BRK207) Wednesday, June 3, 4:00 PM to 4:45 PM PT This is the demo-heavy session on the agents work above, with Mads Kristensen and Nik Karpinsky from the Visual Studio team. You will see agents root-cause bugs using live runtime behavior, pinpoint performance bottlenecks, and build test coverage to catch regressions before they ship. If you work in enterprise C#, .NET, or C++, this is the one.
Make GitHub Copilot Work Your Way: Custom Tools, Context and Workflows (LAB502D) Self-paced lab, opens Tuesday, June 2, at 12:00 PM PT Build custom Copilot agents from scratch, create reusable Agent Skills, and connect to external services via MCP. Works across VS Code, Visual Studio, CLI, and Copilot coding agent. Complete it on your own schedule.
The full Build schedule, including everything streaming online for free, is at build.microsoft.com.
If something we announced today changes how you think about your day-to-day in Visual Studio, I want to hear about it.
On the Friday before Memorial Day, on the eve of a long weekend, the Trump administration announced that it was further gutting legal immigration. The Department of Homeland Security didn't use this language. "This policy allows our immigration system to function as the law intended instead of incentivizing loopholes," the agency said on X. "The era of abusing our nation's immigration system is over." A press release from US Citizenship and Immigration Services, the agency that handles legal immigration, provided few details. Following the Trump playbook, DHS seemingly intended to bury this news by announcing it at a time that hardly anyone …
The rise of AI has been changing the focus of Code.org for the past two years. On Tuesday, the Seattle-based computer science education platform acknowledged the shift and rebranded as CodeAI.
“In the past, the focus of computer science was coding,” co-founder Hadi Partovi said in a video message. “Today, the focus is AI — learning how AI works, learning how to create technology, learning how to problem solve using AI, and most importantly, learning to be a responsible citizen in the age of AI.”
Founded in 2013 by twin brothers Hadi and Ali Partovi, Code.org’s mission has always been to expand computer science education to K-12 students. Backed by nearly $60 million in funding from Microsoft, Amazon, Google and The Ballmer Group, the platform today counts 150 million students and 3 million teachers, with 232 million projects created by students around the world.
Karim Meghji, president and CEO of CodeAI. (CodeAI Photo)
Karim Meghji, who took over as president and CEO in February, is leading CodeAI’s new mission.
“AI has made the doing easy. Protecting critical thinking, and giving kids the knowledge to question this technology and decide what it’s for, is the work of education now,” Meghji said in a news release. “This is the generation that will set the terms for how AI is used. Some are being taught to understand it, direct it, question it, and create with it. Most are not. That’s the gap CodeAI exists to close.”
Meghji joined Code.org in 2022 to serve as chief product officer, leading the shift toward an AI-centered strategy at the organization. The tech vet previously spent 10 years at RealNetworks and is a former CTO at Seattle digital remittance company Remitly.
The CodeAI rebrand has already been in the works, with a change from Hour of Code to Hour of AI — an online learning event that has reached 33 million students. More than 75,000 students have taken the newly released AI Foundations, a free high school course covering how AI works, computational thinking, data literacy, and the ethical impacts of AI.
CodeAI has also led the development of an updated framework to guide state-level implementation of digital science policy, resulting in policies passed in all 50 states and application across the globe.
According to new survey data released Tuesday by CodeAI, 75% of high school students believe AI fluency will be more important than other subjects in their future, and 63% say it’s directly tied to their readiness for life beyond school.
Furthermore, while 84% of students are already using AI, only 16% of high school leaders say all of their students are actually learning about it in school, CodeAI said.
Meghji previously spoke with GeekWire about wanting to move students beyond simply using AI as a tool.
“AI is this black box for most people today in the world. You put a prompt in, you get something back out,” he said earlier this year. “Our perspective is it needs to be a glass box, and we need to give them a screwdriver and a hammer and let them kind of get in there and unpack this thing.”
Partovi has been serving as chairman of the board the past two years. A former Microsoft manager who was an early investor in companies including Facebook, DropBox, Airbnb and Uber, he argued the stakes go beyond future software engineers.
“Nobody knows the jobs of the future, but a sure bet is that every job will involve AI,” he said. “We have a responsibility to prepare the next generation for the biggest change in society since the invention of public education.”
Toolbox App 3.5 focuses on making daily work smoother and managed development environments easier to monitor. The app now supports interface zooming with familiar shortcuts, provides OpenTelemetry metrics for enterprise remote development connections, and handles several long-standing reliability issues more gracefully.
Remote development observability
The Toolbox App now emits OpenTelemetry metrics for remote development connection latency and reliability. You can send them to Grafana, Datadog, Prometheus, or another OTEL-compatible stack to monitor connection health across your developer fleet.
Zoom controls
You can now zoom the Toolbox App interface using familiar keyboard shortcuts: Cmd/Ctrl + to zoom in, Cmd/Ctrl – to zoom out, and Cmd/Ctrl 0 to reset. The setting persists across restarts, so your preferred zoom level is preserved.
Cleaner update progress
Checking for updates no longer hides behind a generic spinner. You’ll now see what the app is checking, what it’s unpacking, and how far along it is – providing a clearer sense of progress.
Enterprise configuration
For enterprise customers using JetBrains IDE Services, the Toolbox App now sends static and dynamic headers together when communicating with backend services. Header updates are also pushed automatically to running IDEs – no need to restart an IDE to pick up new headers.
Bug fixes
IntelliJ-based IDEs no longer randomly disappear from the Toolbox App home view.
Android Studio and other aliased IDEs keep their display name after updates.
The taskbar icon on KDE Plasma 6.6 and the tray and app icons on Pop!_OS now appear reliably.
Remote development fixes
SSH canonicalization failures no longer abort the connection.
The remote development environment list no longer shows an empty page when the canCreateNewEnvironments flag is set.
In 2026, the world of AI is changing at a serious pace. The days of AI systems dealing solely in single-prompt interactions are coming to an end. Instead, these models are evolving into agentic systems – long-running, goal-driven software enabled by agentic frameworks that are becoming a critical layer in modern application architecture.
This rapid shift means that Python developers building autonomous systems are increasingly relying on agentic frameworks to manage reasoning, memory, tools, and collaboration among multiple agents.
You’ve probably already heard of some of the most popular frameworks. LangChain and AutoGen have risen to prominence, but there are dozens more, many of them open-source and only one to two years old. With so many frameworks promising different agentic capabilities, the real challenge is knowing which ones are best suited for the kind of application you want to build.
Let’s take a closer look at some of the most important agentic frameworks on the market in 2026, comparing what each does best and rating them based on our key comparison criteria to help you discover which is best for your projects.
What are AI agents?
An AI agent is a piece of software capable of autonomously reasoning, setting goals, and performing tasks on behalf of a user or another system. As the name suggests, AI agents have a level of agency to learn, adapt, and make decisions independently. This means they can improve their behavior and, over time, choose their own actions to achieve specific goals or outcomes.
AI agents work by following a perceive, reason, act, reflect (PRAR) cycle, which allows them to:
Perceive: Observe the environment, including user input, system state, tools, and memory, to understand the current context and constraints of the task.
Reason: Plan, make decisions, and select actions using a large language model (LLM) or hybrid logic.
Act: Execute actions like calling tools, updating memory, or triggering workflows.
Reflect: Evaluate the outcome of previous actions and adjust future decisions, plans, or prompts to improve results.
AI agents rely on the natural language processing capabilities of large language models, but unlike traditional LLMs and AI chatbots, they don’t require continuous user input to perform tasks. Agents are proactive, working autonomously to achieve a goal based on a specified set of rules and parameters.
What is an agentic framework?
An agentic framework provides the infrastructure needed to build, run, and control AI agents at scale. Most modern frameworks offer three core capabilities:
Orchestration: Controls how agents are sequenced, coordinated, or allowed to collaborate.
Tools: Define how agents interact with external systems like APIs or databases.
Memory: Sets out how agents retain and retrieve information across steps or sessions.
While it’s possible to build an agent without a framework, they’re vital in ensuring agents are reliable, scalable, and safe.
Agentic frameworks help turn experimental agent builds into maintainable software by facilitating:
Multi-agent coordination: When multiple agents communicate to plan, work together, and specialize in different areas of a task.
Human-in-the-loop (HITL) checkpoints: Intentional pause points where a human can review what an agent is about to do.
Observability, control, and reproducibility: The ability to see what an agent is doing, guide agent behavior, or re-run an agent and receive the same results.
Core orchestration paradigms
Before comparing individual frameworks, it’s important to understand how they operate. Let’s look at the three most commonly used orchestration models in 2026.
Graph-based orchestration
Graph-based orchestration provides maximum control by organizing agents and tools as nodes in a directed graph. Instead of letting an agent freely decide what to do next, the flow that agents are allowed to follow is clearly defined.
Strengths
More deterministic control: Predictable behavior is critical for production systems that require reliable results.
Easier debugging: Pinpoint exactly which node failed thanks to clear checkpoints and boundaries.
Production-grade reliability: This approach is ideal for customer-facing applications, enterprise systems, or regulated environments.
Limitations
More upfront design: The workflow must be defined in advance, which slows initial development.
Less “emergent” behavior: Agents are constrained by the graph, leaving less room for experimentation and creativity.
Role-based orchestration
Role-based orchestration is most effective when simplicity is a priority. Agents are assigned specific roles, such as “Planner”, “Researcher”, or “Builder”, and collaborate by sending messages to one another.
Strengths
Intuitive mental model: This type of operation is easy to understand because it effectively mirrors how human teams work.
Rapid prototyping: Minimal setup is required, allowing more time to explore outcomes.
Limitations
Harder-to-constrain behavior: Because agents have the freedom to decide what to do next, it’s difficult to enforce strict execution paths.
Limited determinism: The same input can yield different outcomes, making it tricky to reproduce results and achieve consistency.
Chain-based orchestration
Chain-based orchestration, also known as adaptive orchestration, arguably offers the greatest flexibility. Agents in this model operate in dynamic chains or loops, deciding the next step autonomously.
Strengths
Flexible workflows: Agents are not constrained to a pre-defined path and can freely explore different strategies.
Suitability for creative tasks: This approach is ideal for research, discovery, and experimentation, as agents can iteratively explore ideas, pivot strategies, and adapt their approach.
Limitations
Less predictability: Testing and debugging are more challenging because execution paths are harder to reproduce and trace.
More difficult governance at scale: This unpredictability grows as tasks become more complex.
Best agentic frameworks for your projects
Now that we’re familiar with the key orchestration paradigms of agentic frameworks, it’s time to compare some of the most popular frameworks on the market in 2026. Below, we evaluate each framework’s performance against our key comparison criteria:
Primary orchestration model.
Multi-agent support.
Memory capabilities.
Human-in-the-loop (HITL) support.
Best-fit applications.
Framework
Orchestration model
Multi-agent support
Memory capabilities
HITL support
Best used for
LangChain
Chain-based
Partial
Moderate
Limited to moderate
Rapid LLM app development
LangGraph
Graph-based
Yes
Strong
Strong
Production-grade agent workflows
LlamaIndex
Retrieval-centric
Limited
Strong
Moderate
Knowledge-heavy agents
Haystack
Pipeline-based/modular
Moderate
Strong
Moderate
Production RAG and context-heavy AI systems
AutoGen
Role-based
Strong
Moderate
Limited
Conversational multi-agent systems
CrewAI
Role-based
Strong
Light
Limited
Task-oriented agent teams
Semantic Kernel
Planner-based
Moderate
Moderate
Strong
Enterprise AI
smolagents
Minimalist
Limited
Light
Minimal
Lightweight experiments
OpenAI Agents SDK
Graph-based
Yes
Managed
Strong
Hosted agent applications
Phidata
Agent-centric
Limited to moderate
Strong
Moderate
Data and tool-heavy agents
Let’s take a closer look at the strengths and weaknesses of each framework, along with the applications they’re most suited to.
LangChain
Core design: Chain-based orchestration.
Philosophy: Developer velocity and flexibility.
Launched in 2022, LangChain is one of the most widely adopted frameworks due to its broad ecosystem of integrations. It serves as an accessible interface for nearly any LLM and is an ideal starting point for enthusiasts or startups looking to explore agentic AI. While not strictly “agent-first”, it provides the building blocks for agentic behavior.
LangChain provides less control than other frameworks, but it’s still a fantastic entry point into agentic systems, especially for projects where speed and creativity take precedence over enforcing strict workflows.
Strengths
Huge ecosystem.
Easy tool integration.
Rapid prototyping.
Limitations
Less control than graph-based systems.
Agent logic that can be difficult to understand as it grows in complexity.
Best applications
Prototyping of agentic features.
Tool-augmented chatbots.
LLM-powered backend services.
If you want to go beyond the basics, read our LangChain Python Tutorial: A Complete Guide for 2026. It takes a deeper look at what LangChain offers and walks through real-world use cases for building AI agents in Python.
LangGraph
Core design: Graph-based orchestration.
Philosophy: Explicit control over agent behavior.
LangGraph has emerged as the leading standard for production-grade agent systems. Built on top of LangChain, it replaces implicit chains with explicit graphs, providing strict control over workflows and excellent HITL support via interrupts.
While the graph structure itself can actually make debugging easier by clearly mapping how agents and tools interact, LangGraph does come with a learning curve. Much of this complexity comes from designing the graph and managing explicit state between nodes. Once you understand these concepts, the framework becomes a powerful option for building predictable and controllable agent systems.
Strengths
Deterministic workflows.
Native state management.
Excellent HITL support via interrupts.
Suitability for regulated or mission-critical systems.
Limitations
Higher upfront design effort.
Steeper learning curve due to explicit graph and state management.
Reduced flexibility for open-ended tasks.
Best applications
Autonomous customer support systems.
AI-driven DevOps workflows.
Multi-step decision engines.
LlamaIndex
Core design: Retrieval-centric orchestration.
Philosophy: Data-first agents.
LlamaIndex is a Python framework designed to help AI systems understand, store, and retrieve information from large amounts of documents and data.
Rather than starting with agents and adding data later, LlamaIndex takes the opposite approach – it starts with data and then builds agent behavior around it. This is why it is often described as data-first or retrieval-centric.
Because it operates in this way, LlamaIndex excels at indexing, memory, and retrieval, making it ideal for building agents whose intelligence depends on accessing the right information rather than executing complex actions.
Strengths
Advanced document indexing.
Strong long-term memory patterns.
Limitations
Limited suitability for complex, action-heavy orchestration.
Limited support for multi-agent orchestration.
Best applications
Research assistants.
Knowledge base agents.
Enterprise document intelligence.
Haystack
Core design: Modular pipeline orchestration.
Philosophy: Context engineering and production-ready AI systems.
Haystack is an open-source AI orchestration framework created by deepset for building production-ready AI agents, retrieval-augmented generation (RAG) systems, and multimodal applications.
Instead of focusing purely on agent behavior, Haystack structures applications as explicit pipelines composed of retrievers, routers, memory layers, tools, evaluators, and generators. This modular architecture gives you control over how information flows through a system, allowing each component to be tested and improved independently.
Haystack is particularly strong in applications where the quality of retrieved information determines the quality of the model’s output. Its design also makes it well-suited for enterprise environments that require transparency and reliability in production systems.
Strengths
Highly modular pipeline architecture.
Excellent support for RAG and document processing.
Strong ecosystem, particularly in search and RAG-focused enterprise use cases.
Flexible integrations with models and vector databases.
Limitations
More infrastructure and setup than lightweight frameworks.
Less focus on emergent multi-agent collaboration.
Best applications
Retrieval-augmented generation (RAG) systems.
Enterprise document intelligence.
Data-heavy AI applications.
Production AI pipelines that require strong context control.
AutoGen, an open-source Microsoft framework, popularized the idea of agents collaborating through structured conversation, organizing systems as teams of agents, each with its own specific role. Unlike in other frameworks, there’s no central controller enforcing a strict execution path – the collaboration itself drives progress.
This approach makes AutoGen ideal for exploratory, creative, and research-driven multi-agent systems, at the cost of predictability, HITL, and strict execution control.
Strengths
Natural multi-agent interaction.
Minimal orchestration overhead.
Suitability for emergent problem-solving.
Limitations
Limited execution control.
Weak HITL support.
Best applications
Coding agents.
Brainstorming systems.
AI research experiments.
CrewAI
Core design: Role-based task delegation.
Philosophy: Teams of specialized agents.
CrewAI is centered around building simple, structured multi-agent systems. It is similar to AutoGen, modeling AI agents as members of a “crew” where each agent has a clearly defined role. The goal is to make multi-agent systems approachable, even if you are new to agentic AI.
CrewAI prioritizes simplicity and speed over deep memory and production controls, making it easy to learn and a strong option for prototypes and small teams. However, its limited toolset for observability, HITL, and error handling at scale makes it less suited for larger systems.
Strengths
Very approachable API.
Clear role separation.
Fast setup.
Limitations
Lightweight memory.
Limited production controls.
Best applications
Content pipelines.
Market research automation.
Simple workflow agents.
Semantic Kernel
Core design: Planner-based orchestration.
Philosophy: Enterprise-grade AI integration.
Semantic Kernel is another open-source Microsoft framework, designed for building AI-powered applications that integrate with existing enterprise systems.
It was created with production concerns in mind from the start, emphasizing governance, safety, observability, and human oversight. Rather than maximizing agent autonomy, it focuses on making AI predictable, controllable, and auditable.
By combining structured workflows with LLM reasoning, it trades flexibility and emergent behavior for trust, safety, and operational reliability.
Strengths
Strong HITL support.
Enterprise-friendly architecture.
Good observability.
Limitations
Heavier upfront structure.
Less flexibility for open-ended autonomy.
Steeper learning curve.
Best applications
Internal enterprise tools.
AI copilots.
Business process automation.
smolagents
Core design: Minimalist chain-based.
Philosophy: Simplicity over scale.
smolagents is a bare-bones framework designed to make agentic AI as straightforward and transparent as possible. It prioritizes simple, readable code that makes it easy to understand how an agent works without needing to learn a large framework.
smolagents aims to make agent behavior accessible and easy to experiment with by keeping abstractions minimal and logic transparent. It offers first-class support for code-based and tool-calling agents, broad model and tool compatibility, and lightweight CLI utilities, while intentionally trading large-scale orchestration and production features for simplicity and clarity.
Thanks to ChatGPT’s explosion in popularity, we’ve all heard of OpenAI. The Agents SDK is the company’s effort to provide a managed platform for building and running agents without having to maintain your own orchestration infrastructure.
Rather than assembling agents from scratch, you define agent behavior and workflows, while OpenAI provides orchestration, memory management, monitoring, and safety controls. This makes the Agents SDK particularly attractive for teams that want production-ready agents quickly.
Strengths
Minimal infrastructure burden.
Built-in safety and observability.
Strong multi-agent support.
Limitations
Reduced customization and control.
Limited suitability for experimental research.
Best applications
SaaS agent features.
Customer-facing autonomous systems.
Teams prioritizing speed over customization.
Phidata
Core design: Agent-centric, tool-heavy.
Philosophy: Practical agents for real-world data tasks.
Phidata is designed for building practical, tool-driven AI agents that operate on real-world data.
Rather than focusing on abstract orchestration patterns, Phidata centers the agent around direct interaction with systems such as APIs, databases, and internal services.
Its design reflects the fact that many agents spend most of their time fetching, transforming, and acting on data.
Strengths
Strong tool integration.
Suitability for data-centric workflows.
Limitations
Less emphasis on orchestration.
Limited multi-agent capabilities.
Best applications
Data analysis agents.
Finance and ops automation.
Tool-driven decision systems.
Choosing the right framework
Now that you’re familiar with many of the most popular frameworks in 2026, it’s time to choose the right one for your project. Let’s take a look at some of the key use cases, along with the frameworks that fit them best.
Orchestration model
Where to use
Recommended frameworks
Graph-based
Projects involving complex branching logic and requiring high levels of reliability, auditability, and control.
LangGraph, OpenAI Agents SDK
Role-based
Projects involving rapid development and intuitive design that benefit from emergent collaboration between agents.
AutoGen, CrewAI
Chain-based
Projects requiring maximum flexibility, where agents need to adapt dynamically and determine next steps autonomously.
LangChain
Retrieval-based
Projects where deep, reliable access to knowledge matters more than high levels of autonomy.
LlamaIndex, Haystack
Enterprise-oriented
Projects where strong governance and human-in-the-loop processes are non-negotiable requirements.
Semantic Kernel
Lightweight
Rapid prototyping, educational use, and simple local agents where transparency and control matter more than orchestration complexity.
smolagents
Tool-centric
Building production agents that primarily interact with APIs, databases, and external systems rather than complex multi-step orchestration.
Phidata
In 2026, agentic frameworks have evolved from experimental tools into foundational infrastructure for many applications. The key decision is no longer whether to use agents, but how much control, autonomy, and governance your systems require.
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