AI models are capable of more than most teams are getting out of them. The gap isn't intelligence — it's infrastructure. To move from answering questions to completing workflows, a large language model (LLM) needs memory, tools, orchestration logic, and a way to hand work off to humans when it matters. That infrastructure is what an AI agent framework provides.

There are several widely-adopted AI agent frameworks to choose from, making it important to compare approaches and select the right one for your team. Some frameworks work best for teams starting AI deployment from scratch, others work for non-technical roles looking for a low or no-code approach, and others require extensive engineering knowledge.

What is an AI agent framework?

An AI agent framework gives LLMs the infrastructure agents need to act autonomously. A framework defines how agents perform work, including what they remember from session-to-session and over longer periods of time, which tools they can access, how agents hand-off work from one to another, and where humans need to be kept in the loop for review or approvals.

Core components of agent frameworks

Agentic AI frameworks range from build-your-own LLM agent frameworks like LangGraph, LangChain, CrewAI, and AutoGen, to frameworks that you define within the platform where work gets done. Build-your-own frameworks require technical expertise to connect an LLM and build the necessary memory, tool integrations, and orchestration logic that agents need.

By contrast, low- or no-code platforms make agentic workflows accessible to non-technical teams. For example, Hyperagent is an agent execution platform built on a shared system of record where agents run workflows against structured operational data, connected to all the other tools your business runs on, allowing you to easily define the appropriate level of governance and human oversight.

Both pathways to agentic workflows are effective when you define these core components within your framework:

Top AI agent frameworks in 2026

These four AI orchestration frameworks are the most widely adopted for building LLM-powered agents. Each takes a different approach to AI agent orchestration.

LangGraph

LangGraph is a graph-based orchestration framework from the LangChain team. It models agent workflows as directed graphs, where nodes represent steps and edges define transitions, including loops and conditional branching logic.

Best for: Engineering teams building agents for complex, stateful workflows where agents need to revisit earlier steps based on what they've learned.

Technical lift: High. Requires Python or JavaScript expertise and familiarity with graph-based thinking.

Open source? Yes

LangChain

LangChain is one of the earliest and most widely adopted agent frameworks. It provides modular components — LLM wrappers, memory classes, tool integrations, and chain abstractions — that developers assemble into bespoke agent applications.

Best for: Teams who want a large ecosystem of pre-built components for most common tools and data sources, and are building document-heavy or retrieval-augmented agents.

Technical lift: High. Python-first and developer-oriented.

Open source? Yes

CrewAI

CrewAI is designed specifically for multi-agent systems. The framework introduces the metaphor of a "crew" — a collection of agents with defined roles, goals, and tools that collaborate on a shared task.

Best for: Teams building multi-agent pipelines where different agents handle different parts of a workflow and need to handle agent-to-agent communication and delegation.

Technical lift: Medium to high. More opinionated (the framework makes more decisions for you because it has a prescribed way of doing things) than LangChain (essentially a blank slate). Coding is still required, however.

Open source? Yes

AutoGen

AutoGen, developed by Microsoft Research, focuses on conversational multi-agent systems. It enables agents to converse with each other and with humans, using those conversations to coordinate work.

Best for: Research teams and engineering teams who want agents to debate, critique, or verify each other's outputs before arriving at a final answer.

Technical lift: High. Primarily used by ML engineers and researchers.

Open source? Yes

What other frameworks and technologies come up in AI agent development?

Beyond the four frameworks above, teams will run into a wider set of tools as they build. Semantic Kernel and Mastra are two more open-source AI agent frameworks worth knowing: Semantic Kernel is Microsoft's SDK for integrating LLMs into applications, and Mastra is a TypeScript-based framework for building agents. Agents also sit on top of broader artificial intelligence and machine learning concepts, including generative AI, deep learning, and natural language processing (NLP), which is the branch of AI that lets agents interpret and generate human language.

A few technical mechanisms show up across most frameworks: function calling lets an agent invoke external tools with structured parameters, checkpointing and state management let a workflow pause, resume, or recover without losing progress, and event-driven architecture lets agents react to triggers as they happen instead of running on a fixed schedule. For memory and retrieval, agents combine long-term memory with vector databases and RAG (retrieval-augmented generation) pipelines to ground their answers in your own data, often pulling in models from providers like Hugging Face or Mistral through MCP servers. Teams monitoring or debugging agents in production may also use observability tools like LangSmith.

How to choose the right agent framework

The best AI agent framework depends on your team's needs, the resources available, how quickly you’d like to implement agentic workflows, and where your structured data lives.

Here’s a decision rubric to help you decide:

Build an agent system of record with Airtable

A framework alone doesn’t ensure successful AI agent outcomes. Each framework assumes that agents have access to good data and yet most business data lives in disparate places, like spreadsheets, email threads, project management tools, customer relationship management systems, and Slack channels. Without connecting this data within a shared system of record, agents lack the context they need and their output may miss the mark. When agents work across a single operational surface, where the underlying data is structured and current, they reason across the actual state of the business in real time.

Airtable is a strong system of record for AI agents. As an AI agent platform, Airtable is built for human-agent collaboration and AI workflow automation at scale. Agents work across structured records with explicit relationships, clearly understanding how campaigns connect to assets, how projects connect to dependencies, or how contracts connect to obligations. When workflows live in Airtable, the workflow definition itself becomes the governance layer.

Teams define the boundaries within which agents operate: which data they can access, where human review is required, and how output flows into the next decision. Agent observability lets you see exactly what your AI agents are doing, and step in, if needed.

Build a framework where work gets done

AI agent frameworks give LLMs the infrastructure for agents to act with memory, tool use, orchestration, and human oversight. Build-your-own frameworks like LangGraph, LangChain, CrewAI, and AutoGen are powerful, but require technical expertise and assume your data is agent-ready.

Execution platforms, like Hyperagent, give any team an AI agent builder that includes each of the core components, without reliance on developers. This enables non-technical, operational teams across the business to take advantage of agentic workflows with confidence. In Airtable, workflows are built on structured, connected data and allow both agents and teams visibility into what’s happening.