Agents
Agent Framework
A library that supplies the standard building blocks of an agent, such as calling LLMs, defining and parsing tools, chaining calls, and managing state, so you do not write them from scratch.
In depth
A library that supplies the standard building blocks of an agent, such as calling LLMs, defining and parsing tools, chaining calls, and managing state, so you do not write them from scratch. Examples include the Claude Agent SDK, LangGraph, Mastra, and the Strands Agents SDK. Anthropic's guidance is that frameworks speed up the start but add abstraction that can hide the underlying prompts, so begin with direct API calls and make sure you understand the code beneath any framework you adopt.
Primary source: anthropic.com
Example
A library that supplies the standard building blocks of an agent, such as calling LLMs, defining and parsing tools, chaining calls, and managing state, so you do not write them from scratch.
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Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
Common questions
What is Agent Framework?
A library that supplies the standard building blocks of an agent, such as calling LLMs, defining and parsing tools, chaining calls, and managing state, so you do not write them from scratch.
Why does Agent Framework matter for AI developers?
Agent Framework sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Agent Framework?
Developers Digest publishes tutorials and videos that cover Agents topics including Agent Framework. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
Agent Swarm
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.
Agents
Agent Trajectory Evaluation
Evaluating the sequence of steps an agent took, including its messages and tool calls, rather than only its final answer.
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