AI Agent Frameworks: What They Are and How They Work

AI agent frameworks give developers the tools to build AI agents that can reason, take action, and complete multi-step tasks. Each framework offers different capabilities and approaches to agent development.

This guide explores how AI agent frameworks work, their core components and design patterns, and how leading frameworks compare. It also covers key considerations for choosing a framework, including flexibility, interoperability, and the challenges of deploying agents in production.

AI agent frameworks are software libraries and development toolkits used to build, run, and manage AI agents. An agent is the AI system that plans and acts toward a goal; the framework is the scaffolding developers use to create that system and control how it operates.

Most frameworks provide an interface for calling large language models (LLMs), connecting tools or functions, managing memory or state, and running a control loop that lets an agent decide what to do next. Some focus on a single agent completing a sequence of steps. Others are designed around teams of specialized agents that exchange work, delegate tasks, or collaborate.

How do agents differ from LLMs?

An LLM generates an output from an input. An agent combines a model with instructions, tools, state, and an execution loop to pursue a goal over multiple steps. A chatbot that answers one prompt is not automatically an autonomous agent; agency comes from the surrounding system that lets the model decide, act, observe, and continue.

• Agent. One AI system executing a task loop toward a defined goal.
• Agent framework. The developer toolkit used to build, run, and manage that agent.
• Agentic orchestration. The broader practice of coordinating agents, humans, and systems across an end-to-end business process. Agent frameworks build agents; an orchestration layer governs how those agents participate in the larger process.

Early generative AI applications were largely built around single model calls: send a prompt, receive a response. As models gained function calling, larger context windows, and stronger reasoning capabilities from 2023 through 2025, developers began asking them to do more than generate text. Agents could query APIs, search information, read files, execute code, call other models, and use the results of one action to decide what to do next.

That created a software engineering problem. Every agent needed the same basic plumbing: an execution loop, tool routing, state tracking, and rules for knowing when the task was finished. Hand-coding those pieces for every project was repetitive and harder to maintain. Agent frameworks package those patterns into reusable abstractions so developers can focus on what the agent should do and how its behavior should be controlled.

• Reasoning and planning loop. The framework manages the cycle in which an agent evaluates the current state, selects a next step, acts, observes the result, and continues or stops.
• Tool and function calling. Tools let an agent reach beyond the model by invoking APIs, search, code, databases, or other services. The framework handles how tools are exposed, selected, called, and returned to the agent.
• Memory. Short-term memory preserves context during a task or conversation, while longer-term memory can store information across sessions, often through databases or vector stores.
• Multi-agent communication. Multi-agent frameworks provide ways for agents to pass messages, delegate work, share context, or transfer control to specialized agents.
• State and execution tracking. Frameworks track where an agent is in a multi-step task, what has already happened, and what information is needed to continue or resume execution.

AI agent design patterns are reusable structures for how an agent, or a group of agents, plans work, takes actions, evaluates results, and decides when to stop. Frameworks differ partly because they make different patterns easier to implement and give developers different control over the execution path.

• ReAct (reason + act). The agent alternates between deciding what to do and calling a tool, using each result to inform the next step. It is a common pattern for tool-using single agents.
• Plan-and-execute. The agent creates a plan first and then works through its steps. This can make execution easier to inspect, but a fixed plan may need to be revised when assumptions fail.
• Reflection or self-critique. The agent evaluates its own output and revises it before returning a result. Additional review can improve output quality while also adding model calls, latency, and cost.
• Supervisor or hierarchical. A coordinating agent assigns subtasks to specialized agents and combines their results. This separates responsibilities while retaining a central decision-maker.
• Handoff. One agent transfers control and relevant context to another agent, which becomes responsible for the task. OpenAI Agents SDK supports handoffs as a core coordination primitive.
• Graph-based or state machine. Steps are represented as nodes with explicit transitions, making branches, loops, checkpoints, and approval gates visible in the execution structure. LangGraph, Google Agent Development Kit, and Microsoft Agent Framework support graph-based approaches.

Single-agent vs. multi-agent frameworks

Single-agent frameworks center on one agent working through a task, even when it uses many tools. Multi-agent frameworks make collaboration between specialized agents a first-class pattern. Many modern frameworks support both. The useful question is whether specialization, delegation, or parallel work is central to the application or one agent can own the task.

Code-first vs. visual or low-code

Code-first frameworks expose agents, tools, state, and execution logic through programming interfaces, giving developers direct control over behavior and testing. Visual or low-code approaches move more of that logic into graphical builders or configuration. Some ecosystems support both. The tradeoff is usually between implementation flexibility and how much agent behavior can be built or inspected without writing code.

General-purpose vs. specialized

General-purpose frameworks support a broad range of agent applications, models, tools, and patterns. Specialized frameworks emphasize a narrower problem, such as building agents over private data or managing retrieval-heavy applications. LlamaIndex, for example, grew around data and retrieval use cases and now supports agents and multi-step workflows. Specialization can provide useful abstractions when the application closely matches the framework’s focus.

The frameworks below represent different approaches to building agent applications. This is a descriptive comparison, not a ranking.

The table reflects meaningful differences in emphasis. LangGraph focuses on stateful graph-based control; CrewAI on Crews and Flows; OpenAI Agents SDK on lightweight primitives such as agents, tools, and handoffs; LlamaIndex on data-centric agents and workflows; and Google ADK on code-first agent systems. AutoGen remains available, but Microsoft now directs new users toward Microsoft Agent Framework.

AI agent frameworks increasingly work together through open protocols rather than framework-specific integrations. That reduces the need to build a custom connector for every combination of agent, tool, data source, or external agent.

MCP connects agents to tools and data

Model Context Protocol (MCP) standardizes how AI applications connect to external tools, data sources, and services. Anthropic introduced MCP in November 2024 as an open standard, and in December 2025 donated it to the Agentic AI Foundation, a Linux Foundation directed fund. In practice, an MCP-compatible server can expose capabilities in a standard way so multiple compatible agent applications can use them instead of requiring a separate integration for each application-to-system pairing.

A2A connects agents to other agents

Agent2Agent (A2A) addresses a different layer: communication and collaboration between agents. Google introduced the protocol in April 2025, and the Linux Foundation launched the A2A project in June 2025. A2A is designed to let agents discover capabilities, delegate work, exchange task information, and collaborate across different platforms or frameworks.

MCP and A2A solve complementary problems: MCP connects agents to tools and data; A2A connects agents to other agents. A framework can support either or both while retaining its own execution model.

Choose a framework based on application requirements, not popularity. Work through these steps:

• Define the primary use case. Decide whether one agent can own the task or whether the work benefits from specialized agents collaborating or handing work between them.
• Match the team’s language and ecosystem. A framework that fits existing languages, deployment practices, model providers, and libraries can reduce unnecessary integration work.
• Choose the right level of control. Decide whether the team needs fully programmatic control or benefits from visual or configuration-driven development.
• Assess memory and state requirements. A short conversational task has different persistence needs from a long-running process that must pause, resume, recover, or retain context.
• Plan for production early. Consider reliability, debugging, evaluation, observability, human review, security, and governance before a successful prototype becomes difficult to operate.

Agent frameworks remove a lot of the plumbing, but they do not remove the operational challenges that come with autonomous, model-driven behavior.

• Reliability and non-determinism. The same agent can take different paths or produce different outputs, making testing more complex than deterministic software.
• Debugging execution paths. Multi-step and multi-agent systems can involve many model calls, tool calls, branches, and handoffs, so tracing what happened becomes essential.
• Long-running state. Tasks that span minutes, hours, systems, or human approvals need durable state and a clear way to resume after interruptions or failures.
• Governance and auditability. Frameworks vary in the controls they provide by default. Enterprise applications may require approvals, access boundaries, policy enforcement, and records of agent actions.
• Scaling execution. Running one agent successfully is different from operating many concurrent agent tasks with predictable cost, latency, reliability, and resource usage.

Most agent frameworks are designed to build and run agents. Coordinating them with business systems and human approvals at enterprise scale is a separate architectural concern.