Definition
A short definition of AI agent frameworks, the toolkits that let AI agents plan, remember, use tools, and complete tasks.
What Is an AI Agent Framework?
An AI agent framework is the software layer that gives an AI agent everything it needs to think through a problem, keep track of context, reach out to outside tools, and carry a multi step task through to completion. It sits between a large language model and the actual work you want finished, turning raw reasoning into something that can act.
Most frameworks cover four jobs: breaking a goal into smaller steps, holding memory across those steps, calling on APIs and databases when a tool is needed, and deciding what runs and in what order. LangChain, LangGraph, CrewAI, and AutoGen are among the better known options, each striking its own balance between how much control you keep and how much complexity you take on.
The payoff is speed and reliability. Building an agent without a framework means starting from scratch every time. Building on one means your team ships faster and the agent keeps behaving the way you expect once it leaves the demo and enters production, which is usually where things start to wobble.
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Related terms: AI Agent, Agentic AI, Multi Agent System.
Is an AI agent framework the same thing as an LLM?
No. The large language model supplies the reasoning. The framework is the scaffolding around it that turns that reasoning into steps and action.
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